The Quan algorithm using a 1-year lookback with primary and first secondary diagnoses adequately predicted in-hospital mortality in MI patients (c-statistic 0.701), though not significantly better than other algorithms.
Observational (n=20,280)
Does the Quan algorithm improve the prediction of in-hospital mortality compared to the Halfon and Sundararajan algorithms in patients with myocardial infarction?
The Quan algorithm using a 1-year lookback period with primary and first secondary diagnoses is adequate for predicting in-hospital mortality in MI patients using administrative claims data.
Effect estimate: c-statistic 0.701 (95% CI 0.671-0.739)
Absolute Event Rate: 0.701% vs 0.698%
OBJECTIVES: To compare the performance of three International Statistical Classification of Diseases, 10th Revision translations of the Charlson comorbidities when predicting in-hospital among patients with myocardial infarction (MI). METHODS: MI patients > or =20 years of age with the first admission during 2006 were identified(n=20,280). Charlson comorbidities were drawn from Heath Insurance Claims Data managed by Health Insurance Review and Assessment Service in Korea. Comparisions for various conditions included (a) three algorithms (Halfon, Sundararajan, and Quan algorithms), (b) lookback periods (1-, 3- and 5-years), (c) data range (admission data, admission and ambulatory data), and (d) diagnosis range (primary diagnosis and first secondary diagnoses, all diagnoses). The performance of each procedure was measured with the c-statistic derived from multiple logistic regression adjusted for age, sex, admission type and Charlson comorbidity index. A bootstrapping procedure was done to determine the approximate 95% confidence interval. RESULTS: Among the 20,280 patients, the mean age was 63.3 years, 67.8% were men and 7.1% died while hospitalized. The Quan and Sundararajan algorithms produced higher prevalences than the Halfon algorithm. The c-statistic of the Quan algorithm was slightly higher, but not significantly different, than that of other two algorithms under all conditions. There was no evidence that on longer lookback periods, additional data, and diagnoses improved the predictive ability. CONCLUSIONS: In health services study of MI patients using Health Insurance Claims Data, the present results suggest that the Quan Algorithm using a 1-year lookback involving primary diagnosis and the first secondary diagnosis is adequate in predicting in-hospital mortality.
Kyoung Hoon Kim (Fri,) conducted a observational in Myocardial infarction (n=20,280). Quan algorithm for Charlson Comorbidity Index vs. Halfon and Sundararajan algorithms was evaluated on Performance in predicting in-hospital mortality (c-statistic) (c-statistic 0.701, 95% CI 0.671-0.739). The Quan algorithm using a 1-year lookback with primary and first secondary diagnoses adequately predicted in-hospital mortality in MI patients (c-statistic 0.701), though not significantly better than other algorithms.