Acute lung injury prediction score: derivation and validation in a population-based sample

Abstract

Early recognition of patients at high risk of acute lung injury (ALI) is critical for successful enrollment of patients in prevention strategies for this devastating syndrome. We aimed to develop and prospectively validate an ALI prediction score in a population-based sample of patients at risk. In a retrospective derivation cohort, predisposing conditions for ALI were identified at the time of hospital admission. The score was calculated based on the results of logistic regression analysis. Prospective validation was performed in an independent cohort of patients at risk identified at the time of hospital admission. In a derivation cohort of 409 patients with ALI risk factors, the lung injury prediction score discriminated patients who developed ALI from those who did not with an area under the curve (AUC) of 0.84 (95% CI 0.80-0.89; Hosmer-Lemeshow p = 0.60). The performance was similar in a prospective validation cohort of 463 patients at risk of ALI (AUC 0.84, 95% CI 0.77-0.91; Hosmer-Lemeshow p = 0.88). ALI prediction scores identify patients at high risk for ALI before intensive care unit admission. If externally validated, this model will serve to define the population of patients at high risk for ALI in whom future mechanistic studies and ALI prevention trials will be conducted.

Trial registration: ClinicalTrials.gov NCT00889772.