ArticleMedical science monitor : international medical journal of experimental and clinical research2020
Development and Validation of a Medication Selection Model Under Clinical Application of Renin-Angiotensin Inhibitor Combined with Calcium Channel Blocker for Hypertension Patients.
Article in Medical science monitor : international medical journal of experimental and clinical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors at 3 institutions in 2 countries.
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Abstract
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.
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