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


OBJECTIVE
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.
Center for Health Quality, Outcomes, and Economic Research, Bedford VAMC, Bedford, MA 01730, USA. amb@bu.edu
Ann M
AM
Borzecki
Cindy L
CL
Christiansen
Priscilla
P
Chew
Susan
S
Loveland
Amy K
AK
Rosen
D019985:Benchmarking / Q000706:statistics & numerical data*; D050723:Fractures, Bone / Q000401:mortality; D006471:Gastrointestinal Hemorrhage / Q000401:mortality; D006333:Heart Failure / Q000401:mortality; D017052:Hospital Mortality; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data*; D009203:Myocardial Infarction / Q000401:mortality; D017063:Outcome Assessment, Health Care; D011014:Pneumonia / Q000401:mortality; D019984:Quality Indicators, Health Care / Q000706:statistics & numerical data*; D020521:Stroke / Q000401:mortality; D014481:United States; D016270:United States Agency for Healthcare Research and Quality