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VOLUME 50 | NUMBER 4.1 | AUGUST 2015

Risk-Adjusted In-Hospital Mortality Models for Congestive Heart Failure and Acute Myocardial Infarction: Value of Clinical Laboratory Data and Race/Ethnicity

Keywords: In-hospital mortality; risk-adjusted model; congestive heart failure; acute myocardial infarction

Objective: To examine the impact of key laboratory and race/ethnicity data on the prediction of in-hospital mortality for congestive heart failure (CHF) and acute myocardial infarction (AMI).

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 patients with CHF (n = 5,718) and AMI (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 Patient Identifier was used to link patient-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 Patient 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 CHF and AMI. Laboratory data provide objective evidence of a patient's condition and therefore are accurate determinants of a patient's risk of mortality. Adding race/ethnicity information helps further explain the differences in in-hospital mortality.

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