Evidence map›Paper›PMID 37559024›Full record

ArticleBMC pulmonary medicine2023

Evaluating construct validity of computable acute respiratory distress syndrome definitions in adults hospitalized with COVID-19: an electronic health records based approach.

Neha A Sathe, Su Xian, F Linzee Mabrey, David R Crosslin, Sean D Mooney, Eric D Morrell, Kevin Lybarger, Meliha Yetisgen, Gail P Jarvik, Pavan K Bhatraju and 1 more

Abstract read
In one paragraph

Article in BMC pulmonary medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
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

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Neha A Sathe *Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, 325 9th Avenue HMC #359640, Seattle, WA, 98104-2499, USA. nas212@uw.edu.
Su Xian *Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.
F Linzee MabreyDivision of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, 325 9th Avenue HMC #359640, Seattle, WA, 98104-2499, USA.
David R CrosslinDivision of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA.
Sean D MooneyDepartment of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.
Eric D MorrellDivision of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, 325 9th Avenue HMC #359640, Seattle, WA, 98104-2499, USA.
Kevin LybargerDepartment of Information Sciences and Technology, George Mason University, Fairfax, VA, USA.
Meliha YetisgenDepartment of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.
Gail P JarvikDepartment of Genome Sciences and Division of Medical Genetics, Department of Medicine, University of Washington Medical Center, Seattle, WA, USA.
Pavan K BhatrajuDivision of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, 325 9th Avenue HMC #359640, Seattle, WA, 98104-2499, USA.
Mark M WurfelDivision of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, 325 9th Avenue HMC #359640, Seattle, WA, 98104-2499, USA.

Funding

Transform Dissemination and Implementation Science in CTSA ProgramsUL1TR002319 · NCATS · UNIVERSITY OF WASHINGTON · PI John K. Amory · 2017 to 2026
$100.0M
Variation, Function, and Disease Supplement ProgramU01HG008657 · NHGRI · UNIVERSITY OF WASHINGTON · PI David Russell Crosslin, Gail Pairitz Jarvik · 2015 to 2026
$13.4M
Characterizing clinical and biologic features of persistent hypoxemic respiratory failureF32HL158088 · NHLBI · UNIVERSITY OF WASHINGTON · PI SATHE, NEHA ALHAD · 2021 to 2022
$165k
NCATS NIH HHS UL1 TR002319NHGRI NIH HHS 5U01HG008657NHGRI NIH HHS U01 HG008657NHLBI NIH HHS F32 HL158088NHLBI NIH HHS F32HL158088
6 · The paper itself

Abstract

backgroundEvolving ARDS epidemiology and management during COVID-19 have prompted calls to reexamine the construct validity of Berlin criteria, which have been rarely evaluated in real-world data. We developed a Berlin ARDS definition (EHR-Berlin) computable in electronic health records (EHR) to (1) assess its construct validity, and (2) assess how expanding its criteria affected validity.

methodsWe performed a retrospective cohort study at two tertiary care hospitals with one EHR, among adults hospitalized with COVID-19 February 2020-March 2021. We assessed five candidate definitions for ARDS: the EHR-Berlin definition modeled on Berlin criteria, and four alternatives informed by recent proposals to expand criteria and include patients on high-flow oxygen (EHR-Alternative 1), relax imaging criteria (EHR-Alternatives 2-3), and extend timing windows (EHR-Alternative 4). We evaluated two aspects of construct validity for the EHR-Berlin definition: (1) criterion validity: agreement with manual ARDS classification by experts, available in 175 patients; (2) predictive validity: relationships with hospital mortality, assessed by Pearson r and by area under the receiver operating curve (AUROC). We assessed predictive validity and timing of identification of EHR-Berlin definition compared to alternative definitions.

resultsAmong 765 patients, mean (SD) age was 57 (18) years and 471 (62%) were male. The EHR-Berlin definition classified 171 (22%) patients as ARDS, which had high agreement with manual classification (kappa 0.85), and was associated with mortality (Pearson r = 0.39; AUROC 0.72, 95% CI 0.68, 0.77). In comparison, EHR-Alternative 1 classified 219 (29%) patients as ARDS, maintained similar relationships to mortality (r = 0.40; AUROC 0.74, 95% CI 0.70, 0.79, Delong test P = 0.14), and identified patients earlier in their hospitalization (median 13 vs. 15 h from admission, Wilcoxon signed-rank test P < 0.001). EHR-Alternative 3, which removed imaging criteria, had similar correlation (r = 0.41) but better discrimination for mortality (AUROC 0.76, 95% CI 0.72, 0.80; P = 0.036), and identified patients median 2 h (P < 0.001) from admission.

conclusionsThe EHR-Berlin definition can enable ARDS identification with high criterion validity, supporting large-scale study and surveillance. There are opportunities to expand the Berlin criteria that preserve predictive validity and facilitate earlier identification.

Indexed as

COVID-19Respiratory Distress SyndromeAdultElectronic Health RecordsFemaleHumansMaleMiddle AgedRetrospective StudiesRisk AssessmentAcute respiratory distress syndromeConstruct validityCoronavirus disease 2019Electronic health recordsPhenotyping

Identifiers

PMID37559024
PMCPMC10413524

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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.