1. 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

2. Contribution & framework
Proposed a approach to recommend detailed treatment suggestions (doses and types of medication).
Framework

3. 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)

4. 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))

5. Model formulation
Reward
Quality of life
Costs

6. Result
QALYs in each disease state over patient lifetimes

7. Result
Total QALY gains over patient lifetimes for the Intensive JNC8 strategy and the MDP-based treatment strategy

8. Result
Differences in treatment (mean (SE)) and cost-effectiveness analysis

9. Result
Mean SBP levels of individuals achieved under each treatment strategy.

10. 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.