[Experience of LU Zhi-zheng to apply tenuifolia].

Professor LU Zhi-zheng, one of the first traditional Chinese medicine masters, is good at using tenuifoliain clinical practice, which often brings unexpected surprises. Lu said, tenuifolia is a mild herbal medicine with the nature of upward dispersion and outward penetration but not dryness. Tenuifolia has the following functions: making people conscious, relieving sore throat, diverging incubated diseases, regulating functional activities of qi, sending up Yang, dispelling wind evil and eliminating dampness, and activating collaterals to relieve pain. When well used, it will not only enhance the effect of monarch drug, but also restrict the impetuosity nature in a prescription, achieving better efficacy.

SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation.

Heart failure (HF) is a significant global public health concern with a high readmission rate, posing a serious threat to the health of the elderly population. While several studies have used machine learning (ML) to develop all-cause readmission risk prediction models for elderly patients with HF, few have integrated ML-selected features with those chosen by human experts to assess HF patients readmission. A retrospective analysis of 8396 elderly HF patients hospitalized at the Affiliated Hospital of North Sichuan Medical College from January 1, 2018 to December 31, 2021 was conducted. Variables selected by XGBoost, LASSO regression, and random forest constituted the machine group, while the human expert group comprised variables chosen by two experienced cardiovascular professors. The variables selected by both groups were combined to form a human-machine collaboration group. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) method was used to elucidate the importance of each predictive feature, explain the impact of individual features on the model, and provide visual representation. A total of 73 features were included for model development. The human-machine collaboration model, utilizing CatBoost, achieved an AUC of 0.83617, an F1-score of 0.73521, and a Brier score of 0.16536 on the validation set. This model demonstrated superior predictive performance compared to those created solely by human experts or machine. The SHAP plot was then used to visually display the feature analysis of the human-machine collaboration model, revealing HGB, NT-proBNP, smoking history, NYHA classification, and LVEF as the 5 most important features. This study indicate that the human-machine collaboration model outperforms those relying solely on human expert selection or machine algorithm at predicting all-cause readmission in elderly HF patients. The application of the SHAP method enhanced the interpretability of the model outcomes, aiding clinicians in accurately pinpointing risk factors associated with HF readmission. This advancement enables the formulation of tailored treatment strategies, offering a more personalized approach to patient care.

Development and validation of a machine learning-based readmission risk prediction model for non-ST elevation myocardial infarction patients after percutaneous coronary intervention.

To investigate the factors that influence readmissions in patients with acute non-ST elevation myocardial infarction (NSTEMI) after percutaneous coronary intervention (PCI) by using multiple machine learning (ML) methods to establish a predictive model. In this study, 1576 NSTEMI patients who were hospitalized at the Affiliated Hospital of North Sichuan Medical College were selected as the research subjects. They were divided into two groups: the readmitted group and the non-readmitted group. The division was based on whether the patients experienced complications or another incident of myocardial infarction within one year after undergoing PCI. Common variables selected by univariate and multivariate logistic regression, LASSO regression, and random forest were used as independent influencing factors for NSTEMI patients' readmissions after PCI. Six different ML models were constructed using these common variables. The area under the ROC curve, accuracy, sensitivity, and specificity were used to evaluate the performance of the six ML models. Finally, the optimal model was selected, and a nomogram was created to visually represent its clinical effectiveness. Three different methods were used to select seven representative common variables. These variables were then utilized to construct six different ML models, which were subsequently compared. The findings indicated that the LR model exhibited the most optimal performance in terms of AUC, accuracy, sensitivity, and specificity. The outcome, admission mode (walking and non-walking), communication ability, CRP, TC, HDL, and LDL were identified as independent predicators of readmissions in NSTEMI patients after PCI. The prediction model constructed by the LR algorithm was the best. The established column graph model established proved to be effective in identifying high-risk groups with high accuracy and differentiation. It holds a specific predictive value for the occurrence of readmissions after direct PCI in NSTEMI patients.

[Clinical assessment on treatment of hyperlipidemia with pushen capsule].

OBJECTIVE: To explore the therapeutic effect of Pushen capsule (PSC) in treating primary hyperlipidemia. METHODS: Two hundred and forty patients with primary hyperlipidemia were randomly divided into two groups, the 120 patients in the treated group treated with PSC (4 capsules, tid) and the 120 patients in the control group treated with Zhibituo tablet (3 tablets, tid), and they were administered at the same time with Zhibituo placebo. The therapeutic course for both groups was 4 weeks. The therapeutic effect and the effects on blood lipids and viscosity were observed. RESULTS: The effective rate in the treated group was 76.3%, which was significantly higher than that in the control group (48.7%, P < 0.01). PSC showed a significant lowering effect on TC, TG and LDL-C and raising effect on HDL-C, and the effect in lowering TG was significantly better than that of Zhibituo (P < 0.01). PSC also showed a certain effect in decreasing whole blood viscosity of both high-sheared and low-sheared viscosity. CONCLUSION: Pushen capsule has promising blood lipid regulating effect in patients with hyperlipidemia, and some effects in lowering the blood viscosity.