eARDS: A multi-center validation of an interpretable machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults with COVID-19.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2021
Historique:
received: 20 04 2021
accepted: 21 08 2021
entrez: 24 9 2021
pubmed: 25 9 2021
medline: 5 10 2021
Statut: epublish

Résumé

We present an interpretable machine learning algorithm called 'eARDS' for predicting ARDS in an ICU population comprising COVID-19 patients, up to 12-hours before satisfying the Berlin clinical criteria. The analysis was conducted on data collected from the Intensive care units (ICU) at Emory Healthcare, Atlanta, GA and University of Tennessee Health Science Center, Memphis, TN and the Cerner® Health Facts Deidentified Database, a multi-site COVID-19 EMR database. The participants in the analysis consisted of adults over 18 years of age. Clinical data from 35,804 patients who developed ARDS and controls were used to generate predictive models that identify risk for ARDS onset up to 12-hours before satisfying the Berlin criteria. We identified salient features from the electronic medical record that predicted respiratory failure among this population. The machine learning algorithm which provided the best performance exhibited AUROC of 0.89 (95% CI = 0.88-0.90), sensitivity of 0.77 (95% CI = 0.75-0.78), specificity 0.85 (95% CI = 085-0.86). Validation performance across two separate health systems (comprising 899 COVID-19 patients) exhibited AUROC of 0.82 (0.81-0.83) and 0.89 (0.87, 0.90). Important features for prediction of ARDS included minimum oxygen saturation (SpO2), standard deviation of the systolic blood pressure (SBP), O2 flow, and maximum respiratory rate over an observational window of 16-hours. Analyzing the performance of the model across various cohorts indicates that the model performed best among a younger age group (18-40) (AUROC = 0.93 [0.92-0.94]), compared to an older age group (80+) (AUROC = 0.81 [0.81-0.82]). The model performance was comparable on both male and female groups, but performed significantly better on the severe ARDS group compared to the mild and moderate groups. The eARDS system demonstrated robust performance for predicting COVID19 patients who developed ARDS at least 12-hours before the Berlin clinical criteria, across two independent health systems.

Identifiants

pubmed: 34559819
doi: 10.1371/journal.pone.0257056
pii: PONE-D-21-13104
pmc: PMC8462682
doi:

Substances chimiques

Oxygen S88TT14065

Types de publication

Clinical Trial Comparative Study Journal Article Multicenter Study Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S. Validation Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0257056

Subventions

Organisme : NCATS NIH HHS
ID : TL1 TR002382
Pays : United States
Organisme : NIGMS NIH HHS
ID : T32 GM095442
Pays : United States
Organisme : NIH HHS
ID : 2T32 GM095442
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002378
Pays : United States
Organisme : NIGMS NIH HHS
ID : K23 GM137182
Pays : United States

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

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Auteurs

Lakshya Singhal (L)

Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, United States of America.

Yash Garg (Y)

Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, United States of America.

Philip Yang (P)

Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, Emory University School of Medicine, Atlanta, Georgia, United States of America.

Azade Tabaie (A)

Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, United States of America.

A Ian Wong (AI)

Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, Emory University School of Medicine, Atlanta, Georgia, United States of America.

Akram Mohammed (A)

Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America.

Lokesh Chinthala (L)

Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America.

Dipen Kadaria (D)

Department of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America.

Amik Sodhi (A)

Department of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America.

Andre L Holder (AL)

Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, Emory University School of Medicine, Atlanta, Georgia, United States of America.

Annette Esper (A)

Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, Emory University School of Medicine, Atlanta, Georgia, United States of America.

James M Blum (JM)

Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, United States of America.
Department of Anaesthesia, Emory University School of Medicine, Atlanta, Georgia, United States of America.

Robert L Davis (RL)

Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America.

Gari D Clifford (GD)

Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, United States of America.
Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America.

Greg S Martin (GS)

Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, Emory University School of Medicine, Atlanta, Georgia, United States of America.

Rishikesan Kamaleswaran (R)

Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, United States of America.
Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America.

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