Acute respiratory distress syndrome screening

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Editor-In-Chief: C. Michael Gibson, M.S., M.D. [1]; Associate Editor(s)-in-Chief: Brian Shaller, M.D. [2]


Several screening tools have been devised to aid in the early detection of acute respiratory distress syndrome including Early Acute Lung Injury (EALI), ALI sniffer, and Lung Injury Prediction Study (LIPS) score.


Several screening tools have been proposed and validated for early recognition of progression to ARDS. However, no single biomarker is currently specific or sensitive enough to be incorporated into routine clinical practice.

Levitt et al. proposed the criteria for "Early Acute Lung Injury (EALI)" which include: (1) hospital admission with bilateral opacities on chest radiograph; (2) the absence of isolated left atrial hypertension; and (3) the need for > 2 L/min of supplemental oxygen.[1] The EALI was 73% sensitive and 79% specific for progression to ALI.

Thakur et al. developed and validated an ALI screening tool ("ALI sniffer") based on the American-European Consensus Conference definition using an electronic medical record that facilitates early recognition of specific criteria.[2] The tool demonstrated a sensitivity of 96.3% and a specificity of 89.4%, with a positive predictive value of 46.0% and a negative predictive value of 99.6%.

Trillo-Alvarez et al. devised the Lung Injury Prediction Study (LIPS) score to identify patients at high risk for ALI or ARDS before ICU admission by utilizing parameters that are clearly defined and routinely available in the medical record.[3] Covariates used in model derivation include predisposing conditions (trauma, high-risk surgery, sepsis, shock, pneumonia, aspiration, and pancreatitis) and risk-modifiers (tachypnea, alcohol abuse, hypoalbuminemia, oxygen supplementation, chemotherapy, diabetes mellitus, and smoking history). The LIPS score efficiently discriminated patients who developed ALI from those who did not, with an area under the ROC curve (AUC) of 0.84. The performance of the LIPS score was consistent in a multicenter cohort study with an AUC of 0.80 while maintaining an appropriate negative predictive value of 97% for a screening tool.[4]


  1. Levitt, Joseph E., Harmeet Bedi, Carolyn S. Calfee, Michael K. Gould, and Michael A. Matthay. “Identification of Early Acute Lung Injury at Initial Evaluation in an Acute Care Setting prior to the Onset of Respiratory Failure.” Chest 135, no. 4 (April 2009): 936–43. doi:10.1378/chest.08-2346.
  2. Herasevich, Vitaly, Murat Yilmaz, Hasrat Khan, Rolf D. Hubmayr, and Ognjen Gajic. “Validation of an Electronic Surveillance System for Acute Lung Injury.” Intensive Care Medicine 35, no. 6 (June 2009): 1018–23. doi:10.1007/s00134-009-1460-1.
  3. Trillo-Alvarez, C., R. Cartin-Ceba, D. J. Kor, M. Kojicic, R. Kashyap, S. Thakur, L. Thakur, V. Herasevich, M. Malinchoc, and O. Gajic. “Acute Lung Injury Prediction Score: Derivation and Validation in a Population-Based Sample.” European Respiratory Journal 37, no. 3 (March 1, 2011): 604–9. doi:10.1183/09031936.00036810.
  4. Gajic, Ognjen, Ousama Dabbagh, Pauline K. Park, Adebola Adesanya, Steven Y. Chang, Peter Hou, Harry Anderson, et al. “Early Identification of Patients at Risk of Acute Lung Injury: Evaluation of Lung Injury Prediction Score in a Multicenter Cohort Study.” American Journal of Respiratory and Critical Care Medicine 183, no. 4 (February 15, 2011): 462–70. doi:10.1164/rccm.201004-0549OC.