Development and Validation of an Electronic Health Record-Based, Pediatric Acute Respiratory Distress Syndrome Subphenotype Classifier Model.

Abstract

Objective: To determine if hyperinflammatory and hypoinflammatory pediatric acute respiratory distress syndrome (PARDS) subphenotypes defined using serum biomarkers can be determined solely from electronic health record (EHR) data using machine learning.

Design: Retrospective, exploratory analysis using data from 2014 to 2022.

Setting: Single-center quaternary care PICU.

Patients: Two temporally distinct cohorts of PARDS patients, 2014-2019 and 2019-2022.

Interventions: None.

Measurements and main results: Patients in the derivation cohort (n = 333) were assigned to hyperinflammatory or hypoinflammatory subphenotypes using biomarkers and latent class analysis. A machine learning model was trained on 165 EHR-derived variables to identify subphenotypes. The most important variables were selected for inclusion in a parsimonious model. The model was validated in a separate cohort (n = 114). The EHR-based classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.93 (95% CI, 0.87-0.98), with a sensitivity of 88% and specificity of 83% for determining hyperinflammatory PARDS. The parsimonious model, using only five laboratory values, achieved an AUC of 0.92 (95% CI, 0.86-0.98) with a sensitivity of 76% and specificity of 87% in the validation cohort.

Conclusions: This proof-of-concept study demonstrates that biomarker-based PARDS subphenotypes can be identified using EHR data at 24 hours of PARDS diagnosis. Further validation in larger, multicenter cohorts is needed to confirm the clinical utility of this approach.