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Comparison of in-hospital versus 30-day mortality assessments for selected medical conditions.

Borzecki AM, Christiansen CL, Chew P, Loveland S, Rosen AK. Comparison of in-hospital versus 30-day mortality assessments for selected medical conditions. Medical care. 2010 Dec 1; 48(12):1117-21.

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BACKGROUND: In-hospital mortality measures such as the Agency for Healthcare Research and Quality (AHRQ) Inpatient Quality Indicators (IQIs) are easily derived using hospital discharge abstracts and publicly available software. However, hospital assessments based on a 30-day postadmission interval might be more accurate given potential differences in facility discharge practices. OBJECTIVES: To compare in-hospital and 30-day mortality rates for 6 medical conditions using the AHRQ IQI software. METHODS: We used IQI software (v3.1) and 2004-2007 Veterans Health Administration (VA) discharge and Vital Status files to derive 4-year facility-level in-hospital and 30-day observed mortality rates and observed/expected ratios (O/Es) for admissions with a principal diagnosis of acute myocardial infarction, congestive heart failure, stroke, gastrointestinal hemorrhage, hip fracture, and pneumonia. We standardized software-calculated O/Es to the VA population and compared O/Es and outlier status across sites using correlation, observed agreement, and kappas. RESULTS: Of 119 facilities, in-hospital versus 30-day mortality O/E correlations were generally high (median: r = 0.78; range: 0.31-0.86). Examining outlier status, observed agreement was high (median: 84.7%, 80.7%-89.1%). Kappas showed at least moderate agreement (k > 0.40) for all indicators except stroke and hip fracture (k 0.22). Across indicators, few sites changed from a high to nonoutlier or low outlier, or vice versa (median: 10, range: 7-13). CONCLUSIONS: The AHRQ IQI software can be easily adapted to generate 30-day mortality rates. Although 30-day mortality has better face validity as a hospital performance measure than in-hospital mortality, site assessments were similar despite the definition used. Thus, the measure selected for internal benchmarking should primarily depend on the healthcare system's data linkage capabilities.

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