ObjectiveTo explore the association between triglyceride-glucose （TyG） index and major adverse cardiovascular events （MACEs） risk in coronary heart disease （CHD） patients with blood stasis syndrome after percutaneous coronary intervention （PCI）. MethodsA total of 857 CHD patients with blood stasis syndrome after PCI were enrolled and divided into four groups according to the baseline TyG index quartiles， Q1 （TyG ＜ 8.51）， Q2 （8.51 ≤ TyG ＜ 8.88）， Q3 （8.88 ≤ TyG ＜ 9.22）， and Q4 （TyG ≥ 9.22）. The clinical outcome was defined as a compound endpoint of cardiovascular events including cardiac death， non-fatal myocardial infarction， unplanned revascularization， in-stent restenosis and stroke. The machine learning Boruta algorithm was used for feature selection related to MACEs risk. Kaplan-Meier survival analysis and Cox proportional hazards regression model were used to compare the differences in MACEs risk among the four groups. Restricted cubic spline （RCS） and subgroup analysis were performed to determine the relationship between the TyG index and MACEs risk. The area under the receiver operating characteristic （ROC） curve （AUC）， calibration curve and Hosmer-Lemeshow test， and decision curve analysis （DCA） were plotted to evaluate the predictive value of the TyG index for MACEs risk. ResultsThe median follow-up time of the included patients was 2.45 years. During the follow-up period， 313 cases （36.52%） of new MACEs occurred. The incidence of MACEs in Q1， Q2， Q3， Q4 group was 28.17% （60/213）， 29.05% （61/210）， 39.45% （86/218） and 49.07% （106/216）， respectively. Kaplan-Meier survival analysis suggested statistically significant differences in MACEs risk among the four groups （P＜0.001）. Cox proportional hazards regression model analysis found that the risk of MACEs in patients with high TyG index increased by 60.1% （P＜0.01）. Using Q1 as the reference， the MACEs risk in Q2， Q3 and Q4 groups gradually increased， and the trend was statistically significant （P＜0.05）. RCS model suggested that the TyG index was nonlinearly associated with the MACEs risk （P＜0.001）. The TyG index had a good predictive performance for MACEs risk according to ROC analysis （AUC=0.758， 0.724-0.792） and Hosmer-Lemeshow test （χ² = 4.319， P = 0.827）. Additionally， DCA analysis also suggested a good clinical efficacy of the TyG index for predicting MACEs. Subgroup analysis showed that different baseline TyG index was positively correlated with the MACEs risk in the stratification of age， male， BMI， history of diabetes and hypertension， and low-density lipoprotein cholesterol （LDL-C）≥1.8 mmol·L^-1（P＜0.05）. ConclusionThe elevated TyG index is closely corrected with an increased risk of MACEs in CHD patients with blood stasis syndrome after PCI， indicating a guiding significance for early risk stratification and clinical decisions.

ObjectiveTo explore the impact of blood-invigorating and stasis-dissolving medicinals combined with conventional western medicine on the major adverse cardiovascular events （MACE） in patients with coronary heart disease （CHD） after percutaneous coronary intervention （PCI）. MethodsA prospective cohort study was conducted to collect data on consecutive cases of CHD after PCI. According to whether blood-invigorating and stasis-dissolving medicinals were used， the cases were divided into a Chinese herbal medicinals （CHM） group and control group. The primary outcome was the incidence of MACE one year after PCI， while the secondary outcomes included TCM syndrome score and echocardiography left ventricular ejection fraction （LVEF）. Logistic regression analysis was performed to explore the influencing factors of MACE. ResultsA total of 844 patients who met the criteria were included， with 617 in the CHM group and 227 in the control group. The main blood-invigorating and stasis-dissolving medicinals being used were Danshen （Radix et Rhizoma Salviae Miltiorrhizae， 46.35%）， Chuanxiong （Rhizoma Chuanxiong， 45.87%）， and Chishao （Radix Paeoniae Rubra， 42.30%）. After a median follow-up of 12.73 months， the incidence of MACE in the CHM group （142/617， 23.01%） was significantly lower than that in the control group （68/227， 29.96%） with significant difference （OR=0.70， 95%CI 0.50 to 0.98， P = 0.04）. The LVEF of the CHM group ［（60.06±6.13）%］ was higher than that of the control group ［（58.27±7.36）%］ with significant difference （t = 0.356， P＜0.01）. The TCM syndrome score in the CHM group decreased to 12.66±4.47， while that in the control group increased to 13.81±3.88， with the results favoring the CHM group （t = 2.78， P＜0.01）. Univariate analysis showed correlations between the incidence of MACE after PCI and the use of blood-invigorating and stasis-dissolving medicinals， LVEF， usage of renin-angiotensin-aldosterone system （RAAS） inhibitors， TCM syndrome score， and usage of β blockers （P＜0.05）. Multivariate analysis showed that the use of blood-invigorating and stasis-dissolving medicinals was significantly associated with the reduction of MACE （P＜0.01）， while the baseline LVEF decline， TCM syndrome score increase， no use of RAAS inhibitors or β blockers were the risk factors of MACE after PCI （P＜0.05）. ConclusionThe use of blood-invigorating and stasis-dissolving medicinals based on the conventional western medicine can reduce the risk of MACE one year after PCI of CHD， improve the TCM syndromes and protect heart function.

ObjectiveTo establish and validate a clinical prediction model for 1-year major adverse cardiovascular events（MACEs）risk after percutaneous coronary intervention （PCI） in coronary heart disease （CHD） patients with blood stasis syndrome. MethodThe consecutive CHD patients diagnosed with blood stasis syndrome in the Department of Integrative Cardiology at China-Japan Friendship Hospital from September 1， 2019 to March 31， 2021 were selected for a retrospective study， and basic clinical features and relevant indicators were collected. Eligible patients were classified into a derivation set and a validation set at a ratio of 7∶3， and each set was further divided into a MACEs group and a non-MACEs group. The factors affecting the outcomes were screened out by least absolute shrinkage and selection operator （Lasso） and used to establish a logistic regression model and identify independent prediction variables. The goodness-of-fit of the model was evaluated by the Hosmer-Lemeshow test， and the area under curve （AUC） of the receiver operating characteristic （ROC） curve， calibration curve， decision curve analysis （DCA）， and clinical impact curve （CIC） were employed to evaluate the discrimination， calibration， and clinical impact of the model. ResultA total of 731 consecutive patients were assessed and 404 eligible patients were enrolled， including 283 patients in the derivation set and 121 patients in the validation set. Lasso identified ten variables influencing outcomes， which included age， sex， fasting plasma glucose （FPG）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C）， homocysteine （Hcy）， brachial-ankle pulse wave velocity （baPWV）， flow-mediated dilatation （FMD）， left ventricular ejection fraction （LVEF）， and Gensini score. The multivariate Logistic regression preliminarily identified age， FPG， TG， Hcy， LDL-C， LVEF， and Gensini score as the independent variables that influenced the outcomes. Of these variables， male， high FMD and high LVEF were protective factors， and the rest were risk factors. The prediction model for 1-year MACEs risk after PCI in CHD patients with blood stasis syndrome showed χ²=12.371 （P=0.14） in Hosmer-Lemeshow test and the AUC of 0.90. With the threshold probability > 10%， the model showed better prediction performance for 1-year MACEs risk after PCI in CHD patients with blood stasis syndrome than for that in all the patients. With the threshold probability > 60%， the estimated value was much closer to the real number of patients. ConclusionThe established clinical prediction model facilitates the early prediction of 1-year MACEs risk after PCI in CHD patients with blood stasis syndrome， which can provide ideas for the precise treatment of CHD patients after PCI and has guiding significance for improving the prognosis of the patients. Meanwhile， multi-center studies with larger sample sizes are expected to further validate， improve， and update the model.