Tingting Wang, Juntao Tan, Tiantian Wang, Shoushu Xiang, Yang Zhang, Chang Jian, Jie Jian, Wenlong Zhao
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引用次数: 0
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.
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.