Risk-Adjusted In-Hospital Mortality Models for XXXX and Acute @entity583 : Value of Clinical Laboratory Data and Race/Ethnicity.

@entity1 @entity583 @entity712
OBJECTIVE: To examine the impact of key laboratory and race/ethnicity data on the prediction of in-hospital mortality for @entity712 ( @entity712 ) and @entity583 ( @entity583 ). DATA SOURCES: Hawaii adult hospitalizations database between 2009 and 2011, linked to laboratory database. STUDY DESIGN: Cross-sectional design was employed to develop risk-adjusted in-hospital mortality models among @entity1 with @entity712 (n = 5,718) and @entity583 (n = 5,703). DATA COLLECTION/EXTRACTION METHODS: Results of 25 selected laboratory tests were requested from hospitals and laboratories across the state and mapped according to Logical Observation Identifiers Names and Codes standards. The laboratory data were linked to administrative data for each discharge of interest from an all-payer database, and a Master @entity1 Identifier was used to link @entity1 -level encounter data across hospitals statewide. PRINCIPAL FINDINGS: Adding a simple three-level summary measure based on the number of abnormal laboratory data observed to hospital administrative claims data significantly improved the model prediction for inpatient mortality compared with a baseline risk model using administrative data that adjusted only for age, gender, and risk of mortality (determined using 3M's All @entity1 Refined Diagnosis Related Groups classification). The addition of race/ethnicity also improved the model. CONCLUSIONS: The results of this study support the incorporation of a simple summary measure of laboratory data and race/ethnicity information to improve predictions of in-hospital mortality from @entity712 and @entity583 . Laboratory data provide objective evidence of a @entity1 's condition and therefore are accurate determinants of a @entity1 's risk of mortality. Adding race/ethnicity information helps further explain the differences in in-hospital mortality.
