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. 2023 Sep 14:14:1208621.
doi: 10.3389/fphar.2023.1208621. eCollection 2023.

The effect of Shengmai injection in patients with coronary heart disease in real world and its personalized medicine research using machine learning techniques

Jing Ma #  1 , Ze Yu #  2 , Ting Chen  1 , Ping Li  1 , Yan Liu  1 , Jihui Chen  1 , Chunming Lyu  3 , Xin Hao  4 , Jinyuan Zhang  5 , Shuang Wang  4 , Fei Gao  5 , Jian Zhang  1 , Shuhong Bu  1
Affiliations

The effect of Shengmai injection in patients with coronary heart disease in real world and its personalized medicine research using machine learning techniques

Jing Ma et al. Front Pharmacol. .

Abstract

Objective: 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. Methods: 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. Results: Patients using Shengmai injection had shorter length of hospital stay than those not using Shengmai injection (median 10.00 days vs. 11.00 days, p = 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. Conclusion: This study provides evidence supporting the clinical effectiveness of Shengmai injection, and established its personalized medicine model, which may help clinicians make better decisions.

Keywords: Shengmai; coronary heart disease; machine learning; personalized medicine; real world.

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Conflict of interest statement

Authors XH and SW were employed by Dalian Medicinovo Technology Co., Ltd. and Authors JYZ and FG were employed by Beijing Medicinovo Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Workflow of data cleaning.
FIGURE 2
FIGURE 2
Workflow of data processing and modelling.
FIGURE 3
FIGURE 3
Accuracy of XGBoost model corresponding to the number of ranked features.
FIGURE 4
FIGURE 4
SHAP values of the important variables. The dot color is redder when the feature value gets higher and bluer when the feature value gets lower. When the SHAP value gets higher, the impact of the variable on model output is larger.
FIGURE 5
FIGURE 5
Confusion matrix in XGBoost model.
See this image and copyright information in PMC

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