By using the Registry for Catastrophic Illness database from a national health insurance program in Taiwan, we obtained demographic data, medication history, and diagnostic codes based on the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM; www.icd9-data.com/2007) for the analyses. In the current study, we included ESRD subjects undergoing hemodialysis or peritoneal dialysis, and ≥ 18 years between 1995 and 2008. We excluded subjects with history of acute coronary syndrome (ACS) or previous stroke before dialysis. All enrolled subjects were followed from 1995 to 2009 and the medium follow-up time was 1428 days. By reviewing of the pharmacy prescription database, we also gathered information on prescribed drugs, dosage, and duration. A total of 607 dialysis patients with ADPKD were enrolled and, among them, 193 patients were excluded if ever experienced with CVD before starting dialysis while 27 subjects were excluded due to follow-up less than 3 months. Finally, there were 387 subjects included in the final analyses. The designed patient flow diagram is shown in Figure 1. The study was approved by the Research Ethics Committee of the National Taiwan University Hospital, Taipei, Taiwan. All methods were carried out in accordance with relevant guidelines and regulations.
Comorbidities and outcomes:
After the index use of an ACEI or an ARB, we defined the comorbidities by searching the database for the presence of hypertension (ICD-9-CM codes: 401.X–405.X), diabetes mellitus (250.X, 249.X), hyperlipidemia (272.X), CVD events including coronary artery disease (411.X– 414.X, V17.3, V81.0), atrial fibrillation (427.31, 427.3), valvular heart disease (394.X-396.X,) and liver cirrhosis (571.X, 572.X). The endpoints of the present study were death, new onset ACS (410.X, A270, 411.1), coronary intervention (CI): percutaneous coronary intervention (00.66, 36.0X), ischemic stroke: (434.X, A293, A292), hemorrhagic stroke: (430.X, 431.X, 432.X), peripheral arterial disease (250.7, 443.X, 444.2), heart failure (428.0–428.3, 428.9).
Propensity score-based matching
Propensity score (PS) matching is a statistical technique used to control the covariates to make two groups more comparable in observation study. In the current study, the PS dependent variable was receiving ACEI/ARB treatment or not. Other covariates, such as age, gender, hypertension, DM, dyslipidemia, comorbidities, and medications (antiplatelet, warfarin, beta-blocker, Statin), were put into a non-parsimonious logistic regression model. Participants were excluded from further analysis if an appropriate PS match could not be found. In the final analysis, the remaining subjects composed a matched 1:1 or 1:2 according to the original case number in each group.
All analyses were performed with SPSS 15.0 for WINDOWS 7 (SPSS Inc., Chicago, IL, USA). Student’s t test was performed to compare continuous variables while chi-squared test was used to test categorical covariates. Fisher’s exact test was used instead for categorical variable if any expected value within a 2x2 table was below 5. Cox’s proportional hazard models to adjust covariates including age, gender, risk profile, and medications were used to estimate the risk of outcome associated with and without taking ACEI/ARB. The event-free survival time was defined as the time from the day of dialysis therapy to an endpoint. If an event did not occur, the case was regarded as censorship at the end of the study, withdraws from the insurance, loss contact, and receiving kidney transplantation. Moreover, we performed a subgroup analysis by including patients with more aggressive ACEI/ARB treatment (defined as ACEI/ARB treatment over 50% of the follow-up period) to test the consistency. Kaplan–Meier curves were performed to show the event-free survival trend between subjects with and without taking ACEI/ARB and tested by log-rank test. A P value < 0.05 was considered statistically significant in all analyses.