Observational studyThe Lancet. Digital health2021
Performance of intensive care unit severity scoring systems across different ethnicities in the USA: a retrospective observational study.
Observational study in The Lancet. Digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it, 91 citations in OpenAlex.
- Cell-free DNA as diagnostic and prognostic biomarkers for adult sepsis: a systematic review and meta-analysis.Scientific reports · 2023Pooled it
- The Impact of Black Race on Pneumonia Severity Scoring Systems.CHEST pulmonary · 2026Article
- AI alignment in medical imaging: Unveiling hidden biases through counterfactual analysis.PLOS digital health · 2026Article
- Systematic Review and Meta-Analysis of Intensive Care Unit Scoring Systems' Performance in Patients with Pre-Existing Kidney Disease.Kidney international reports · 2026Article
- Article
- Performance fairness of neural network models in early risk assessment of inpatients with varying severity: a retrospective study.BMC medical informatics and decision making · 2026Article
- Augmenting the Simplified Acute Physiology Score II for intensive care unit mortality prediction and benchmarking: a French nationwide study.Critical care (London, England) · 2026Article
- Explainable AI for mortality prediction: a comparative study using the MIMIC-III dataset.BMJ health & care informatics · 2026Article
- A Research Agenda to Mitigate Racial and Ethnic Disparities in U.S. Critical Care Medicine: An Official American Thoracic Society Research Statement.American journal of respiratory and critical care medicine · 2025Article
- GRACE-ICU: A multimodal nomogram-based approach for illness severity assessment of older adults in the ICU.NPJ digital medicine · 2025Article
- Bias in vital signs? Machine learning models can learn patients' race or ethnicity from the values of vital signs alone.BMJ health & care informatics · 2025Article
- Development and Validation of a Dynamic Real-Time Risk Prediction Model for Intensive Care Units Patients Based on Longitudinal Irregular Data: Multicenter Retrospective Study.Journal of medical Internet research · 2025Article
- Dexmedetomidine improves prognosis in septic patients with myocardial injury and lower APACHE IV scores: a retrospective cohort study.BMC anesthesiology · 2025Article
- Introducing the Team Card: Enhancing governance for medical Artificial Intelligence (AI) systems in the age of complexity.PLOS digital health · 2025Article
- Association Between Lactic Dehydrogenase-to-Albumin Ratio and Short-Time Mortality in Patients with Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary disease · 2025Article
- The stress hyperglycemia ratio as a predictor of short- and long-term mortality in patients with acute brain injury: a retrospective cohort study.Frontiers in neurology · 2025Article
- Association between lactate/albumin ratio and 28-day all-cause mortality in critically ill patients with acute myocardial infarction.Scientific reports · 2024Article
- Minimizing bias when using artificial intelligence in critical care medicine.Journal of critical care · 2024Article
- The Glycemic Ratio Is Strongly and Independently Associated With Mortality in the Critically Ill.Journal of diabetes science and technology · 2024Article
- Clinical data mining: challenges, opportunities, and recommendations for translational applications.Journal of translational medicine · 2024Review
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Authors and funding
6 authors at 6 institutions in 2 countries.
Funding
Abstract
backgroundDespite wide use of severity scoring systems for case-mix determination and benchmarking in the intensive care unit (ICU), the possibility of scoring bias across ethnicities has not been examined. Guidelines on the use of illness severity scores to inform triage decisions for allocation of scarce resources, such as mechanical ventilation, during the current COVID-19 pandemic warrant examination for possible bias in these models. We investigated the performance of the severity scoring systems Acute Physiology and Chronic Health Evaluation IVa (APACHE IVa), Oxford Acute Severity of Illness Score (OASIS), and Sequential Organ Failure Assessment (SOFA) across four ethnicities in two large ICU databases to identify possible ethnicity-based bias.
methodsData from the electronic ICU Collaborative Research Database (eICU-CRD) and the Medical Information Mart for Intensive Care III (MIMIC-III) database, built from patient episodes in the USA from 2014-15 and 2001-12, respectively, were analysed for score performance in Asian, Black, Hispanic, and White people after appropriate exclusions. Hospital mortality was the outcome of interest. Discrimination and calibration were determined for all three scoring systems in all four groups, using area under receiver operating characteristic (AUROC) curve for different ethnicities to assess discrimination, and standardised mortality ratio (SMR) or proxy measures to assess calibration.
findingsWe analysed 166 751 participants (122 919 eICU-CRD and 43 832 MIMIC-III). Although measurements of discrimination were significantly different among the groups (AUROC ranging from 0·86 to 0·89 [p=0·016] with APACHE IVa and from 0·75 to 0·77 [p=0·85] with OASIS), they did not display any discernible systematic patterns of bias. However, measurements of calibration indicated persistent, and in some cases statistically significant, patterns of difference between Hispanic people (SMR 0·73 with APACHE IVa and 0·64 with OASIS) and Black people (0·67 and 0·68) versus Asian people (0·77 and 0·95) and White people (0·76 and 0·81). Although calibrations were imperfect for all groups, the scores consistently showed a pattern of overpredicting mortality for Black people and Hispanic people. Similar results were seen using SOFA scores across the two databases.
interpretationThe systematic differences in calibration across ethnicities suggest that illness severity scores reflect statistical bias in their predictions of mortality.
fundingThere was no specific funding for this study.
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