A Real-World Study on the Short-Term Efficacy of Amlodipine in Treating Hypertension Among Inpatients.

IF 2.3 Q2 MEDICINE, GENERAL & INTERNAL
Pragmatic and Observational Research Pub Date : 2024-08-06 eCollection Date: 2024-01-01 DOI:10.2147/POR.S464439
Tingting Wang, Juntao Tan, Tiantian Wang, Shoushu Xiang, Yang Zhang, Chang Jian, Jie Jian, Wenlong Zhao
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Abstract

Purpose: Hospitalized hypertensive patients rely on blood pressure medication, yet there is limited research on the sole use of amlodipine, despite its proven efficacy in protecting target organs and reducing mortality. This study aims to identify key indicators influencing the efficacy of amlodipine, thereby enhancing treatment outcomes.

Patients and methods: In this multicenter retrospective study, 870 hospitalized patients with primary hypertension exclusively received amlodipine for the first 5 days after admission, and their medical records contained comprehensive blood pressure records. They were categorized into success (n=479) and failure (n=391) groups based on average blood pressure control efficacy. Predictive models were constructed using six machine learning algorithms. Evaluation metrics encompassed the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). SHapley Additive exPlanations (SHAP) analysis assessed feature contributions to efficacy.

Results: All six machine learning models demonstrated superior predictive performance. Following variable reduction, the model predicting amlodipine efficacy was reconstructed using these algorithms, with the light gradient boosting machine (LightGBM) model achieving the highest overall performance (AUC = 0.803). Notably, amlodipine showed enhanced efficacy in patients with low platelet distribution width (PDW) values, as well as high hematocrit (HCT) and thrombin time (TT) values.

Conclusion: This study utilized machine learning to predict amlodipine's effectiveness in hypertension treatment, pinpointing key factors: HCT, PDW, and TT levels. Lower PDW, along with higher HCT and TT, correlated with enhanced treatment outcomes. This facilitates personalized treatment, particularly for hospitalized hypertensive patients undergoing amlodipine monotherapy.

氨氯地平治疗住院病人高血压短期疗效的真实世界研究。
目的:住院高血压患者依赖降压药,尽管氨氯地平在保护靶器官和降低死亡率方面的疗效已得到证实,但有关单独使用氨氯地平的研究却十分有限。本研究旨在确定影响氨氯地平疗效的关键指标,从而提高治疗效果:在这项多中心回顾性研究中,870 名住院的原发性高血压患者在入院后的前 5 天均接受了氨氯地平治疗,他们的病历中包含了全面的血压记录。根据平均血压控制效果将他们分为成功组(479 人)和失败组(391 人)。使用六种机器学习算法构建了预测模型。评估指标包括曲线下面积(AUC)、准确性、灵敏度、特异性、阳性预测值(PPV)和阴性预测值(NPV)。SHapley Additive exPlanations(SHAP)分析评估了特征对疗效的贡献:结果:所有六个机器学习模型都表现出卓越的预测性能。在减少变量后,使用这些算法重建了预测氨氯地平疗效的模型,其中轻梯度提升机(LightGBM)模型的整体性能最高(AUC = 0.803)。值得注意的是,氨氯地平在血小板分布宽度(PDW)值低、血细胞比容(HCT)和凝血酶时间(TT)值高的患者中显示出更强的疗效:本研究利用机器学习预测了氨氯地平在高血压治疗中的疗效,指出了关键因素:HCT、PDW 和 TT 水平。较低的 PDW 以及较高的 HCT 和 TT 与更好的治疗效果相关。这有助于个性化治疗,尤其是对接受氨氯地平单药治疗的住院高血压患者。
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来源期刊
Pragmatic and Observational Research
Pragmatic and Observational Research MEDICINE, GENERAL & INTERNAL-
自引率
0.00%
发文量
11
期刊介绍: Pragmatic and Observational Research is an international, peer-reviewed, open-access journal that publishes data from studies designed to closely reflect medical interventions in real-world clinical practice, providing insights beyond classical randomized controlled trials (RCTs). While RCTs maximize internal validity for cause-and-effect relationships, they often represent only specific patient groups. This journal aims to complement such studies by providing data that better mirrors real-world patients and the usage of medicines, thus informing guidelines and enhancing the applicability of research findings across diverse patient populations encountered in everyday clinical practice.
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