Costs of payment in Thai acute coronary syndrome patients.

BACKGROUND: Acute coronary syndrome (ACS) is a major health care syndrome that can financially burden patients throughout the world, including Thailand. Few studies purposed estimating the costs of treatment. The data from the ACS registry database represented the costs of hospital charges paid by ACS patients. Although these were not the actual treatment costs, the authors can approximately estimate the total expenditure for the first admission. OBJECTIVES: First, calculate the cost of ACS to the patients, including diagnostic, demographic data, treatment modalities, type of payers, hospital profile, and outcomes. Second, find the appropriate model to identify the independent factors for predicting the treatment costs. MATERIAL AND METHOD: The present study collected data from the second and third phase of a national multicenter prospective registry of ACS in Thailand, Thai ACS registry (TACSR). 3,552 patients with new onset of ACS were analyzed. RESULTS: Median age was 67 years (range 26.5-105.5) with predominately male and median length of stay (LOS) was 7 days (range, 1-184). 42% referred from other hospitals. The median cost of the total population was 47,908 baht (range, 633-1,279,679). When classified into those of STEMI, NSTEMI, and UA, the costs were 82,848.5, 40,531 and 26,116 baht respectively, p < 0.0001. Patients in the government hospital had to pay the total cost with PCI and CABG, 152,081-161,374 baht and 203,139-223,747 baht respectively, while the private hospital charged almost twice as much. For the types of payers, private insurance including private employee security fund paid significantly more than others. Costs in patients paid by "30 baht na tional health scheme and social security fund" were significantly less than those of others. For modality of treatment in STEMI, primary PCI was significantly more costly than thrombolytics and no reperfusion therapy, 161,096.5 vs. 60,043.0 and 33,335.0 baht respectively p < 0.0001. Early invasive groups in NSTEMI/UA had much higher median costs 145,794.0 baht when compared to those of the conservative group, 47,908 baht, p < 0.0001. Two multiple linear regression models according to the diagnostic group identified the independent factors for predicting cost. PCI, LOS, CABG, admission in a private hospital, Death, GPIIb/IlIa inhibitors use, major bleeding, coronary angiogram, thrombolytics use, age and diabetes were independent predictors for the cost in STEMI patients, R2 = 0.58. For those of NSTEMI/UA, the independent predictors for the cost were PCI, LOS, CABG, admission in a private hospital, death, GP IIb/IIIa inhibitors use, major bleeding, coronary angiogram, age, ventricular arrhythmia, CHF and referred patients, R2 =0.62. CONCLUSION: Costs in ACS patients were markedly different among diagnostic groups. The clinical risk factors were hospital type, type of payers, referred system, treatment procedures, drugs used and complications including outcome. Some of these factors could independently predict the costs.