Derivation and validation of the In-hospital Mortality for XXXX using Claims daTa (IMPACT) prediction rule.

@entity1 @entity1151
INTRODUCTION: Existing prediction rules for prospectively prognosticating early mortality following @entity1151 ( @entity1151 ) require clinical and/or laboratory data, and are rarely suitable for claims database analyses. We sought to develop a claims-based prediction rule that retrospectively classifies @entity1151 @entity1 into low- or higher-risk in-hospital mortality categories. MATERIALS AND METHODS: We randomly assigned MarketScan database @entity1 admitted for @entity1151 between April 2010 and September 2013 into derivation (80%) and validation (20%) cohorts. A prediction rule (In-hospital Mortality for @entity1151 using Claims daTa or IMPACT) was derived using multivariable logistic regression, with in-hospital mortality as the dependent variable and demographic/comorbidity data available in claims databases as independent variables. In-hospital mortality rates for low- and higher-risk @entity1 were compared across the derivation and validation cohorts, and prediction rule performance was assessed by evaluating sensitivity and specificity estimates. RESULTS: A total of 27,833 @entity1 admitted for @entity1151 were included. The IMPACT rule consisted of 12 risk factors, and categorized 46% of @entity1 as low-risk in both cohorts. @entity1 classified as low-risk by IMPACT (defined as an estimated in-hospital mortality risk  <=1.5%) had average in-hospital mortality rates of 0.81% (95% confidence interval [CI], 0.65-1.00) in the derivation and 0.77% (95% CI, 0.50-1.18) in the validation cohort. Higher-risk @entity1 had average in-hospital mortality rates of 4.61% (95% CI, 4.25-5.01) and 5.02% (95% CI, 4.30-5.85), respectively. The IMPACT rule had high sensitivity for classifying in-hospital mortality risk (0.87 in both cohorts), but moderate specificity (0.47 for both cohorts). LIMITATIONS: We were unable to assess 30 day mortality as an endpoint. IMPACT was only validated in an internal sample. CONCLUSIONS: The IMPACT prediction rule may be able to retrospectively classify @entity1151 @entity1 ' in-hospital mortality risk with high sensitivity and moderate specificity.
