Evidence map›Paper›PMID 33766288›Full record

Observational studyThe Lancet. Digital health2021

Performance of intensive care unit severity scoring systems across different ethnicities in the USA: a retrospective observational study.

Rahuldeb Sarkar, Christopher Martin, Heather Mattie, Judy Wawira Gichoya, David J Stone, Leo Anthony Celi

Open access · goldAbstract readComparative StudyObservational Study
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 1 pooled it
11.4field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

44 citing papers in PubMed, 1 synthesis or guideline pooled it, 91 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 6 institutions in 2 countries.

Rahuldeb SarkarDepartment of Respiratory Medicine, Medway NHS Foundation Trust, Gillingham, Kent, UK; Department of Critical Care, Medway NHS Foundation Trust, Gillingham, Kent, UK; Faculty of Life Sciences, King's College London, London, UK.
Christopher MartinUCL Institute for Health Informatics, London, UK; Crystallise, Essex, UK.
Heather MattieDepartment of Biostatistics, Harvard T H Chan School of Public Health, Boston, MA, USA.
Judy Wawira GichoyaInterventional Radiology and Informatics, Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA.
David J StoneDepartment of Anesthesiology, University of Virginia School of Medicine, Charlottesville, VA, USA; Department of Neurosurgery, University of Virginia School of Medicine, Charlottesville, VA, USA; Center for Advanced Medical Analytics, University of Virginia School of Medicine, Charlottesville, VA, USA.
Leo Anthony CeliDepartment of Biostatistics, Harvard T H Chan School of Public Health, Boston, MA, USA; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA; Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA. Electronic address: lceli@mit.edu.
Emory University · USHarvard University · USMassachusetts Institute of Technology · USMedway NHS Foundation Trust · GBUniversity College London · GBUniversity of Virginia · US

Funding

Critical Care Informatics: Ethical considerations around the use and sharing of health-related dataR01EB017205 · NIBIB · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI CELI, LEO ANTHONY G, MARK, ROGER GREENWOOD · 2014 to 2021
$3.8M
Critical Care InformaticsR56EB017205 · NIBIB · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI CELI, LEO ANTHONY G, GHASSEMI, MARZYEH · 2018 to 2023
$783k
NIBIB NIH HHS R01 EB017205NIBIB NIH HHS R56 EB017205
6 · The paper itself

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.

Indexed as

Intensive Care UnitsRacismSeverity of Illness IndexAdolescentAdultAgedAged, 80 and overEthnicityFemaleHospital MortalityHumansMaleMiddle AgedOrgan Dysfunction ScoresRacial GroupsRetrospective Studies

Identifiers

PMID33766288
PMCPMC8063502
OpenAlexW3137590022

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.