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Published byGwen Wiggins Modified over 6 years ago
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Dynamic treatment selection and medication for personalised blood pressure therapy using a Markov decision process model: a cost- effectiveness analysis Choi SE, Brandeau ML, Basu S. 1Department of Management Science and Engineering, Stanford University, Stanford, California, USA First published on BMJ Open in 2017 Piao Liying
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Contribution & framework
Proposed a approach to recommend detailed treatment suggestions (doses and types of medication). Framework
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Model formulation States: 3 actions:
Demographic information (age, sex, race) Patient’s CVD-related covariates (age-, sex- and race-dependent chronic kidney disease status, type 2 diabetes status, lipid profile and tobacco smoking status) 7 patient’s health state Well Adverse event without CVD history MI (심근경색 myocardial infraction ) Stroke Post CVD Adverse event with CVD history Death 3 actions: Stop a medication treatment Remain on the current medication treatment(s) and dose level(s) Change medication treatment (by increasing a dosage of a current medication, and/or changing the medication)
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Model formulation Objective: Reward
Maximizes the patient’s expected discounted quality-adjusted life years (QALYs) over a simulated time horizon. Reward Effect of drugs From one drug at standard dose in lowering blood pressure from a pre- treatment blood pressure P : ( (P−154)) From half standard doses : (R+n*0.078(P−150))
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Model formulation Reward Quality of life Costs
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Result QALYs in each disease state over patient lifetimes
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Result Total QALY gains over patient lifetimes for the Intensive JNC8 strategy and the MDP-based treatment strategy
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Result Differences in treatment (mean (SE)) and cost-effectiveness analysis
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Result Mean SBP levels of individuals achieved under each treatment strategy.
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Limitations Limitations of survey studies
Recall biases, acceptability biases and under-reporting that may lead to mis-estimation of baseline. Did not incorporate adherence parameters in the model.
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