A formula for prediction of posttraumatic XXXX based on early anatomic and physiologic parameters.

@entity1 @entity55 @entity772 @entity2489 @entity3813 @entity1566 @entity130
BACKGROUND: Identification of risks for development of @entity2489 ( @entity2489 ), which might be identified early after injury, would allow for prognostic estimates and targeting of high-risk cohorts for clinical trials of preventive strategies. This study was performed to develop an equation that can be applied to estimate the probability of @entity55 based on parameters collected in the early postinjury interval. METHODS: Over a 28-month period, @entity1 admissions were reviewed for mechanism and severity of injury, patterns of injury, shock, and need for emergent intubation. Early deaths (<48 hours) were excluded. @entity2489 diagnosis required > or = 10(5) colony-forming units/mL organisms in the bronchoalveolar lavage effluent. Multiple logistic regression analysis was used to develop the prediction equation and estimate odds ratios. The equation was then tested on consecutive @entity1 admitted over a 2-month period. RESULTS: We reviewed 9,721 admissions (77% blunt, 23% penetrating). @entity2489 incidence was 5.6%. Overall mortality was 2% (21% for @entity1 with @entity2489 vs. 1% for no @entity2489 ; p < 0.0001). Multiple logistic regression analysis for all @entity1 produced the following equation: f(x) = -3.08 - 1.56 (MOI) - 0.12 (GCS) + 1.37 (SCI) + 0.30 (chest AIS) + 1.87 ( @entity3813 ) + 0.67 (tx) + 0.05 (ISS) + 0.66 (int), where MOI is mechanism of injury (penetrating = 1, blunt = 0), GCS is Glasgow @entity772 Scale score, SCI is @entity1566 (yes = 1, no = 0), @entity3813 is emergent laparotomy (yes = 1, no = 0), ISS is Injury Severity Score, tx is units of blood transfused in the resuscitation room, and int is intubation in either the field or the resuscitation room (yes = 1, no = 0). The probability of @entity2489 was calculated as follows: P( @entity2489 ) = e(f)(x)/1 + e(f)(x). This formula was concordant in 95% and discordant in 5%. CONCLUSION: It is possible to accurately predict risk for @entity2489 in @entity130 @entity1 based on data available early after injury. This calculation could be useful for counseling families relative to prognosis and research protocols, and addressing hospitalization issues with third-party payors.
