• Neha A Sathe, Leila R Zelnick, Eric D Morrell, Pavan K Bhatraju, V Eric Kerchberger, Catherine L Hough, Lorraine B Ware, Alison E Fohner, and Mark M Wurfel.
• Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA.
• Crit. Care Med. 2024 May 1; 52 (5): 764774764-774.

ObjectivesImproving the efficiency of clinical trials in acute hypoxemic respiratory failure (HRF) depends on enrichment strategies that minimize enrollment of patients who quickly resolve with existing care and focus on patients at high risk for persistent HRF. We aimed to develop parsimonious models predicting risk of persistent HRF using routine data from ICU admission and select research immune biomarkers.DesignProspective cohorts for derivation ( n = 630) and external validation ( n = 511).SettingMedical and surgical ICUs at two U.S. medical centers.PatientsAdults with acute HRF defined as new invasive mechanical ventilation (IMV) and hypoxemia on the first calendar day after ICU admission.InterventionsNone.Measurements And Main ResultsWe evaluated discrimination, calibration, and practical utility of models predicting persistent HRF risk (defined as ongoing IMV and hypoxemia on the third calendar day after admission): 1) a clinical model with least absolute shrinkage and selection operator (LASSO) selecting Pa o2 /F io2 , vasopressors, mean arterial pressure, bicarbonate, and acute respiratory distress syndrome as predictors; 2) a model adding interleukin-6 (IL-6) to clinical predictors; and 3) a comparator model with Pa o2 /F io2 alone, representing an existing strategy for enrichment. Forty-nine percent and 69% of patients had persistent HRF in derivation and validation sets, respectively. In validation, both LASSO (area under the receiver operating characteristic curve, 0.68; 95% CI, 0.64-0.73) and LASSO + IL-6 (0.71; 95% CI, 0.66-0.76) models had better discrimination than Pa o2 /F io2 (0.64; 95% CI, 0.59-0.69). Both models underestimated risk in lower risk deciles, but exhibited better calibration at relevant risk thresholds. Evaluating practical utility, both LASSO and LASSO + IL-6 models exhibited greater net benefit in decision curve analysis, and greater sample size savings in enrichment analysis, compared with Pa o2 /F io2 . The added utility of LASSO + IL-6 model over LASSO was modest.ConclusionsParsimonious, interpretable models that predict persistent HRF may improve enrichment of trials testing HRF-targeted therapies and warrant future validation.Copyright © 2024 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

20 keywords...

hide…