Derivation of a prediction rule for post-traumatic acute lung injury
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AbstractThe purpose of this study was to derive an early, highly sensitive and specific prediction rule for the development of post-traumatic acute lung injury (ALI). In a prospective, non-interventional study a convenience sample of 92 adults admitted to the resuscitation room following blunt trauma was studied in order to derive this prediction rule. The study was conducted in the emergency department of a university hospital in the New Territories of Hong Kong. One emergency physician assessed each patient for 25 variables, which might predict post-traumatic ALI, and the primary outcome measure was the presence or absence of ALI 48 h post-injury. Eleven variables associated with ALI were entered into a classification and regression tree (CART) in order to derive models predictive of ALI. Two models were developed and used to derive the decision rule. Acute lung injury was likely if either: (1) the patient had an ISS > 27 and a haematocrit < 0.37, or (2) the patient had a haematocrit < 0.36 and a total leucocyte count > 15. The first guideline had a classification rate of 96.7% (95% confidence interval (CI), 90.8-99.3%), a sensitivity of 100% (CI 65.2-100%) and a specificity of 96.5% (CI 90.0-99.3%). The second guideline had a classification rate of 96.7% (CI 90.8-99.3%), a sensitivity of 85.7% (CI 42.1-99.6%) and a specificity of 97.7% (CI 91.8-99.7%). Practical highly sensitive and specific prediction guidelines for post-traumatic acute lung injury have been derived and now require prospective validation. (C) 1999 Elsevier Science Ireland Ltd. All rights reserved.
All Author(s) ListRainer TH, Lam PKW, Wong EMC, Cocks RA
Journal nameResuscitation
Volume Number42
Issue Number3
Pages187 - 196
LanguagesEnglish-United Kingdom
Keywordsacute lung injury; multiple trauma; prediction rules; respiratory distress syndrome; wounds and injuries
Web of Science Subject CategoriesCritical Care Medicine; CRITICAL CARE MEDICINE; Emergency Medicine; EMERGENCY MEDICINE; General & Internal Medicine

Last updated on 2020-13-10 at 02:26