The Measurement Of Hospital Performance For Acute Myocardial Infarction And Estimating A Composite Measure Of Hospital Quality Based On A Bayesian Hierarchical Latent Variable Model

Objective: Acute myocardial infarction has a high incidence and fatality rate,and has serious damage to health and life. Improve quality of care is one of the mostimportant means reduce the burden of disease and the economic burden of, but thereis not complete quality evaluation system in China. This research is combined withChinese medical environment, to solve the problems in the quality evaluation, studythe whole evaluation process. We try to build up a set of quality indicators for patientswith acute myocardial infarction, to define each indicator in detailed, analysiscorrelation between care processes and with the patient outcome, to calculate hospitalstandardized rates for fair comparison, study a composite measure of hospital qualitybased on a bayesian hierarchical latent variable model (BLVM).Methods: A literature review practice guidelines of acute myocardial infarctionidentified existing quality indicators for AMI care. A list of potential indicators wasassessed by a panel of clinicians from a variety of disciplines using amodified-Delphipanel process, form the final indicators; Collected clinical data form20hospitals, and,used multi-level logistic regression model to calculate risk adjusted rate by using theidea of risk adjustment; Calculated composite quality index by weight sum;Comprehensive evaluation used the bayesian hierarchical latent variable model:assume a latent variable that decide the hospital the utilization rate of each indicators,the latent variables is unobservable quality of care, build on a latent variables modelwith random effects, the use of bayesian inference estimates posterior parameters,estimated a composite measure of hospital quality based on BLVM.Results: The main research results as follows: Eighty-five potential indicators for AMI care were established. In the first roundof consultation,25indicators were deleted; the rest of the60were reviewed byexperts. The second round of consultation, experts rated each indicator according tothe following six criteria; there are23indicators into the next round process. The thirdround of consultation, after the expert face-to-face discussion, the second round of23indicators had good reliability and feasibility, including three structure indicators,fifteen process indicators and five outcome indicators.We collected2203patients with AMI from20first-class hospitals, can actuallyget data in10process indicators and1outcame indicators. Clopidogrel and statinshave higher utilization rates, thrombolysis drug and percutaneous coronaryintervention had high-usage. Rates for care process indicators between differenthospitals had a great difference; the mortality between hospitals also had a variance,risk-adjusted mortality from4.54%to11.62%,51%of the variation was from thequality of care,49%of the variation was caused by mixed factors; there is acorrelation between different indicators, aspirin and beta blockers (r=0.61), aspirinand angiotensin-converting enzyme inhibitors (ACEI)(r=0.50), clopidogrel andthrombolysis (r=0.53), clopidogrel and percutaneous coronary intervention (PCI)(r=0.47), clopidogrel and coronarography (r=0.45), statins and coronarography (r=0.50), PCI and coronarography (r=0.91) are significant; Weighted composite measureshows: the third hospital had best quality of care best, the17th hospital was the worst.We chose9indicators to build overall evaluation model. The compare betweenmodels showed that BLVM had better convergence than other models. The posteriordistribution of parameters in the model indicated that all the indicators had positivecorrelation with quality of care besides in-hospital mortality and thrombolytic.95%confidence interval (CI) of9indicators’ posterior regression coefficient showed thatthe relationship between PCI, coronarography, thrombolytic, clopidogrel, statins andquality of care was statistically significant, and the correlation between PCI, coronarography and quality of care was stronger; aspirin, beta blockers, ACEI andstatins had bigger random effect. All the hospitals were divided into three groupsbased on the95%CI of quality of care index which was calculated through BLVM.The quality of care of hospital3,19and16was higher than average, in contrast,hospital2,10and17was lower than average.Conclusion: We have developed a set of quality indicators for patients with AMI,including23indicators. It had been demonstrated that11of23indicators can becollected through medical record. There was large variation on the use of processindicators and in-hospital mortality between hospitals. The method of risk-adjustmentprovides equity to compare hospitals due to adjusting confounding factors. BLVMallowed for random effect is more suitable for overall evaluation of quality ofhealthcare. It not only provided statistical theoretical framework to integratemulti-dimension indicators into a synthetic indicator, but also inferred compositeindexes and their ranks and accounted for the sources of hospitals variation. Inconclusion, BLVM is more suitable for overall evaluation of quality of healthcare, andit lays statistical theory foundation for the calculation of composite index.