Application of Bayesian additive regression tree model in the evaluation of individualized efficacy of hypertension drugs

ZHAO Hong-yu; FENG Yi-ping; MA Xiao-tian; PAN Feng-ming; WU Xin-ying; WANG Shu-kang; JI Xiao-kang; ZHANG Zhen-tang; WANG Qing; XUE Fu-zhong

doi:10.16462/j.cnki.zhjbkz.2021.06.004

Volume 25 Issue 6

Jul. 2021

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2021 > 25(6): 637-643

ZHAO Hong-yu, FENG Yi-ping, MA Xiao-tian, PAN Feng-ming, WU Xin-ying, WANG Shu-kang, JI Xiao-kang, ZHANG Zhen-tang, WANG Qing, XUE Fu-zhong. Application of Bayesian additive regression tree model in the evaluation of individualized efficacy of hypertension drugs[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(6): 637-643. doi: 10.16462/j.cnki.zhjbkz.2021.06.004

Citation:

ZHAO Hong-yu, FENG Yi-ping, MA Xiao-tian, PAN Feng-ming, WU Xin-ying, WANG Shu-kang, JI Xiao-kang, ZHANG Zhen-tang, WANG Qing, XUE Fu-zhong. Application of Bayesian additive regression tree model in the evaluation of individualized efficacy of hypertension drugs[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(6): 637-643. doi: 10.16462/j.cnki.zhjbkz.2021.06.004

Citation:

ZHAO Hong-yu, FENG Yi-ping, MA Xiao-tian, PAN Feng-ming, WU Xin-ying, WANG Shu-kang, JI Xiao-kang, ZHANG Zhen-tang, WANG Qing, XUE Fu-zhong. Application of Bayesian additive regression tree model in the evaluation of individualized efficacy of hypertension drugs[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(6): 637-643. doi: 10.16462/j.cnki.zhjbkz.2021.06.004

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Application of Bayesian additive regression tree model in the evaluation of individualized efficacy of hypertension drugs

doi: 10.16462/j.cnki.zhjbkz.2021.06.004

1.
Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Institute of Health and Medical Research, Shandong University, Jinan 250012, China
2.
Department of Health Management, Center for Disease Control and Prevention of Qingdao West Coast New Area, Qingdao 266400, China

ZHAO Hong-yu and FENG Yi-ping contributed equally to this article

Funds:

National Key Research and Development Program of China 2020YFC2003500

National Natural Science Foundation of China 81773547

Natural Science Foundation of Shandong Province ZR2019ZD02

More Information

Corresponding author: WANG Qing, E-mail: wangqing1984@126.com; XUE Fu-zhong, E-mail: xuefzh@sdu.edu.cn
Received Date: 2021-04-19
Rev Recd Date: 2021-05-17
Publish Date: 2021-06-10

Abstract

Abstract

Objective To evaluate the personalized efficacy of captopril and its drug combination with nitrendipine on blood pressure control by Bayesian additive regression tree (BART) model, and to identify subgroup characteristics of high-yield patients, taking people with hypertension and diabetes as samples. Methods According to different medication modes, the patients with hypertension and diabetes who were included in the "Comprehensive Prevention and Control Project of Hypertension and Diabetes for All Populations" in Jiaonan, Shandong Province from January 2011 to July 2015 were divided into three groups. The propensity score was used to randomize the matching formation and to get comparable samples, then BART model was established to explore the personalized efficacy of medication. Results In the comparison between captopril alone and no medication, combination medication and no medication, combined medication and captopril alone, the area under curre (AUC) of the three groups of models and their 95% CI were 0.710 (0.686-0.734), 0.796 (0.754-0.838) and 0.768 (0.725-0.812), respectively. And for most patients, the combined medication effect was better than the single captopril and no medicine. Among them, lower systolic blood pressure and good exercise habits were the common characteristics of those who used captopril alone and combination drugs that had better efficacy compared with those who did not use drugs. Conclusion The constructed BART model is used to predict the personalized efficacy of hypertension drugs in patients with hypertension and diabetes, which can be used to summarize the characteristics of high-yield subgroups, and also provide the basis for the practice of precision medicine in the treatment of hypertension.
- Bayesian additive regression tree,
- Hypertension,
- Captopril,
- Precision medicine

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References(19)

References

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