Sex specific differences in short-term mortality after ICU-delirium

Sex specific differences in short-term mortality after ICU-delirium | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Sex specific differences in short-term mortality after ICU-delirium Nikolaus Schreiber, Michael Eichlseder, Simon Orlob, Christoph Klivinyi, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5176203/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Dec, 2024 Read the published version in Critical Care → Version 1 posted 11 You are reading this latest preprint version Abstract Introduction Delirium is a frequent complication in critically ill patients and is associated with adverse outcomes such as long-term cognitive impairment and increased mortality. It is unknown whether there are sex-related differences in ICU-delirium and associated outcomes. We aimed to assess sex-specific differences in short-term mortality following ICU-delirium. Methods We conducted a retrospective cohort study using the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Adult ICU patients who were diagnosed with delirium using the Confusion Assessment Method for the ICU (CAM-ICU) or the Intensive Care Delirium Screening Checklist (ICDSC) were included. The primary outcome was 30-day mortality following delirium onset. To control for baseline differences in demographics, illness severity, and comorbidities, we applied 1:1 propensity score matching. Cox proportional hazards regression models were used to evaluate the association between sex and mortality. Results A total of 8950 ICU patients with delirium were analyzed, of whom 42.6% were female. In univariable analysis, women had higher crude mortality (26.0% vs. 23.4%; HR 1.16, 95% CI 1.071–1.267, p < 0.001). After propensity score matching, the cohort included 3811 females and 3811 males. Thirty-day mortality was again higher in women (HR 1.14, 95% CI 1.046–1.252; p = 0.003). Conclusion Our study suggests that women with ICU-delirium have a significantly higher risk of short-term mortality than men. Further research is needed to understand the biological and clinical factors driving this disparity and to inform sex-specific interventions for ICU-delirium. ICU-delirium sex differences mortality personalized ICU-care Figures Figure 1 Introduction Delirium is a heterogeneous syndrome of acute brain dysfunction, characterized by acute and fluctuating disturbance of consciousness, cognition and attention ( 1 ). It affects up to half of critically ill patients and is associated with adverse outcomes, including ICU stays and long-term cognitive impairment ( 2 ). ICU-delirium remains a heterogenous syndrome resulting from a variety of risk factors and precipitants, and currently there is no pharmacologic intervention with substantial evidence for benefit ( 3 ). Emerging research has identified significant differences in how women and men experience, respond to, and recover from other entities in critical care such as, cardiogenic shock ( 4 ), sepsis ( 5 ) or acute kidney injury ( 6 ). To ensure a nuanced interpretation of current evidence, it is crucial to differentiate between gender and sex: gender involves socially constructed roles and behaviors considered appropriate by a society, while sex refers to biological attributes ( 7 ). To enhance patient care and outcomes, it is essential to understand and address sex and gender differences in the ICU setting, allowing for personalized management that ensures equitable, patient-centered care ( 8 ). However, the impact of sex-specific differences on ICU-delirium and related outcomes remains poorly understood, with current data being scarce and inconclusive. ( 9 , 10 ). The aim of this study was to explore whether critically ill patients with ICU-delirium exhibit sex-specific differences in short-term mortality. Methods Data source and study design To ensure transparency and reproducibility, we utilized data from the openly accessible Medical Information Mart for Intensive Care-IV (MIMIC-IV) database ( 11 ), available via the PhysioNet repository ( 12 ). The MIMIC-IV database was developed by the Massachusetts Institute of Technology (MIT) and researchers who agree to the data use agreement and have completed "protecting human subjects training" can request access. The database includes detailed clinical information for over 60000 critically ill patients admitted to the ICUs at Beth Israel Deaconess Medical Center (BIDMC) between 2008 and 2019. The MIMIC-IV database was approved by the institutional review boards of the Beth Israel Deaconess Medical Center (2001-P-001699/14) and the MIT (No. 0403000206), which waived the requirement for individual patient consent because the datasets contained deidentified information. The MIMIC-IV database was accessed through PostgreSQL, with variables extracted using SQL queries provided by the official MIMIC GitHub repository. All subsequent data preparation and analyses were conducted using Python version 3.12.4. Our study adhered to the Strengthening-the-reporting-of-observational-studies-in-epidemiology (STROBE) guideline for observational research ( 13 ) and has been registered on the Open Science Framework ( https://osf.io/g6fr8 ). The code to fully reproduce our analysis is available ( https://github.com/schrnik/sex_specific_differences_delirium ). Study population Patients aged 18 years or older who were admitted to the ICU and screened positive for delirium during their stay at the ICU using either the Confusion Assessment Method for the ICU (CAM-ICU) ( 14 ) or the Intensive Care Delirium Screening Checklist (ICDSC) ( 15 ) were eligible for analysis. Patients were excluded if they screened negative for delirium, lacked documentation of delirium screening or had incomplete data required for time-to-event analysis. Outcome The censoring date of the study was set the latest at 30 days from delirium onset. The primary outcome of interest was 30-day mortality following the onset of delirium and was defined as the time interval from delirium onset to death-from-any-cause or the censoring date when being still alive 30 days after delirium onset. Statistical analysis Continuous variables were reported as medians with interquartile ranges (IQRs) and compared between sexes using the Wilcoxon rank-sum test. Categorical variables were summarized as counts and percentages and compared using the Chi-Square test. The magnitude of differences between groups was quantified using standardized mean differences (SMDs). To examine the association between sex and 30-day mortality, we performed Cox proportional hazards regression. Results were expressed as hazard ratios (HRs) with 95% confidence intervals (CIs). The proportional hazards assumption was assessed using Schoenfeld residuals. Survival probabilities were visualized with Kaplan-Meier curves, and differences between sexes were assessed using the log-rank test. To account for potential imbalances in baseline demographics, illness severity, and comorbidities between sexes, we applied propensity score matching (PSM). We derived the propensity score \(\:e\) from a multivariable logistic regression model with sex as the dependent variable. Conditional on the propensity score, the distribution of baseline covariates was expected to be similar between female and male patients ( 16 , 17 ). Covariates for the logistic regression model were selected based on existing literature ( 18 ) and included age, illness severity (measured by the Simplified Acute Physiology Score (SAPS) II), invasive ventilation prior to delirium onset, Charlson Comorbidity Index, admission type (surgical versus medical), sepsis at admission, coronary artery disease, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic pulmonary disease, rheumatic disease, peptic ulcer disease, liver disease, diabetes, renal disease, malignant cancer, metastatic solid tumor, and acquired immunodeficiency syndrome (AIDS). We performed 1:1 nearest-neighbor matching with a caliper width of 0.1. After matching, balance between groups was assessed by re-estimating SMDs within the matched cohort to ensure that baseline covariates were well balanced between sexes. To assess the robustness of our findings, we performed sensitivity analyses, the details of which are provided in the Additional Files. Results A total of 8950 ICU patients developed delirium during their stay and were eligible for analysis (Study Flow Chart, Additional Figure A1). Of these, 42.6% were female. Women were significantly older than men (median age: 71 [58–81] vs. 66 [54–77], p < 0.001) and had higher illness severity (SAPS II: 39.0 [31.0–50.0] vs. 38.0 [30.0–49.0], p < 0.001), though they were less likely to receive invasive ventilation (48.4% vs. 53.5%, p < 0.001). The baseline demographics and comorbidities of the entire cohort, stratified by sex, are detailed in Table 1 . Table 1 Demographics, illness severity and comorbidities stratified by sex. Variable Overall n = 8950 Female n = 3811 Male = 5139 Age 68.0 [56.0–79.0] 71.0 [58.0–81.0] 66.0 [54.0–77.0] Comorbidity and Illness severity scores Charlson Comorbidity Index 5.0 [3.0–7.0] 5.0 [3.0–7.0] 5.0 [3.0–7.0] SAPS II 39.0 [31.0–49.0] 39.0 [31.0–50.0] 38.0 [30.0–49.0] Diagnosis, admission and treatment modality Type of admission Surgical 2208 (24.7%) 912 (23.9%) 1296 (25.2%) Medical 6742 (75.3%) 2899 (76.1%) 3843 (74.8%) Invasive ventilation before onset of delirium 4593 (51.3%) 1843 (48.4%) 2750 (53.5%) Sepsis at admission 6477 (72.4%) 2699 (70.8%) 3778 (73.5%) Comorbidities Peripheral vascular disease 1008 (11.3%) 382 (10.0%) 626 (12.2%) Coronary artery disease 1507 (16.8%) 533 (14.0%) 974 (19.0%) Cerebrovascular disease 1961 (21.9%) 923 (24.2%) 1038 (20.2%) Congestive heart failure 2478 (27.7%) 1057 (27.7%) 1421 (27.7%) Renal disease 1750 (19.6%) 645 (16.9%) 1105 (21.5%) Dementia 671 (7.5%) 346 (9.1%) 325 (6.3%) Chronic pulmonary disease 2226 (24.9%) 1104 (29.0%) 1122 (21.8%) Malignant cancer 1004 (11.2%) 386 (10.1%) 618 (12.0%) Rheumatic disease 276 (3.1%) 185 (4.9%) 91 (1.8%) Peptic ulcer disease 252 (2.8%) 107 (2.8%) 145 (2.8%) Mild liver disease 1201 (13.4%) 424 (11.1%) 777 (15.1%) Severe liver disease 630 (7.0%) 225 (5.9%) 405 (7.9%) Diabetes without complications 1981 (22.1%) 836 (21.9%) 1145 (22.3%) Diabetes with complications 916 (10.2%) 330 (8.7%) 586 (11.4%) Paraplegia 838 (9.4%) 388 (10.2%) 450 (8.8%) Metastatic solid tumor 449 (5.0%) 189 (5.0%) 260 (5.1%) Acquired immune deficiency syndrome (AIDS) 35 (0.4%) 10 (0.3%) 25 (0.5%) For continuous variables medians with 25 th -75 th percentile in brackets are depicted, whereas for categorical variables absolute values and percent in brackets are presented. Abbreviations: AIDS - Acquired immune deficiency syndrome; SAPS II - Simplified Acute Physiology Score II; At 30 days, 992 of 3811 females and 1202 of 5139 males (26.0% vs. 23.4%, p = 0.004) had died, resulting in a crude HR of 1.16 (95% CI 1.071–1.267, p < 0.001). After propensity score matching, the cohort included 3811 females and 3811 males. In the matched cohort, 909 males and 992 females had died after 30 days, corresponding to a HR of 1.14 (95% CI 1.046–1.252, p = 0.003) for female sex. Baseline characteristics in the matched cohort were well-balanced, with all SMDs < 0.1 (Additional Figure A2). The distribution of the propensity score before and after matching is shown in Additional Figure A3. Discussion This study identified a significantly higher risk of short-term mortality for women with ICU-delirium compared to men. The existing literature on sex-specific differences in delirium is limited and somewhat contradictory ( 8 ). While some studies identify male sex as a risk factor for ICU-delirium, others have found an increased risk among women ( 18 ). However, the impact of sex on short-term mortality in ICU patients in general remains uncertain, with research suggesting higher risk-adjusted ICU mortality in women ( 19 ), whereas other studies suggest no difference ( 17 ). Despite these findings, the relationship between sex and outcomes specifically in ICU-delirium has not been thoroughly explored. Hence, current guidelines do not explicitly address sex-specific differences in the prevention or management of ICU-delirium, despite the growing recognition of sex differences in critical care literature ( 20 ). To the best of our knowledge, our study is the first to specifically assess sex-related differences in short-term mortality among ICU-delirium patients. This study provides new insights but also raises important questions: i.) Why do women with ICU-delirium experience a higher risk of mortality? What are the implications of identifying a mortality difference between male and female patients with delirium? i.) It is possible that men and women follow different trajectories of recovery or deterioration after ICU-delirium due to differences in genetic predisposition, hormonal factors, and immunological responses to acute brain dysfunction. Moreover, emerging evidence suggests that women are undertreated in the ICU despite experiencing higher illness severity, and our findings may indirectly reflect this disparity ( 8 ). ii.) Within the context of personalized medicine, our findings reinforce the need for special focus on female patients with ICU-delirium, both in everyday clinical practice and in future interventional trials, where women recently were underrepresented ( 3 ). The critical care literature has shown significant progress in understanding sex-specific differences in other conditions, such as sepsis and cardiogenic shock ( 8 ), whereas further research into ICU-delirium is essential. Special attention should be directed toward uncovering the mechanisms driving higher mortality in female patients. The strengths of our study are the use of a large, openly available dataset and availability of detailed methodology and code to facilitate the reproduction and extension of our findings. Additionally, the substantial sample size allowed us to adjust for a wide range of covariates, maximizing the reliability of our results. By incorporating the propensity score within a causal inference framework, we aimed to achieve the highest level of certainty possible with observational data ( 16 ). Several limitations warrant consideration. We were unable to perform a subgroup analysis of delirium subtypes, which may influence short-term mortality and requires further investigation. Furthermore, this study was a post hoc analysis of single-center observational data, which inherently limits the generalizability of our findings. Despite using stringent propensity score matching, residual confounding cannot be excluded. Lastly, we were not able to investigate potential sex-related biases in the assessment of delirium, which might have played a role. Therefore, replication and validation of our findings in different cohorts are needed. Conclusion Our findings indicate a higher risk of short-term mortality in female patients with ICU-delirium. These findings underscore the importance of considering sex-specific differences in delirium management and research. Future studies should aim to replicate these results, investigate the underlying mechanisms, and explore how personalized care can address potential differences in the needs of female and male ICU patients with delirium. Abbreviations AIDS – Acquired immune deficiency syndrome BIDMC - Beth Israel Deaconess Medical Center CAM-ICU - Confusion Assessment Method for the ICU (CAM-ICU) CI – Confidence interval HR – Hazard ratio ICDSC - Intensive Care Delirium Screening Checklist ICU – Intensive Care Unit IQR – Interquartile range MIMIC-IV - Medical Information Mart for Intensive Care-IV MIT - Massachusetts Institute of Technology PSM – Propensity score matching SAPS II - Simplified Acute Physiology Score II SMD – standardized mean difference STROBE - Strengthening the reporting of observational studies in epidemiology Declarations Ethics approval and consent to participate The MIMIC database was approved by the institutional review boards of the Beth Israel Deaconess Medical Center (2001-P-001699/14) and the Massachusetts Institute of Technology (No. 0403000206), which waived the requirement for individual patient consent because the datasets contained deidentified information. Consent for publication Not applicable. Availability of data and materials To ensure transparency and reproducibility, we utilized data from the openly accessible Medical Information Mart for Intensive Care-IV (MIMIC-IV) database, available via the PhysioNet repository (https://physionet.org/content/mimiciv/3.0/). The code for reproduction of our analysis is available on Github (https://github.com/schrnik/sex_specific_differences_delirium). We used MIMIC-IV version 3.0, released on July 23, 2024. Competing interests None. Funding None. Authors' contributions NS and PE designed the study and drafted the first manuscript. NS, SO, SFH, CK and PE analyzed the data. LS, SFH, PZ, ME, AP and JB gave conceptual input and revised the manuscript significantly. All authors read final manuscript, and approved the final version submitted for publication. Acknowledgements The authors thank Prof. Andrea Kurz and Prof. Alexander Rosenkranz for their invaluable support. 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Devlin JW, Skrobik Y, Gélinas C, Needham DM, Slooter AJC, Pandharipande PP, et al. Clinical Practice Guidelines for the Prevention and Management of Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption in Adult Patients in the ICU. Crit Care Med. 2018 Sep;46(9):e825–73. Additional Declarations No competing interests reported. Supplementary Files AdditionalMaterialSexspecificFINAL.docx Cite Share Download PDF Status: Published Journal Publication published 18 Dec, 2024 Read the published version in Critical Care → Version 1 posted Editorial decision: Revision requested 06 Nov, 2024 Reviews received at journal 02 Nov, 2024 Reviews received at journal 27 Oct, 2024 Reviewers agreed at journal 25 Oct, 2024 Reviews received at journal 12 Oct, 2024 Reviewers agreed at journal 10 Oct, 2024 Reviewers agreed at journal 02 Oct, 2024 Reviewers invited by journal 01 Oct, 2024 Editor assigned by journal 30 Sep, 2024 Submission checks completed at journal 30 Sep, 2024 First submitted to journal 29 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Graz","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Schörghuber","suffix":""},{"id":374779844,"identity":"070ee97e-91fc-4b39-bba6-a1da955b31e2","order_by":11,"name":"Philipp Eller","email":"data:image/png;base64,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","orcid":"","institution":"Medical University of Graz","correspondingAuthor":true,"prefix":"","firstName":"Philipp","middleName":"","lastName":"Eller","suffix":""}],"badges":[],"createdAt":"2024-09-29 17:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5176203/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5176203/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13054-024-05204-7","type":"published","date":"2024-12-18T15:57:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69352501,"identity":"8748a6b6-f3c4-4a95-bdeb-81f0026e58c2","added_by":"auto","created_at":"2024-11-19 13:15:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":495981,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier Survival Curve by Sex.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e30-day survival probability after delirium onset compared between male patients (in blue) and female patients (in orange). 95%-Confidence Intervals are depicted as shaded areas. Log-Rank Test p-value: 0.0004.\u003c/p\u003e","description":"","filename":"KaplanMeierSurvivalCurvebySex.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5176203/v1/f1c2c10f6ead08c3d2cc58cc.jpg"},{"id":72201726,"identity":"e964c0c8-2a33-42bd-9544-068cb361d069","added_by":"auto","created_at":"2024-12-23 16:10:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":942085,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5176203/v1/23e53b49-24d3-4118-88dc-7005627d2a6b.pdf"},{"id":69351213,"identity":"a0791e36-2046-48f3-9e6b-369ecd72eb1d","added_by":"auto","created_at":"2024-11-19 13:07:56","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":647877,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalMaterialSexspecificFINAL.docx","url":"https://assets-eu.researchsquare.com/files/rs-5176203/v1/1011e447cfd4efa08db75a2d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sex specific differences in short-term mortality after ICU-delirium","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDelirium is a heterogeneous syndrome of acute brain dysfunction, characterized by acute and fluctuating disturbance of consciousness, cognition and attention (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It affects up to half of critically ill patients and is associated with adverse outcomes, including ICU stays and long-term cognitive impairment (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). ICU-delirium remains a heterogenous syndrome resulting from a variety of risk factors and precipitants, and currently there is no pharmacologic intervention with substantial evidence for benefit (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEmerging research has identified significant differences in how women and men experience, respond to, and recover from other entities in critical care such as, cardiogenic shock (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), sepsis (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) or acute kidney injury (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). To ensure a nuanced interpretation of current evidence, it is crucial to differentiate between gender and sex: gender involves socially constructed roles and behaviors considered appropriate by a society, while sex refers to biological attributes (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). To enhance patient care and outcomes, it is essential to understand and address sex and gender differences in the ICU setting, allowing for personalized management that ensures equitable, patient-centered care (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, the impact of sex-specific differences on ICU-delirium and related outcomes remains poorly understood, with current data being scarce and inconclusive. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The aim of this study was to explore whether critically ill patients with ICU-delirium exhibit sex-specific differences in short-term mortality.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eData source and study design\u003c/p\u003e\u003cp\u003eTo ensure transparency and reproducibility, we utilized data from the openly accessible Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), available via the PhysioNet repository (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The MIMIC-IV database was developed by the Massachusetts Institute of Technology (MIT) and researchers who agree to the data use agreement and have completed \"protecting human subjects training\" can request access. The database includes detailed clinical information for over 60000 critically ill patients admitted to the ICUs at Beth Israel Deaconess Medical Center (BIDMC) between 2008 and 2019.\u003c/p\u003e\u003cp\u003e The MIMIC-IV database was approved by the institutional review boards of the Beth Israel Deaconess Medical Center (2001-P-001699/14) and the MIT (No. 0403000206), which waived the requirement for individual patient consent because the datasets contained deidentified information.\u003c/p\u003e\u003cp\u003eThe MIMIC-IV database was accessed through PostgreSQL, with variables extracted using SQL queries provided by the official MIMIC GitHub repository. All subsequent data preparation and analyses were conducted using Python version 3.12.4.\u003c/p\u003e\u003cp\u003eOur study adhered to the Strengthening-the-reporting-of-observational-studies-in-epidemiology (STROBE) guideline for observational research (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) and has been registered on the Open Science Framework (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/g6fr8\u003c/span\u003e\u003cspan address=\"https://osf.io/g6fr8\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The code to fully reproduce our analysis is available (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/schrnik/sex_specific_differences_delirium\u003c/span\u003e\u003cspan address=\"https://github.com/schrnik/sex_specific_differences_delirium\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStudy population\u003c/p\u003e\u003cp\u003ePatients aged 18 years or older who were admitted to the ICU and screened positive for delirium during their stay at the ICU using either the Confusion Assessment Method for the ICU (CAM-ICU) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) or the Intensive Care Delirium Screening Checklist (ICDSC) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) were eligible for analysis. Patients were excluded if they screened negative for delirium, lacked documentation of delirium screening or had incomplete data required for time-to-event analysis.\u003c/p\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003cp\u003eThe censoring date of the study was set the latest at 30 days from delirium onset. The primary outcome of interest was 30-day mortality following the onset of delirium and was defined as the time interval from delirium onset to death-from-any-cause or the censoring date when being still alive 30 days after delirium onset.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables were reported as medians with interquartile ranges (IQRs) and compared between sexes using the Wilcoxon rank-sum test. Categorical variables were summarized as counts and percentages and compared using the Chi-Square test. The magnitude of differences between groups was quantified using standardized mean differences (SMDs).\u003c/p\u003e\u003cp\u003eTo examine the association between sex and 30-day mortality, we performed Cox proportional hazards regression. Results were expressed as hazard ratios (HRs) with 95% confidence intervals (CIs). The proportional hazards assumption was assessed using Schoenfeld residuals.\u003c/p\u003e\u003cp\u003eSurvival probabilities were visualized with Kaplan-Meier curves, and differences between sexes were assessed using the log-rank test.\u003c/p\u003e\u003cp\u003eTo account for potential imbalances in baseline demographics, illness severity, and comorbidities between sexes, we applied propensity score matching (PSM). We derived the propensity score \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:e\\)\u003c/span\u003e\u003c/span\u003e from a multivariable logistic regression model with sex as the dependent variable. Conditional on the propensity score, the distribution of baseline covariates was expected to be similar between female and male patients (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCovariates for the logistic regression model were selected based on existing literature (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and included age, illness severity (measured by the Simplified Acute Physiology Score (SAPS) II), invasive ventilation prior to delirium onset, Charlson Comorbidity Index, admission type (surgical versus medical), sepsis at admission, coronary artery disease, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic pulmonary disease, rheumatic disease, peptic ulcer disease, liver disease, diabetes, renal disease, malignant cancer, metastatic solid tumor, and acquired immunodeficiency syndrome (AIDS).\u003c/p\u003e\u003cp\u003eWe performed 1:1 nearest-neighbor matching with a caliper width of 0.1. After matching, balance between groups was assessed by re-estimating SMDs within the matched cohort to ensure that baseline covariates were well balanced between sexes.\u003c/p\u003e\u003cp\u003eTo assess the robustness of our findings, we performed sensitivity analyses, the details of which are provided in the Additional Files.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 8950 ICU patients developed delirium during their stay and were eligible for analysis (Study Flow Chart, Additional Figure A1). Of these, 42.6% were female. Women were significantly older than men (median age: 71 [58\u0026ndash;81] vs. 66 [54\u0026ndash;77], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and had higher illness severity (SAPS II: 39.0 [31.0\u0026ndash;50.0] vs. 38.0 [30.0\u0026ndash;49.0], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), though they were less likely to receive invasive ventilation (48.4% vs. 53.5%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The baseline demographics and comorbidities of the entire cohort, stratified by sex, are detailed in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDemographics, illness severity and comorbidities stratified by sex.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall n\u0026thinsp;=\u0026thinsp;8950\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFemale n\u0026thinsp;=\u0026thinsp;3811\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMale\u0026thinsp;=\u0026thinsp;5139\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.0 [56.0\u0026ndash;79.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.0 [58.0\u0026ndash;81.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.0 [54.0\u0026ndash;77.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eComorbidity and Illness severity scores\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCharlson Comorbidity Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.0 [3.0\u0026ndash;7.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.0 [3.0\u0026ndash;7.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.0 [3.0\u0026ndash;7.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSAPS II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.0 [31.0\u0026ndash;49.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.0 [31.0\u0026ndash;50.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.0 [30.0\u0026ndash;49.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eDiagnosis, admission and treatment modality\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eType of admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurgical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2208 (24.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e912 (23.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1296 (25.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6742 (75.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2899 (76.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3843 (74.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInvasive ventilation before onset of delirium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4593 (51.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1843 (48.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2750 (53.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSepsis at admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6477 (72.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2699 (70.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3778 (73.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eComorbidities\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeripheral vascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1008 (11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e382 (10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e626 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1507 (16.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e533 (14.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e974 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCerebrovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1961 (21.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e923 (24.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1038 (20.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCongestive heart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2478 (27.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1057 (27.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1421 (27.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRenal disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1750 (19.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e645 (16.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1105 (21.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDementia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e671 (7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e346 (9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e325 (6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2226 (24.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1104 (29.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1122 (21.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalignant cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1004 (11.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e386 (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e618 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRheumatic disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e276 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeptic ulcer disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e252 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMild liver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1201 (13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e424 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e777 (15.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSevere liver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e630 (7.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e225 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e405 (7.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes without complications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1981 (22.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e836 (21.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1145 (22.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes with complications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e916 (10.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330 (8.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e586 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParaplegia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e838 (9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e388 (10.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e450 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetastatic solid tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e449 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcquired immune deficiency syndrome (AIDS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFor continuous variables medians with 25\u003csup\u003eth\u003c/sup\u003e-75\u003csup\u003eth\u003c/sup\u003e percentile in brackets are depicted, whereas for categorical variables absolute values and percent in brackets are presented.\u003c/p\u003e\n\u003cp\u003eAbbreviations: AIDS - Acquired immune deficiency syndrome; SAPS II - Simplified Acute Physiology Score II;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt 30 days, 992 of 3811 females and 1202 of 5139 males (26.0% vs. 23.4%, p\u0026thinsp;=\u0026thinsp;0.004) had died, resulting in a crude HR of 1.16 (95% CI 1.071\u0026ndash;1.267, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After propensity score matching, the cohort included 3811 females and 3811 males. In the matched cohort, 909 males and 992 females had died after 30 days, corresponding to a HR of 1.14 (95% CI 1.046\u0026ndash;1.252, p\u0026thinsp;=\u0026thinsp;0.003) for female sex.\u003c/p\u003e\n\u003cp\u003eBaseline characteristics in the matched cohort were well-balanced, with all SMDs\u0026thinsp;\u0026lt;\u0026thinsp;0.1 (Additional Figure A2). The distribution of the propensity score before and after matching is shown in Additional Figure A3.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study identified a significantly higher risk of short-term mortality for women with ICU-delirium compared to men.\u003c/p\u003e \u003cp\u003eThe existing literature on sex-specific differences in delirium is limited and somewhat contradictory (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). While some studies identify male sex as a risk factor for ICU-delirium, others have found an increased risk among women (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). However, the impact of sex on short-term mortality in ICU patients in general remains uncertain, with research suggesting higher risk-adjusted ICU mortality in women (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), whereas other studies suggest no difference (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Despite these findings, the relationship between sex and outcomes specifically in ICU-delirium has not been thoroughly explored.\u003c/p\u003e \u003cp\u003eHence, current guidelines do not explicitly address sex-specific differences in the prevention or management of ICU-delirium, despite the growing recognition of sex differences in critical care literature (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). To the best of our knowledge, our study is the first to specifically assess sex-related differences in short-term mortality among ICU-delirium patients.\u003c/p\u003e \u003cp\u003eThis study provides new insights but also raises important questions:\u003c/p\u003e\n\u003ch3\u003ei.) Why do women with ICU-delirium experience a higher risk of mortality?\u003c/h3\u003e\n\u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eWhat are the implications of identifying a mortality difference between male and female patients with delirium?\u003c/em\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ei.)\u003c/em\u003e It is possible that men and women follow different trajectories of recovery or deterioration after ICU-delirium due to differences in genetic predisposition, hormonal factors, and immunological responses to acute brain dysfunction. Moreover, emerging evidence suggests that women are undertreated in the ICU despite experiencing higher illness severity, and our findings may indirectly reflect this disparity (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eii.)\u003c/em\u003e Within the context of personalized medicine, our findings reinforce the need for special focus on female patients with ICU-delirium, both in everyday clinical practice and in future interventional trials, where women recently were underrepresented (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The critical care literature has shown significant progress in understanding sex-specific differences in other conditions, such as sepsis and cardiogenic shock (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), whereas further research into ICU-delirium is essential. Special attention should be directed toward uncovering the mechanisms driving higher mortality in female patients.\u003c/p\u003e \u003cp\u003eThe strengths of our study are the use of a large, openly available dataset and availability of detailed methodology and code to facilitate the reproduction and extension of our findings. Additionally, the substantial sample size allowed us to adjust for a wide range of covariates, maximizing the reliability of our results. By incorporating the propensity score within a causal inference framework, we aimed to achieve the highest level of certainty possible with observational data (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral limitations warrant consideration. We were unable to perform a subgroup analysis of delirium subtypes, which may influence short-term mortality and requires further investigation. Furthermore, this study was a post hoc analysis of single-center observational data, which inherently limits the generalizability of our findings. Despite using stringent propensity score matching, residual confounding cannot be excluded. Lastly, we were not able to investigate potential sex-related biases in the assessment of delirium, which might have played a role. Therefore, replication and validation of our findings in different cohorts are needed.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings indicate a higher risk of short-term mortality in female patients with ICU-delirium. These findings underscore the importance of considering sex-specific differences in delirium management and research. Future studies should aim to replicate these results, investigate the underlying mechanisms, and explore how personalized care can address potential differences in the needs of female and male ICU patients with delirium.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAIDS \u0026ndash; Acquired immune deficiency syndrome\u003c/p\u003e\n\u003cp\u003eBIDMC - Beth Israel Deaconess Medical Center\u003c/p\u003e\n\u003cp\u003eCAM-ICU - Confusion Assessment Method for the ICU (CAM-ICU) \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCI \u0026ndash; Confidence interval\u003c/p\u003e\n\u003cp\u003eHR \u0026ndash; Hazard ratio\u003c/p\u003e\n\u003cp\u003eICDSC -\u0026nbsp;Intensive Care Delirium Screening Checklist\u003c/p\u003e\n\u003cp\u003eICU \u0026ndash; Intensive Care Unit\u003c/p\u003e\n\u003cp\u003eIQR \u0026ndash; Interquartile range\u003c/p\u003e\n\u003cp\u003eMIMIC-IV - Medical Information Mart for Intensive Care-IV\u003c/p\u003e\n\u003cp\u003eMIT - Massachusetts Institute of Technology\u003c/p\u003e\n\u003cp\u003ePSM \u0026ndash; Propensity score matching\u003c/p\u003e\n\u003cp\u003eSAPS II - Simplified Acute Physiology Score II\u003c/p\u003e\n\u003cp\u003eSMD \u0026ndash; standardized mean difference\u003c/p\u003e\n\u003cp\u003eSTROBE - Strengthening the reporting of observational studies in epidemiology\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MIMIC database was approved by the institutional review boards of the Beth Israel Deaconess Medical Center (2001-P-001699/14) and the Massachusetts Institute of Technology (No. 0403000206), which waived the requirement for individual patient consent because the datasets contained deidentified information.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure transparency and reproducibility, we utilized data from the openly accessible Medical Information Mart for Intensive Care-IV (MIMIC-IV) database, available via the PhysioNet repository (https://physionet.org/content/mimiciv/3.0/).\u003c/p\u003e\n\u003cp\u003eThe code for reproduction of our analysis is available on Github (https://github.com/schrnik/sex_specific_differences_delirium). We used MIMIC-IV version 3.0, released on July 23, 2024.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNS and PE designed the study and drafted the first manuscript. NS, SO, SFH, CK and PE analyzed the data. LS, SFH, PZ, ME, AP and JB gave conceptual input and revised the manuscript significantly. All authors read final manuscript, and approved the final version submitted for publication.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Prof. Andrea Kurz and Prof. Alexander Rosenkranz for their invaluable support.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript and presented results will be part of a thesis project (NS).\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eStollings JL, Kotfis K, Chanques G, Pun BT, Pandharipande PP, Ely EW. Delirium in critical illness: clinical manifestations, outcomes, and management. Intensive Care Med. 2021 Oct;47(10):1089\u0026ndash;103.\u003c/li\u003e\n\u003cli\u003eKotfis K, van Diem-Zaal I, Williams Roberson S, Sietnicki M, van den Boogaard M, Shehabi Y, et al. The future of intensive care: delirium should no longer be an issue. Crit Care Lond Engl. 2022 Jul 5;26(1):200.\u003c/li\u003e\n\u003cli\u003eSmit L, Slooter AJC, Devlin JW, Trogrlic Z, Hunfeld NGM, Osse RJ, et al. Efficacy of haloperidol to decrease the burden of delirium in adult critically ill patients: the EuRIDICE randomized clinical trial. Crit Care Lond Engl. 2023 Oct 30;27(1):413.\u003c/li\u003e\n\u003cli\u003eFisher T, Hill N, Kalakoutas A, Lahlou A, Rathod K, Proudfoot A, et al. Sex differences in treatments and outcomes of patients with cardiogenic shock: a systematic review and epidemiological meta-analysis. Crit Care Lond Engl. 2024 Jun 6;28(1):192.\u003c/li\u003e\n\u003cli\u003eAntequera A, Lopez-Alcalde J, Stallings E, Muriel A, Fern\u0026aacute;ndez F\u0026eacute;lix B, Del Campo R, et al. Sex as a prognostic factor for mortality in critically ill adults with sepsis: a systematic review and meta-analysis. BMJ Open. 2021 Sep 22;11(9):e048982.\u003c/li\u003e\n\u003cli\u003eNeugarten J, Golestaneh L. Female sex reduces the risk of hospital-associated acute kidney injury: a meta-analysis. BMC Nephrol. 2018 Nov 8;19(1):314.\u003c/li\u003e\n\u003cli\u003eLarsson E. Sex matters: Is it time for a SOFA makeover? Crit Care Lond Engl. 2024 Aug 8;28(1):268.\u003c/li\u003e\n\u003cli\u003eMerdji H, Long MT, Ostermann M, Herridge M, Myatra SN, De Rosa S, et al. Sex and gender differences in intensive care medicine. Intensive Care Med. 2023 Oct;49(10):1155\u0026ndash;67.\u003c/li\u003e\n\u003cli\u003eTrzepacz PT, Franco JG, Meagher DJ, Lee Y, Kim JL, Kishi Y, et al. Delirium Phenotype by Age and Sex in a Pooled Data Set of Adult Patients. J Neuropsychiatry Clin Neurosci. 2018;30(4):294\u0026ndash;301.\u003c/li\u003e\n\u003cli\u003eKrewulak KD, Stelfox HT, Ely EW, Fiest KM. Risk factors and outcomes among delirium subtypes in adult ICUs: A systematic review. J Crit Care. 2020 Apr;56:257\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eJohnson AEW, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023 Jan 3;10(1):1.\u003c/li\u003e\n\u003cli\u003eGoldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000 Jun 13;101(23):E215-220. \u003c/li\u003e\n\u003cli\u003evon Elm E, Altman DG, Egger M, Pocock SJ, G\u0026oslash;tzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet Lond Engl. 2007 Oct 20;370(9596):1453\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eEly EW, Inouye SK, Bernard GR, Gordon S, Francis J, May L, et al. Delirium in mechanically ventilated patients: validity and reliability of the confusion assessment method for the intensive care unit (CAM-ICU). JAMA. 2001 Dec 5;286(21):2703\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eBergeron N, Dubois MJ, Dumont M, Dial S, Skrobik Y. Intensive Care Delirium Screening Checklist: evaluation of a new screening tool. Intensive Care Med. 2001 May;27(5):859\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eAustin PC. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivar Behav Res. 2011 May;46(3):399\u0026ndash;424.\u003c/li\u003e\n\u003cli\u003eHollinger A, Gayat E, F\u0026eacute;liot E, Paugam-Burtz C, Fournier MC, Duranteau J, et al. Gender and survival of critically ill patients: results from the FROG-ICU study. Ann Intensive Care. 2019 Mar 29;9(1):43.\u003c/li\u003e\n\u003cli\u003eOrmseth CH, LaHue SC, Oldham MA, Josephson SA, Whitaker E, Douglas VC. Predisposing and Precipitating Factors Associated With Delirium: A Systematic Review. JAMA Netw Open. 2023 Jan 3;6(1):e2249950.\u003c/li\u003e\n\u003cli\u003eModra LJ, Higgins AM, Pilcher DV, Bailey MJ, Bellomo R. Sex Differences in Mortality of ICU Patients According to Diagnosis-related Sex Balance. Am J Respir Crit Care Med. 2022 Dec 1;206(11):1353\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eDevlin JW, Skrobik Y, G\u0026eacute;linas C, Needham DM, Slooter AJC, Pandharipande PP, et al. Clinical Practice Guidelines for the Prevention and Management of Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption in Adult Patients in the ICU. Crit Care Med. 2018 Sep;46(9):e825\u0026ndash;73.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"critical-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cric","sideBox":"Learn more about [Critical Care](http://ccforum.biomedcentral.com/)","snPcode":"13054","submissionUrl":"https://submission.nature.com/new-submission/13054/3","title":"Critical Care","twitterHandle":"@Crit_Care","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ICU-delirium, sex differences, mortality, personalized ICU-care","lastPublishedDoi":"10.21203/rs.3.rs-5176203/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5176203/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e \u003cp\u003eDelirium is a frequent complication in critically ill patients and is associated with adverse outcomes such as long-term cognitive impairment and increased mortality. It is unknown whether there are sex-related differences in ICU-delirium and associated outcomes. We aimed to assess sex-specific differences in short-term mortality following ICU-delirium.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort study using the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Adult ICU patients who were diagnosed with delirium using the Confusion Assessment Method for the ICU (CAM-ICU) or the Intensive Care Delirium Screening Checklist (ICDSC) were included. The primary outcome was 30-day mortality following delirium onset. To control for baseline differences in demographics, illness severity, and comorbidities, we applied 1:1 propensity score matching. Cox proportional hazards regression models were used to evaluate the association between sex and mortality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 8950 ICU patients with delirium were analyzed, of whom 42.6% were female. In univariable analysis, women had higher crude mortality (26.0% vs. 23.4%; HR 1.16, 95% CI 1.071\u0026ndash;1.267, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After propensity score matching, the cohort included 3811 females and 3811 males. Thirty-day mortality was again higher in women (HR 1.14, 95% CI 1.046\u0026ndash;1.252; p\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study suggests that women with ICU-delirium have a significantly higher risk of short-term mortality than men. Further research is needed to understand the biological and clinical factors driving this disparity and to inform sex-specific interventions for ICU-delirium.\u003c/p\u003e","manuscriptTitle":"Sex specific differences in short-term mortality after ICU-delirium","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-19 13:07:51","doi":"10.21203/rs.3.rs-5176203/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-06T10:07:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-02T19:38:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-27T15:56:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87006220794253295862562794098890584926","date":"2024-10-25T14:31:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-12T14:24:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252993642876318528316674578085935779893","date":"2024-10-10T06:12:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333124994356071494025858377560737383468","date":"2024-10-02T13:51:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-01T20:16:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-01T01:32:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-01T01:31:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Critical Care","date":"2024-09-29T17:51:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"critical-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cric","sideBox":"Learn more about [Critical Care](http://ccforum.biomedcentral.com/)","snPcode":"13054","submissionUrl":"https://submission.nature.com/new-submission/13054/3","title":"Critical Care","twitterHandle":"@Crit_Care","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"09d4e100-ab01-4ee8-a4ef-c04e6f62c977","owner":[],"postedDate":"November 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T16:01:47+00:00","versionOfRecord":{"articleIdentity":"rs-5176203","link":"https://doi.org/10.1186/s13054-024-05204-7","journal":{"identity":"critical-care","isVorOnly":false,"title":"Critical Care"},"publishedOn":"2024-12-18 15:57:33","publishedOnDateReadable":"December 18th, 2024"},"versionCreatedAt":"2024-11-19 13:07:51","video":"","vorDoi":"10.1186/s13054-024-05204-7","vorDoiUrl":"https://doi.org/10.1186/s13054-024-05204-7","workflowStages":[]},"version":"v1","identity":"rs-5176203","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5176203","identity":"rs-5176203","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}