Early identification of patients at risk of acute lung injury: Evaluation of lung injury prediction score in a multicenter cohort study

Ognjen Gajic, Ousama Dabbagh, Pauline K. Park, Adebola Adesanya, Steven Y. Chang, Peter Hou, Harry Anderson, J. Jason Hoth, Mark E. Mikkelsen, Nina T. Gentile, Michelle N. Gong, Daniel Talmor, Ednan Bajwa, Timothy R. Watkins, Emir Festic, Murat Yilmaz, Remzi Iscimen, David A. Kaufman, Annette M. Esper, Ruxana SadikotIvor Douglas, Jonathan Sevransky, Michael Malinchoc

Research output: Contribution to journalArticlepeer-review

380 Scopus citations


Rationale: Accurate, early identification of patients at risk for developing acute lung injury (ALI) provides the opportunity to test and implement secondary prevention strategies. Objectives: To determine the frequency and outcome of ALI development inpatients at risk andvalidate a lunginjury predictionscore (LIPS). Methods: In this prospective multicenter observational cohort study, predisposing conditions and risk modifiers predictive of ALI development were identified from routine clinical data available during initial evaluation. The discrimination of the model was assessed with area under receiver operating curve (AUC). The risk of death from ALI was determined after adjustment for severity of illness and predisposing conditions. Measurements and Main Results: Twenty-two hospitals enrolled 5,584 patients at risk. ALI developed a median of 2 (interquartile range 1-4) days after initial evaluation in 377 (6.8%; 148 ALI-only, 229 adult respiratory distress syndrome) patients. The frequency of ALI varied according to predisposing conditions (from 3% in pancreatitis to 26% after smoke inhalation). LIPS discriminated patients who developed ALI from those who did not with an AUC of 0.80 (95% confidence interval, 0.78-0.82). When adjusted for severity of illness and predisposing conditions, development of ALI increased the risk of in-hospital death (odds ratio, 4.1; 95% confidence interval, 2.9-5.7). Conclusions: ALI occurrence varies according to predisposing conditions and carries an independently poor prognosis. Using routinely available clinical data, LIPS identifies patients at high risk for ALI early in the course of their illness. This model will alert clinicians about the risk of ALI and facilitate testing and implementation of ALI prevention strategies. Clinical trial registered with www.clinicaltrials.gov (NCT00889772).

Original languageEnglish (US)
Pages (from-to)462-470
Number of pages9
JournalAmerican journal of respiratory and critical care medicine
Issue number4
StatePublished - Feb 15 2011


  • Acute respiratory failure
  • Adult
  • Prediction model
  • Prevention
  • Respiratory distress syndrome

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine
  • Critical Care and Intensive Care Medicine


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