SpringerOpen Newsletter

Receive periodic news and updates relating to SpringerOpen.

Open Access Highly Accessed Original Research

Lung injury prediction score for the emergency department: first step towards prevention in patients at risk

Marie-Carmelle Elie-Turenne12*, Peter C Hou3456, Aya Mitani1457, Jonathan M Barry45, Erica Y Kao1535, Jason E Cohen1689, Gyorgy Frendl17567, Ognjen Gajic10111218, Nina T Gentile13 and On Behalf of US Critical Illness and Injury Trials Group: Lung Injury Prevention Study Investigators (USCIITG–LIPS 1

  • * Corresponding author: Marie-Carmelle Elie-Turenne elie@ufl.edu

Author Affiliations

1 Department of Emergency Medicine, University of Florida College of Medicine, PO Box 100186, 1329 SW 16th Street, Gainesville, FL 32610, USA

2 Emergency Department, Shands University of Florida Medical Center, Gainesville, FL, USA

3 Department of Emergency Medicine, Brigham and Women’s Hospital, 75 Francis Street, Boston, MA 02115, USA

4 Division of Burn, Trauma, and Surgical Critical Care, Brigham and Women’s Hospital, Boston, MA, USA

5 Surgical Intensive Care Unit Translational Research (STAR) Center, Brigham and Women’s Hospital, Boston, MA, USA

6 Harvard Medical School, Department of Emergency Medicine & Division of Burn, Trauma, and Surgical Critical Care, Department of Surgery, Brigham and Women’s Hospital, 75 Francis Street, Neville House 312-B, Boston, MA 02115, USA

7 Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Boston, MA, USA

8 Department of Emergency Medicine, Albany Medical Center, Albany, NY, USA

9 Albany Medical College, Albany, NY, USA

10 Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA

11 Multidisciplinary Epidemiology and Translational Research in Intensive Care (METRIC), Mayo Clinic, Rochester, MN, USA

12 Mayo Medical School, Rochester, MA, USA

13 Department of Emergency Medicine, Temple School of Medicine, Philadelphia, PA, USA

14 Department of Medicine, Stanford Hospitals and Clinincs, 300 Pasteur Drive, Room: S102, MC: 5110, Stanford, CA 94305, USA

15 F. Edward Hebert School of Medicine, Uniform Services University of the Health Sciences, 4301 Jones Bridge Road, Bethesda, MD 20814-4712, USA

16 Albany Medical Center Emergency Medicine Group, 47 New Scotland Avenue, MC 139, Albany, NY 12208, USA

17 Department of Anesthesiology Perioperative and Pain Medicine, Brigham and Women’s Hospital, 75 Francis Street, Boston, MA 02115, USA

18 Mayo Clinic, Pulmonary and Critical Care Medicine, Old Marian Hall, Second Floor, Room 115, 200 First St. SW, Rochester, MN 5590, USA

For all author emails, please log on.

International Journal of Emergency Medicine 2012, 5:33  doi:10.1186/1865-1380-5-33

Published: 3 September 2012

Abstract

Background

Early identification of patients at risk of developing acute lung injury (ALI) is critical for potential preventive strategies. We aimed to derive and validate an acute lung injury prediction score (EDLIPS) in a multicenter sample of emergency department (ED) patients.

Methods

We performed a subgroup analysis of 4,361 ED patients enrolled in the previously reported multicenter observational study. ED risk factors and conditions associated with subsequent ALI development were identified and included in the EDLIPS model. Scores were derived and validated using logistic regression analyses. The model was assessed with the area under the receiver-operating curve (AUC) and compared to the original LIPS model (derived from a population of elective high-risk surgical and ED patients) and the Acute Physiology and Chronic Health Evaluation (APACHE II) score.

Results

The incidence of ALI was 7.0% (303/4361). EDLIPS discriminated patients who developed ALI from those who did not with an AUC of 0.78 (95% CI 0.75, 0.82), better than the APACHE II AUC 0.70 (p ≤ 0.001) and similar to the original LIPS score AUC 0.80 (p = 0.07). At an EDLIPS cutoff of 5 (range −0.5, 15) positive and negative likelihood ratios (95% CI) for ALI development were 2.74 (2.43, 3.07) and 0.39 (0.30, 0.49), respectively, with a sensitivity 0.72(0.64, 0.78), specificity 0.74 (0.72, 0.76), and positive and negative predictive value of 0.18 (0.15, 0.21) and 0.97 (0.96, 0.98).

Conclusion

EDLIPS may help identify patients at risk for ALI development early in the course of their ED presentation. This novel model may detect at-risk patients for treatment optimization and identify potential patients for ALI prevention trials.