TY - JOUR
T1 - Development and External Validation of Models to Predict Persistent Hypoxemic Respiratory Failure for Clinical Trial Enrichment∗
AU - Sathe, Neha A.
AU - Zelnick, Leila R.
AU - Morrell, Eric D.
AU - Bhatraju, Pavan K.
AU - Kerchberger, V. Eric
AU - Hough, Catherine L.
AU - Ware, Lorraine B.
AU - Fohner, Alison E.
AU - Wurfel, Mark M.
N1 - Publisher Copyright:
© 2024 Lippincott Williams and Wilkins. All rights reserved.
PY - 2024/5/1
Y1 - 2024/5/1
N2 - 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 Pao2/Fio2, 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 Pao2/Fio2alone, 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 Pao2/Fio2(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 Pao2/Fio2. 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.
AB - 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 Pao2/Fio2, 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 Pao2/Fio2alone, 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 Pao2/Fio2(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 Pao2/Fio2. 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.
KW - acute hypoxemic respiratory failure
KW - acute respiratory distress syndrome
KW - clinical prediction rule
KW - precision medicine
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U2 - 10.1097/CCM.0000000000006181
DO - 10.1097/CCM.0000000000006181
M3 - Article
C2 - 38197736
AN - SCOPUS:85190749764
SN - 0090-3493
VL - 52
SP - 764
EP - 774
JO - Critical care medicine
JF - Critical care medicine
IS - 5
ER -