BACKGROUND: This study evaluated the impact of clinical features and concomitant conditions on the clinical selection of different renin-angiotensin system (RAS) inhibitors in patients with hypertension, and built a renin-angiotensin inhibitors selection model (RAISM) to provide a reference for clinical decision making.

MATERIAL AND METHODS: We included 213 hypertensive patients in the study cohort; patients were divided into two groups: the angiotensin-converting enzyme inhibitor (ACEI) combined with calcium channel blocker (CCB) group (ACEI+CCB group) and the angiotensin receptor antagonist (ARB) combined with CCB group (ARB+CCB group). Basic demographic characteristics and concomitant conditions of the patients were compared. Single-factor and multi-factor analysis was performed by adopting logistic regression model. The RAISM was established by utilizing the nomograph technology. C-index and calibration curve were used to evaluate the model’s efficacy.

RESULTS: In the study, 34.27% of the patients used ACEI+CCB and 65.73% of patients used ARB+CCB. The difference in age, body mass index (BMI), elderly patient, diabetes, renal dysfunction, and hyperlipidemia between the 2 groups determined medication selection. To be specific, compared to the group using ARB+CCB, the odds ratios and 95% confidence interval (CI) of the aforementioned factors for the ACEI+CCB group were 0.476 (0.319–0.711), 1.274 (1.001–1.622), 0.365 (0.180–0.743), 0.471 (0.203–1.092), 0.542 (0.268–1.094), and 0.270 (0.100–0.728), respectively; The C-index of RAISM acquired from the model construction parameters was 0.699, and the correction curve demonstrated that the model has good discriminative ability.

CONCLUSIONS: The outcome of our study suggests that independent discriminating factors that influence the clinical selection of different RAS inhibitors were elderly patient, renal insufficiency, and hyperlipidemia; and the RAISM constructed in this study has good predictability and clinical benefit.

Keywords: Artificial Intelligence, Decision Support Systems, Clinical, Hypertension, Knowledge management, Medication Therapy Management, angiotensin receptor antagonists, Angiotensin-Converting Enzyme Inhibitors, Antihypertensive Agents, Calcium Channel Blockers, Clinical Decision-Making, Drug Therapy, Combination, Models, Theoretical, Renin-Angiotensin System