Ma Jing, Yu Ze, Chen Ting, Li Ping, Liu Yan, Chen Jihui, Lyu Chunming, Hao Xin, Zhang Jinyuan, Wang Shuang, Gao Fei, Zhang Jian, Bu Shuhong
Department of Pharmacy, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Front Pharmacol. 2023 Sep 14;14:1208621. doi: 10.3389/fphar.2023.1208621. eCollection 2023.
Shengmai injection is a common treatment for coronary heart disease. The accurate dose regimen is important to maximize effectiveness and minimize adverse reactions. We aim to explore the effect of Shengmai injection in patients with coronary heart disease based on real-world data and establish a personalized medicine model using machine learning and deep learning techniques. 211 patients were enrolled. The length of hospital stay was used to explore the effect of Shengmai injection in a case-control study. We applied propensity score matching to reduce bias and Wilcoxon rank sum test to compare results between the experimental group and the control group. Important variables influencing the dose regimen of Shengmai injection were screened by XGBoost. A personalized medicine model of Shengmai injection was established by XGBoost selected from nine algorithm models. SHapley Additive exPlanations and confusion matrix were used to interpret the results clinically. Patients using Shengmai injection had shorter length of hospital stay than those not using Shengmai injection (median 10.00 days vs. 11.00 days, = 0.006). The personalized medicine model established via XGBoost shows accuracy = 0.81 and AUC = 0.87 in test cohort and accuracy = 0.84 and AUC = 0.84 in external verification. The important variables influencing the dose regimen of Shengmai injection include lipid-lowering drugs, platelet-lowering drugs, levels of GGT, hemoglobin, prealbumin, and cholesterol at admission. Finally, the personalized model shows precision = 75%, recall rate = 83% and F1-score = 79% for predicting 40 mg of Shengmai injection; and precision = 86%, recall rate = 79% and F1-score = 83% for predicting 60 mg of Shengmai injection. This study provides evidence supporting the clinical effectiveness of Shengmai injection, and established its personalized medicine model, which may help clinicians make better decisions.
生脉注射液是冠心病的常用治疗药物。准确的剂量方案对于最大化疗效和最小化不良反应至关重要。我们旨在基于真实世界数据探索生脉注射液对冠心病患者的疗效，并使用机器学习和深度学习技术建立个性化用药模型。共纳入211例患者。在一项病例对照研究中，使用住院时间来探究生脉注射液的疗效。我们应用倾向得分匹配来减少偏倚，并使用Wilcoxon秩和检验比较实验组和对照组的结果。通过XGBoost筛选影响生脉注射液剂量方案的重要变量。从九个算法模型中选择XGBoost建立生脉注射液的个性化用药模型。使用SHapley加法解释和混淆矩阵对结果进行临床解释。使用生脉注射液的患者住院时间比未使用生脉注射液的患者短（中位数10.00天对11.00天，P = 0.006）。通过XGBoost建立的个性化用药模型在测试队列中的准确率为0.81，AUC为0.87；在外部验证中的准确率为0.84，AUC为0.84。影响生脉注射液剂量方案的重要变量包括降脂药物、降血小板药物、入院时的谷氨酰转肽酶、血红蛋白、前白蛋白和胆固醇水平。最后，个性化模型预测40mg生脉注射液时的精确率为75%，召回率为83%，F1分数为79%；预测60mg生脉注射液时的精确率为86%，召回率为79%，F1分数为83%。本研究提供了支持生脉注射液临床疗效的证据，并建立了其个性化用药模型，这可能有助于临床医生做出更好的决策。
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