An Alternative Method of Acute Lung Injury Classification for Use in Observational Studies

Overview

Journal Chest

Publisher Elsevier

Specialty Pulmonary Medicine

Date 2010 Jun 26

PMID 20576730

Citations 29

Authors

Chirag V Shah

Paul N Lanken

A Russell Localio

Robert Gallop

Scarlett Bellamy

Shwu-Fan Ma

Carlos Flores

Jeremy M Kahn

Barbara Finkel

Barry D Fuchs

Joe G N Garcia

Jason D Christie

Affiliations

Soon will be listed here.

Abstract

Background: In observational studies using acute lung injury (ALI) as an outcome, a spectrum of lung injury and difficult-to-interpret chest radiographs (CXRs) may hamper efforts to uncover risk factor associations. We assessed the impact of excluding patients with difficult-to-classify or equivocal ALI diagnosis on clinical and genetic risk factor associations for ALI after trauma.

Methods: This study was of a prospective cohort of 280 critically ill trauma patients. The primary outcome was the development of ALI. Patients were classified into one of three groups: (1) definite ALI (patients who fulfilled the American-European Consensus Conference [AECC] criteria for ALI), (2)equivocal ALI (patients who had difficult-to-interpret CXRs), and (3) definite non-ALI. We compared clinical and genetic ALI risk factor associations between two classification schemes: AECC classification (definite ALI vs rest) and alternative classification (definite ALI vs definite non-ALI, excluding equivocal ALI).

Results: Ninety-three (35%) patients were classified as definite ALI, 67 (25%) as equivocal, and 104 (39%) as definite non-ALI. Estimates of clinical and genetic ALI risk factor associations were farther from the null using the alternative classification. In a multivariable risk factor model, the C statistic of the alternative classification was significantly higher than that derived from the AECC classification (0.82 vs 0.74; P < .01).

Conclusions: The ability to detect ALI risk factors may be improved by excluding patients with equivocal or difficult-to-classify ALI. Such analyses may provide improved ability to detect clinical and genetic risk factor associations in future epidemiologic studies of ALI.

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