Lung injury prediction score for the emergency department: first step towards prevention in patients at risk
| Autor: | Ognjen Gajic, Nina T. Gentile, Gyorgy Frendl, Marie-Carmelle Elie-Turenne, Aya A. Mitani, Jason Cohen, Jonathan M Barry, Erica Y Kao, Peter C. Hou |
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| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Předmět: |
medicine.medical_specialty
education.field_of_study APACHE II business.industry Incidence (epidemiology) Population Subgroup analysis Emergency department Lung injury Logistic regression 3. Good health 03 medical and health sciences 0302 clinical medicine 030228 respiratory system Internal medicine Emergency medicine Emergency Medicine medicine Observational study 030212 general & internal medicine business education Original Research |
| Zdroj: | International Journal of Emergency Medicine |
| ISSN: | 1865-1380 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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