Evidence map›Paper›PMID 33501459›Full record

ArticlemedRxiv : the preprint server for health sciences2021

Performance of intensive care unit severity scoring systems across different ethnicities.

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

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 6 institutions in 2 countries.

Rahuldeb SarkarDepartments of Respiratory Medicine and Critical Care, Medway NHS Foundation Trust, Gillingham, Kent, UK.
Christopher MartinUCL Institute for Health Informatics, London, UK.
Heather MattieDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA, 02115.
Judy Wawira GichoyaInterventional Radiology & Informatics, Department of Radiology & Imaging Sciences, Emory University, 1364 Clifton Rd NE Suite AG08 Atlanta, GA 30322.
David J StoneDepartments of Anesthesiology and Neurosurgery, and the Center for Advanced Medical Analytics, University of Virginia School of Medicine, Charlottesville, VA, 22908.
Leo Anthony CeliLaboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA 20139.
Beth Israel Deaconess Medical Center · USEmory Healthcare · USHarvard University · USKing's College London · GBUniversity College London · GBUniversity of Virginia · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite wide utilisation of severity scoring systems for case-mix determination and benchmarking in the intensive care unit, the possibility of scoring bias across ethnicities has not been examined. Recent 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 three severity scoring systems (APACHE IVa, OASIS, SOFA) across ethnic groups in two large ICU databases in order to identify possible ethnicity-based bias.

methodData from the eICU Collaborative Research Database and the Medical Information Mart for Intensive Care were analysed for score performance in Asians, African Americans, Hispanics and Whites after appropriate exclusions. Discrimination and calibration were determined for all three scoring systems in all four groups.

findingsWhile measurements of discrimination -area under the receiver operating characteristic curve (AUROC) -were significantly different among the groups, they did not display any discernible systematic patterns of bias. In contrast, measurements of calibration -standardised mortality ratio (SMR) -indicated persistent, and in some cases significant, patterns of difference between Hispanics and African Americans versus Asians and Whites. The differences between African Americans and Whites were consistently statistically significant. While calibrations were imperfect for all groups, the scores consistently demonstrated a pattern of over-predicting mortality for African Americans and Hispanics.

interpretationThe systematic differences in calibration across ethnic groups suggest that illness severity scores reflect bias in their predictions of mortality.

fundingLAC is funded by the National Institute of Health through NIBIB R01 EB017205. There was no specific funding for this study.

Identifiers

PMID33501459
PMCPMC7836131
OpenAlexW3112638307

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Registered trials

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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.