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Racial Differences in Accuracy of Predictive Models for High-Flow Nasal Cannula Failure in COVID-19.
Yang, Philip; Gregory, Ismail A; Robichaux, Chad; Holder, Andre L; Martin, Greg S; Esper, Annette M; Kamaleswaran, Rishikesan; Gichoya, Judy W; Bhavani, Sivasubramanium V.
Affiliation
  • Yang P; Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Emory University, Atlanta, GA.
  • Gregory IA; Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Emory University, Atlanta, GA.
  • Robichaux C; Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA.
  • Holder AL; Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Emory University, Atlanta, GA.
  • Martin GS; Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Emory University, Atlanta, GA.
  • Esper AM; Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Emory University, Atlanta, GA.
  • Kamaleswaran R; Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA.
  • Gichoya JW; Department of Surgery, Duke University School of Medicine, Durham, NC.
  • Bhavani SV; Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA.
Crit Care Explor ; 6(3): e1059, 2024 Mar.
Article de En | MEDLINE | ID: mdl-38975567
ABSTRACT

OBJECTIVES:

To develop and validate machine learning (ML) models to predict high-flow nasal cannula (HFNC) failure in COVID-19, compare their performance to the respiratory rate-oxygenation (ROX) index, and evaluate model accuracy by self-reported race.

DESIGN:

Retrospective cohort study.

SETTING:

Four Emory University Hospitals in Atlanta, GA. PATIENTS Adult patients hospitalized with COVID-19 between March 2020 and April 2022 who received HFNC therapy within 24 hours of ICU admission were included.

INTERVENTIONS:

None. MEASUREMENTS AND MAIN

RESULTS:

Four types of supervised ML models were developed for predicting HFNC failure (defined as intubation or death within 7 d of HFNC initiation), using routine clinical variables from the first 24 hours of ICU admission. Models were trained on the first 60% (n = 594) of admissions and validated on the latter 40% (n = 390) of admissions to simulate prospective implementation. Among 984 patients included, 317 patients (32.2%) developed HFNC failure. eXtreme Gradient Boosting (XGB) model had the highest area under the receiver-operator characteristic curve (AUROC) for predicting HFNC failure (0.707), and was the only model with significantly better performance than the ROX index (AUROC 0.616). XGB model had significantly worse performance in Black patients compared with White patients (AUROC 0.663 vs. 0.808, p = 0.02). Racial differences in the XGB model were reduced and no longer statistically significant when restricted to patients with nonmissing arterial blood gas data, and when XGB model was developed to predict mortality (rather than the composite outcome of failure, which could be influenced by biased clinical decisions for intubation).

CONCLUSIONS:

Our XGB model had better discrimination for predicting HFNC failure in COVID-19 than the ROX index, but had racial differences in accuracy of predictions. Further studies are needed to understand and mitigate potential sources of biases in clinical ML models and to improve their equitability.
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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Canule / COVID-19 Limites: Aged / Female / Humans / Male / Middle aged Langue: En Journal: Crit Care Explor Année: 2024 Type de document: Article Pays d'affiliation: Gabon

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Canule / COVID-19 Limites: Aged / Female / Humans / Male / Middle aged Langue: En Journal: Crit Care Explor Année: 2024 Type de document: Article Pays d'affiliation: Gabon