Development and External Validation of Models to Predict Persistent Hypoxemic Respiratory Failure for Clinical Trial Enrichment

Abstract

Objectives: Improving 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. Design: Prospective cohorts for derivation (n = 630) and external validation (n = 511). Setting: Medical and surgical ICUs at two U.S. medical centers. Patients: Adults with acute HRF defined as new invasive mechanical ventilation (IMV) and hypoxemia on the first calendar day after ICU admission. Interventions: None. Measurements and Main Results: We 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 Pao 2/Fio 2, 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 Pao 2/Fio 2 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 Pao 2/Fio 2 (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 Pao 2/Fio 2. The added utility of LASSO + IL-6 model over LASSO was modest. Conclusions: Parsimonious, interpretable models that predict persistent HRF may improve enrichment of trials testing HRF-targeted therapies and warrant future validation.