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Racial Disparities in Invasive ICU Treatments Among Septic Patients: High Resolution Electronic Health Records Analysis from MIMIC-IV
Open accessAbstract
Background: Low-resolution administrative databases can give biased results, whereas high-resolution, time-stamped variables from clinical databases like MIMIC-IV might provide nuanced insights.We evaluated racial-ethnic disparities in life-sustaining ICU-treatments (Invasive Mechanical Ventilation (IMV), Renal Replacement Therapy (RRT), and Vasopressors (VP)) among patients with sepsis.Methods: In this observational retrospective cohort study, patients fulfilling sepsis-3 criteria were categorized by treatment assignment within the first 4 days.The outcomes were treatment allocations.The likelihood of receiving treatment was calculated by race-ethnicity (Racial-ethnic group (REG) or White group (WG)) using 5-fold sub-sampling nested logistic regression and XGBoost.Results: In 23,914 admissions, 82% were White, 42% were women.REG were less likely to receive IMV across all eligibility days (day 1 odds ratio (OR) 0.87, 95% confidence interval (CI) 0.83-0.94,day 4 OR 0.80, 95% CI 0.72 -0.87).There were no differences in RRT (day 1 OR 1.00, 95% CI 0.96-1.09,day 4 OR 1.00, 95% CI 0.94-1.06).REG were also less likely to be treated with VP at days 1 to 3 (day 1 OR 0.87, 95% CI 0.76-0.94),but not at day 4 (OR 0.95, 95% CI 0.87-1.01).These findings remained robust when relaxing eligibility criteria for treatment allocation.Conclusion: Our findings reveal significant disparities in the use of invasive life-saving ICU treatments among septic patients from racial and ethnic minority backgrounds, particularly with respect to IMV and VP use.These disparities underscore not only the need to address inequality in critical care settings, but also highlight the importance of high-resolution data.
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The MIMIC-IV dataset has a disproportionate representation: 81.8% White, 10.5% Black, 4.3% Hispanic, and 3.3% Asian populations.
“the dataset has a disproportionate representation, with 81.8% White, 10.5% Black, 4.3% Hispanic, and 3.3% Asian populations [29].”
From AI-Driven Predictions of Readmission and Mortality for Improved Discharge Decisions in Critical Care: A Retrospective Study (2026) · cites this paper as [29]
The Results (Baseline Study Cohort) give exactly these percentages, but for the final cohort of 23,914 ICU admissions selected for the sepsis study, not for the entire MIMIC-IV dataset. The Methods (Exposure and Outcome) instead describe MIMIC-IV as approximately 70% White. (LLM, conf=0.90)
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MIMIC-IV, a freely accessible electronic health record dataset
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Time to treatment and mortality during mandated emergency care for sepsis.
Seymour, C. W., Gesten, F., Prescott, H. C., Friedrich, M. E., Iwashyna, T. J., Phillips, G. S., Lemeshow, S., Osborn, T., Terry, K. M., & Levy, M. M. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1703058 · Cited by 2,225
Weiskopf, N. G., & Weng, C. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000681 · Cited by 1,216 · cited 1×
“However, most of these studies come from claims-based databases that are prone to variable mis-classification or do not contain important timestamped information [15-17].”
Hersh, W. R., Weiner, M. G., Embi, P. J., Logan, J. R., Payne, P. R. O., Bernstam, E. V., Lehmann, H. P., Hripcsak, G., Hartzog, T. H., Cimino, J. J., et al. (2013) · Medical Care · doi:10.1097/mlr.0b013e31829b1dbd · Cited by 567
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Self-citationRacial Variation in the Incidence, Care, and Outcomes of Severe Sepsis
Barnato, A. E., Alexander, S. L., Linde-Zwirble, W. T., & Angus, D. C. (2008) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200703-480oc · Cited by 284
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Heo, Y., Kim, M., Han, S.-S., Kim, T.-H., Heo, J., Kim, D., Kim, W. J., Lee, S.-J., Kwon, O. B., Kim, Y., et al. (2026) · Diagnostics · doi:10.3390/diagnostics16060874
Metadata
- Authors
- Mohammed, S., Matos, J., Doutreligne, M., Celi, L. A., & Struja, T.
- Affiliations
- Harvard University (US)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Beth Israel Deaconess Medical Center (US)
- Kantonsspital Aarau (CH)
- Corresponding author
- Struja, Tristan
- Venue
- The Yale Journal of Biology and Medicine
- Publisher
- Yale Journal of Biology and Medicine
- Published
- 2023-09-29
- Volume
- 96
- Issue
- 3
- Pages
- 293-312
- ISSN
- 0044-0086 (ISSN-L)
- 1551-4056
- PMC
- PMC10524813
- OpenAlex
- W4387241272
- Cited by
- 13
- References
- 8
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 2
- FWCI
- 2.60
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc
- Locations
- The Yale Journal of Biology and Medicine (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (194497, P400PM_194497/1)
- Fundação Luso-Americana para o Desenvolvimento
- National Institute of Biomedical Imaging and Bioengineering (R01EB001659)
- Topics
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Sepsis Diagnosis and Treatment
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Palliative Care and End-of-Life Issues
- Keywords
- racial and ethnic disparities, sepsis, septic shock, invasive mechanical ventilation, renal replacement therapy, vasopressors, critical care access, MIMIC-IV, treatment allocation
- MeSH terms
- Critical Care, Ethnicity, Female, Humans, Intensive Care Units, Male, Minority Groups, Retrospective Studies, Sepsis, Healthcare Disparities, Electronic Health Records
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