Cargando…

Dynamic treatment selection and modification for personalised blood pressure therapy using a Markov decision process model: a cost-effectiveness analysis

OBJECTIVE: Personalised medicine seeks to select and modify treatments based on individual patient characteristics and preferences. We sought to develop an automated strategy to select and modify blood pressure treatments, incorporating the likelihood that patients with different characteristics wou...

Descripción completa

Detalles Bibliográficos
Autores principales: Choi, Sung Eun, Brandeau, Margaret L, Basu, Sanjay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5695480/
https://www.ncbi.nlm.nih.gov/pubmed/29146652
http://dx.doi.org/10.1136/bmjopen-2017-018374
_version_ 1783280320660897792
author Choi, Sung Eun
Brandeau, Margaret L
Basu, Sanjay
author_facet Choi, Sung Eun
Brandeau, Margaret L
Basu, Sanjay
author_sort Choi, Sung Eun
collection PubMed
description OBJECTIVE: Personalised medicine seeks to select and modify treatments based on individual patient characteristics and preferences. We sought to develop an automated strategy to select and modify blood pressure treatments, incorporating the likelihood that patients with different characteristics would benefit from different types of medications and dosages and the potential severity and impact of different side effects among patients with different characteristics. DESIGN, SETTING AND PARTICIPANTS: We developed a Markov decision process (MDP) model to incorporate meta-analytic data and estimate the optimal treatment for maximising discounted lifetime quality-adjusted life-years (QALYs) based on individual patient characteristics, incorporating medication adjustment choices when a patient incurs side effects. We compared the MDP to current US blood pressure treatment guidelines (the Eighth Joint National Committee, JNC8) and a variant of current guidelines that incorporates results of a major recent trial of intensive treatment (Intensive JNC8). We used a microsimulation model of patient demographics, cardiovascular disease risk factors and side effect probabilities, sampling from the National Health and Nutrition Examination Survey (2003–2014), to compare the expected population outcomes from adopting the MDP versus guideline-based strategies. MAIN OUTCOME MEASURES: Costs and QALYs for the MDP-based treatment (MDPT), JNC8 and Intensive JNC8 strategies. RESULTS: Compared with the JNC8 guideline, the MDPT strategy would be cost-saving from a societal perspective with discounted savings of US$1187 per capita (95% CI 1178 to 1209) and an estimated discounted gain of 0.06 QALYs per capita (95% CI 0.04 to 0.08) among the US adult population. QALY gains would largely accrue from reductions in severe side effects associated with higher treatment doses later in life. The Intensive JNC8 strategy was dominated by the MDPT strategy. CONCLUSIONS: An MDP-based approach can aid decision-making by incorporating meta-analytic evidence to personalise blood pressure treatment and improve overall population health compared with current blood pressure treatment guidelines.
format Online
Article
Text
id pubmed-5695480
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher BMJ Publishing Group
record_format MEDLINE/PubMed
spelling pubmed-56954802017-11-24 Dynamic treatment selection and modification for personalised blood pressure therapy using a Markov decision process model: a cost-effectiveness analysis Choi, Sung Eun Brandeau, Margaret L Basu, Sanjay BMJ Open Health Services Research OBJECTIVE: Personalised medicine seeks to select and modify treatments based on individual patient characteristics and preferences. We sought to develop an automated strategy to select and modify blood pressure treatments, incorporating the likelihood that patients with different characteristics would benefit from different types of medications and dosages and the potential severity and impact of different side effects among patients with different characteristics. DESIGN, SETTING AND PARTICIPANTS: We developed a Markov decision process (MDP) model to incorporate meta-analytic data and estimate the optimal treatment for maximising discounted lifetime quality-adjusted life-years (QALYs) based on individual patient characteristics, incorporating medication adjustment choices when a patient incurs side effects. We compared the MDP to current US blood pressure treatment guidelines (the Eighth Joint National Committee, JNC8) and a variant of current guidelines that incorporates results of a major recent trial of intensive treatment (Intensive JNC8). We used a microsimulation model of patient demographics, cardiovascular disease risk factors and side effect probabilities, sampling from the National Health and Nutrition Examination Survey (2003–2014), to compare the expected population outcomes from adopting the MDP versus guideline-based strategies. MAIN OUTCOME MEASURES: Costs and QALYs for the MDP-based treatment (MDPT), JNC8 and Intensive JNC8 strategies. RESULTS: Compared with the JNC8 guideline, the MDPT strategy would be cost-saving from a societal perspective with discounted savings of US$1187 per capita (95% CI 1178 to 1209) and an estimated discounted gain of 0.06 QALYs per capita (95% CI 0.04 to 0.08) among the US adult population. QALY gains would largely accrue from reductions in severe side effects associated with higher treatment doses later in life. The Intensive JNC8 strategy was dominated by the MDPT strategy. CONCLUSIONS: An MDP-based approach can aid decision-making by incorporating meta-analytic evidence to personalise blood pressure treatment and improve overall population health compared with current blood pressure treatment guidelines. BMJ Publishing Group 2017-11-15 /pmc/articles/PMC5695480/ /pubmed/29146652 http://dx.doi.org/10.1136/bmjopen-2017-018374 Text en © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted. This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
spellingShingle Health Services Research
Choi, Sung Eun
Brandeau, Margaret L
Basu, Sanjay
Dynamic treatment selection and modification for personalised blood pressure therapy using a Markov decision process model: a cost-effectiveness analysis
title Dynamic treatment selection and modification for personalised blood pressure therapy using a Markov decision process model: a cost-effectiveness analysis
title_full Dynamic treatment selection and modification for personalised blood pressure therapy using a Markov decision process model: a cost-effectiveness analysis
title_fullStr Dynamic treatment selection and modification for personalised blood pressure therapy using a Markov decision process model: a cost-effectiveness analysis
title_full_unstemmed Dynamic treatment selection and modification for personalised blood pressure therapy using a Markov decision process model: a cost-effectiveness analysis
title_short Dynamic treatment selection and modification for personalised blood pressure therapy using a Markov decision process model: a cost-effectiveness analysis
title_sort dynamic treatment selection and modification for personalised blood pressure therapy using a markov decision process model: a cost-effectiveness analysis
topic Health Services Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5695480/
https://www.ncbi.nlm.nih.gov/pubmed/29146652
http://dx.doi.org/10.1136/bmjopen-2017-018374
work_keys_str_mv AT choisungeun dynamictreatmentselectionandmodificationforpersonalisedbloodpressuretherapyusingamarkovdecisionprocessmodelacosteffectivenessanalysis
AT brandeaumargaretl dynamictreatmentselectionandmodificationforpersonalisedbloodpressuretherapyusingamarkovdecisionprocessmodelacosteffectivenessanalysis
AT basusanjay dynamictreatmentselectionandmodificationforpersonalisedbloodpressuretherapyusingamarkovdecisionprocessmodelacosteffectivenessanalysis