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Designing Risk-Adjusted Therapy for Patients with Hypertension

机译:设计高血压患者的风险调整疗法

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Limited guidance is available for providing patient-specific care to hypertensive patients, although this chronic condition is the leading risk factor for cardiovascular diseases. To address this issue, we develop an analytical model that takes into account the most relevant risk factors including age, sex, blood pressure, diabetes status, smoking habits, and blood cholesterol. Using the Markov Decision Process framework, we develop a model to maximize expected quality-adjusted life years, as well as characterize the optimal sequence and combination of antihypertensive medications. Assuming the physician uses the standard medication dose for each drug, and the patient fully adheres to the prescribed treatment regimen, we prove that optimal treatment policies exhibit a threshold structure. Our findings indicate that our recommended thresholds vary by age and other patient characteristics, for example (1) the optimal thresholds for all medication prescription are nonincreasing in age, and (2) the medications need to be prescribed at lower thresholds for males who smoke than for males who have diabetes. The improvements in quality-adjusted life years associated with our model compare favorably with those obtained by following the British Hypertension Society's guideline, and the gains increase with the severity of risk factors. For instance, in both genders (although at different rates), diabetic patients gain more than non-diabetic patients. Our sensitivity analysis results indicate that the optimal thresholds decrease if the medications have lower side-effects and vice versa.
机译:尽管为慢性病是心血管疾病的主要危险因素,但对于为高血压患者提供针对患者的护理的指导很少。为了解决这个问题,我们开发了一种分析模型,该模型考虑了最相关的风险因素,包括年龄,性别,血压,糖尿病状况,吸烟习惯和胆固醇。使用马尔可夫决策过程框架,我们开发了一个模型,以最大化预期的质量调整寿命,并确定抗高血压药物的最佳顺序和组合。假设医生对每种药物使用标准药物剂量,并且患者完全遵守规定的治疗方案,那么我们证明最佳治疗策略具有阈值结构。我们的发现表明,我们建议的阈值随年龄和其他患者特征而异,例如(1)所有药物处方的最佳阈值都不会随着年龄的增长而增加,(2)吸烟男性的处方阈值应低于较低阈值适用于患有糖尿病的男性。与我们的模型相关的质量调整生命年的改善与遵循英国高血压协会的指南所获得的改善相比,且随着风险因素的严重性而增加。例如,在两种性别中(尽管比率不同),糖尿病患者比非糖尿病患者受益更多。我们的敏感性分析结果表明,如果药物的副作用较小,则最佳阈值会降低,反之亦然。

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