case study research in primary education

A Case for Case Study Research in Education

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• Kit Grauer  

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This chapter makes the case that case study research is making a comeback in educational research because it allows researchers a broad range of methodological tools to suit the needs of answering questions of “how” and “why” within a particular real-world context. As Stake (1995) suggests, case study is often a preferred method of research because case studies may be epistemologically in harmony with the reader’s experience and thus to that person a natural basis for generalization.

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Grauer, K. (2012). A Case for Case Study Research in Education. In: Klein, S.R. (eds) Action Research Methods. Palgrave Macmillan, New York. https://doi.org/10.1057/9781137046635_4

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Strategies for promoting autonomous reading motivation: A multiple case study research in primary education

Frontline Learning Research

It is important to reveal strategies which foster students’ reading motivation in order to break through the declining trend in reading motivation throughout children’s educational careers. Consequently, the present study advances an underexposed field in reading motivation research by studying and identifying the strategies of teachers excellent in promoting fifth-grade students’ volitional or autonomous reading motivation through multiple case study analysis. Data on these excellent teachers were gathered from multiple sources (interviews with teachers, SEN coordinators, and school leaders; classroom observations; teacher and student questionnaires) and analysed. The results point to the teaching dimensions of autonomy support, structure, and involvement – as indicated by self-determination theory – as well as to reading aloud as critical strategies to promote students’ autonomous reading motivation in the classroom. A school culture supporting students’ and teachers’ interest in reading is also an essential part of reading promotion. The theoretical and practical significance of the study is discussed.

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The purpose of this study was to explore antecedents and outcomes of English as a Foreign Language (EFL) learners' self-determined classroom engagement within a self-system model of motivation process development framework. Grounded on the modern motivation theory, Self-determination theory (Deci & Ryan, 1985), a mixed-method research was conducted with 412 EFL learners answering self-report questionnaires and randomly chosen 18 interviewees in preparatory classes of a foreign languages vocational school at a medium-scaled Turkish university. Data triangulation showed that quantitative and qualitative findings were consistent with one another according to general tendencies about context (perceived autonomy-support), self (basic psychological needs), action (behavioural, emotional, agentic and cognitive engagement) and outcome (achievement and attendance) variables. The hypothesized path models among context, self, action and outcome, highlighted that learners' perceptions of classroom social context facilitate or undermine their intrinsic desires to act, which in turn have a substantial impact on their achievement and attendance in English language course. Themes from the interviews also underscored that course teacher is a motivation supporter in EFL classrooms and plays a pivotal role in learners' self-related ideas, multidimensional classroom engagement and positive outcomes in English course. By presenting details on underlying structures of EFL learners' motivational self-systems within antecedents and outcomes of classroom engagement framework, it provided significant insights into many questions about classroom engagement. The findings of the study have implications for those in charge of English as a foreign language teaching who want to foster learners' engagement more or have to cope with a high number of unmotivated language learners in their classrooms.

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The present study investigates whether productive classroom discourse in the form of instructional and motivational classroom discourse (Turner et al., Journal of Educational Psychology 94: 88-106, 2002) provides a supportive social context for students that fosters the fulfilment of the basic psychological needs of autonomy and competence (Meyer and Turner, Educational Psychologist 37(1): 17-25, 2002; Ryan and Deci, Contemporary Educational Psychology 25(1): 54-67, 2000). In order to explore this, we studied the ways in which a teacher professional development programme (Dialogic Video Cycle; Gröschner et al. 2015) might affect the quality of teachers' instructional and motivational discourse. The programme provided video-based professional development on productive classroom discourse for an entire school year. During the same period of time, we explored changes in students' perceptions of autonomy and competence support and possible implications for students' intrinsic learning motivation. The study followed a two-group field-experiment design; 10 STEM classes participated (N = 226 students). The intervention group (IG) was compared to a control group (CG), which participated in traditional professional development on classroom Eur

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Research supports the connection between engagement, achievement, and school behavior across levels of economic and social advantage and disadvantage. Despite increasing interest and scientific findings, a number of interrelated conceptual and methodological issues must be addressed to advance this construct, particularly for designing data-supported interventions that promote school completion and enhanced educational outcomes for all students. Of particular concern is the need to (a) develop consensus on the name of the construct, (b) identify reliable measures of the dimensions of the construct, and (c) complete the construct validation studies needed to move research and intervention forward. © 2008 Wiley Periodicals, Inc.

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Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

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Effectiveness of audio-lingual teaching method to enhance students’ english speaking and listening proficiency: the case of selected primary schools in nyagatare district, evode nshimiyimana, gabriel bazimaziki.

This study is qualitative research that explored the effectiveness of the audio-lingual method to improve students’ English speaking and listening proficiency in primary schools. The study was conducted in three primary schools located in Nyagatare District, in Rwanda. Both interview and observation were used to collect data from 174 participants. The observation was carried out on 160 students while interview questions were administered to 14 teachers selected purposively. Qualitative analysis reveals that applying the Audio-lingual method in the English classroom boosts learners’ English proficiency viz speaking and listening skills by enabling them to communicate using the target language within and outside the school context. In light of the study findings, curriculum implementers and other education stakeholders are recommended to provide needful facilities for effective use of this teaching method. Equally important, teachers should carefully use the audio-lingual method to help learners acquire communicative competencies. Lastly, further studies should explore the effectiveness of the audio-lingual method in teaching language to adult learners in the context of Rwanda.

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Whitney Hough received the 2024-25 U.S. Institute of Peace fellowship

Whitney hough received the 2024-25 u.s. institute of peace fellowship for her doctoral research.

Our doctoral student Whitney Hough received the U.S. Institute of Peace (USIP) and the Minerva Research Initiative Peace Scholar Fellowship for pursuing her doctoral research. 

Whitney, a third-year Ph.D. student in the Comparative and International Education Program, is deeply passionate about her research. Her focus lies at the intersection of education, conflict, development, and peacebuilding. She is particularly intrigued by the challenges of providinghigh-quality secondary education in emergencies, the agency of teachers in crisis and conflict-affected contexts, and the transformative role of education in protracted conflict. With 15 years of progressive experience in the international education and nonprofit sectors, including a recent role as the Deputy Project Director for Fulbright Teacher Exchanges at IREX, a global international education development nonprofit, Whitney brings a wealth of practical knowledge to her academic pursuits. She holds a master’s degree in Conflict, Security and Development from the University of Bradford UK, and a Bachelor’s in Psychology and Cross Cultural Studies from Carleton College.

The fellowship that Whitney has been awarded is a testament to her academic prowess and the importance of her research. The 2024-25 USIP-Minerva Peace and Security Scholar Fellowship, funded by the US Institute of Peace and the Minerva Research Initiative, is a highly prestigious award. It provides full-time support for dissertation research from September 1, 2024, to June 30, 2025. The Fellowship also includes opportunities for academic engagement, such as attendance at the annual US Institute of Peace Workshop, regular dissertation progress reports, contributions to the USIP Peace Scholar newsletter, and participation in Peace Scholar roundtable discussions.

Whitney Hough, third-year Ph.D. student in CIE program

The project is entitled “Teachers as Transformative Agents During Protracted Conflict: A Case Study of Cameroon” . It is a qualitative case study that explores the role of teachers in conflict and peacebuilding across six secondary schools in the Anglophone regions of Northwest and Southwest Cameroon. The study aims to analyze what teachers perceive as their responsibilities in fostering peace in conflict-affected contexts, the opportunity and risk factors they associate with adopting those responsibilities, and how they use agency to adopt, adapt, and/or resist peacebuilding efforts.

When asked about how her experience at TC contributed to her research,  Whitney told us: “ I've appreciated the opportunity at TC to be involved in Dr. Mendenhall's UNHCR Typology of Teachers of Persons of Concern Project, which involved fieldwork in Chad and Uganda, along with Dr. Mendenhall's BRICE Teacher and Student Well-Being project, and Dr. Garnett Russell's Newcomer and Refugee Youth Project. Those initiatives helped me learn how best to approach fieldwork, study design, and data analysis.”

Congratulations, Whitney, on this fantastic achievement! We are excited to see the fruits of this work in the future!  

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• Nicholas S. Peters 4 &
• Jennifer K. Quint 1  

BMC Pulmonary Medicine volume  24 , Article number:  221 ( 2024 ) Cite this article

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An immediate, temporal risk of heart failure and arrhythmias after a Chronic Obstructive Pulmonary Disease (COPD) exacerbation has been demonstrated, particularly in the first month post-exacerbation. However, the clinical profile of patients who develop heart failure (HF) or atrial fibrillation/flutter (AF) following exacerbation is unclear. Therefore we examined factors associated with people being hospitalized for HF or AF, respectively, following a COPD exacerbation.

We conducted two nested case-control studies, using primary care electronic healthcare records from the Clinical Practice Research Datalink Aurum linked to Hospital Episode Statistics, Office for National Statistics for mortality, and socioeconomic data (2014-2020). Cases had hospitalization for HF or AF within 30 days of a COPD exacerbation, with controls matched by GP practice (HF 2:1;AF 3:1). We used conditional logistic regression to explore demographic and clinical factors associated with HF and AF hospitalization.

Odds of HF hospitalization (1,569 cases, 3,138 controls) increased with age, type II diabetes, obesity, HF and arrhythmia history, exacerbation severity (hospitalization), most cardiovascular medications, GOLD airflow obstruction, MRC dyspnea score, and chronic kidney disease. Strongest associations were for severe exacerbations (adjusted odds ratio (aOR)=6.25, 95%CI 5.10-7.66), prior HF (aOR=2.57, 95%CI 1.73-3.83), age≥80 years (aOR=2.41, 95%CI 1.88-3.09), and prior diuretics prescription (aOR=2.81, 95%CI 2.29-3.45).

Odds of AF hospitalization (841 cases, 2,523 controls) increased with age, male sex, severe exacerbation, arrhythmia and pulmonary hypertension history and most cardiovascular medications. Strongest associations were for severe exacerbations (aOR=5.78, 95%CI 4.45-7.50), age≥80 years (aOR=3.15, 95%CI 2.26-4.40), arrhythmia (aOR=3.55, 95%CI 2.53-4.98), pulmonary hypertension (aOR=3.05, 95%CI 1.21-7.68), and prescription of anticoagulants (aOR=3.81, 95%CI 2.57-5.64), positive inotropes (aOR=2.29, 95%CI 1.41-3.74) and anti-arrhythmic drugs (aOR=2.14, 95%CI 1.10-4.15).

Conclusions

Cardiopulmonary factors were associated with hospitalization for HF in the 30 days following a COPD exacerbation, while only cardiovascular-related factors and exacerbation severity were associated with AF hospitalization. Understanding factors will help target people for prevention.

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Exacerbations of Chronic Obstructive Pulmonary Disease (COPD) are associated with an increased risk of cardiovascular disease (CVD) [ 1 ] likely due to linked pathophysiology, hypoxic state, and an amplified systemic inflammatory response [ 2 , 3 ]. Previous studies have demonstrated an increased, immediate period of risk for hospitalized cardiovascular events following a COPD exacerbation compared with non-exacerbating patients, [ 1 , 4 ] with the risk highest within the first month and following a severe (hospitalized) exacerbation [ 1 , 5 , 6 ]. The 30 day risk of arrhythmias and heart failure (HF) following an exacerbation, [ 6 , 7 , 8 ] approach [ 4 , 6 ] or even surpass [ 7 , 9 ] 3-fold, compared to those who did not have an exacerbation.

Globally, the prevalence of HF in COPD is high, [ 10 ] and, of patients hospitalized for exacerbation, 20% have existing, undertreated HF [ 11 ]. Incident HF attributed to exacerbations is thought to come from the increase in pulmonary arterial pressures, low blood oxygen levels [ 12 ] and activation of adrenoceptors of the autonomic nervous system [ 12 , 13 ]. However, shared symptomology of HF and COPD exacerbations makes new HF diagnosis difficult, with HF often missed [ 3 , 12 , 14 ] or occurring in tandem; approximately 8% of people primarily diagnosed with HF also have a secondary diagnosis of exacerbation [ 15 ]. Studies have investigated COPD progression in HF [ 12 ] and conversely, HF progression across COPD phenotypes, [ 12 , 16 ] but few have focused on exacerbating COPD alone [ 15 ] and no studies have examined factors associated with hospitalization with a HF diagnosis following a COPD exacerbation.

Arrhythmias are also common among people with COPD, with prevalence ranging from 5 to 15% globally, [ 10 ] and atrial fibrillation (AF) is the most common arrhythmia [ 17 ]. AF diagnoses at the time of an exacerbation are also common, with estimates around 17% [ 18 ], but due to shared, nonspecific symptoms between AF and COPD, differential diagnosis can be difficult [ 19 ]. During a COPD exacerbation, changes in blood gases from hypoxia and rising pulmonary pressure can lead to electrophysiological and structural changes of the atrium, and together are thought to contribute to exacerbation-related disturbances in rhythm and progression of AF [ 6 , 10 , 19 , 20 , 21 ] The understanding of COPD progression alongside AF progression is unclear [ 19 ]. Few studies have examined factors associated with hospitalization for incident AF after an exacerbation, with the focus on the short-term [ 21 , 22 ]. Several have however investigated factors associated with concurrent AF at time of an exacerbation [ 18 , 23 ].

Therefore, using a primary-care defined COPD cohort from the Clinical Practice Research Datalink (CPRD) Aurum database linked with hospital, mortality, and socioeconomic deprivation data, we explored factors associated with incident hospitalization for non-fatal HF, or AF or atrial flutter, within the 30 days following a COPD exacerbation.

Study design and methods

Data source.

We obtained pseudonymized, routinely-collected electronic healthcare record data from CPRD Aurum, [ 24 ] a primary care database broadly representative by age, sex, deprivation, and regional distribution, [ 25 ] and covering approximately 20% of GP-registered patients in England [ 24 ]. Primary care data from the May 2022 build [ 24 ] were linked to the Office for National Statistics (ONS) for mortality, socioeconomic data from the 2015 Index of Multiple Deprivation (IMD), and secondary care data from NHS England’s Hospital Episode Statistics (HES) Admitted Patient Care (APC) database.

Source population

The source population were people with a COPD diagnosis who had been included in the Exacerbations of COPD and their OutcomeS on CardioVascular diseases (EXACOS-CV) observational open cohort study [ 4 ]. People were eligible for inclusion in that original cohort study if they met the following criteria: 1) aged 40 years or older, 2) had a COPD diagnosis using a validated algorithm (86.5% PPV) [ 26 ] 3) were eligible for linkage with HES, ONS, and IMD data, 4) had a smoking history (i.e., current or ex-smoker), 5) had continuous GP practice registration with acceptable quality data in the year before start of follow up, and 6) had data recorded after 1 st of January 2014.

Study design and population

We conducted two nested-case control studies, in parallel. The study population for each study consisted of COPD patients in the EXACOS-CV source population, who 1) experienced an exacerbation within cohort follow-up, 2) among the pool of eligible controls had at least 30 days of contributing data post-exacerbation, and 3) had no evidence of HF or arrhythmias in the year before exacerbation. People were excluded from the AF/flutter study population if, within the 30 days post-exacerbation, they had evidence of other non-AF arrhythmias (e.g., cardiac arrest) (Fig. 1 for study designs; Supplementary Figures 1 and 2 for HF and AF/flutter eligibility diagram respectively).

Study design for the parallel, nested case control studies. Abbreviations: HF=heart failure, AF/f = atrial fibrillation/flutter, GP=general practice, CV=cardiovascular. Two nested case control studies, for HF and AF/f respectively, were conducted among all patients who experienced an exacerbation at start of cohort follow up. Full definitions of demographic and clinical factors of interest are in the Supplementary materials

Cases were individuals with a hospitalized, cardiovascular event (HF or AF/flutter, respectively) within 1-30 days post COPD exacerbation. Cases were determined in the HES database using ICD-10 codes in the primary diagnostic position across all episodes in a single hospitalization. The date of admission was the date of the case (index date, HF or AF/flutter, respectively). Extended case definitions are in Supplementary Table 3 .

The set of controls per case was drawn among individuals in the study population who had no in-hospital diagnosis of the cardiovascular event-of-interest (HF or AF/flutter, respectively) and who had 30 days of contributing data post-exacerbation. Controls were assigned a pseudo-end date (i.e., 30 days post-exacerbation) on which to match. Controls were individually matched to cases on GP practice to account for unmeasurable potential clinical differences in disease management by clinicians, within a 30-day window of the index date, and could be used as a control for more than one case. For the HF analysis, controls were matched 2:1 and for the AF/flutter analysis, 3:1.

The choice of matching factors and ratios were chosen based on considerations of 1) maximizing the ratio itself for reasons of power and precision while 2) minimizing cases lost without a full set of controls. Only case sets with the full ratio of controls per case were included for analysis.

We examined factors potentially associated with hospitalization for HF or AF/flutter in the 30 days following an exacerbation of COPD. Factors included demographic characteristics: age (categorized into four age bands; 40-69, 70-74, 75-79, ≥80), sex, IMD quintiles, and ethnicity; and smoking status. Comorbidities included (hypertension, anxiety, depression and depressive symptoms, type II diabetes, chronic kidney disease (CKD), BMI [body mass index; using World Health Organization classification]). COPD factors included GOLD grade of airflow limitation (defined as 1 mild, 2 moderate, 3-4 severe/very severe), Medical Research Council (MRC) dyspnea score (1-2; 3; 4-5), exacerbation severity at cohort entry (moderate/severe) prior exacerbation frequency (infrequent (≤1) versus frequent (≥2) history in a year window preceding one year to cohort entry). Prior CVD history was evidence of the following anytime preceding the year before exacerbation date [acute coronary syndrome (ACS), arrhythmias, HF, ischemic stroke, pulmonary hypertension PH], COPD inhaled therapies, and major classes of cardiovascular medications (prescriptions defined two years before cohort entry). Covariate definitions are in Supplementary Table 4 .

We used validated definitions for COPD exacerbations. A moderate exacerbation was defined as a COPD-related primary care (GP) visit with either a code for exacerbation (including Lower Respiratory Tract Infection (LRTI) SNOMED-CT codes) and/or prescription for respiratory antibiotics and oral systemic corticosteroids not on the same day as an annual review, as validated in CPRD [ 27 ]. A severe exacerbation was defined as a hospitalization with an acute respiratory event code including COPD or bronchitis as a primary diagnosis, or a secondary diagnosis of COPD, as validated in HES [ 28 ]. We considered exacerbations to be the same event if recorded within 14 days in which case the highest level of severity was chosen.

We checked covariate missingness to assess use in adjusted models. Imputation was not considered for covariates with missing data, given the missingness mechanism was Missing-Not-At-Random (MNAR), violating the Missing-Completely-At-Random (MCAR) assumption premising imputation [ 29 ].

Codelists for primary care factors were generated using SNOMED-CT and British National Formulary ontologies; we used our standardizable, reproducible methodology, available on GitHub: for drug [ 30 ] and medical/phenotype codelists , respectively. Codelists for hospitalizations used ICD-10 codes. Codelists are available on our EXACOS-CV GitHub repository .

Statistical analysis

We compared separately the odds of hospitalization for HF or AF/flutter between the comparator groups for each factor, using conditional logistic regression. Our final model was adjusted for all covariates without substantial amount of missing data, including demographic factors, comorbidities, and COPD inhaled therapies and cardiovascular medications. In three sensitivity analyses, we repeated main analyses additionally adjusting for variables-of-interest with substantial missing data, respectively: GOLD, MRC, and CKD.

Analyses were performed using STATA v17.

These data were collected and provided by CPRD. Ethical approval was obtained through CPRD’s Research Data Governance Process (protocol number: 22_002377). The RECORD checklist for observational studies is in Supplementary Table 5 .

Characteristics of study participants

The HF dataset consisted of 1,569 cases experiencing an HF event within the first 30 days post-exacerbation, matched to 3,138 controls. The AF/flutter dataset consisted of 841 cases experiencing an AF/flutter event within the first 30 days post-exacerbation, matched to 2,523 controls. Tables 1 and 2 show the characteristics of the participants for HF and AF/flutter, respectively.

Across both study populations, cases tended to have had a severe exacerbation, were more likely to be older, male, an ex-smoker, have comorbidities including prior prevalent cardiovascular disease, and be prescribed cardiovascular medications. Cases and controls both tended to have infrequent exacerbation history, have prescriptions for long-acting therapies, but tended to not have prescriptions for short-acting inhaled therapies.

Factors associated with HF hospitalization 1-30 days post exacerbation

Age, type II diabetes, obesity, prior HF diagnosis, prior arrhythmia diagnosis, having a severe exacerbation, and most cardiovascular medications were associated with increased odds of being hospitalized for HF within 30 days of a COPD exacerbation (Table 1 ). The factors most strongly associated with HF were exacerbation severity (aOR=6.25, 95%CI 5.10-7.66), a prior HF diagnosis (aOR=2.57, 95%CI 1.73-3.83), age at least 80 years (≥80 vs. 40-69; aOR=2.41, 95%CI 1.88-3.09), and, of the cardiovascular medications, diuretics (aOR=2.81, 95%CI 2.29-3.45).

In sensitivity analyses, GOLD grade, MRC score, and history of CKD were all associated with an increased odds of being hospitalized for HF within the month post exacerbation.(Supplementary Table 1 ) The strongest associations were for CKD (aOR=1.85, 95%CI 1.46-2.35) and higher levels of airflow limitation and breathlessness (GOLD grade 3-4 Severe/Very Severe aOR=1.83, 95%CI 1.32-2.54, versus GOLD grade 1 Mild) (Score 4-5 MRC aOR=1.87, 95%CI 1.42-2.46, versus MRC 1-2).

Factors associated with AF/flutter hospitalization 1-30 days post exacerbation

Age, male sex, prior arrhythmia, prior PH, and having a severe exacerbation were associated with AF/flutter in the 30 days following an exacerbation. Most cardiovascular medications were also associated with AF/flutter (Table 2 ). The factors most strongly associated with AF were exacerbation severity (aOR=5.78 95%CI 4.45-7.50), age ≥80 years (aOR=3.15 95%CI 2.26-4.40), prior arrhythmia and PH (aOR=3.55, 95%CI 2.53-4.98; aOR=3.05, 95%CI 1.21-7.68), and of the cardiovascular medications, anticoagulants (aOR=3.81, 95%CI 2.57-5.64), positive inotropes (aOR=2.29, 95%CI 1.41-3.74) and anti-arrhythmic drugs (aOR=2.14, 95%CI 1.10-4.15).

In sensitivity analyses, GOLD grade, MRC score, and CKD had no association with AF/flutter (Supplementary Table 2 ).

In a primary care defined COPD population, this study examined the clinical profiles of people hospitalized for HF and for AF within a month post exacerbation. We observed that the odds of HF and of AF hospitalization were higher for people with severe, hospitalized exacerbations and with cardiovascular-related history. For HF analyses only, the odds of HF were also higher for people with microvascular factors (i.e., type II diabetes; obesity; CKD) and for people with pulmonary factors, namely those with worse GOLD grade of airflow limitation and higher levels of MRC breathlessness scores.

Heart failure

The pathophysiological links between COPD exacerbations and HF are recognized [ 31 , 32 , 33 ]. Upon exacerbation, dynamic lung hyperinflation from airflow limitation alongside heightened inflammation and hypoxia, can lead to increased strain on both the lungs and heart. The increased cardiopulmonary pressure can then lead to impaired contraction or filling of the left ventricle, namely HF with preserved or with reduced ejection fraction, respectively [ 31 , 32 , 33 ].

Few studies have investigated factors associated with HF in COPD alone, [ 15 , 34 , 35 , 36 ] of which only one [ 15 ] investigated hospitalized exacerbation with concurrent HF, but did not quantify this relationship with ORs, and was conducted in the US National Inpatient Sample (NIS) database. The remaining were post-hoc analyses of trials focused on stable COPD [ 34 , 35 , 36 ].

The strong magnitude of the association for exacerbation severity (hospitalization) but not for exacerbation frequency, suggests two points. First, from a healthcare service-level standpoint, patients hospitalized for exacerbations are more likely to be hospitalized for a future HF (i.e., re-admission) compared with patients whose exacerbation was managed in primary care. Second, clinically, exacerbation severity (e.g., greater intensity of inflammation) has a greater indication of a patients’ future cardiac state, rather than past exacerbation occurrence and management. Findings for older age were anticipated and align with previous studies [ 15 , 34 , 35 , 36 ].

The associations for history of HF, arrhythmia, type II diabetes, and CKD with post-exacerbation HF are unsurprising given their known independent relationships each with HF and exacerbations alone. Chronic, unmanaged HF can lead to future health service utilization for HF [ 16 , 37 ]. Arrhythmia-attributed cardiac remodeling can contribute to development of cardiomyopathy [ 38 ]. Diabetes is a risk factor for substantial HF progression [ 16 , 39 ], and separately a population-based study in COPD patients demonstrated an increased risk of cardiovascular mortality with type II diabetes [ 40 ]. Impaired renal hemodynamics and activation of the renin-angiotensin-aldosterone system (RAAS) can lead to HF, [ 41 ] and separately reduced kidney function is associated with future HF [ 42 ].

Our findings for cardiovascular medications indicate a certain treatment profile in primary care, leading up to the post-exacerbation HF hospitalization. The strongest association for diuretics suggests that leading up to future HF, patients perhaps are receiving treatment indicated for uncontrolled edema from existing HF, diabetic cardiomyopathy, or CKD for example.

GOLD and MRC as factors for post-exacerbation HF likewise were expected. Increased breathlessness and reduced lung function are not only symptoms of an imminent exacerbation or HF; equally, these factors can also indicate delayed diagnosis of unstable COPD or HF, [ 16 , 43 ] given their shared symptomology [ 3 , 12 , 14 ]. Reduced lung function can contribute to worsening prognosis and precipitate a future exacerbation or HF [ 16 , 43 ].

Atrial fibrillation

The pathophysiological mechanisms implicating AF post COPD exacerbation are also established [ 19 , 44 ]. At time of exacerbation, drastic increases in lung hyperinflation and impaired intrathoracic pressures can cause increased pulmonary vascular resistance and damage, leading to alterations to atrial electrophysiology [ 19 , 44 ]. Compromised gas exchange in the lungs can induce systemic inflammation and oxidative stress too, and also put strain on pulmonary vasculature, leading to abnormal atrial structure and ion-channel remodeling, [ 19 , 44 ] while certain treatments prescribed upon exacerbation are arrhythmogenic [ 19 , 44 ].

Only four studies have investigated patient profiles for AF development in unstable COPD, all hospital-based [ 18 , 21 , 22 , 23 ], of which two conducted in the US National Inpatient Sample (NIS) database [ 18 , 22 ]. Two examined factors associated with AF diagnosis after exacerbation [ 21 , 22 ] one of which patients had existing AF [ 21 ]. Two failed to quantify with ORs, only comparing baseline characteristics of exacerbating patients by status of concurrent AF [ 18 , 23 ].

Our findings for older age and male gender are not unexpected; studies similarly found these associations in exacerbating [ 18 , 22 ] and in stable COPD [ 45 ]. Unsurprisingly, exacerbation severity associated with future AF, again adding to the existing evidence of stronger associations for hospitalized exacerbation [ 1 , 6 ] and again, likewise to HF, suggests a distinction between healthcare service-level patient pathways, and intensity versus frequency.

The associations we found for history of PH and arrhythmias aligns with what was anticipated clinically. Electrophysiological and structural changes to the atrium over time, from either AF itself [ 20 ] or from chronic atrial stretching and fibrosis attributed to PH, [ 46 ] can lead to future AF. While a study using Euro Heart Survey data showed COPD as a factor for progression of paroxysmal to persistent AF (aOR=1.51, 95%CI 0.95-2.39) [ 20 ], neither of the two studies looking at patient profiles for post-exacerbation AF, looked at chronic, prevalent arrhythmias itself as a factor [ 21 , 22 ]. No studies have looked at PH, although the study among end-stage COPD patients in the NIS database found a weak association for pulmonary circulatory disorders (aOR=1.44, 95%CI 1.37-1.52) [ 22 ], compared the OR of about 3 for PH. A study found raised pulmonary artery pressure to be associated with AF (p<0.05), but failed to quantify, and it was small, underpowered, and not generalizable as it restricted to hospitalized COPD patients with existing AF [ 21 ].

The lack of associations for prior ACS, ischemic stroke, HF, and hypertension somewhat contradict the study among hospitalized, end-stage COPD patients, [ 22 ] where an association was found for HF (aOR=2.42; 95%CI: 2.36-2.48) and coagulopathy (aOR=1.23; 95%CI:1.16-1.31), but again this may reflect the more severe prognosis of these patients versus those in our study.

Likewise to HF, findings for cardiovascular medications indicate a certain treatment profile in primary care, leading up to the post-exacerbation AF hospitalization. Although we were unable to adjudicate by specific subtype of AF,(e.g., paroxysmal, persistent) the strongest associations for positive inotropes, anti-arrhythmic drugs, and anticoagulants may suggest that leading up to future AF, patients perhaps are receiving treatment to manage abnormal heart rate and/or rhythm, and/or to prevent clotting. Future research could investigate the respective treatment profile relative to paroxysmal AF and to persistent AF [ 47 ] to confirm and extend our findings.

Our null results for other comorbidities (i.e., depression and depressive symptoms, anxiety, BMI, CKD, and type-II diabetes) goes against studies finding an association for diabetes, [ 18 , 22 ] mixed findings for depression, [ 18 , 22 ] among other comorbidities. Yet these observed associations could be due to differing context; using the NIS database in a study population of only hospitalized, exacerbating, insured payors. GOLD airflow obstruction and MRC dyspnea score were not associated with post-exacerbation AF hospitalization, possibly as AF is often associated with vague symptoms of onset and not necessarily immediately thought about as a cause of increasing breathlessness in someone with COPD [ 44 , 48 , 49 ].

Methodological considerations

A key strength is our generalizable COPD cohort, defined within the electronic healthcare record with detailed data to examine and adjust for a range of factors. Unlike other studies, this allowed us to look at two patient pathways: cardiovascular-related hospitalizations post primary-care exacerbation, and re-admissions post hospitalized exacerbation. Our exclusion criteria allowed us to quantify the odds of new onset HF and AF hospitalizations following exacerbation, by ensuring no evidence of AF or HF in the year prior to exacerbation. We chose to study two common cardiovascular conditions in COPD, AF and HF. We could not subdivide HF and AF more granularly, because of insufficient statistical power and the inability to obtain electrocardiogram or echocardiogram results to adjudicate.

We used validated codes to define COPD [ 26 ] and COPD exacerbations [ 27 , 28 ] so misclassification is unlikely. Where possible, we used previously tested methods [ 30 ] and codes to define our factors-of-interest and codes were checked by a pulmonologist and/or cardiologist. The nested case control matched design allowed us to control for unmeasurable potential clinical differences in disease management by clinicians, by matching patients on GP practice.

To minimize selection bias among patients with measured factors only, we adjusted only for covariates without substantial missing data. We reserved GOLD, MRC, and CKD for sensitivity analyses; the associations of these factors with HF are generalizable only to patients with measurements (e.g., patients with greater healthcare monitoring, provision, or access). For this reason, the relationship for ethnicity could not be quantified, and given the data sparsity. For the HF analysis, we were unable to quantify B-type natriuretic peptide testing as a factor (BNP or NT-proBNP) because of 90% missing data for BNP (data not shown).

Confounding by indication is possible, particularly for the associations observed for cardiovascular medications (cases could have been more likely prescribed cardiovascular medications to manage a prevalent co-morbidity (perhaps with delayed diagnosis) that posed future cardiovascular risk, compared with controls) [ 50 ]. For example, although 12.8% of HF cases had prevalent HF diagnoses at baseline (201/1569), over 70% of HF cases were prescribed diuretics (1122/1569). Diuretics, particularly extended use of loop diuretics, can indicate possible, pre-HF diagnoses [ 51 ], given HF diagnoses tend to be delayed in COPD patients [ 14 , 37 , 51 ]. Taking this information together, this suggests a substantial proportion of cases could have been prescribed diuretics to manage possible-yet-undiagnosed HF, in which case, the later case-defining hospitalization was the delayed, first-time diagnosis of HF.

Our results for cardiovascular medications do not imply these medications are increasing the cardiovascular risk, rather they add to an understanding of the exacerbating patient profile. Furthermore, although these medications could indicate delayed CVD diagnosis, alternatively they could be medically indicated for management of a co-morbidity we did not adjust for, e.g., beta-blockers can be prescribed for thyroid conditions [ 52 ].

Implications for clinical practice

Within the month-window following an exacerbation, largely exacerbation intensity and cardiovascular-related management and disease history were associated with odds of incident HF and AF. For HF specifically, existing type II diabetes, CKD, lung function (GOLD grade), and levels of breathlessness (MRC) had an association too—but not for AF. These factors can help better identify patients most at-risk for HF and AF, to streamline efforts to allocate screening, vigilant monitoring, and prevention.

At the time of a COPD exacerbation, particularly hospitalized exacerbation, we recommend preemptively monitoring markers of possible HF, through taking medication history of loop diuretics, [ 51 ] and through BNP testing [ 53 ]. At present however, HF prevention is narrow in scope, with guidelines for early identification of HF not explicitly considering unstable COPD [ 53 , 54 ]. Our results suggest that HF monitoring should widen to include patients with COPD exacerbations.

Upon exacerbation, particularly hospitalized exacerbation, we recommend proactively screening for AF (e.g., electrocardiogram) [ 19 ] as AF commonly presents subclinically [ 17 ]. Still, at present, AF screening is narrow in scope; it is primarily conducted in patients with existing or suspected AF with the goal of preventing stroke, with AF guidelines not explicitly considering unstable COPD [ 17 , 55 ]. Our results suggest that AF screening should widen to include patients with COPD exacerbations, to help prevent future AF—even before stroke.

Cardiopulmonary factors were associated with hospitalization for HF in the 30 days following a COPD exacerbation, while only cardiovascular-related factors and exacerbation severity were associated with AF hospitalization. Understanding risk factors will help to target people for prevention. Future studies with the intent of improving risk prediction for HF and AF should consider exacerbations of COPD.

Availability of data and materials

Data are available on request from the CPRD. Their provision requires the purchase of a license, and this license does not permit the authors to make them publicly available to all. This work used data from the CPRD Aurum version collected in May 2022 and have clearly specified the data selected within the Methods section, and linked data in the Supplementary Materials . To allow identical data to be obtained by others, via the purchase of a license, the code lists will be provided upon request. Licenses are available from the CPRD ( http://www.cprd.com ): The Clinical Practice Research Datalink Group, The Medicines and Healthcare products Regulatory Agency, 10 South Colonnade, Canary Wharf, London E14 4PU.

Abbreviations

Chronic Obstructive Pulmonary Disease

Clinical Practice Research Datalink

• Cardiovascular Disease

General practice

Hospital Episode Statistics

Index of Multiple Deprivation

Medical Research Council

• Heart Failure
• Atrial Fibrillation

Chronic Kidney Disease

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Acknowledgements

This study is based in part on data from the Clinical Practice Research Datalink obtained under license from the UK Medicines and Healthcare products Regulatory Agency. The data is provided by patients and collected by the National Health Service (NHS) as part of their care and support. The interpretation and conclusions contained in this study are those of the author/s alone.

This study was funded by AstraZeneca UK. CN, KR, and SM of the funding source took part in initial conceptualization and protocol design and the interpretation of results.

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JQ takes responsibility for the content of the manuscript, including the data and analysis. EG, JQ, CN, KR, and SM conceptualized the study and designed the protocol. JQ, NP and EG contributed to the development of the codelists that defined the study variables. EG, CK contributed to the methodology. EG, HW, CK and AI accessed and verified the data. EG, CK and AI were responsible for data curation and management. EG, CK were responsible for formal analysis. EG wrote the original draft of the manuscript. EG, MA, JQ contributed to the literature review and clinical implications. All authors contributed and approved the final manuscript. All authors had final responsibility for the decision to submit for publication.

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CPRD has NHS Health Research Authority (HRA) Research Ethics Committee (REC) approval to allow the collection and release of anonymized primary care data for observational research [NHS HRA REC reference number: 05/MRE04/87]. Each year CPRD obtains Section 251 regulatory support through the HRA Confidentiality Advisory Group (CAG), to enable patient identifiers, without accompanying clinical data, to flow from CPRD contributing GP practices in England to NHS Digital, for the purposes of data linkage [CAG reference number: 21/CAG/0008]. The protocol for this research was approved by CPRD’s Research Data Governance (RDG) Process (protocol number: 22_002377 ) and the approved protocol is available upon request. Linked pseudonymized data was provided for this study by CPRD. Data is linked by NHS Digital, the statutory trusted third party for linking data, using identifiable data held only by NHS Digital. Select general practices consent to this process at a practice level with individual patients having the right to opt-out.

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JQ reports grants from GlaxoSmithKline, Health Data Research UK, MRC, Asthma+Lung UK, Bayer, BI, AZ and Chiesi, outside this work and AZ for the conduct of this study. JQ has received personal fees for advisory board participation, consultancy or speaking fees from GlaxoSmithKline, Evidera, AstraZeneca, and Insmed. CN, KR, and SM are employees of AZ and hold stock and/or options in the company. HW reports grants from Health Data Research UK outside the submitted work. EG, CK, AI, and MA have nothing to disclose.

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Graul, E.L., Nordon, C., Rhodes, K. et al. Factors associated with non-fatal heart failure and atrial fibrillation or flutter within the first 30 days post COPD exacerbation: a nested case-control study. BMC Pulm Med 24 , 221 (2024). https://doi.org/10.1186/s12890-024-03035-4

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• Chronic Obstructive Pulmonary Disease Exacerbations
• Electronic Health Records
• Epidemiology

BMC Pulmonary Medicine

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Triglyceride-glucose index as a potential predictor for in-hospital mortality in critically ill patients with intracerebral hemorrhage: a multicenter, case–control study

• Yang Yang 1   na1 ,
• Shengru Liang 2   na1 ,
• Jiangdong Liu 1   na1 ,
• Minghao Man 3 ,
• Dengfeng Jia 1 ,
• Jianwei Li 1 ,
• Xiaoxi Tian 1   na2 &
• Lihong Li 1   na2  

BMC Geriatrics volume  24 , Article number:  385 ( 2024 ) Cite this article

174 Accesses

Metrics details

The correlation between the triglyceride-glucose index (TyG) and the prognosis of ischemic stroke has been well established. This study aims to assess the influence of the TyG index on the clinical outcomes of critically ill individuals suffering from intracerebral hemorrhage (ICH).

Patients diagnosed with ICH were retrospectively retrieved from the Medical Information Mart for Intensive Care (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). Various statistical methods, including restricted cubic spline (RCS) regression, multivariable logistic regression, subgroup analysis, and sensitivity analysis, were employed to examine the relationship between the TyG index and the primary outcomes of ICH.

A total of 791 patients from MIMIC-IV and 1,113 ones from eICU-CRD were analyzed. In MIMIC-IV, the in-hospital and ICU mortality rates were 14% and 10%, respectively, while in eICU-CRD, they were 16% and 8%. Results of the RCS regression revealed a consistent linear relationship between the TyG index and the risk of in-hospital and ICU mortality across the entire study population of both databases. Logistic regression analysis revealed a significant positive association between the TyG index and the likelihood of in-hospital and ICU death among ICH patients in both databases. Subgroup and sensitivity analysis further revealed an interaction between patients' age and the TyG index in relation to in-hospital and ICU mortality among ICH patients. Notably, for patients over 60 years old, the association between the TyG index and the risk of in-hospital and ICU mortality was more pronounced compared to the overall study population in both MIMIC-IV and eICU-CRD databases, suggesting a synergistic effect between old age (over 60 years) and the TyG index on the in-hospital and ICU mortality of patients with ICH.

Conclusions

This study established a positive correlation between the TyG index and the risk of in-hospital and ICU mortality in patients over 60 years who diagnosed with ICH, suggesting that the TyG index holds promise as an indicator for risk stratification in this patient population.

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Introduction

Spontaneous, nontraumatic, intracerebral hemorrhage (ICH) is a catastrophic disease making up approximately 10–20% of all types of stroke [ 1 ]. Epidemiological data indicate that 30% of ICH patients requiring intensive care unit (ICU) management and 40% of them die within 30 days [ 2 ]. Despite ongoing research and advancements in this medical field, effective therapeutic options for improving the prognosis of patients with ICH are still lacking [ 3 ]. Consequently, there is an urgent need to identify remediable factors that may impact the outcomes of ICH, as this information could potentially lead to the development of new therapeutic targets.

Insulin resistance (IR), a pathological condition where tissue does not respond normally to insulin stimulation, plays a crucial role in the development of metabolic disorders [ 4 ]. More importantly, studies have revealed that compared with peripheral tissue, IR appears earlier in the central nervous system, indicating that brain is more vulnerable to IR, especially under various pathological states such as ICH and ischemic stroke (IS) [ 5 ]. Therefore, the indicators associated with IR may be closely related to the initiation of ICH and its prognosis.

The triglyceride-glucose (TyG) index, consisting of fasting triglyceride (FTG) and fasting blood glucose (FBG), is a valuable tool for analyzing lipid and glucose metabolism [ 6 , 7 ]. It is also recognized as an accurate indicator of IR [ 8 , 9 ]. Some researchers have observed a positive correlation between the TyG index and the incidence and mortality rates of progressed coronary artery disease [ 10 , 11 ]. Additional studies have indicated that the TyG index may have the potential to forecast negative cardiovascular events in individuals with coronary artery disease [ 12 ]. Moreover, multiple studies have demonstrated the predictive ability of the TyG index for the onset and mortality of IS [ 13 , 14 ]. These findings collectively highlight the association of the TyG index with cardiovascular and cerebrovascular diseases. However, the relationship between ICH and the TyG index, as well as the prognostic role of the TyG index in this condition, remains unexplored.

Therefore, the objective of this study is to evaluate the impact of the TyG index on the prognosis of critically ill patients with ICH, which may establish its potential utility as a risk stratification tool in ICH cases.

Data sources

Data used in this study were extracted from the Medical Information Mart for Intensive Care (MIMIC-IV version 2.2) and the eICU Collaborative Research Database (eICU-CRD) [ 15 , 16 ]. MIMIC-IV consists of medical records between 2008 and 2019 from over 190,000 patients who were treated in various types of ICU of the Beth Israel Deaconess Medical Center. The eICU-CRD included medical records of over 200,000 patients receiving clinical management in ICUs from over 200 medical centers between 2014 and 2015. Since data in these two databases are de-identified to hide patients’ information, the informed consent and ethics approval are not essential.

Data extraction

Structure query language (SQL), executed on the PostgresSQL (version 13.7.2), was utilized for data extraction from MIMIC-IV and eICU-CRD. One researcher (Yang Yang) with authorization to access both databases (Record ID: 48,776,647) conducted the data extraction. Inclusive criteria encompassed patients who were (1) aged 18 years and above; (2) diagnosed with ICH in accordance with International Classification of Diseases, 9th and 10th Revision (ICD9 and ICD10). Exclusion criteria included: (1) patients with multiple hospitalization entries, only data from the initial hospitalization due to ICH were considered; (2) patients lacking data of FTG and FBG on the first day of ICU admittance were omitted; (3) individuals who expired or were released within 24 h of ICU admission were excluded due to their significant missing data for key variables used in the regression analysis. Therefore, excluding this group of patients was necessary to ensure the reliability of the results of the regression analysis.

The following information was extracted for the final study cohort: (1) patients’ age and gender; (2) comorbidities identified by ICD-9 and ICD-10 codes; (3) initial FBG and FTG results within 24 h post-ICU admission; (4) average values of laboratory parameters within 24 h of ICU admittance; (5) minimum Glasgow Coma Scale (GCS) score on the first day of ICU admittance; (6) maximum Acute Physiology Score III (APSIII) and Sequential Organ Failure Assessment (SOFA) scores on the first day of ICU management; (7) treatment-related data that may impact the prognosis of ICH patients were extracted, which includes invasive mechanical ventilation, the use of anticoagulants, and the use of antiplatelet agents during hospitalization.

Assessment of the TyG index

The TyG index is calculated using the formula: TyG index = ln [FTG (mg/dl) × FBG (mg/dl)/2], where FTG and FBG represent the first recorded values of FBG and FTG since ICU admission [ 17 , 18 ]. In the subsequent statistical analysis, the TyG index was considered both as a continuous and categorical variable. When treated as a categorical variable, it was divided into four groups based on quartiles. The data extraction process is illustrated in Fig.  1 .

The flow chart for extracting data from the MIMIC-IV and eICU databases

Primary outcomes

The primary outcomes of interest were all-cause in-hospital mortality and ICU mortality, which were defined as deaths occurring during hospitalization and ICU staying, respectively.

Statistical analysis

Continuous variables were expressed as median (interquartile ranges) and categorical variables were described as number (percentages). Comparisons between groups were performed by Mann–Whitney U or Kruskal–Wallis test for continuous variables, and chi-squared or Fisher’s exact test for categorical ones.

In order to investigate the relationship between the TyG index and the primary outcomes, an initial analysis utilizing restricted cubic splines (RCS) with four knots was carried out to assess any potential non-linear associations between the TyG index and the risk of in-hospital and ICU mortality. If a non-linear relationship was not detected, logistic regression analysis were performed using three different models: model 1 included only the TyG index, model 2 adjusted for age and gender, and model 3 further adjusted for various potential confounders relevant to the clinical outcomes of ICH, including GCS, hypertension, congestive heart failure, white blood cell count (WBC), serum urea nitrogen (BUN), serum creatinine, red cell distribution width (RDW), serum bilirubin, serum aspartate aminotransferase (AST), prothrombin time (PT), use of anticoagulants, and use of antiplatelet agents. Additionally, to check for multicollinearity in the logistic regression analysis, a Spearman rank correlation test was carried out and the square root of the variance inflation factor (VIF) was calculated.

To explore potential variations within specific populations, subgroup analysis was conducted by stratifying patients according to gender, age (> 60 vs. ≤ 60 years), diabetes, hypertension, use of anticoagulants, and use of antiplatelet agents. The interaction between the TyG index and the other variables utilized for stratification in subgroup analysis was evaluated through likelihood ratio test. Finally, a sensitivity analysis was performed by using Cox proportional hazard regression to verify the relationship between the TyG index and in-hospital and ICU mortality. The follow-up period was measured from the date of hospital or ICU admission to the date of death during the hospitalization or ICU stay. The Cox regression model was adjusted for possible confounders as outlined in the fully adjusted model (model 3) of logistic regression mentioned above.

All statistical analysis were performed using R software (version 4.3.1). The“VIM”package was used to visualize the distribution of missing values, from which we can see that all variables had missing ratio less than 20% (Additional file 1 : Figure S1). The “mice” package was adopted to address missing values by multiple imputation to obtain 5 imputation datasets in the process of logistic regression. Besides, the “corrplot” package was used to visualize the associations between continuous variables. The“plotRCS” package was used to perform RCS. The“forestploter” package was adopted to visualized the results of subgroup analysis. The “survminer” package was used to conduct Cox regression analysis. Statistically significant was set as a two tailed P  <  0.05 .

Baseline characteristics

A total of 791 patients from MIMIC-IV and 1,113 from eICU-CRD were included in the final analysis. Among them, 418 (53%) individuals in MIMIC-IV and 627 (56%) in eICU-CRD were male. The in-hospital mortality rates were 14% in MIMIC-IV and 16% in eICU-CRD, with ICU mortality rates of 10% and 8% respectively. The median age was 72.25 (60.63, 82.59) years in MIMIC-IV and 66 (55, 77) years in eICU-CRD. Besides, the average value of TyG index was 8.72 (8.38, 9.17) in MIMIC-IV and 8.76 (8.33, 9.21) in eICU-CRD.

When dividing participants into groups based on the quartiles of the TyG index, patients in the upper quartiles had significantly higher APSIII scores, and higher proportion of invasive ventilation than those in the lower quartiles ( P  <  0.001 for all ). Furthermore, hospital stay time, ICU stay time, in-hospital mortality, and ICU mortality all exhibited a gradual increase from the first to the fourth quartile of the TyG index. However, there was no significant difference in the mean hospital and ICU survival time of patients who died in the hospital or ICU across the quartiles of the TyG index. (Table  1 and Additional file 2 : Table S1).

Baseline data of participants divided by the hospital and ICU outcomes are presented in Table  2 and Additional file 3 : Table S2, respectively. Compared to in-hospital and ICU survivors, non-survivors in both the MIMIC-IV and eICU-CRD databases showed significantly higher APSIII and SOFA scores, shorter hospital stays, and a higher proportion of invasive ventilation. However, compared to in-hospital and ICU survivors, ICU stay time was shorter in non-survivors from eICU-CRD and longer in non-survivors from MIMIC-IV. Furthermore, GCS scores were lower in in-hospital and ICU non-survivors compared to survivors in eICU-CRD, but there was no significant difference in GCS scores between in-hospital and ICU survivors and non-survivors in MIMIC-IV. Interestingly, despite the potential risk of secondary hemorrhage associated with antiplatelet agents, their usage was more common among in-hospital and ICU non-survivors than survivors in MIMIC-IV. Moreover, the TyG index was notably higher in the in-hospital non-survivors compared to survivors (MIMIC-IV: 8.94 (8.51–9.48) vs. 8.70 (8.36–9.09); P  <  0.001 . eICU-CRD: 8.98 (8.51–9.49) vs. 8.70 (8.31–9.16); P  <  0.001 ). Similarly, the TyG index was significantly elevated in ICU non-survivors in contrast to ICU survivors (MIMIC-IV: 9.00 (8.51–9.48) vs. 8.70 (8.36–9.10); P  < 0.001. eICU-CRD: 9.09 (8.76–9.65) vs. 8.71 (8.31–9.17); P  < 0.001) (Additional file 4 : Figure S2).

Association between the TyG index and the primary outcomes

We initially conducted a nonlinear correlation analysis between the TyG index and the primary outcomes using RCS. Findings suggested no significant nonlinear correlation between the TyG indicator and the likelihood of either in-hospital or ICU mortality (In-hospital mortality: P for nonlinear  =  0.751 in MIMIC-IV , P for nonlinear  =  0.562 in eICU-CRD. ICU mortality: P for nonlinear  =  0.986 in MIMIC-IV , P for nonlinear  =  0.431 in eICU-CRD) (Fig.  2 ). Subsequently, logistic regression analysis was conducted to assess the linear relationship between the TyG index and the primary outcomes. In the fully adjusted model (model 3) that adjusted for various potential confounders related to the clinical outcomes of ICH, a positive correlation was found between the TyG index and the risk of in-hospital mortality (MIMIC-IV: OR 1.75 [95%CI 1.20–2.52], P  =  0.003 . eICU-CRD: OR 1.37 [95%CI 1.05–1.80], P  <  0.001 ) and ICU mortality (MIMIC-IV: OR 2.15 [95%CI 1.45–3.17], P  <  0.001. eICU-CRD: OR 1.61 [95%CI 1.13–2.27], P  <  0.001 ). Moreover, compared to the first quartile (Q1) of the TyG index, the results of model 3 indicated that the fourth quartile (Q4) was linked to a higher risk of in-hospital mortality (MIMIC-IV: OR 2.31 [95%CI 1.18–4.67], P  =  0.017 . eICU-CRD: 1.73 [95%CI 1.02–3.06], P  =  0.036 ) and ICU mortality (MIMIC-IV: OR 3.24 [95%CI 1.54–7.11], P  =  0.002 . eICU-CRD: 2.30 [95%CI 1.09–5.16], P  =  0.034 ) (Table 3 , Additional file 5 : Table S3).

Restricted cubic spline analysis for the nonlinear association between the TyG index and the risk of, A  in-hospital mortality of ICH patients from MIMIC-IV; B  ICU mortality of ICH patients from MIMIC-IV; C  in-hospital mortality of ICH patients from eICU-CRD; D  ICU mortality of ICH patients from eICU-CRD

To assess multicollinearity in the logistic regression model, the Spearman rank correlation coefficient and VIF were calculated, respectively. Findings revealed that there was no linear correlation between the TyG index and the other continuous variables incorporated in model 3 (Additional file 6 : Figure S3). Additionally, none of the variables in model 3 exhibited a square root of VIF ≥ 2 (data not shown). Taken together, these results suggest that there is no multicollinearity present in the logistic regression model, indicating the reliability of the results.

Subgroup analysis

To investigate potential variations within specific populations, logistic regression analysis was conducted across various subgroups, including gender, age, diabetes, hypertension, use of anticoagulant agents, and use of antiplatelet agents. The forest plot revealed a significant positive correlation between the TyG index and in-hospital mortality among participants over 60 years (MIMIC-IV: OR 2.58 [95%CI 1.92–4.27], P  =  0.005 . eICU-CRD: OR 1.56 [95%CI 1.10–2.22], P  =  0.019 ), those without diabetes (MIMIC-IV: OR 2.20 [95%CI 1.34–3.57], P  =  0.002 . eICU-CRD: OR 1.69 [95%CI 1.18–2.41], P  =  0.004 ), and those with hypertension (MIMIC-IV: OR 1.97 [95%CI 1.28–3.01], P  =  0.002 . eICU-CRD: OR 1.84 [95%CI 1.28–2.99], P  =  0.014 ). Similarly, there was a positive association for ICU mortality in patients over 60 years (MIMIC-IV: OR 3.86 [95%CI 1.31–6.99], P  <  0.001 . eICU-CRD: OR 1.70 [95%CI 1.23–2.90], P  =  0.036 ), those without diabetes (MIMIC-IV: OR 2.62 [95%CI 1.56–4.40], P  <  0.001 . eICU-CRD: OR 2.10 [95%CI 1.34–3.32], P  =  0.001 ), and those with hypertension (MIMIC-IV: OR 2.44 [95%CI 1.54–3.84], P  <  0.001 . eICU-CRD: OR 1.83 [95%CI 1.13–2.97], P  =  0.013 ). Furthermore, in both MIMIC-IV and eICU-CRD, significant interactions were found between the TyG index and patients' age, diabetic status, and history of hypertension concerning in-hospital and ICU outcomes of individuals with ICH ( P for interaction  <  0.05 for all) (Figs. 3 and 4 ).

Subgroup analysis for the correlation between the TyG index and the risk of in-hospital mortality in patients with ICH from MIMIC-IV and eICU-CRD databases

Subgroup analysis for the correlation between the TyG index and the risk of ICU mortality in patients with ICH from MIMIC-IV and eICU-CRD databases

Sensitivity analysis

To further verify the association between the TyG index and in-hospital and ICU mortality, as well as the significant interactions between the TyG index and patients' age regarding in-hospital and ICU outcomes, a sensitivity analysis was performed using Cox proportional hazard regression. Following fully adjusted, a positive correlation was observed between the TyG index and the risk of in-hospital mortality in ICH patients from both MIMI-IV and eICU-CRD datasets. The positive association between the TyG index and in-hospital and ICU mortality was also present in patients over 60 years old, those without diabetes, and those with hypertension in both databases. Importantly, significant interactions were only found between patients' age and the TyG index concerning in-hospital and ICU outcomes of ICH patients in both MIMIC-IV and eICU-CRD datasets (Table  4 and Additional file 7 : Table S4). These findings collectively suggest that the TyG index has the potential to serve as a prognostic indicator for ICH in patients over 60 years of age.

In this retrospective multicenter study, the impact of the TyG index on the prognosis of critically ill patients with ICH was evaluated, uncovering two important findings. Firstly, a positive correlation was found between the TyG index and the risk of in-hospital and ICU all-cause mortality in ICH patients. Secondly, this correlation was notably stronger in patients over 60 years old, especially in those with hypertension or lacking diabetes.

The association between the TyG index and the course of IS has been extensively studied. Wang et al. reported that individuals in the highest quartile of the TyG index face a 1.45 times greater risk of developing IS compared to those in the lowest quartile [ 19 ]. Results from a 9-year prospective study showed that keeping TyG index elevated was strongly related to an increased morbidity of IS, suggesting that monitoring and regulating the TyG index at an appropriate level could be beneficial in preventing IS [ 20 ]. Additionally, various studies have explored the capability of the TyG index in predicting the outcome of IS. Lee and colleagues found that the TyG index could forecast an adverse functional outcome three months post-reperfusion in IS patients [ 21 ]. Yang et al. noted a correlation between higher TyG index and elevated rates of both recurrence and mortality within one year following an IS event [ 22 ]. In critically ill patients, Cai W et al. found that the TyG index may assist in identifying IS patients at high risk of all-cause mortality [ 14 ]. Despite these findings highlight the significant relationship between the TyG index and IS as well as its prognosis, research on the association between the TyG index and ICH remains scarce.

To address this gap in knowledge, we conducted this study and found a positive correlation between the TyG index and the likelihood of either in-hospital or ICU mortality in individuals with ICH. The positive correlation persisted even after adjusting for potential confounders, suggesting that the TyG index could serve as an independent predictor of hospitalization outcomes in patients with ICH, potentially aiding clinicians in their decision-making process. More importantly, subgroup analysis revealed that there is a synergistic effect of old age (over 60 years), hypertension, and non-diabetic status on the TyG index’s impact on hospitalization outcomes in patients with ICH. Sensitivity analysis using Cox regression model further confirmed the synergistic effect between old age (over 60 years) and the TyG index on the in-hospital and ICU mortality of ICH patients in both MIMIC-IV and eICU-CRD databases. These results underscored the population-specific influence of the TyG index on ICH prognosis, highlighting the importance of focusing on elderly patients with ICH.

The exact mechanisms connecting the TyG index with the poor prognosis of ICH are still unclear, but evidence supports a key role of IR in this process. Patients with IR are more susceptible to hyperglycemia. A study has found that hyperglycemia could inhibit the expression of Aquaporin-4, resulting in the aggravation of vasogenic brain edema and blood–brain barrier (BBB) destruction [ 23 ]. Autophagy is a vital cellular process for maintaining homeostasis, but hyperglycemia can decrease autophagic activity in the brain during ICH, leading to the accumulation of macromolecular debris and damaged cells, ultimately causing neuronal injury [ 24 ]. In stoke rat treated with type plasminogen activator, hyperglycemia could enhance superoxide production in brain tissue and blood vessels, increasing BBB permeability in the peri-ischemic area and leading to a 3- to fivefold rise in the volume of secondary hemorrhage after thrombolysis [ 25 ]. This finding establishes a link between hyperglycemia-induced superoxide production and the increased risk of hematoma expansion in IS. The generation of reactive oxygen species has been demonstrated during ICH [ 26 , 27 ]. Moreover, studies have shown that an increase in blood glucose can exacerbate hematoma expansion in a rat model of ICH [ 28 ]. Therefore, it is plausible to infer that IR related hyperglycemia may result in poor outcomes of ICH by promoting superoxide production, thereby increasing the risk of hematoma expansion.

Adequate cerebral perfusion is crucial for determining the prognosis of patients with ICH. The automatic regulation ability of cerebral blood vessels plays a significant role in maintaining appropriate cerebral perfusion during cerebral hemorrhage. Studies have indicated that elevated intracranial pressure in patients with acute ICH can impair cerebrovascular autoregulation within a two-week period [ 29 ]. The myogenic response, which refers to the ability of smooth muscle cells to react to changes in blood pressure, is essential for preserving cerebrovascular autoregulation [ 30 ]. Animal studies have proved that IR can heighten the tension of cerebrovascular myogenic response, leading to a reduction in the diameter of the cerebrovascular lumen, subsequently causing brain tissue ischemia and nerve cell injury [ 31 ]. In addition, the smooth muscle activity of distal cerebral arteries is notably higher than that of proximal ones, making them more vulnerable to IR and resulting in impaired cerebral circulation function [ 30 ].

Chronic inflammatory response is one pathogenesis of IR, and IR in turn can reflect the level of systemic inflammation in the body [ 32 ]. Interestingly, our study revealed a gradual increase in WBC across quartile intervals of the TyG index, with values exceeding the upper limit of the reference range in the fourth quartile, suggesting an escalation of systemic inflammation during the course of ICH in patients with IR. Furthermore, the integrity of BBB was compromised during ICH, allowing the infiltration of peripheral immune cells and pro-inflammatory cytokines into the central nervous system. This heightened inflammatory response in the brain can further compromise the BBB, creating a detrimental cycle [ 33 ]. In addition, study has shown that dysregulation of the insulin signaling pathway can activate NF-κB, leading to the transcription and expression of inflammatory factors in the brain, thereby exacerbating neuroinflammation [ 34 ].

Intriguingly, although the evidence presented supports the significant role of IR in linking the TyG index to the unfavorable prognosis of intracerebral hemorrhage (ICH), our study found no correlation between the TyG index and either in-hospital or ICU mortality in ICH patients with a history of diabetes, a group known to have a higher risk of IR compared to non-diabetic individuals. Explaining the cause of this paradox is challenging. One potential reason could be reverse causality [ 14 , 35 ], where patients diagnosed with diabetes may be more likely to accept appropriate treatment or adopt healthy lifestyle habits. This could lead to their analytical parameters being similar to or even lower than those of non-diabetic counterparts. Consistent to this theory, in our study, no significant differences were observed in FBG, FTC, and TyG indexes between diabetic and non-diabetic individuals in both the MIMC-IV and eICU-CRD study cohorts (TyG index: 8.71 (8.38, 9.17) vs 8.77 (8.39, 9.14), P  = 0.673 for MIMIC-IV. 8.75 (8.34, 9.22) vs 8.78 (8.3, 9.17), P  = 0.925 for eICU-CRD). Additionally, as diabetic patients may have adopted a healthier lifestyle than their non-diabetic counterparts, their prognosis might be improved in subgroup analyses stratified by diabetes status.

The hyper-insulinemic-euglycemic clamp is considered the most accurate method for detecting IR, but its practicality is limited due to high costs, time-consuming procedures, and invasiveness. The homeostasis model assessment index for IR (HOMA-IR) is commonly used in clinical settings to assess beta-cell function and detect IR [ 36 ]. Nevertheless, its applicability is restricted in patients undergoing insulin therapy or those with ineffective beta cells [ 37 ]. Besides, HOMA-IR relies on measuring insulin levels, which are not routinely checked in clinical practice. Therefore, researchers have introduced the TyG index as a potentially reliable and cost-effective alternative marker for IR. Using hyper-insulin-normoglycemia clamp technique as the gold standard, a study showed excellent predictive efficiency of the TyG index for IR, with sensitive and specificity of 96.5% and 85.0%, respectively [ 8 ]. David and colleagues also demonstrated that the TyG index outperforms FBG and FTG in diagnosing type 2 diabetes and monitoring its progression [ 38 ]. Given that FBG and FTG measurements are available in most healthcare facilities, the TyG index has the potential to be widely utilized in blood glucose management and could serve as a valuable tool for risk assessment in patients with ICH.

The study has several limitations. First, the location and volume of hemorrhage, which are crucial factors influencing the prognosis of ICH, could not be extracted from the databases. Therefore, future research should incorporate these indicators to further validate the current findings. Secondly, specific population such as the Chinese or African are scarce in the study cohort. Consequently, the conclusions derived from this study should be cautiously interpreted in these population. Thirdly, the impact of dynamic changes in the TyG index on the prognosis of ICH patients was not assessed in this study. Given that variability in the TyG index has been linked to the incidence of IS [ 20 ], further studies are needed to investigate the cumulative effect of the TyG index on the incidence and outcome of ICH. Fourthly, exclusion of patients without FTG and FBG data on the first day of ICU admission may introduce bias if the missing data pattern is not completely random. Last but not the least, the utilization of propofol, fibrate, and glucose, along with insulin infusion prior to hospitalization, could have a notable effect on FTG and FBG levels. Nevertheless, neither MIMIC-IV nor eICU-CRD databases contains information on pre-hospitalization medications. Therefore, further investigation is required to confirm the current findings by incorporating these treatment-related information before ICU admission.

This study identified a positive correlation between the TyG index and in-hospital as well as ICU all-cause mortality in patients with ICH, particularly among individuals aged over 60 years with a history of hypertension. The findings indicate that the TyG index may be a useful tool for risk stratification in elderly patients with ICH, assisting clinicians in identifying high-risk individuals and providing timely intervention.

Availability of data and materials

The available data for MIMIC-IV can be accessed from the website https://mimic.physionet.org/ . The available data for eICU-CRD can be accessed from the website https://eicu-crd.mit.edu/ . The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Acute Physiology Score III

Serum aspartate aminotransferase

Blood–brain barrier

Serum urea nitrogen

The eICU Collaborative Research Database

Fasting blood glucose

Fasting triglyceride

Glasgow coma scale

The homeostasis model assessment index for IR

International Classification of Diseases

• Intensive care unit
• Intracerebral hemorrhage

Insulin resistance

Ischemic stroke

The Medical Information Mart for Intensive Care

Odds ratios

Prothrombin time

Restricted cubic spline

Red cell distribution width

Sequential Organ Failure Assessment

Structure Query Language

Triglyceride glucose index

Variance inflation factor

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Acknowledgements

The present study utilized data from the MIMIC-IV and eICU database. We express our gratitude to all the staff and patients who contributed to the development of the MIMIC-IV and eICU database.

This research was supported by Innovation Science Fund of Tangdu hospital, China (No. 2023BTDQN001), Shaanxi Province Key Research and Development Plan Project (2024SF-YBXM-210), and Air Force Medical University Clinical Research Program (2023LC2319).

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Yang Yang, Shengru Liang and Jiangdong Liu contribute equally to this work and are co-first authors.

Lihong Li and Xiaoxi Tian contributed equally to this work and are co-corresponding authors.

Authors and Affiliations

Department of Emergency, Tangdu Hospital, Fourth Military Medical University, Xi’an, 710038, China

Yang Yang, Jiangdong Liu, Yue Si, Dengfeng Jia, Jianwei Li, Xiaoxi Tian & Lihong Li

Department of Endocrinology, Tangdu Hospital, Fourth Military Medical University, Xi’an, 710038, China

Shengru Liang

Department of Neurosurgery, Tangdu Hospital, Fourth Military Medical University, Xi’an, 710038, China

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YY extracted data from MIMIC-IV and eICU-database, analyzed the data, and wrote the original draft. SL conducted literature review, analyzed the data, and wrote the original draft. JDL conducted literature review, and operated software. MM and DJ organized the data and checked the integrity of the data. YS and JWL assisted in statistical analysis. XT designed the study, and checked the final results. LL designed the study, conceptualized the research aims, and revised the paper. All authors have made an intellectual contribution to the manuscript and approved the final submission.

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Supplementary Information

Additional file 1: figure s1..

The proportion and distribution of missing data for variables in (A) MIMIC-IV database and (B) eICU-CRD database.

Additional file 2.

Additional file 3., additional file 4: figure s2..

The boxplot of the TyG index stratified by the in-hospital and ICU outcomes. (A) The level of the TyG index in hospital survivors and non-survivors from the MIMIC-IV database. (B) The level of the TyG index in hospital survivors and non-survivors from the eICU-CRD database. (C) The level of the TyG index in ICU survivors and non-survivors from the MIMIC-IV database. (D) The level of the TyG index in ICU survivors and non-survivors from the eICU-CRD database.

Additional file 5.

Additional file 6: figure s3..

The correlation between continuous variables in the cohort derived from (A) MIMIC-IV and (B) eICU-CRD.

Additional file 7.

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Yang, Y., Liang, S., Liu, J. et al. Triglyceride-glucose index as a potential predictor for in-hospital mortality in critically ill patients with intracerebral hemorrhage: a multicenter, case–control study. BMC Geriatr 24 , 385 (2024). https://doi.org/10.1186/s12877-024-05002-4

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DOI : https://doi.org/10.1186/s12877-024-05002-4

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