30-day in-hospital mortality after acute myocardial infarction in Tuscany (Italy): an observational study using hospital discharge data

Abstract

Background: Coronary heart disease is the leading cause of mortality in the world. One of the outcome indicators recently used to measure hospital performance is 30-day mortality after acute myocardial infarction (AMI). This indicator has proven to be a valid and reproducible indicator of the appropriateness and effectiveness of the diagnostic and therapeutic process for AMI patients after hospital admission. The aim of this study was to examine the determinants of inter-hospital variability on 30-day in-hospital mortality after AMI in Tuscany. This indicator is a proxy of 30-day mortality that includes only deaths occurred during the index or subsequent hospitalizations.

Methods: The study population was identified from hospital discharge records (HDRs) and included all patients with primary or secondary ICD-9-CM codes of AMI (ICD-9 codes 410.xx) that were discharged between January 1, 2009 and November 30, 2009 from any hospital in Tuscany. The outcome of interest was 30-day all-cause in-hospital mortality, defined as a death occurring for any reason in the hospital within 30 days of the admission date. Because of the hierarchical structure of the data, with patients clustered into hospitals, random-effects (multilevel) logistic regression models were used. The models included patient risk factors and random intercepts for each hospital.

Results: The study included 5,832 patients, 61.90% male, with a mean age of 72.38 years. During the study period, 7.99% of patients died within 30 days of admission. The 30-day in-hospital mortality rate was significantly higher among patients with ST segment elevation myocardial infarction (STEMI) compared with those with non-ST segment elevation myocardial infarction (NSTEMI). The multilevel analysis which included only the hospital variance showed a significant inter-hospital variation in 30-day in-hospital mortality. When patient characteristics were added to the model, the hospital variance decreased. The multilevel analysis was then carried out separately in the two strata of patients with STEMI and NSTEMI. In the STEMI group, after adjusting for patient characteristics, some residual inter-hospital variation was found, and was related to the presence of a cardiac catheterisation laboratory.

Conclusion: We have shown that it is possible to use routinely collected administrative data to predict mortality risk and to highlight inter-hospital differences. The distinction between STEMI and NSTEMI proved to be useful to detect organisational characteristics, which affected only the STEMI subgroup.

Figure 1

RSMRs with 95% CIs by hospital. Red line: overall 30-day in-hospital mortality rate; green bars: significantly higher RSMRs compared with the overall 30-day in-hospital mortality rate. RSMRs with 95% CIs are not reported for hospitals with only one patient (# 36 and 37). Hospitals with a cardiac catheterisation laboratory are 1, 2, 3, 5, 6, 7, 8, 9, 13, 14 and 19. RSMR, Risk-standardised mortality rate.

Figure 2

RSMRs with 95% CIs by hospital (only patients with STEMI). Red line: overall 30-day in-hospital mortality rate; green bars: significantly higher RSMRs compared with the overall 30-day in-hospital mortality rate. RSMRs with 95% CIs are not reported for hospitals with only one patient (# 36 and 37). Hospitals with a cardiac catheterisation laboratory are 1, 2, 3, 5, 6, 7, 8, 9, 13, 14 and 19.

Figure 3

Funnel plot for unadjusted 30-day in-hospital mortality rate with 95% and 99.8% control limits (STEMI). Red line: overall 30-day in-hospital mortality rate; green lines: 95% control limits; grey lines: 99.8% control limits; labelled data points: hospitals falling above the upper 95% limit or below the lower 95% limit.

Figure 4

Funnel plot for unadjusted 30-day in-hospital mortality rate with 95% and 99.8% control limits (NSTEMI). Red line: overall 30-day in-hospital mortality rate; green lines: 95% control limits; grey lines: 99.8% control limits; labelled data points: hospitals falling above the upper 95% limit or below the lower 95% limit.