Zhiguo Zhao1,2, Nancy Wickersham3, Kirsten N Kangelaris4, Addison K May5, Gordon R Bernard3, Michael A Matthay6,7, Carolyn S Calfee6,7, Tatsuki Koyama1, Lorraine B Ware8,9. 1. Department of Biostatistics, Vanderbilt University, Nashville, TN, USA. 2. The Institution for Medicine and Public Health, Vanderbilt University, Nashville, TN, USA. 3. Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University, T1218 MCN, 1161 21st Avenue S., Nashville, TN, 37232-2650, USA. 4. Division of Hospital Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA. 5. Division of Trauma and Surgical Critical Care, Vanderbilt University, Nashville, TN, USA. 6. Department of Medicine, University of California San Francisco, San Francisco, CA, USA. 7. Department of Anesthesia and Cardiovascular Research Institute, University of California San Francisco, San Francisco, CA, USA. 8. Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University, T1218 MCN, 1161 21st Avenue S., Nashville, TN, 37232-2650, USA. lorraine.ware@vanderbilt.edu. 9. Department of Pathology, Microbiology and Immunology, Vanderbilt University, Nashville, TN, USA. lorraine.ware@vanderbilt.edu.
Abstract
PURPOSE: Mortality prediction in ARDS is important for prognostication and risk stratification. However, no prediction models have been independently validated. A combination of two biomarkers with age and APACHE III was superior in predicting mortality in the NHLBI ARDSNet ALVEOLI trial. We validated this prediction tool in two clinical trials and an observational cohort. METHODS: The validation cohorts included 849 patients from the NHLBI ARDSNet Fluid and Catheter Treatment Trial (FACTT), 144 patients from a clinical trial of sivelestat for ARDS (STRIVE), and 545 ARDS patients from the VALID observational cohort study. To evaluate the performance of the prediction model, the area under the receiver operating characteristic curve (AUC), model discrimination, and calibration were assessed, and recalibration methods were applied. RESULTS: The biomarker/clinical prediction model performed well in all cohorts. Performance was better in the clinical trials with an AUC of 0.74 (95% CI 0.70-0.79) in FACTT, compared to 0.72 (95% CI 0.67-0.77) in VALID, a more heterogeneous observational cohort. The AUC was 0.73 (95% CI 0.70-0.76) when FACTT and VALID were combined. CONCLUSION: We validated a mortality prediction model for ARDS that includes age, APACHE III, surfactant protein D, and interleukin-8 in a variety of clinical settings. Although the model performance as measured by AUC was lower than in the original model derivation cohort, the biomarker/clinical model still performed well and may be useful for risk assessment for clinical trial enrollment, an issue of increasing importance as ARDS mortality declines, and better methods are needed for selection of the most severely ill patients for inclusion.
RCT Entities:
PURPOSE: Mortality prediction in ARDS is important for prognostication and risk stratification. However, no prediction models have been independently validated. A combination of two biomarkers with age and APACHE III was superior in predicting mortality in the NHLBI ARDSNet ALVEOLI trial. We validated this prediction tool in two clinical trials and an observational cohort. METHODS: The validation cohorts included 849 patients from the NHLBI ARDSNet Fluid and Catheter Treatment Trial (FACTT), 144 patients from a clinical trial of sivelestat for ARDS (STRIVE), and 545 ARDS patients from the VALID observational cohort study. To evaluate the performance of the prediction model, the area under the receiver operating characteristic curve (AUC), model discrimination, and calibration were assessed, and recalibration methods were applied. RESULTS: The biomarker/clinical prediction model performed well in all cohorts. Performance was better in the clinical trials with an AUC of 0.74 (95% CI 0.70-0.79) in FACTT, compared to 0.72 (95% CI 0.67-0.77) in VALID, a more heterogeneous observational cohort. The AUC was 0.73 (95% CI 0.70-0.76) when FACTT and VALID were combined. CONCLUSION: We validated a mortality prediction model for ARDS that includes age, APACHE III, surfactant protein D, and interleukin-8 in a variety of clinical settings. Although the model performance as measured by AUC was lower than in the original model derivation cohort, the biomarker/clinical model still performed well and may be useful for risk assessment for clinical trial enrollment, an issue of increasing importance as ARDS mortality declines, and better methods are needed for selection of the most severely ill patients for inclusion.
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