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Critical Analysis – Types, Examples and Writing Guide

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Critical Analysis

Definition:

Critical analysis is a process of examining a piece of work or an idea in a systematic, objective, and analytical way. It involves breaking down complex ideas, concepts, or arguments into smaller, more manageable parts to understand them better.

Types of Critical Analysis

Types of Critical Analysis are as follows:

Literary Analysis

This type of analysis focuses on analyzing and interpreting works of literature , such as novels, poetry, plays, etc. The analysis involves examining the literary devices used in the work, such as symbolism, imagery, and metaphor, and how they contribute to the overall meaning of the work.

Film Analysis

This type of analysis involves examining and interpreting films, including their themes, cinematography, editing, and sound. Film analysis can also include evaluating the director’s style and how it contributes to the overall message of the film.

Art Analysis

This type of analysis involves examining and interpreting works of art , such as paintings, sculptures, and installations. The analysis involves examining the elements of the artwork, such as color, composition, and technique, and how they contribute to the overall meaning of the work.

Cultural Analysis

This type of analysis involves examining and interpreting cultural artifacts , such as advertisements, popular music, and social media posts. The analysis involves examining the cultural context of the artifact and how it reflects and shapes cultural values, beliefs, and norms.

Historical Analysis

This type of analysis involves examining and interpreting historical documents , such as diaries, letters, and government records. The analysis involves examining the historical context of the document and how it reflects the social, political, and cultural attitudes of the time.

Philosophical Analysis

This type of analysis involves examining and interpreting philosophical texts and ideas, such as the works of philosophers and their arguments. The analysis involves evaluating the logical consistency of the arguments and assessing the validity and soundness of the conclusions.

Scientific Analysis

This type of analysis involves examining and interpreting scientific research studies and their findings. The analysis involves evaluating the methods used in the study, the data collected, and the conclusions drawn, and assessing their reliability and validity.

Critical Discourse Analysis

This type of analysis involves examining and interpreting language use in social and political contexts. The analysis involves evaluating the power dynamics and social relationships conveyed through language use and how they shape discourse and social reality.

Comparative Analysis

This type of analysis involves examining and interpreting multiple texts or works of art and comparing them to each other. The analysis involves evaluating the similarities and differences between the texts and how they contribute to understanding the themes and meanings conveyed.

Critical Analysis Format

Critical Analysis Format is as follows:

I. Introduction

• Provide a brief overview of the text, object, or event being analyzed
• Explain the purpose of the analysis and its significance
• Provide background information on the context and relevant historical or cultural factors

II. Description

• Provide a detailed description of the text, object, or event being analyzed
• Identify key themes, ideas, and arguments presented
• Describe the author or creator’s style, tone, and use of language or visual elements

III. Analysis

• Analyze the text, object, or event using critical thinking skills
• Identify the main strengths and weaknesses of the argument or presentation
• Evaluate the reliability and validity of the evidence presented
• Assess any assumptions or biases that may be present in the text, object, or event
• Consider the implications of the argument or presentation for different audiences and contexts

IV. Evaluation

• Provide an overall evaluation of the text, object, or event based on the analysis
• Assess the effectiveness of the argument or presentation in achieving its intended purpose
• Identify any limitations or gaps in the argument or presentation
• Consider any alternative viewpoints or interpretations that could be presented
• Summarize the main points of the analysis and evaluation
• Reiterate the significance of the text, object, or event and its relevance to broader issues or debates
• Provide any recommendations for further research or future developments in the field.

VI. Example

• Provide an example or two to support your analysis and evaluation
• Use quotes or specific details from the text, object, or event to support your claims
• Analyze the example(s) using critical thinking skills and explain how they relate to your overall argument

VII. Conclusion

• Reiterate your thesis statement and summarize your main points
• Provide a final evaluation of the text, object, or event based on your analysis
• Offer recommendations for future research or further developments in the field
• End with a thought-provoking statement or question that encourages the reader to think more deeply about the topic

How to Write Critical Analysis

Writing a critical analysis involves evaluating and interpreting a text, such as a book, article, or film, and expressing your opinion about its quality and significance. Here are some steps you can follow to write a critical analysis:

• Read and re-read the text: Before you begin writing, make sure you have a good understanding of the text. Read it several times and take notes on the key points, themes, and arguments.
• Identify the author’s purpose and audience: Consider why the author wrote the text and who the intended audience is. This can help you evaluate whether the author achieved their goals and whether the text is effective in reaching its audience.
• Analyze the structure and style: Look at the organization of the text and the author’s writing style. Consider how these elements contribute to the overall meaning of the text.
• Evaluate the content : Analyze the author’s arguments, evidence, and conclusions. Consider whether they are logical, convincing, and supported by the evidence presented in the text.
• Consider the context: Think about the historical, cultural, and social context in which the text was written. This can help you understand the author’s perspective and the significance of the text.
• Develop your thesis statement : Based on your analysis, develop a clear and concise thesis statement that summarizes your overall evaluation of the text.
• Support your thesis: Use evidence from the text to support your thesis statement. This can include direct quotes, paraphrases, and examples from the text.
• Write the introduction, body, and conclusion : Organize your analysis into an introduction that provides context and presents your thesis, a body that presents your evidence and analysis, and a conclusion that summarizes your main points and restates your thesis.
• Revise and edit: After you have written your analysis, revise and edit it to ensure that your writing is clear, concise, and well-organized. Check for spelling and grammar errors, and make sure that your analysis is logically sound and supported by evidence.

When to Write Critical Analysis

You may want to write a critical analysis in the following situations:

• Academic Assignments: If you are a student, you may be assigned to write a critical analysis as a part of your coursework. This could include analyzing a piece of literature, a historical event, or a scientific paper.
• Journalism and Media: As a journalist or media person, you may need to write a critical analysis of current events, political speeches, or media coverage.
• Personal Interest: If you are interested in a particular topic, you may want to write a critical analysis to gain a deeper understanding of it. For example, you may want to analyze the themes and motifs in a novel or film that you enjoyed.
• Professional Development : Professionals such as writers, scholars, and researchers often write critical analyses to gain insights into their field of study or work.

Critical Analysis Example

An Example of Critical Analysis Could be as follow:

Research Topic:

The Impact of Online Learning on Student Performance

Introduction:

The introduction of the research topic is clear and provides an overview of the issue. However, it could benefit from providing more background information on the prevalence of online learning and its potential impact on student performance.

Literature Review:

The literature review is comprehensive and well-structured. It covers a broad range of studies that have examined the relationship between online learning and student performance. However, it could benefit from including more recent studies and providing a more critical analysis of the existing literature.

Research Methods:

The research methods are clearly described and appropriate for the research question. The study uses a quasi-experimental design to compare the performance of students who took an online course with those who took the same course in a traditional classroom setting. However, the study may benefit from using a randomized controlled trial design to reduce potential confounding factors.

The results are presented in a clear and concise manner. The study finds that students who took the online course performed similarly to those who took the traditional course. However, the study only measures performance on one course and may not be generalizable to other courses or contexts.

Discussion :

The discussion section provides a thorough analysis of the study’s findings. The authors acknowledge the limitations of the study and provide suggestions for future research. However, they could benefit from discussing potential mechanisms underlying the relationship between online learning and student performance.

Conclusion :

The conclusion summarizes the main findings of the study and provides some implications for future research and practice. However, it could benefit from providing more specific recommendations for implementing online learning programs in educational settings.

Purpose of Critical Analysis

There are several purposes of critical analysis, including:

• To identify and evaluate arguments : Critical analysis helps to identify the main arguments in a piece of writing or speech and evaluate their strengths and weaknesses. This enables the reader to form their own opinion and make informed decisions.
• To assess evidence : Critical analysis involves examining the evidence presented in a text or speech and evaluating its quality and relevance to the argument. This helps to determine the credibility of the claims being made.
• To recognize biases and assumptions : Critical analysis helps to identify any biases or assumptions that may be present in the argument, and evaluate how these affect the credibility of the argument.
• To develop critical thinking skills: Critical analysis helps to develop the ability to think critically, evaluate information objectively, and make reasoned judgments based on evidence.
• To improve communication skills: Critical analysis involves carefully reading and listening to information, evaluating it, and expressing one’s own opinion in a clear and concise manner. This helps to improve communication skills and the ability to express ideas effectively.

Importance of Critical Analysis

Here are some specific reasons why critical analysis is important:

• Helps to identify biases: Critical analysis helps individuals to recognize their own biases and assumptions, as well as the biases of others. By being aware of biases, individuals can better evaluate the credibility and reliability of information.
• Enhances problem-solving skills : Critical analysis encourages individuals to question assumptions and consider multiple perspectives, which can lead to creative problem-solving and innovation.
• Promotes better decision-making: By carefully evaluating evidence and arguments, critical analysis can help individuals make more informed and effective decisions.
• Facilitates understanding: Critical analysis helps individuals to understand complex issues and ideas by breaking them down into smaller parts and evaluating them separately.
• Fosters intellectual growth : Engaging in critical analysis challenges individuals to think deeply and critically, which can lead to intellectual growth and development.

Advantages of Critical Analysis

Some advantages of critical analysis include:

• Improved decision-making: Critical analysis helps individuals make informed decisions by evaluating all available information and considering various perspectives.
• Enhanced problem-solving skills : Critical analysis requires individuals to identify and analyze the root cause of a problem, which can help develop effective solutions.
• Increased creativity : Critical analysis encourages individuals to think outside the box and consider alternative solutions to problems, which can lead to more creative and innovative ideas.
• Improved communication : Critical analysis helps individuals communicate their ideas and opinions more effectively by providing logical and coherent arguments.
• Reduced bias: Critical analysis requires individuals to evaluate information objectively, which can help reduce personal biases and subjective opinions.
• Better understanding of complex issues : Critical analysis helps individuals to understand complex issues by breaking them down into smaller parts, examining each part and understanding how they fit together.
• Greater self-awareness: Critical analysis helps individuals to recognize their own biases, assumptions, and limitations, which can lead to personal growth and development.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Critical writing: Descriptive vs critical

• Managing your reading
• Source reliability
• Critical reading
• Descriptive vs critical
• Deciding your position
• The overall argument
• Individual arguments
• Signposting
• Alternative viewpoints
• Critical thinking videos

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“Descriptions: they report information about something, but they don't perform any kind of reasoning - and nor do they pass judgement on or analyse the information they contain.” Tom Chatfield, Critical Thinking

Many of students are told that their writing is too descriptive and not critical enough. But what does this actually mean? This page describes both sorts of writing so that you can see the difference and gives examples of how to make your writing less descriptive and more critical.

Descriptive versus critical writing

Descriptive writing.

This is an essential element of academic writing but it is used to set the background and to provide evidence rather than to develop argument. When writing descriptively you are informing your reader of things that they need to know to understand and follow your argument but you are not transforming that information in any way. This is usually writing about things you have read, done (often as part of reflective writing ) or observed.

Critical writing

When writing critically, you are developing a reasoned argument and participating in academic debate. Essentially you are persuading your reader of your position on the topic at hand. This is about taking the information you have described and using it in some way. This could be writing things like:

• why it is relevant to your argument,
• how it relates to other literature,
• how it relates to the focus of your assignment
• how a theory can be put into practice,
• why it is significant,
• why you are not persuaded by it,
• how it leads you to reach your conclusion.

The University of Leeds gives some good examples of descriptive vs critical writing on their website: Critical writing .

Table comparing functions of descriptive and critical writing

The table below gives more examples of the difference between descriptive and critical writing 

Persuade don't inform

To summarise, when you are writing critically you are persuading the reader of your position on something whereas when you are writing descriptively you are just informing them of something you have read, observed or done. We take you through the process of deciding on, and demonstrating your position in your writing on the next page: Deciding your position .

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Chapter 6: Thinking and Analyzing Rhetorically

6.8 What is Critical Analysis

Julie A. Townsend

What is critical analysis?

Critical analysis is a term that students may hear often, especially as they progress through university courses and move into the twenty-first century workforce. Teachers and future employers want to see critical analysis applied in a variety of ways. Every context will have different ways that are standard for critical analysis of situations, data, and problems. Broadly, critical thinking is a way of looking at a situation that goes beyond first impressions and cliches. This section will describe specific techniques for critical analysis that can be used across different situations, especially for discovering more about writing and topics relevant to writing studies.

How can I do critical analysis?

William Thelin in Writing Without Formulas offers eight concrete ways to perform critical analysis: “interrogating the obvious,” “seeing patterns,” “finding what’s not there,” looking at “race, class, and gender,” “twisting the cliché,” “unearthing agendas,” and asking, “who profits?” (28—47). The following sections are originally derived from Thelin’s categories but are modified to better study writing in context, since many first-year writing classes at CSU following the “writing-about-writing” theme (as described by Downs and Wardle in “Teaching about writing, righting misconceptions”).

This chapter will work from an example scenario in which the writer aims to detail and understand the reading, writing, communication, and education that is taking place in one online asynchronous course. The writer’s originating research question is: What kinds of reading, writing, communication, and education takes place in this one asynchronous course? After the writer has written down their initial thoughts on the course and how communication works in the specific situation, they can use the following guidelines to write more and dig deeper into the context they are studying.

Detailing the Basics

Before the writer can use critical analysis, they need to clearly identify and describe details in the context. Details can help the writer more clearly understand the situation they are studying. Details are also necessary for readers to follow along with the critical analysis that the writer is performing.

Questions to help the writer detail the basics for studying communication in one asynchronous online course

• What did the instructor write?
• What are the students expected to write?
• Where, how, and why are they expected to write?
• How does communication between students occur?
• What about communication with the teacher?
• How is the course organized?
• What kinds of resources are used in the course?
• Are students expected to read every word on the course page? What words are they required to read?
• What kinds of external documents does the teacher expect students to use?

When the writer begins critical analysis with details of the basic situation, nothing is too mundane or obvious to skip over in the writing process. Specific details help the context come to life for both the writer and the readers. Writers should aim to draw a living picture of the situation. Then, from that living picture, the writer can work to analyze the situation in a more complete manner using the following suggestions.

Look for clusters, patterns, and coordination

After the writer has a drawn a clear picture for themselves and for the reader of what kinds of reading, writing, and communication are going on in the context they are describing, they can look for connections and links among these texts, resources, and people.

• A cluster includes technologies, people, texts, or ideas that exist near one another in a situation.
• o Clusters and patterns can help writers see the relationships between different elements and can help the writer see and understand a situation differently.
• o Coordination can help the writer see how separate acts of reading, writing, and communication work together to complete larger tasks.

Questions to help the writer find clusters, patterns, and coordination while studying communication in one asynchronous online course

• How does the student in the course group together texts to perform a task?
• Has the instructor supplied readings that the students need to write about?
• How does the student use assigned texts (possibly with other texts or technologies) in their writing process?
• What about external texts that the student needs to gather? How do those texts work into their writing process?
• Are certain texts often grouped together in the instruction or writing process?
• What kinds of resources do students tie together to complete assignments?
•  How do technologies outside of the course (like using social media or messaging classmates) work in conjunction with other texts and resources when the student is completing course work?

A deeper look at coordination

In writing studies, researchers can look for how texts are used in coordination with one another to learn more about the writing process and to describe how exactly people write and get work done. The concept of textual coordination (Slattery, “Technical writing as textual coordination”; Pigg, “Coordinating constant invention”) helps researchers to better understand how writers use resources (from computer programs to emails to syllabi to dictionaries) to write.

For research writing especially, writers tend to have multiple tabs or windows open on their computers with articles, websites, and the word processor they are using. The tying together of these resources by the writer is textual coordination. According to Shaun Slattery in “Undistributing Work through Writing”, the study of textual coordination emerged from researchers looking into how distributed work takes place in environments that are often mediated by computers (313). Many twenty-first century knowledge-working careers use a model of distributed work and rely on “the ability to identify, rearrange, circulate, abstract, and broker information” (Johnson-Eilola qtd. on Slattery 312). While most first-year writers may not have much career experience in knowledge working, they do have experience tying together resources and technologies. For example: reading a homework assignment and taking notes in a separate document and then using those texts in an essay is an example of textual coordination.

Looking through the lens of intersectionality

This section is borrowed (using Creative Commons Licensing) from “Unit I: An Introduction to Women, Gender, and Sexuality Studies” in the open-education resource textbook An Introduction to Women, Gender, and Sexuality Studies . “Within intersectional frameworks, race, class, gender, sexuality, age, ability, and other aspects of identity are considered mutually constitutive; that is, people experience these multiple aspects of identity simultaneously and the meanings of different aspects of identity are shaped by one another. In other words, notions of gender and the way a person’s gender is interpreted by others are always impacted by notions of race and the way that person’s race is interpreted. For example, a person is never received as just a woman, but how that person is racialized impacts how the person is received as a woman. So, notions of blackness, brownness, and whiteness always influence gendered experience, and there is no experience of gender that is outside of an experience of race. In addition to race, gendered experience is also shaped by age, sexuality, class, and ability; likewise, the experience of race is impacted by gender, age, class, sexuality, and ability.” For more information on intersectionality, read more in their chapter and textbook .

By asking questions about race, class, gender, ability, sexuality, and the intersections between these categories, writers can perform more critical analysis.

Questions to help the writer perform analysis with intersectional lenses while studying communication in one asynchronous online course

• What is the race, class, gender, sexuality, and ability of the authors of the readings we are assigned? How do these categories intersect in the lives of the authors?
• Do the statistics of the authors assigned for students to read match with the demographics of experts in the field?
• How are race, class, gender, ability, and sexuality distributed in the field overall?
• If there are inequalities in the demographics of professionals in the field, are there initiatives that work towards inviting more diversity into the field?
• What kinds of reading, writing, and communication are missing or different from similar contexts?
• Could resources be added to enhance communication, representation, understanding, or ease of access? What would those resources be?

What could be added?

In this stage of analysis, the writer should take a few steps back from the details of the context they are studying so that they might be able to see what could be added to the environment they are studying . The writer could compare the context they are studying to other contexts to help see what might be missing.

Questions to help the writer perform analysis on what could be added?

If the writer is performing critical analysis in a context where the previously discussed categories might not apply, “What is Critical Analysis?” by The University of Bradford offers a broad framework for critical analysis that can be applied beyond topics relevant to writing, reading, and communication. The University of Bradford describes critical analysis as part of the process that includes: “description,” “analysis,” and “evaluation” (2). For description, it suggests that writers focus on answering questions starting with “what”, “where”, “who”, and “when” (2). For the analysis stage, it suggests answering “how”, “why”, and “what if?” (2). Evaluation includes “so what?” and “what next?” Writers can use the categories outlined here to perform critical analysis that adds depth, texture, and details to thoughts and observations.

Works Cited

Downs, Douglas, and Elizabeth Wardle. “Teaching about writing, righting misconceptions:(Re)envisioning” first-year composition” as” Introduction to Writing Studies”.”  College composition and communication  (2007): 552-584.

Kang, Miliann, Donovan Lessard, Laura Heston, and Sonny Nordmarken. Introduction to Women, Gender, and Sexuality Studies . UMassAmherst Libraries, Pressbooks.

Pigg, Stacey. “Coordinating constant invention: Social media’s role in distributed work.” Technical Communication Quarterly 23.2 (2014): 69-87.

Slattery, Shaun. “Technical writing as textual coordination: An argument for the value of writers’ skill with information technology.” Technical Communication 52.3 (2005): 353.

Slattery, Shaun. “Undistributing work through writing: How technical writers manage texts in complex information environments.” Technical Communication Quarterly 16.3 (2007):    311-325.

Thelin, William. Writing Without Formulas. Second edition. Cengage, 2009. “What is Critical Analysis?” Academic Skills Advice. The University of Bradford. Accessed 17 October 2019.

A Guide to Rhetoric, Genre, and Success in First-Year Writing by Julie A. Townsend is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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4 The difference between descriptive and critical writing

It is important that you understand the difference between descriptive writing and adopting a critical stance, and are able to show clear evidence of your understanding in your writing. Table 2 provides some examples of this.

Writing a Critical Analysis

What is in this guide, definitions, putting it together, tips and examples of critques.

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This guide is meant to help you understand the basics of writing a critical analysis. A critical analysis is an argument about a particular piece of media. There are typically two parts: (1) identify and explain the argument the author is making, and (2), provide your own argument about that argument. Your instructor may have very specific requirements on how you are to write your critical analysis, so make sure you read your assignment carefully.

Critical Analysis

A deep approach to your understanding of a piece of media by relating new knowledge to what you already know.

Part 1: Introduction

• Identify the work being criticized.
• Present thesis - argument about the work.
• Preview your argument - what are the steps you will take to prove your argument.

Part 2: Summarize

• Provide a short summary of the work.
• Present only what is needed to know to understand your argument.

Part 3: Your Argument

• This is the bulk of your paper.
• Provide "sub-arguments" to prove your main argument.
• Use scholarly articles to back up your argument(s).

Part 4: Conclusion

• Reflect on  how  you have proven your argument.
• Point out the  importance  of your argument.
• Comment on the potential for further research or analysis.
• Cornell University Library Tips for writing a critical appraisal and analysis of a scholarly article.
• Queen's University Library How to Critique an Article (Psychology)
• University of Illinois, Springfield An example of a summary and an evaluation of a research article. This extended example shows the different ways a student can critique and write about an article
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The difference between descriptive and critical writing

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Categories: LearnHigher Resources | Critical Thinking and Reflection

Published: 26/01/2012

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• Reading Critically

What is Critical Analysis?

Analysis is a word that is also often used when taking a critical approach to something. It could be that you look at some evidence and if you think it is good quality, you may choose to include that in your essay or writing to help support your argument. When you have analysed different sets of evidence you may  synthesize all the ideas gathered from multiple sources bringing together the relevant information into a different argument or idea. 

To evaluate something or someone, you think and consider it or them in order to make a judgment about it/them; this could be as simple as how good or bad they are. When you critically evaluate something or someone you consider how judgments vary from different perspectives and how some judgments are stronger than others. This often means creating an objective, reasoned argument for your overall case, based on the evaluation from different perspectives.

Taking a critical approach when you are studying involves constantly asking questions and keeping an open mind.

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How to write a critical analysis

Unlike the name implies a critical analysis does not necessarily mean that you are only exploring what is wrong with a piece of work. Instead, the purpose of this type of essay is to interact with and understand a text. Here’s what you need to know to create a well-written critical analysis essay.

What is a critical analysis?

A critical analysis examines and evaluates someone else’s work, such as a book, an essay, or an article. It requires two steps: a careful reading of the work and thoughtful analysis of the information presented in the work.

Although this may sound complicated, all you are doing in a critical essay is closely reading an author’s work and providing your opinion on how well the author accomplished their purpose.

Critical analyses are most frequently done in academic settings (such as a class assignment). Writing a critical analysis demonstrates that you are able to read a text and think deeply about it. However, critical thinking skills are vital outside of an educational context as well. You just don’t always have to demonstrate them in essay form.

How to outline and write a critical analysis essay

Writing a critical analysis essay involves two main chunks of work: reading the text you are going to write about and writing an analysis of that text. Both are equally important when writing a critical analysis essay.

Step one: Reading critically

The first step in writing a critical analysis is to carefully study the source you plan to analyze.

If you are writing for a class assignment, your professor may have already given you the topic to analyze in an article, short story, book, or other work. If so, you can focus your note-taking on that topic while reading.

Other times, you may have to develop your own topic to analyze within a piece of work. In this case, you should focus on a few key areas as you read:

• What is the author’s intended purpose for the work?
• What techniques and language does the author use to achieve this purpose?
• How does the author support the thesis?
• Who is the author writing for?
• Is the author effective at achieving the intended purpose?

Once you have carefully examined the source material, then you are ready to begin planning your critical analysis essay.

Step two: Writing the critical analysis essay

Taking time to organize your ideas before you begin writing can shorten the amount of time that you spend working on your critical analysis essay. As an added bonus, the quality of your essay will likely be higher if you have a plan before writing.

Here’s a rough outline of what should be in your essay. Of course, if your instructor gives you a sample essay or outline, refer to the sample first.

• Background Information

Critical Analysis

Here is some additional information on what needs to go into each section:

Background information

In the first paragraph of your essay, include background information on the material that you are critiquing. Include context that helps the reader understand the piece you are analyzing. Be sure to include the title of the piece, the author’s name, and information about when and where it was published.

“Success is counted sweetest” is a poem by Emily Dickinson published in 1864. Dickinson was not widely known as a poet during her lifetime, and this poem is one of the first published while she was alive.

After you have provided background information, state your thesis. The thesis should be your reaction to the work. It also lets your reader know what to expect from the rest of your essay. The points you make in the critical analysis should support the thesis.

Dickinson’s use of metaphor in the poem is unexpected but works well to convey the paradoxical theme that success is most valued by those who never experience success.

The next section should include a summary of the work that you are analyzing. Do not assume that the reader is familiar with the source material. Your summary should show that you understood the text, but it should not include the arguments that you will discuss later in the essay.

Dickinson introduces the theme of success in the first line of the poem. She begins by comparing success to nectar. Then, she uses the extended metaphor of a battle in order to demonstrate that the winner has less understanding of success than the loser.

The next paragraphs will contain your critical analysis. Use as many paragraphs as necessary to support your thesis.

Discuss the areas that you took notes on as you were reading. While a critical analysis should include your opinion, it needs to have evidence from the source material in order to be credible to readers. Be sure to use textual evidence to support your claims, and remember to explain your reasoning.

Dickinson’s comparison of success to nectar seems strange at first. However the first line “success is counted sweetest” brings to mind that this nectar could be bees searching for nectar to make honey. In this first stanza, Dickinson seems to imply that success requires work because bees are usually considered to be hard-working and industrious.

In the next two stanzas, Dickinson expands on the meaning of success. This time she uses the image of a victorious army and a dying man on the vanquished side. Now the idea of success is more than something you value because you have worked hard for it. Dickinson states that the dying man values success even more than the victors because he has given everything and still has not achieved success.

This last section is where you remind the readers of your thesis and make closing remarks to wrap up your essay. Avoid summarizing the main points of your critical analysis unless your essay is so long that readers might have forgotten parts of it.

In “Success is counted sweetest” Dickinson cleverly upends the reader’s usual thoughts about success through her unexpected use of metaphors. The poem may be short, but Dickinson conveys a serious theme in just a few carefully chosen words.

What type of language should be used in a critical analysis essay?

Because critical analysis papers are written in an academic setting, you should use formal language, which means:

• No contractions
• Avoid first-person pronouns (I, we, me)

Do not include phrases such as “in my opinion” or “I think”. In a critical analysis, the reader already assumes that the claims are your opinions.

Your instructor may have specific guidelines for the writing style to use. If the instructor assigns a style guide for the class, be sure to use the guidelines in the style manual in your writing.

Additional t ips for writing a critical analysis essay

To conclude this article, here are some additional tips for writing a critical analysis essay:

• Give yourself plenty of time to read the source material. If you have time, read through the text once to get the gist and a second time to take notes.
• Outlining your essay can help you save time. You don’t have to stick exactly to the outline though. You can change it as needed once you start writing.
• Spend the bulk of your writing time working on your thesis and critical analysis. The introduction and conclusion are important, but these sections cannot make up for a weak thesis or critical analysis.
• Give yourself time between your first draft and your second draft. A day or two away from your essay can make it easier to see what you need to improve.

Frequently Asked Questions about critical analyses

In the introduction of a critical analysis essay, you should give background information on the source that you are analyzing. Be sure to include the author’s name and the title of the work. Your thesis normally goes in the introduction as well.

A critical analysis has four main parts.

• Introduction

The focus of a critical analysis should be on the work being analyzed rather than on you. This means that you should avoid using first person unless your instructor tells you to do otherwise. Most formal academic writing is written in third person.

How many paragraphs your critical analysis should have depends on the assignment and will most likely be determined by your instructor. However, in general, your critical analysis paper should have three to six paragraphs, unless otherwise stated.

Your critical analysis ends with your conclusion. You should restate the thesis and make closing remarks, but avoid summarizing the main points of your critical analysis unless your essay is so long that readers might have forgotten parts of it.

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Academic writing

• Thinking about grammar
• Correct punctuation
• Writing in an academic style

Styles of writing

Descriptive writing, analytical writing, reflective writing.

• Effective proof reading

When you’re asked to write an assignment you might immediately think of an essay, and have an idea of the type of writing style that’s required.

This is likely to be a ‘discursive’ style, as often in an essay you’re asked to discuss various theories, ideas and concepts in relation to a given question. For those coming to study maths and science subjects, you might be mistaken in thinking that any guidance on writing styles is not relevant to you.

In reality, you’ll probably be asked to complete a range of different assignments, including reports, problem questions, blog posts, case studies and reflective accounts. So for the mathematicians and scientists amongst you, you’re likely to find that you’ll be expected to write more at university than perhaps you’re used to. You may be asked to write essays, literature reviews or short discussions, in which all the advice on this course, including the advice below, is of relevance. In addition, even writing up your equations or findings will require you to carefully consider your structure and style. Different types of assignment often call for different styles of writing, but within one assignment you might use a variety of writing styles depending on the purpose of that particular section.

Three main writing styles you may come across during your studies are:  descriptive ,  analytical  and  reflective . These will require you to develop your writing style and perhaps think more deeply about what you’ve read or experienced, in order to make more meaningful conclusions.

The diagram below shows examples of each of these writing styles. It takes as an example a trainee teacher’s experience within a classroom and shows how the writing style develops as they think more deeply about the evidence they are presented with.

All of these forms of writing are needed to write  critically . You’ll come across the term  critical  in lots of contexts during your studies, for example critical thinking, critical writing and critical analysis. Critical writing requires you to:

• view a topic from a variety of angles

• evaluate evidence

• present a clear conclusion

• and reflect on the limitations of your own argument.

So how will you know which writing style is needed in your assignments? As mentioned, most academic assignments will require a certain amount of description but your writing should mainly be analytical and critical. You’ll be given an assignment brief and marking guidelines, which will make clear the expectations with regards to writing style. Make sure you read these carefully before you begin your assignment.

It’s unlikely that you’ll be asked to only write  descriptively , though this form of writing is useful in certain sections of reports and essays. You’ll need elements of description in essays before you go on to analyse and evaluate. The description is there to help the reader understand the key principles and for you to set the scene. But description, in this case, needs to be kept to a minimum.

However  descriptive  writing is often used in the methodology and findings sections of a report, or when completing a laboratory report on an experiment. Here you’re describing (for instance) the methods you adopted so that someone else can replicate them should they need to. Descriptive writing focuses on answering the ‘what?’ ‘when’ and ‘who’ type questions.

Analytical  writing style is often called for at university level. It involves reviewing what you’ve read in light of other evidence. Analytical writing shows the thought processes you went through to arrive at a given conclusion and discusses the implications of this. Analytical writing usually follows a brief description and focuses on answering questions like: ‘why?’ ‘how?’ and ‘so what?’

Have a look at our LibGuide on Critical Analysis: Thinking, Reading, and Writing for more in-depth guidance on analytical writing.

Not all writing will require you to write  reflectively . However you might be asked to write a reflective account after a work placement or find that at the end of a report it might be appropriate to add some personal observations. This style of writing builds on analysis by considering the learning you’ve gained from practical experience. The purpose of the reflection is to help you to make improvements for the future and, as such, it is a more ‘personal’ form of academic writing often using the first person. Evidence still has a key role in reflective writing; it’s not just about retelling your story and how you felt. And evidence in the case of reflection will include your own personal experience which adds to the discussion. Reflective writing focuses on future improvements and answers questions like ‘what next?’

• Practice-based and reflective learning LibGuide More details on thinking and writing reflectively.
• << Previous: Writing in an academic style
• Next: Effective proof reading >>
• Last Updated: Apr 30, 2024 10:24 AM
• URL: https://libguides.reading.ac.uk/writing

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Distinguishing between description and analysis in academic writing

When I switched from chemical engineering (my undergraduate degree) to political science and human geography (my doctoral degree), I went through economics of technical change and international marketing (my Masters). But the chemical engineering component was still very strong during my Masters. I remember reading comments from a professor’s marker (yes, my professor didn’t even grade my essay!) saying “ lacks analysis “.

WHAT THE HELL IS ANALYSIS IF NOT WHAT I AM WRITING THEN?! Now, when I read student essays, or Masters/PhD theses, I find myself writing similar comments: “ this is a very good description, but lacks real analysis “. I asked both the Political Scientists Facebook group (of which I’m proud of being part of) and the Research Companion Facebook group (a fantastic resource created by Dr. Petra Boynton, author of the book “The Research Companion”).

I received A LOT of really good feedback on both groups (who said that Facebook was only good for posting photos of your kids?) which I am detailing here (I’ve asked for permission to attribute whoever recommended a particular book or reading).

Political Scientists

• The Craft of Research . (by Booth et al) Shane Gunderson, Cheryl Van Den Handel, and Jay De Sart recommended this book, which I have read and own. This is a book on how to undertake social science research, and it’s one I definitely recommend too.
• They Say, I Say . Omar Wasow recommended this book, seconded by Jackie Gehring. Erin Ackerman, author of the “Analyze This: Writing in the Social Sciences” chapter of “They Say, I Say” book, mentioned that her chapter Chapter 13 is focused on social sciences’ writing and a few political science examples.
• Empirical Research in Political Science (by Leanne Powner). I had heard of Leanne’s work before and I *thought* I had a copy of this book, but I think it’s one of the ones I lost at MPSA 2016 (don’t ask). So, I’ve requested an examination copy and will report back once I’ve read it.
• Writing a Research Paper in Political Science: A Practical Guide to Inquiry, Structure, and Method (by Lisa Baglioni). Recommended by Mirya Holman, Mary Anne Mendoza, and Jay De Sart. I don’t own this book either, but the comments I read were that the book walks the student through the process of writing a research paper quite clearly. I’ve also requested an examination copy, and will report back once I’ve read it
• Matthew Parent recommended a handout by John Gerring et al (yes, Gerring from case studies! The excerpt is from Gerring and Dino Christenson’s forthcoming book). I love both Gerring and Christenson’s work so I’m always happy to promote it.

I found through Google a few handouts, but these three were the ones that stood out to me, and were also the simplest for me to refer my students for a reading.

• Summary vs. Description vs. Analysis vs. Argument . One handout I found clearly describes the differences between summary, description, analysis and argument. This one is an anthropology-focused one .
• This checklist tells the reader how to distinguish between description (telling things how they are, detailed accounts of facts and data) and analysis (explaining the implications, tying theory and empirical evidence to the description).
• This short guide from the University of Birmingham Writing Centre on critical thinking and the differences between analytical and descriptive writing really outlines when you use description, when you should be analyzing and how to differentiate between both.

Over on The Research Companion Facebook group, I got a few responses.

• Dr. Helen Kara recommended her book: Research and evaluation for busy students and practitioners. A time saving guide (having read some of her work and writing, I can vouch for it!).
• Sarah Howcutt shared with me a couple of handouts where she clearly explains what description is and how to insert analysis into your paragraphs.

I then searched my own Mendeley library for examples of good articles I had read that could show my students what analysis looks like, vis-a-vis descriptive text. Here are a few examples I tweeted.

Describing refers to providing details. Analyzing implies comparing, contrasting, weighing the evidence for additional insight, critiquing — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017

The first one is from a World Development 2014 article by Alison Post and Veronica Herrera on public service delivery in Latin America (focusing on water and wastewater). Here, I wanted the reader to see how Herrera and Post set up a comparison between what the literature says versus what their own analysis shows.

. @veromsherrera and Alison Post offer an excellent example of the “They Say/I Say” model, showing what literature says vs their analysis pic.twitter.com/SYUMxtd543 — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017

. @veromsherrera Note that here @veromsherrera and Post analyze the literature on privatization and offer their own analysis of what it fails to account for. — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017

. @veromsherrera This is important when teaching our students: contrast what the literature says with your own empirical findings. Also, model They Say/I Say — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017

. @veromsherrera If you’re wondering what I mean by the “They Say/I Say” model, it’s based on Graff & Birkenstein book https://t.co/Nu7SkRPKT4 h/t @owasow — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017

This example comes from Kathryn Harrison’s 2002 Governance article comparing US/Canada/Sweden and dioxins control policy. This paper investigates the role of ideas, interests and institutions on policy change. In this example, I wanted to show how Harrison weighs evidence from each one of the three case studies and evaluates the differential impact that ideas, interests and institutions had on policy evolution.

In comparison of pulp and paper policies US/Canada/Sweden, @khar1958 weighs evidence & explanatory power of ideas, interests & institutions pic.twitter.com/ce2tbqxhmb — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017

. @khar1958 Note that while @khar1958 finds compelling evidence of impact of ideas, she points out to interplay of ideas, interests AND institutions. — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017

. @khar1958 This is important when we teach students to offer evidence. We need to tell them to offer alternative explanations, weigh evidence/results. — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017

I then used Josh Cousins and Josh Newell’s article on political-industrial ecology in Los Angeles’ water supply infrastructure to show the reader how Cousins and Newell present descriptive text on Los Angeles and its water supply and then connect it to the literature through analysis.

In their paper on the urban industrial-political ecology of Los Angeles water supply, @JoshJCousins & Newell link description w/analysis pic.twitter.com/IPesNK1uSx — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017

. @JoshJCousins I used pink to denote descriptive text, and orange to show where Cousins & Newell link the description above with theoretical underpinnings. — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017

I used Megan Hatch and Elizabeth Rigby’s article on state-level governments as laboratories of democracy and their study of state-level inequality to show how you can use data (quantitative, in this case) to create an argument and dispel previously held beliefs/preconceived ideas/previous theoretical and empirical findings with their own.

Here, @meganehatch & E. Rigby show (graph above screenshot) how their results counter our traditional understanding of inequality in states pic.twitter.com/Ew3wTHcsYc — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017

I also used a paper by Melissa Merry on tweeting and the framing of gun policy using the Narrative Policy Framework. In this example I wanted to show how Merry mobilizes her empirical findings to construct a new measure and to explain the theoretical and empirical implications of her findings.

. @melpoague offers good example of ANALYSIS – “here is how I constructed an index, and what my results imply” (on gun policy narratives) pic.twitter.com/yXUyPCijWk — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017

From David Carter and Chris Weible’s study of smoking bans in Colorado in 1977 and 2006, I drew an example where I show how Carter and Weible set up an empirical question (a hypothesis) and then use their data to explain differences between both smoking bans.

In their paper comparing Colorado smoking bans 1977 vs 2006 @DCarterSLC and @chris_weible answer 1 of their questions w data & analysis pic.twitter.com/0SY5E1VAhs — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017

Another way in which researchers show they’ve done analysis is in case study selection. In this paper by Rob de Leo and Donnelly, they do a study of policy transfer and the adoption of the Affordable Care Act in Massachusetts. De Leo and Donnelly clearly outline the various reasons why choosing this particular case makes sense.

In their paper on policy transfer and implementation of the Affordable Care Act, @r_deLeo and Donelly clearly outline case study selection pic.twitter.com/afeHzT8amM — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017

I am thankful to everyone who provided me with links to books, handouts, etc. And I hope this blog post will be useful to anybody who needs to teach analysis vs. description. I certainly will be using it with my own students and research assistants!

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Posted in academia , writing .

Tagged with academic writing , analysis , synthesis , writing .

By Raul Pacheco-Vega – May 7, 2017

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Analytical vs. Descriptive Writing: Definitions and Examples

Scholars at all levels are expected to write. People who are not students or scholars often engage in writing for work, or to communicate with friends, family, and strangers through email, text messages, and social media. Academia recognizes two major types of writing—descriptive writing and analytical writing—which are both used in non-academic situations as well. As you might expect, descriptive writing focuses on clear descriptions of facts or things that have happened, while analytical writing provides additional analysis.

Descriptive writing is the most straightforward type of academic writing. It provides accurate information about "who", "what", "where", and "when". Examples of descriptive writing include:

• Summarizing an article (without offering additional insight)
• Stating the results of an experiment (without analyzing the implications)
• Describing a newsworthy event (without discussing possible long-term consequences)

High school students and undergraduates are most commonly asked to write descriptively, to show that they understand the key points of a specific topic (e.g. the major causes of World War II).

Analytical writing goes beyond summarizing information and instead provides evaluation, comparison, and possible conclusions. It addresses the questions of "why?", "so what?", and "what next?". Examples of analytical writing include:

• The discussion section of research papers
• Opinion pieces about the likely consequences of newsworthy events and the steps that should be taken in response.

High school students and undergraduates are sometimes asked to write analytically to "stretch their thinking". Possible topics might include "Could World War II have been avoided?" and "How can CRISPR-Cas9 technology improve human health?". The value of any such analysis is entirely dependent on the writer's ability to understand and clearly explain relevant information, which would be explained through descriptive writing. For graduate students and professional researchers, the quality of their work is at least partially based on the quality of their analysis.

The following table from The Study Skills Handbook by Stella Cottrell (2013, 4th edition, Palgrave Macmillan, page 198) is commonly used to summarize the differences between descriptive writing and analytical writing.

Description and analysis are also used in spoken communication such as presentations and conversations, and in visual communication such as diagrams and memes. In all of these cases, it is important to communicate clearly and effectively, and to use reliable sources of information.

Descriptive writing and analytical writing are often used in combination. In job application cover letters and essays for university admission, adding analytical text can provide context for otherwise unremarkable statements.

• Descriptive text: "I graduated from Bear University in 2020 with a B.S. in Chemistry and a cumulative GPA of 3.056."
• Analytical text: "While I struggled with some of my introductory courses, I proactively sought help to fill gaps in my understanding, and earned an "A" grade for all five of my senior year science courses. Therefore, I believe I am a strong candidate for . . ."

Combining description and analysis can also be very effective when discussing the significance of research results.

• Descriptive text: "Our study found significant (>2 ug/L) concentrations of polyfluoroalkyl substances (PFAS) in blood samples from all 5,478 study participants."
• Analytical text: "These results are alarming because the sample population included people who range in age from 1 month old to 98 years old, who live on five different continents, who reside in extremely rural areas and in urban areas, and who have little to no direct contact with products containing PFAS. PFAS are called "forever chemicals" because they are estimated to take hundreds or thousands of years to degrade. According to the US Centers for Disease Control (CDC), PFAS can move through soils to contaminate drinking water, and bioaccumulate in animals. Further research is urgently needed to better understand the adverse effects that PFAS have on human health, to identify the source of PFAS in rural communities, and to develop a method to sequester or destroy PFAS that have already entered the environment."

In both of the examples above, the analytical text includes additional facts (e.g. "A" grade for senior science courses; 1 month old to 98 years old) that help strengthen the argument. The student's transcript and the research paper's results section would contain these same facts—along with many others—written descriptively or presented in graphs, tables, or lists. For the analytical text, the author is trying to persuade the reader, and has therefore selected relevant facts to support their argument.

In the example about PFAS, the author's argument is further strengthened by citing additional information from a reputable source (the CDC). In reports where the author is supposed to be unbiased (e.g. a journalist writing descriptively), a similar effect can be obtained by quoting reputable sources. For example, "Professor of environmental science Kim Lee explains that PFAS are. . ." In these situations, it is often appropriate to present opposing views, as long as they come from reputable sources. This strategy of quoting or citing reputable sources can also be effective for students and professionals who do not have strong credentials in the topic under discussion.

Analytical writing supports a point of view

People cannot choose their own facts, but the same facts can be used to support very different points of view. Let's consider some different points of view that can be supported by the PFAS example from above.

• Scientific point of view: "Further research is urgently needed to better understand the adverse effects that PFAS have on human health, to identify the source of PFAS in rural communities, and to develop a method to sequester or destroy PFAS that have already entered the environment."
• Policy point of view: "Legislative action is urgently needed to ban the use of all PFAS, instead of banning new PFAS one at a time. Abundant and reliable data strongly indicates that all PFAS have similar effects, even if they have small differences in chemical composition. Given such evidence, the impetus must be on the chemical industry to prove safety, rather than on the general public to prove harm."
• Legal point of view: "Chemical companies have known about the danger of PFAS for years, but hid the evidence and continued to use these chemicals. Therefore, individuals and communities who have been harmed have the right to sue for damages."

These three points of view focus on three different fields (science, policy, and law), but all have a negative view of PFAS. The next example shows how the same factual information can be used to support opposing views.

• Descriptive text: " According to Data USA , the average fast food worker in 2019 was 26.1 years old, and earned a salary of $12,294 a year."
• Point of view #1: "These data show why raising the minimum wage is unnecessary. Most fast food workers are young, with many being teenagers who are making extra money while living with their parents. The majority will eventually transition to jobs that require more skills, and that are rewarded with higher pay. If we mandate that companies pay low-skill workers more than required by the free market, then more highly skilled workers will also demand a pay raise. This will hurt businesses, contribute to inflation, and have no net benefit."
• Point of view #2: "These data show why raising the minimum wage is so important. On average, for every 16-year-old working in fast food for extra money, there is a 36-year-old trying to make ends meet. As factory jobs have moved overseas, employees without specialized skills have turned to fast food for steady employment. According to the UC Berkeley Labor Center , for families with someone working full-time (40 hours/week) in fast food, more than half are enrolled in public assistance programs. These include Medicaid, food stamps, and the Earned Income Tax Credit. Therefore, taxpayers are subsidizing companies that pay poverty wages, so that their employees can have access to basic necessities like food and healthcare."

A primary purpose of analytical writing is to show how facts (explained through descriptive writing) support a particular conclusion or a particular path forward. This often requires explaining why an alternative interpretation is less satisfactory. This is how scholarly work—and good discussions in less formal situations—contribute to our collective understanding of the world.

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NBA Draft Lottery Winners’ Recent Impact vs. Historic Legends: A Critical Analysis

The Hawks won the NBA Draft lottery, but recent No. 1 overall picks have not had the transformative impact they once did, raising concerns about the evolving landscape of top draft selections.

• In the 2021-22 and 2022-23 seasons, no player chosen first overall made it to the first or second All-NBA teams.
• Over the past 10 years, there have been 14 instances of players drafted outside the top 10 earning first-team All-NBA honors.

The decline in impact from No. 1 overall picks may also reflect a combination of teams' drafting inefficiencies and a potential decrease in talent development within various basketball systems.

• Recent NBA Drafts have seen a lack of immediate game-changing talents at the No. 1 spot, contrasting with historical trends of impactful top draft selections.
• The emergence of top-tier NBA talents outside the top 10 picks in recent years indicates a shift in the drafting landscape towards undervalued players.

The NBA hopes upcoming drafts may witness the rise of new elite talents like Edwards, Wembanyama, and Banchero, potentially ushering in a new era of impactful top picks.

As the nature of No. 1 overall picks' impact evolves, NBA teams may need to reconsider their drafting strategies and explore untapped talent pools to secure future success in a changing draft landscape.

Read more at @sportingnews

The summary of the linked article was generated with the assistance of artificial intelligence technology from OpenAI

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The Hawks won the NBA Draft lottery, but No. 1 overall picks haven't been the game-changers they once were | Sporting News

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Elections Today

Recent projections, delegate tracker, maryland, west virginia and nebraska primaries 2024: alsobrooks beats trone, gop incumbents survive, biden once slammed trump's china tariffs. now he's building on them: analysis.

Biden’s tariffs on $18B worth of Chinese goods focus on strategic industries.

Although the Biden administration won't admit it, new tariffs on China announced Tuesday represent a major shift for President Joe Biden .

Back in 2019, Biden slammed then-President Donald Trump 's move to impose tariffs on $300 billion worth of Chinese imports.

"Trump doesn't get the basics. He thinks his tariffs are being paid by China," Biden said at the time. "Any freshman econ student could tell you that the American people are paying his tariffs."

Then in 2020, while campaigning for the White House, Biden vowed to remove Trump's tariffs if elected.

Studies have shown that American consumers largely bore the brunt of those taxes.

But now, not only is Biden keeping those Trump-era tariffs in place, he actually is building on them.

It's true that Biden's new tariffs on $18 billion worth of Chinese imports are narrowly focused on a few strategic industries. At a Rose Garden event unveiling the new actions, Biden touted it as a "smart approach" to target goods such as electric vehicles, solar cells, steel, aluminum and certain medical equipment.

But he is maintaining many of the broad-based tariffs from the Trump-era that he was once highly critical of.

MORE: Biden announces new China tariffs on electric vehicles, solar, chips and more

U.S. Trade Representative Katherine Tai was repeatedly pressed on the apparent reversal during the White House daily press briefing.

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Predicting outcome after aneurysmal subarachnoid hemorrhage by exploitation of signal complexity: a prospective two-center cohort study

• Stefan Yu Bögli 1 ,
• Ihsane Olakorede 1 ,
• Michael Veldeman 3 ,
• Erta Beqiri 1 ,
• Miriam Weiss 3 , 4 ,
• Gerrit Alexander Schubert 3 , 4 ,
• Jan Folkard Willms 2 ,
• Emanuela Keller 2 &
• Peter Smielewski 1  

Critical Care volume  28 , Article number:  163 ( 2024 ) Cite this article

Metrics details

Signal complexity (i.e. entropy) describes the level of order within a system. Low physiological signal complexity predicts unfavorable outcome in a variety of diseases and is assumed to reflect increased rigidity of the cardio/cerebrovascular system leading to (or reflecting) autoregulation failure. Aneurysmal subarachnoid hemorrhage (aSAH) is followed by a cascade of complex systemic and cerebral sequelae. In aSAH, the value of entropy has not been established yet.

aSAH patients from 2 prospective cohorts (Zurich—derivation cohort, Aachen—validation cohort) were included. Multiscale Entropy (MSE) was estimated for arterial blood pressure, intracranial pressure, heart rate, and their derivatives, and compared to dichotomized (1–4 vs. 5–8) or ordinal outcome (GOSE—extended Glasgow Outcome Scale) at 12 months using uni- and multivariable (adjusted for age, World Federation of Neurological Surgeons grade, modified Fisher (mFisher) grade, delayed cerebral infarction), and ordinal methods (proportional odds logistic regression/sliding dichotomy). The multivariable logistic regression models were validated internally using bootstrapping and externally by assessing the calibration and discrimination.

A total of 330 (derivation: 241, validation: 89) aSAH patients were analyzed. Decreasing MSE was associated with a higher likelihood of unfavorable outcome independent of covariates and analysis method. The multivariable adjusted logistic regression models were well calibrated and only showed a slight decrease in discrimination when assessed in the validation cohort. The ordinal analysis revealed its effect to be linear. MSE remained valid when adjusting the outcome definition against the initial severity.

Conclusions

MSE metrics and thereby complexity of physiological signals are independent, internally and externally valid predictors of 12-month outcome. Incorporating high-frequency physiological data as part of clinical outcome prediction may enable precise, individualized outcome prediction. The results of this study warrant further investigation into the cause of the resulting complexity as well as its association to important and potentially preventable complications including vasospasm and delayed cerebral ischemia.

Introduction

Aneurysmal subarachnoid hemorrhage (aSAH) remains a serious disease with often poor prognosis even after successful securing of the aneurysm [ 1 ]. Patients who survive the initial hemorrhage remain at risk for developing secondary brain injury, such as delayed cerebral ischemia (DCI) [ 2 ]. DCI is a major cause of death and disability after aSAH [ 3 ]. It is the consequence of complex interactions of neuronal activity, cerebral and systemic hemodynamics, and feedback mechanisms—neurovascular (un)coupling, cerebral autoregulation, and CO 2 reactivity [ 2 ]. Dynamic changes of multiple interacting factors including cerebral vasospasm [ 4 ], inflammatory markers [ 5 ], oxygenation [ 6 ], blood pressure, and cardiac output [ 7 ] precede DCI occurrence. The paramount goal of neurocritical care is to predict, counteract or even prevent these secondary injuries to improve patients’ outcome. Consequently, the acute period following the hemorrhage is accompanied by extensive multimodal monitoring within a neurocritical care unit (NCCU) environment. The monitoring comprises aspects of cerebral physiology and hemodynamics (incl. intracranial pressure (ICP), cerebral perfusion pressure (CPP)) integrated with systemic physiological parameters (arterial blood pressure, cardiac output, heart rate (HR), oxygenation, and ventilation) [ 8 ].

Signal complexity (i.e. entropy) describes the level of apparent disorder within a system. Low signal complexity predicts unfavorable outcome in a variety of diseases and is assumed to reflect increased rigidity of the cardio/cerebrovascular feedback/regulating system leading to (or reflecting) autoregulation failure [ 9 , 10 , 11 , 12 ] This, in turn, leaves the brain susceptible to secondary injury. Physiological systems are regulated by multiple, interacting, mechanisms leading to dynamically changing biosignals across different temporal scales. 14 Multiscale entropy (MSE), a version of signal complexity, estimates sample entropy over a range of increasingly downsampled (i.e. averaged) data [ 13 , 14 ] In comparison to sample entropy of a single scale MSE has the following benefits: 1. It allows for the evaluation of complex physiological systems that operate across different time scales; 2. It suppresses the impact of noise on the resulting metric. In 2012 Lu et al. described the association between decreased ICP signal complexity and unfavorable outcome after traumatic brain injury [ 9 ]. Zeiler et al. validated the concepts presented in a large multi-center cohort and extended the description to include other biosignals [ 15 ]. In aSAH, a metric related to signal complexity, heart rate variability, has shown, to a degree, an association with complications and unfavorable outcome [ 16 , 17 , 18 ]. However, other patho-physiological states such as sepsis decrease heart rate variability, whereby its use in clinical practice for prediction of specific complications has remained limited. We aimed to exploit the abundance of monitoring data acquired from each patient within an NCCU environment to assess the potential use of MSE as an outcome predictor after aSAH.

Materials and methods

The study was approved by the local ethics committees Zurich and Aachen and was in accordance with the ethical standards laid down in the 2013 Declaration of Helsinki for research involving human subjects. Informed consent was received before inclusion by the patient or their legal medical representative. Data from two prospective observational cohorts (University Hospital Zurich, Switzerland; the Rheinisch-Westfälische Technische Hochschule Aachen, Germany) was analyzed. The Zurich cohort was used as the derivation cohort to establish models and analyses, while the Aachen cohort was used for external validation.

Study population

For the Zurich cohort a total of 244 consecutively admitted adult patients with aSAH were recruited as part of the ICU Cockpit Prospective Cohort Study between 2016 and 2022. All of these received multimodal monitoring data acquisition and were consequently evaluated for inclusion. For the Aachen cohort a total of 316 consecutively admitted adult patients with aSAH were collected as part of a prospective cohort between 2014 and 2021. 102 of these received multimodal monitoring data acquisition and were consequently evaluated for inclusion. Inclusion criteria were: 1. aSAH due to an angiography confirmed ruptured aneurysm; 2. Admission to the NCCU and recording of high-resolution monitoring data. The only exclusion criterion was loss to follow up with missing 12-month outcome. Patients at both centers were treated according to the guidelines of the Neurocritical Care Society, American Heart Association guidelines, and the respective standard therapies of the two centers [ 19 , 20 ].

Data acquisition

The following relevant clinical data were prospectively included in the respective databases: Demographics, World Federation of Neurological Surgeons scale (WFNS)[ 21 ], modified Fisher Score (mFisher) [ 22 ], clinical course incl. aneurysm occlusion modality, occurrence of angiographic vasospasm (defined as narrowing of the vessels in neuroimaging independent of clinical symptoms), delayed cerebral infarction (DCI—infarction within neuroimaging not present on imaging performed within 24–48 h after aneurysm occlusion, and not attributable to other causes [ 23 ]), and outcome at 12 months (represented by the Glasgow Outcome Scale Extended—GOSE [ 24 ]). WFNS was evaluated after neurological resuscitation (i.e. after insertion of EVD and/or hematoma evacuation). In either center outcome was assessed during routine outpatient follow-up consultations or by contacting the patient, their next of kin, or caregiver by telephone in a structured interview. Physiological high-resolution data (at least 100 Hz—BP, ICP, HR) was collected in Zurich (Moberg Component Neuromonitoring Systems (CNS)—Moberg Research Inc, PA, USA) and Aachen (MPR2 logO Datalogger (Raumedic, Helmbrechts, Germany) or, after July 2018, Moberg Component Neuromonitoring Systems (CNS)—Moberg Research Inc, PA, USA). The data acquisition was started after admission to the respective NCCUs (after neurological resuscitation and generally after securing of the aneurysm) and stopped either when the patient was discharged to the ward or if invasive monitoring was deemed unnecessary.

Data preprocessing

The high-resolution (i.e. waveform) monitoring data from either center was transformed into an HDF5 format for streamlined analysis of the different formats. NCCU high-resolution waveform data contains, without exception, artifacts which are not representative of the patients’ physiology. Thus, raw waveform data was preprocessed using ICM + ® (Cambridge Enterprise Ltd, Cambridge, United Kingdom). Data was curated to remove artifacts using both manual and automated methods. The manual methods were applied to remove sections with arterial line failure (continuous reduction of the arterial blood pressure amplitude followed by flushing) and sections with manipulation or opening of the external ventricular drain (EVD—high frequency artefacts with or without sudden changes of ICP level). Automated methods for cleaning of arterial blood pressure were removal of pressure below 0 or above 300 mmHg and removal of sections with pulse amplitude of less than 15 mmHg. Automated methods for cleaning of ICP included removal of values below − 20 or above 200 mmHg, removal of sections with low amplitude (< 0.04 mmHg) corresponding to noise or EVD opening, and removal of values with a 95% Spectral edge frequency above 10 Hz (high-frequency noise). Only the remaining data (termed artifact-free) is used for further processing mitigating the effect of artificial, non-physiological sections.

Data was then processed to acquire 10 s averages of mean arterial blood pressure (ABP), systolic blood pressure (SBP), diastolic blood pressure (DBP), ICP, ICP amplitude (AMP), CPP (difference between ABP and ICP), and HR. Averaging, in effect, allowed for the removal of cardiac and respiratory components.

Multiscale entropy analysis

MSE was calculated as previously described based on the estimation of sample entropy [ 13 ]. Sample entropy describes the probability that matching sequences of length m will exhibit the same behavior (i.e. will also match) when extended by one point. It is estimated as the negative natural logarithm of the ratio between the number of m  + 1 length patterns to the corresponding m length patterns [ 25 ]. We estimated sample entropy using m = 2 and a tolerance of 0.15. MSE describes the process of calculating sample entropy over different time scales. A total of 20 scales starting from 1 up to 20 (produced by averaging based coarse graining i.e. Step 1—no averaging, step 2—averaging of 2 consecutive samples … step 20—averaging of 20 consecutive samples) covering the range of slow waves was used. MSE is the resulting area under the curve (AUC) of the plotted sample entropies. Higher values represent higher signal entropy/complexity. MSE was calculated for each of the 10 s biosignals resulting in the metrics MSE abp, MSE sbp, MSE dbp, MSE cpp, MSE hr, MSE icp, MSE amp.

Statistical analysis

Statistical analysis was performed in R Studio (R version 4.3.2— https://www.r-project.org/ —packages used: rstatix, pROC, boot, rms, MASS, ResourceSelection, predtools, brant ).

Descriptive variables are reported as counts/percentages or mean ± standard deviation. Distribution of the different continuous variables was assessed using the Shapiro–Wilk test. Equality of variances was tested using the Bartlett test or the Levene test. Different statistical methods were explored to assess the association between MSE and outcome. Both univariable as well as multivariable analysis (covariates: age, WFNS, mFisher, and occurrence of DCI) were performed. A significance level of p  < 0.05 was set due to the exploratory nature of the study and the different tests used for exploration. The Bonferroni corrected adjusted significance level would be p  = 0.00089.

Univariable: First the different MSE variables were compared to outcome as dichotomized by GOSE (1–4 vs. 5–8) using independent t-tests. To assess the overall diagnostic performance of the different MSE metrics, ROC curves (receiver operating characteristic curves) were plotted and evaluated by calculating the AUC (overall diagnostic performance) and its confidence interval (CI), and by estimating the optimal threshold (based on the Youden Index) to assess sensitivity, specificity, positive/negative predictive values, and accuracy. MSE metrics were then plotted against outcome as grouped into Dead/Vegetative (GOSE 1–2), Severe Disability (GOSE 3–4), Moderate Disability (GOSE 5–6), and Good Recovery (GOSE 7–8) and evaluated by analysis of variance (ANOVA).

Multivariable: Covariate adjusted logistic regression models were built with dichotomized GOSE (1–4 vs. 5–8) as endpoint to assess the independence of the MSE metrics as predictors of outcome. Effect of the metrics on model performance was described using the odds ratio (OR) including its CI. Diagnostic performance of the models was assessed using the AUC, the Nagelkerke R 2 (R 2 ), and the Brier Score. The effect of MSE metric inclusion was evaluated using the DeLong’s test comparing the different AUCs to a base model without the inclusion of MSE metrics. The established models were validated both internally as well as externally. Internal validation was performed by bootstrapping (1000 replications with replacement). During this process prediction models were derived from each bootstrap sample and applied to both the bootstrap and the original dataset allowing for the estimation of optimism (i.e. the difference between the AUC/R 2 /Brier scores of the results derived from the original vs. the different bootstrapping data sets). External validity was assessed by: 1. Evaluating the calibration (agreement between predicted and observed outcome described using its intercept and slope and assessed using the Hosmer–Lemeshow-goodness-of-fit test) 2. Evaluating the discrimination (AUC) when applying the derivation-dataset-based model to the validation cohort.

Ordinal multivariable: Due to the ordinal nature of the outcome score we additionally performed a proportional odds logistic regression and a sliding dichotomy analysis. Both, proportional odds logistic regression as well as sliding dichotomy allow for exploiting the range of the outcome scale by providing either the assessment of OR across different cutoffs or the assessment of baseline adjusted outcome definitions thereby increasing statistical power [ 26 ]. Proportional odds logistic regression adjusted for covariates was applied to the same scales as described above with moving cutoffs (Dead/Vegetative vs. Severe Disability, Severe Disability vs. Moderate Disability, Moderate Disability vs. Good Recovery) to assess the common odds ratio. The proportional odds assumption was tested using the Brant-Wald test. Lastly a sliding dichotomy approach was used to assess the importance of MSE metrics for a baseline severity adjusted outcome definition. For each patient, based on the baseline covariates (age, WFNS, mFisher score, and occurrence of DCI), a prognostic risk probability for unfavorable outcome was estimated. The resulting scores were then divided into 3 prognostic groups of roughly equal size corresponding to low, intermediate, and high likelihood of unfavorable outcome. For each prognostic group a separate cutoff was defined to dichotomize outcome into favorable and unfavorable, with the adjusted favorable outcome classified as:

GOSE 7–8: for the group with low likelihood for unfavorable outcome,

GOSE 5–8: for the group with intermediate likelihood for unfavorable outcome

GOSE 3–8: for the group with high likelihood for unfavorable outcome.

The resulting baseline severity adjusted outcome variable was then assessed against the MSE metrics using logistic regression. For both methods bootstrapping was applied for internal validation and to acquire the CI.

Secondary analysis

Three additional secondary analyses were performed to assess further aspects associated with the metric MSE based on the most promising metrics found. First: To assess, whether early outcome prediction using MSE is feasible, a secondary analysis was performed including only data acquired within the first 48 h after NCCU admission. Second: To evaluate whether MSE was associated with specific clinical aspects of the disease, values were assessed against clinical events. For this purpose, the following additional clinical parameters were extracted from the electronic patient records (occurrence of rebleeding, global cerebral edema, brain herniation, and seizures) and evaluated using t-tests. The raw metrics (ABP, HR, ICP) were assessed against the derived MSE metric to reveal possible intercorrelations. Third: The stability of the metric was assessed by evaluating the change when considering longer amounts of data within one patient (between 1 and 24 h) as well as when comparing the results of the metrics to the duration of the measurement in the whole cohort.

Patient characteristics and high-resolution data availability

Derivation cohort: 241 patients were included as part of the derivation cohort (3 were excluded due to loss to follow up). ABP/HR data was available in all patients, ICP data was available in 150 (62%) patients. The following amount of artefact free data was available: ABP—239 h/patient (total of 57′257 monitoring hours), HR—267 h/patient (total of 63′955 monitoring hours), ICP—205 h/patient (total of 30′778 monitoring hours). Validation cohort: 89 Patients were included as part of the derivation cohort (13 were excluded due to loss to follow up). ABP/HR data was available in 101 (99%), and ICP data was available in 73 (72%) patients. The following amount of artefact free data was available: ABP/HR—268 h/patient (total of 23′553 monitoring hours), ICP—290 h/patient (total of 21′169 monitoring hours). The median time between the initial hemorrhage and start of multimodality monitoring was 18 h in the derivation and 31 h in the validation cohort. The distributions of available data of the derivation and validation cohort can be found in the supplement including overall lengths of datasets as well as the density with respect to the timing from the initial hemorrhage (Additional file 1 : A). The highest density of data was available between day 3 and 14 after the initial hemorrhage in either center. Overall descriptions of physiology metrics can be found in the supplement (Additional file 1 : B). As this was a cohort undergoing active treatment, ICP within either cohort was mostly below 20 mmHg and ABP was around 90–100 mmHg. The clinical characteristics of the derivation and validation cohort can be found in Table  1 . The outcome at 12 months (assessed using GOSE) is shown in Fig.  1 .

Glasgow Outcome Scale Extended (GOSE) at 12 months ( A —derivation cohort; B —validation cohort)

Univariable analysis

In a first step, the different MSE metrics were evaluated against dichotomized outcome (GOSE 1–4 vs. GOSE 5–8). Overall, there was a difference between the outcome groups irrespective of the MSE metric. The specific p -values were: MSE abp (9.15 e-16), MSE sbp (3.81 e-19), MSE dbp (7.17 e-13), MSE cpp (1.77 e-6), MSE hr (6.65 e-19) MSE icp (4.70 e-10), MSE amp (6.72 e-6). The respective data is shown in form of boxplots in Fig.  2 panel A. The predictive value of the different metrics (ROC curves) is shown in Fig.  2 panel B. The specific AUC (CI) can be found in Table  2 . AUC ranged between 0.71 and 0.83. The highest values were found for MSE hr (AUC 0.83 (0.78–0.89)) and MSE sbp (AUC 0.82 (0.77–0.87)). The Youden Index was established for each MSE metric and used to calculate related metrics and accuracy (Table  2 ). The accuracy of the metrics was between 68% (MSE amp) and 77% (MSE hr) when using the Youden Index as a cutoff. To assess the MSE metrics against a higher granularity of outcome, they were then plotted against Dead/Vegetative, Severe Disability, Moderate Disability, and Good Recovery (Fig.  3 ). The respective p -values can be found in Table  3 . Overall, there were monotonic decreases of MSE with higher values found in more favorable outcomes.

MSE vs. Dichotomized Outcome. Panel A. The different MSE metrics are shown using boxplots comparing unfavourable (GOSE 1–4; pink) and favourable (5–8; green) outcome. An independent t-test was used for statistical analysis. Significant differences are shown using asterisks (*** =  p  < 0.001). The specific p -values were: MSE abo (9.15 e-16), MSE sbp (3.81 e-19), MSE dbp (7.17 e-13), MSE cpp (1.77 e-6), MSE hr (6.65 e-19) MSE icp (4.70 e-10), MSE amp (6.72 e-6). Panel B shows the corresponding ROC curves describing the predictive value of the different MSE scores

MSE vs. Ordinal Outcome. The different MSE metrics are shown using boxplots grouped by ordinal outcome: Dead/Vegetative (GOSE 1–2), Severe Disability (GOSE 3–4), Moderate Disability (GOSE 5–6), and Good Recovery (GOSE 7–8). The color coding ranges from intense pink (dead/vegetative) to intense green (good recovery). The respective p-values of the performed ANOVA can be found in Table  3

Multivariable analysis

To assess the independence of MSE metrics when corrected for covariates, multivariable logistic regression models were built. The results describing adjusted effect of the metric (OR), as well as the overall model performance (AUC, Nagelkerke R 2 , Brier Score) are shown in Table  4 (top panel). All MSE metrics remained independently associated with outcome with OR between 0.78 (MSE sbp) and 0.86 (MSE amp). AUCs ranged between 0.79 and 0.87 and R 2 between 0.32 and 0.51. Overall MSE sbp and MSE hr showed the highest effect and discriminatory value. MSE abp ( p  = 0.0068), MSE sbp ( p  = 0.0028), MSE dbp ( p  = 0.024), MSE cpp ( p  = 0.032), MSE hr ( p  = 0.003), MSE icp ( p  = 0.004), MSE amp ( p  = 0.029) all increased the AUC when compared to the model excluding the MSE metrics.

To assess internal validity, optimism-corrected performance estimates (AUC, Nagelkerke R 2 , and Brier Scores) were established using bootstrapping and are shown in Table  4 (middle panel). AUC optimism was at most 0.01 and R 2 optimism was between 0.01 and 0.02. To assess external validity, the models were applied to the validation cohort describing discriminatory performance (AUC) and calibration using the Hosmer–Lemeshow-goodness-of-fit test and the calibration intercepts and slopes (Table  4 , bottom panel). AUCs of the models when applied to the validation cohort were between 0.72 and 0.80. The Hosmer–Lemeshow-goodness-of-fit test found good model fits (test statistics were non-significant). The calibration slope was between 0.79 and 0.88 with the intercept being close to 0 in all cases.

Ordinal multivariable analysis

To assess the effect of MSE on the outcome in form of an ordinal scale, a proportional odds logistic regression model adjusted for covariates was produced. The proportional odds assumptions were met for all MSE metrics and the common OR and p -values of the proportional odds regression are shown in Table  5 . Common OR ranged between 0.79 and 0.88. Lastly, a sliding dichotomy approach was used to estimate the added value of MSE metrics when outcome was dichotomized based on individualized outcome prediction. The OR and p-values of the sliding dichotomy approach can be found in Table  5 . Overall, OR ranged between 0.82 and 0.91.

Different further aspects of MSE were evaluated within the secondary analysis. Firstly, to evaluate the potential for early outcome prediction, MSE based on only the data acquired within the first 48 h after NCCU admission was evaluated. MSE sbp, MSE hr, and MSE icp all remained associated with outcome both when considered within univariable as well as multivariable and ordinal analyses (Additional file 1 : C). After correction for the confounders (age, WFNS, mFisher, and occurrence of DCI) the OR of MSE sbp, MSE hr, and MSE icp were 0.87 (0.81–0.94), 0.86 (0.81–0.92), and 0.88 (0.83–0.95) per 1 step increase respectively. Overall, the models showed good discrimination with AUCs of 0.81 (0.75–0.85), 0.83 (0.77–0.87), 0.78 (0.70–0.85) for the multivariable logistic regression models including MSE sbp, MSE hr, and MSE icp respectively. The additional analyses can be found in Additional file 1 : C.

In a second step, the MSE metrics MSE sbp, MSE hr, and MSE icp were evaluated against different clinical aspects and events (Additional file 1 : D). Higher WFNS grade was associated with a decrease in all MSE metrics (MSE sbp: 25.4 ± 4.5 vs. 21.7 ± 4.2, p  < 0.001; MSE hr: 24.6 ± 4.8 vs. 20.0 ± 5.1, p  < 0.001; MSE icp: 21 ± 7 vs. 16 ± 6, p  < 0.001 for low vs. high WFNS respectively). While no MSE metric was associated with mFisher, MSE icp was higher in patients who received coiling (19 ± 7) as compared to clipping (16 ± 6, p  = 0.003). Conditions associated with or resulting from high ICP (cerebral edema, brain herniation) were associated with decreases in all three MSE metrics. Rebleeding and hydrocephalus on the other hand were only associated with a decrease in MSE sbp (rebleeding: 23.9 ± 4.6 vs. 20.0 ± 5.8, p  = 0.008; hydrocephalus: 25.2 ± 5.1 vs. 22.4 ± 4.3, p  < 0.001) and MSE hr (rebleeding: 23.1 ± 5.5 vs. 19.2 ± 6.7, p  = 0.032; hydrocephalus: 24.1 ± 5.8 vs. 21.0 ± 5.2, p  < 0.001) but not MSE icp. Similarly, DCI was associated with a decrease in MSE sbp (23.7 ± 5.0 vs. 22.5 ± 4.1, p  = 0.039) and MSE hr (22.8 ± 5.6 vs. 20.2 ± 5.3, p  < 0.001), but not MSE icp. The additional results can be found Additional file 1 : D.

Lastly, the stability of MSE was assess by evaluating the change when including longer amounts of data as well as when comparing the results of the metrics to the duration of the measurement (Additional file 1 : E). Starting from 3 to 6 h, stable MSE values could be found. There was no difference in absolute value of MSE compared to the duration of the recording when considering all patients.

Entropy, and in particular its multiscale version, MSE, builds on the previously described concept that physiological systems are regulated by multiple, interacting, mechanisms that collectively result in dynamically changing, irregular, fluctuations of biosignals across different temporal scales [ 14 ]. Lower MSE represents higher regularity of a system. While a “stable”, “regular” system would at first glance seemingly give the impression of being ‘healthy’, in reality, such a system is more rigid, with impaired capacity to counteract ever-present, random environmental triggers. The disease course of aSAH includes a variety of pathological processes necessitating continuous and rapid adjustments to equilibrate the system [ 27 ]. Patients that survive the initial hemorrhage remain at risk for developing secondary brain injury due to numerous pathophysiological cascades and complications. As part of the early brain injury, due to the initial hemorrhage, ICP rises either immediately (due to the volume of the bleeding itself [ 28 ]) or with a certain delay (i.e. resulting hydrocephalus [ 29 ] and/or brain edema). In the worst-case scenario, either one can lead to a relevant reduction in CPP. If the system fails to counteract this decrease in cerebral perfusion, this may lead to transient cerebral hypoxia or, ultimately, even infarction. Various injury cascades (upregulation of inflammatory pathways [ 30 ], coagulopathy [ 31 ]) further damage the system in case of cerebral hypoxia. A non-reactive system, represented consequently by low MSE, has been associated with higher pressure reactivity index (PRx) values supporting the notion that MSE represents the activity level of the physiological regulation systems, including cerebral blood flow autoregulation [ 9 , 15 ]. While both metrics clearly share similar mechanisms, to equate MSE with cerebrovascular reactivity (as assessed using PRx) would be an oversimplification, considering Lu et al. also showed that PRx loses its predictive value when MSE is added to multivariable regression models for outcome prediction after traumatic brain injury [ 9 ].

The main driver of secondary brain injury in aSAH is DCI [ 3 ]. Although, DCI is the consequence of complex interacting pathophysiological sequelae, a well described and potentially reversible cause is vasospasm, which describes the narrowing of cerebral vessels [ 4 ]. Depending on the severity of such narrowing, ischemia or even infarction can occur. In aSAH, intact cerebral autoregulation is essential to counteract such dynamic reductions of vessel diameter by automatically and immediately increasing CPP. Autoregulation failure is detrimental, as symptomatic treatment (using vasoactive medications or intra-arterial spasmolysis) can only be initiated with a significant delay.

In addition to cerebral complications, aSAH leads to various systemic and most prominently cardiac complications [ 32 , 33 ]. Both, myocardial ischemia and neurogenic stunned myocardium are found after aSAH leading to wall motion abnormalities and consequently reduced cardiac output. In the worst case, cardiac complications coincide with (or cause) pulmonary edema leading to further impairment of cardiac function and oxygenation [ 34 , 35 , 36 ]. Sufficient cardiac output is necessary to counteract impaired perfusion due to vasospasm. Cardiac output guided therapy is beneficial for managing cerebral oxygenation in patients with vasospasm [ 7 , 37 ]. Myocardial injury might indeed be one cause of decreased entropy with previous reports describing good discrimination when assessing heart rate variability after aSAH and diagnosis of neurocardiogenic injury [ 17 ]. To date, the most commonly evaluated complexity related metric in aSAH is heart rate variability due to its simple determination based on short electrocardiograms [ 18 ]. Considering these results, it is not surprising that MSE hr was a predictor of outcome. However, variability and entropy cannot be used interchangeably due to the large number of metrics used for description some of which depend on distribution while others depend on specific patterns.

In aSAH, due to the complex nature and course of disease, NCCU monitoring plays a pivotal role for guiding treatment. Neurocritical care bioinformatics allow for the acquisition, integration, and synchronization of the various biosignals within the same environment, thereby permitting exploitation of advanced data-driven methods for guiding treatment and outcome prediction [ 38 ]. Yet, computational methods remain underutilized and are generally not readily available for direct bedside implementation. Commonly, single (at times arithmetic mean or worse, single snapshot) targets are used for guiding treatment, ignoring the potential benefits of complex integration. The results of this study underline the potential of advanced analytical tools for improving the understanding of the complex pathophysiology of multimodal monitoring dynamics after aSAH. However, relevant limitations exist.

Limitations

Although this study included over 300 patients from 2 centres, they were all treated at highly specialized, high resource centers with relatively high patient volumes. On a similar note, patients underwent active treatment throughout their NCCU stay. It is unclear if, and to what extent interventions and complications are associated with changes in MSE. This is underlined by the results showing changes in MSE depending on various events. Either aspect, however, implies that the biosignals acquired do not represent the “natural” course of the disease, but patients undergoing active treatment. Due to the exploratory nature of this study, we did not explore time-trends of MSE, thus we cannot comment on the patterns of variability of MSE over the course of the NCCU stay (e.g. depending on intervention, medication or similar) or whether there were specific turning points (e.g. refractory ICP increase, DCI etc.). Overall, many complications and disease aspects were associated with changes in MSE and it is likely that the resulting metric represents a composite of various aspects. It is important to note that while the results were adjusted for the relevant and known outcome predictors age, clinical and imaging severity, and the important complication DCI, many other clinical variables are of importance when predicting outcome. To date, no multimodal monitoring based metric alone can or should replace clinical examination. However, metrics such as MSE should be seen as complementary allowing for additional physiology information.

This study provides the first description of MSE as an outcome predictor after aSAH. MSE metrics and thereby complexity of physiological signals are independent, internally and externally valid predictors of 12 month outcome after aSAH. MSE decreases monotonically with worse outcomes and remains a valid outcome predictor when adjusting the outcome definition to the initial severity of disease and age. The promising results of this study warrant further investigation into the cause of the resulting complexity as well as its association with important and potentially preventable complications (i.e. vasospasm and DCI). Of particular importance will be the assessment of time-trends and the evaluation of intraindividual episodes of decreased entropy and their association to specific events. Promising targets for such analysis are MSE sbp and MSE hr since neither requires continuous neuromonitoring and can therefore be applied to the whole aSAH population.

Data availability

The processed data is available upon reasonable request by the corresponding author.

Abbreviations

Aneurysmal subarachnoid hemorrhage

Mean arterial blood pressure

Intracranial pressure amplitude

Area under the curve

Confidence interval

Cerebral perfusion pressure

Diastolic blood pressure

Delayed cerebral ischemia

Glasgow outcome scale extended

Intracranial pressure

Modified Fisher scale

• Multiscale entropy

Neurocritical care unit

Negative predictive value

Positive predictive value

Receiver operating characteristic

Systolic blood pressure

World Federation of Neurosurgical Societies grading scale

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Acknowledgements

We wish to thank all the patients, family members and staff that participated in the study.

This project was made possible by the generous funding from the Swiss National Science Foundation (Grant Number: 210839) received by Stefan Yu Bögli. Erta Beqiri is supported by the Medical Research Council (Grant No.: MR N013433-1) and by the Gates Cambridge Scholarship.

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Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK

Stefan Yu Bögli, Ihsane Olakorede, Erta Beqiri & Peter Smielewski

Neurocritical Care Unit, Institute for Intensive Care and Department for Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland

Jan Folkard Willms & Emanuela Keller

Department of Neurosurgery, RWTH Aachen University, Aachen, Germany

Michael Veldeman, Miriam Weiss & Gerrit Alexander Schubert

Department of Neurosurgery, Cantonal Hospital Aarau, Aarau, Switzerland

Miriam Weiss & Gerrit Alexander Schubert

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The study was conceived by SYB. SYB, EK, PS contributed to the study design. Data collection was performed by SYB, MV, MW, GAS, JFW and EK. Data processing and formal analysis were performed by SYB and IO. SYB wrote the first draft of the manuscript. All authors commented and revised the manuscript and all authors read and approved the final manuscript.

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Supplementary data describing data coverage (A), physiological metrics (B), and the results of the secondary analyses (C-E).

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Bögli, S.Y., Olakorede, I., Veldeman, M. et al. Predicting outcome after aneurysmal subarachnoid hemorrhage by exploitation of signal complexity: a prospective two-center cohort study. Crit Care 28 , 163 (2024). https://doi.org/10.1186/s13054-024-04939-7

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Ukraine war latest: Zelenskyy cancels foreign trips as official sounds alarm from border town under attack

The Ukrainian president has cancelled visits to Spain and Portugal after Moscow's forces began a new offensive in the northeast of the country, where Kyiv says it is moving troops to new positions. Submit your question on the war for our experts to answer in the box below.

Wednesday 15 May 2024 09:54, UK

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• Zelenskyy cancels foreign trips as fighting intensifies
• Situation in Kharkiv town under Russian attack 'extremely difficult'
• Ukrainian troops move into new positions in Kharkiv
• Russia downs missiles launched at Crimea
• Analysis:  Putin's 'baffling' reshuffle explained
• Live reporting by Lauren Russell

Ask a question or make a comment

US secretary of state Antony Blinken is in Kyiv meeting Ukrainian leaders.

Yesterday he also found time for an impromptu visit to a local nightclub where he took to the stage with an electric guitar to perform a version of Neil Young's 1989 hit Rockin' In The Free World. 

Before he began playing, he told the crowd: "The US is with you, so much of the world is with you."

For rights reasons, unfortunately, we can't show you him singing, but we can report he made a solid effort.

Russia's foreign ministry spokeswoman has said Moscow will destroy all US military equipment supplied to Ukraine.

Maria Zakharova's comments come after US secretary of state Antony Blinken told Ukrainian leaders in Kyiv that despite a month-long delay more US weaponry was coming and some had already arrived.

The US-aid package sets aside $61bn (£48.1bn) for Ukraine, much of which will go toward replenishing badly depleted artillery and air defence systems.

Ms Zakharova also took aim at attempts from the West to use frozen Russian assets to benefit Ukraine.

She said to do so would be in violation of Russian law and risked undermining the international financial system. 

If Russian assets in the US were seized, it could mean another $5bn (£3.9bn)  in assistance for Ukraine, coming from Russian Central Bank holdings.

However it is unlikely the US will seize the assets without agreement from other members of the G7 and the European Union.

The head of the Ukrainian police force in the Kharkiv region has described the situation in the town of Vovchansk as "extremely difficult". 

Oleksiy Kharkivskiy, Vovchansk's patrol police chief, said on Facebook that Russian forces are establishing positions inside the town. 

Yesterday Russia claimed it had taken parts of Vovchansk and the border village of Buhruvatka.

It is part of Moscow's pressing offensive on the Kharkiv region, which began on Friday after weeks of speculation that Russia was preparing to establish a new frontline there, and is forcing Ukraine to rush in reinforcements.

Russia has said it repelled a Ukrainian drone attack on the region of Tatarstan - around 497 miles east of Moscow.

The defence ministry said the attack occurred at around 7.30am local time.

It said Russian air defences had destoyed an aeroplane-style drone.

President Volodymyr Zelenskyy has cancelled a trip to Spain and Portugal, with some media outlets reporting it is because of renewed fighting in Ukraine.

King Felipe of Spain was due to hold a reception for Mr Zelenskyy on Friday. 

The Ukrainian leader was then expected to sign a bilateral security cooperation agreement with Portugal's Prime Minister Pedro Sanchez.

A spokesperson for the Portuguese government said Mr Zelenskyy had cancelled the visit but did not give a reason, while the Spanish government said it could not comment for security reasons. 

As we have been reporting, Ukrainian troops remain outgunned by Russian forces.

Yesterday, the western and northern parts of Vovchansk in Ukraine's Kharkhiv region fell under the control of Russian forces, according to the TASS state news agency.

Emergency teams have been working at the scene of a Russian airstrike in Kharkiv.

The region is where Russian forces are pressing on with an offensive, forcing Ukraine to rush in reinforcements.

Apart from the devastation and the blow to Ukrainian morale in the region, home to Ukraine's second largest city of Kharkiv, the incursion is a distraction for Kyiv's defensive operations in the east where Russia has focused its offensive for months.

Fires at an oil depot and power substation in Russia's Belgorod and Lipetsk regions were caused by drones launched by the Security Service of Ukraine (SBU), a Ukrainian intelligence source has told Reuters.

The attack damaged Oskolneftesnab oil depot near the city of Staryi Oskol in Russia's Belgorod region and Yeletskaya power substation in the Lipetsk region.

"Russian industry which works to wage war with Ukraine will remain a legitimate target for the SBU," the intelligence source said.

"Measures to undermine the enemy's military potential will continue."

For context : Ukraine has been stepping up its strikes on oil and gas facilities across Russia in an effort to disrupt military logistics and combat operations - it is these facilities that supply fuel for Russian tanks, ships and fighter jets.

Experts also say that striking these sites will most likely cause disruption to Russia's energy network.

The region of Chasiv Yar, in the eastern Bakhmut region, has long been under fire.

It is in eastern areas like this that Russia's advance has been far more significant and strategically important.

But it is hoped that a weapons package from the US could change that.

Here, our  security and defence editor Deborah Haynes  visits an artillery position on the outskirts of the frontline region ...

The Russian defence ministry has said its air forces destroyed 10 long-range missiles known as ATACMS that Ukraine's military launched overnight at Crimea.

The ministry did not say whether there was any damage, but the Russia-installed governor of the Crimean port of Sevastopol said that missile debris fell onto a residential area.

According to early information, no one was injured, Mikhail Razvozhayev, the governor, said on Telegram .

Russia annexed Crimea from Ukraine 10 years ago in a move broadly condemned by Kyiv's Western allies.

And Russia has often said, without providing evidence, that Ukraine had started using the US-supplied ATACMS.

Yesterday, a US official said that ATACMS and air defence interceptors approved by Joe Biden on 24 April were already reaching the Ukrainian forces.

Ukraine's military has said it is moving troops to new positions in two areas of the northeastern Kharkiv region where Moscow is pressing an offensive, and warned of a Russian force buildup to the north near its Sumy region.

A cross-border attack on a new flank in Sumy region would likely stretch Kyiv's depleted defenders even further after Russia's incursion into the Kharkiv region opened a new front on Friday, forcing Ukraine to rush in reinforcements.

Russia has made inroads into the north of Kharkiv region and said yesterday it had taken parts of the town of Vovchansk and the border village, Buhruvatka.

Military spy chief Kyrylo Budanov has said Moscow has already committed all the troops it has in the border areas for the Kharkiv operation, but that it has other reserve forces that he expected to be used in the coming days.

"A rapid trend towards a stabilisation of the situation had emerged - that is, the enemy is, in principle, already blocked at the lines that it was able to reach," he said in televised comments.

Mr Budanov said Russia had small groups of forces in the border areas near Ukraine's Sumy region in the vicinity of the Russian town of Sudzha from where Russian natural gas transits into Ukraine by pipe on its way to European customers.

"As for the Sumy region, the Russians actually planned an operation in the Sumy region from the very beginning... but the situation did not allow them to take active actions and start the operation," he said.

Top Ukrainian officials say they do not believe Russia has the troop numbers to capture the city of Kharkiv.

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What Stormy Daniels said happened in Trump’s hotel suite, from the transcript

Despite objections from Donald Trump’s attorneys, adult-film actress Daniels shared details of the alleged sexual encounter in the New York hush money trial.

Adult-film actress Stormy Daniels appeared on the witness stand at Donald Trump’s hush money trial Tuesday, offering an account of her alleged one-night stand with the former president . Despite the repeated objections of Trump’s lawyers, Daniels went into great detail about the 2006 sexual encounter; her description at times made it sound like the sex could be viewed as nonconsensual . The presumptive Republican nominee could be heard muttering profanities at certain points in the proceedings.

Trump, who denies ever having sex with Daniels, has pleaded not guilty to charges of falsifying business records to conceal hush money payments to Daniels during the 2016 election.

Here are key moments from Tuesday’s trial in New York, based on an early transcript and lightly edited for brevity and clarity.

Trump hush money trial

Trump’s lawyer seeks to block Daniels from sharing sexual details

At the opening of Tuesday’s court session, Trump’s attorney Susan Necheles began by objecting — in advance of testimony by Daniels, whose legal name is Stephanie Clifford — to the prosecution seeking “any details of any sexual acts.” The charges, the defense noted, are not over alleged sexual acts.

Susan Necheles, defense attorney: We think that this is irrelevant. It has nothing to do with the charges in this case. And to the extent that it has any relevance, it’s unduly prejudicial. And there really is no reason for it to be coming into the case about books and records here.

Justice Juan Merchan: And when you say “details of any sexual act,” what do you mean? Do you mean more than just “we had sex?”

Necheles: Yes, your honor.

The prosecution argued that Daniels’s account — particularly of a conversation with Trump in a hotel suite where Daniels says they had sex — was important for establishing her credibility as a witness.

Susan Hoffinger, prosecution attorney: The general details of what occurred, including the sex act, barring certain details that are unnecessary, are a significant part of the story, but also very important for us in terms of her credibility.

Merchan : So when you say that some details are necessary, can you give me a sense of what you have in mind?

The prosecution argued that the details are important if the jury is to understand why Trump would have been motivated to pay money to prevent a story about the alleged encounter with Daniels from appearing in news reports.

Hoffinger : How she even ended up having a sexual act with him, and then in terms of the sexual act, it will be just very basic. It’s not going to involve any descriptions of genitalia or anything of that nature.

The judge ruled that prosecution lawyers can ask Daniels for background information about the events leading up to the encounter.

Merchan : But we don’t need to know the details of the intercourse.

Daniels describes alleged sexual encounter with Trump

Daniels, who appeared nervous and spoke quickly, gave an incredibly detailed account of the evening she went to Trump’s suite at a Lake Tahoe resort following a golf event, where she said they had sex.

When she arrived for what she thought was just dinner, Daniels said Trump was wearing silk or satin pajamas, but that he changed into a suit after she made a joke about it. When they sat down at the dining room table, Trump asked her questions about the adult-film business including whether she tested for sexually transmitted diseases, she said. At one point, she says she spanked him with a rolled-up magazine.

After emerging from the bathroom, Daniels said she was “startled” to find Trump on the bed in a T-shirt and boxer shorts. Daniels told jurors she did not want to have sex with him.

Daniels: Then I just thought, oh, my God, what did I misread to get here. Because the intention was pretty clear, somebody stripped down in their underwear and posing on the bed, like waiting for you.

Daniels said she never felt physically or verbally threatened by Trump, but did feel a power imbalance between them. Trump was bigger than her, she said, and there was a bodyguard outside the door.

Daniels: The next thing I know, I was on the bed, somehow on the opposite side of the bed from where we had been standing. I had my clothes and shoes off. I believe my bra, however, was still on.

Daniels’s account also included how she stared at the ceiling during sex, since she was “trying to think about anything other than what was happening there” — to which the judge sustained an objection. She also said Trump didn’t wear a condom, and described the position in which they had sex. After the encounter, Daniels said she remained silent as she gathered her possessions.

Daniels: My hands were shaking so hard. I was having a hard time getting dressed. He said, “Oh, great. Let’s get together again honey bunch. We were great together.” I just wanted to leave.

Daniels said they stayed in touch because she hoped Trump would let her appear on his hit reality show, “The Apprentice.”

Judge requests Trump stop cursing

Even before Daniels described the alleged sexual encounter, the judge was concerned about Trump’s reaction to her initial testimony. As the court rose for its morning recess, Merchan beckoned Trump’s defense lawyers to a sidebar.

Merchan : I understand that your client is upset at this point, but he is cursing audibly, and he is shaking his head visually and that’s contemptuous. It has the potential to intimidate the witness and the jury can see that.

Trump’s attorney Todd Blanche said he would talk to his client.

Merchan : One time I noticed when Ms. Daniels was testifying about rolling up the magazine, and presumably smacking your client, and after that point he shook his head and he looked down. And, later, I think he was looking at you, Mr. Blanche, later when we were talking about The Apprentice, at that point he again uttered a vulgarity and looked at you this time. Please talk to him at the break.

The judge rejects a mistrial request from Trump’s lawyers

Immediately after the lunch break, Trump’s attorneys argued that Daniels’s testimony about the sex act was irrelevant and prejudicial to the point that it warranted a mistrial.

Trump’s lawyer Blanche : The Court set guardrails for this testimony. And the guardrails by this witness, answering questions from the government, were just thrown to the side.

In particular, Trump’s attorney argued that many of the details were simply intended to embarrass his client and “inflame the jury” in a trial fundamentally about business records, including any suggestion there were “safety concerns” in the encounter.

The prosecution responded that it had been “extremely mindful of not eliciting too much testimony about the actual act.”

Hoffinger: But, at the end of the day, your Honor, this is what defendant was trying to hide … in terms of the payoff in 2016 before the election. This is an exhibit. If you were … of what Mr. Trump wanted to make sure didn’t get disclosed. We have carved back details.

The judge said it “would have been better” if the prosecution hadn’t gone into certain areas, but suggested that “in fairness,” the witness was “a little difficult to control.”

Merchan: I do think that there were some things that were better left unsaid. Having said that, I don’t believe we are at the point where a mistrial is warranted.

Daniels admits to signing a false statement in 2018

In a statement in 2018 ahead of an appearance on “Jimmy Kimmel Live,” Daniels denied getting paid to keep quiet about her encounter with Trump — one of several claims that she has since renounced but that Trump and his allies have seized on to undermine her credibility. “I am denying it [the affair] because it never happened,” the statement said.

But in Tuesday’s testimony, Daniels said she signed the statement differently than how she usually signs things as a “tip off” to indicate that the statement was false .

Hoffinger: What kind of a tip off?

Daniels: That I didn’t — that either I didn’t sign it — that I didn’t sign it willingly.

Hoffinger: Is that because you were upset about signing it?

Daniels: Yes.

Hoffinger: And is this statement false?

Daniels’s 2018 statement echoed a similar one provided by Daniels’s lawyer to the Wall Street Journal the same year, when it reported that Trump’s attorney Michael Cohen had negotiated a secret $130,000 payment to secure Daniels’s silence ahead of the 2016 presidential election. On Tuesday, Daniels said she did not want to sign off on that statement to the Journal “because it’s not true,” but worried about violating a nondisclosure agreement.

Marianne LeVine contributed to this report.

Trump New York hush money case

Former president Donald Trump’s criminal hush money trial is underway in New York. Follow live updates from the trial .

Key witnesses: Several key witnesses, including David Pecker and Stormy Daniels, have taken the stand. Here’s what Daniels said during her testimony . Read full transcripts from the trial .

Gag order: New York Supreme Court Justice Juan Merchan has twice ruled that Trump violated his gag order , which prohibits him from commenting on jurors and witnesses in the case, among others. Here are all of the times Trump has violated the gag order .

The case: The investigation involves a $130,000 payment made to Daniels, an adult-film actress , during the 2016 presidential campaign. It’s one of many ongoing investigations involving Trump . Here are some of the key people in the case .

The charges: Trump is charged with 34 felony counts of falsifying business records. Falsifying business records is a felony in New York when there is an “intent to defraud” that includes an intent to “commit another crime or to aid or conceal” another crime. He has pleaded not guilty . Here’s what to know about the charges — and any potential sentence .