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DOI 10.1186/s12911-021-01739-9

Медицинская библиотека · BMC medical informatics and decision making · 2021

Identifying drug-related attributes to personalise antihypertensive agents: the outcome report of patients receiving metoprolol therapy.

Liu Chunyu, Xu Jing, Liu Ran, Wang Miye, Zhuo Yixuan, Su Lan, Yan Hongmei, Zhang Qing

СтатьяПоддержка исследования: вне правительства СШАПеревод в очереди

Аннотация

BACKGROUND

Currently, numerous antihypertensive drugs from different pharmacological classes are available; however, blood pressure control is achieved in only less than a third of patients treated for hypertension. Moreover, providing optimal and personalised treatment for hypertension is challenging. Therefore, in this study, we propose a 'drug-related attributes' sensitive spectrum. This novel concept can assist clinicians in selecting an optimal antihypertensive drug and improve blood pressure control after examining the attributes of a patient.

METHODS

We collected clinical data on attributes related to hypertension and its therapy of inpatients from West China Hospital who received metoprolol therapy and constructed the sensitive spectrum using data-visualisation tools.

RESULTS

Our analysis revealed that haematocrit, haemoglobin, serum creatinine, serum cystatin C, serum urea, age, sex, systolic pressure, diastolic pressure, pulse pressure, and heart rate are metoprolol-related attributes.

CONCLUSION

Our study showed that all metoprolol-related attributes identified are reasonable and helpful in improving the personalisation of metoprolol therapy. The proposed drug-related attributes spectrum can help personalise antihypertensive medication. Moreover, data-visualisation tools can be effectively used to mine the drug-related attributes sensitive spectrum.

Для цитирования: Liu Chunyu, Xu Jing, Liu Ran, Wang Miye, Zhuo Yixuan, Su Lan, Yan Hongmei, Zhang Qing. Identifying drug-related attributes to personalise antihypertensive agents: the outcome report of patients receiving metoprolol therapy.. BMC medical informatics and decision making. 2021. DOI: 10.1186/s12911-021-01739-9.

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