Designing Risk-Adjusted Therapy for Patients with Hypertension
成果类型:
Article
署名作者:
Zargoush, Manaf; Gumus, Mehmet; Verter, Vedat; Daskalopoulou, Stella S.
署名单位:
McMaster University; McGill University; McGill University
刊物名称:
PRODUCTION AND OPERATIONS MANAGEMENT
ISSN/ISSBN:
1059-1478
DOI:
10.1111/poms.12872
发表日期:
2018
页码:
2291-2312
关键词:
hypertension treatment
personalized healthcare
medical decision making
Markov decision processes
threshold policy
摘要:
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.