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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.
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Objective:To explore the risk factors of the incidence of arrhythmia and the prediction of baseline ventricular late potential in patients with first depression episode.Methods:The cohort study was used to observe the relationship between the baseline status of ventricular late potential, the severity of baseline depression symptoms, the extent of remission of depressive symptoms within the treatment duration and arrhythmia incidence in the 3 years progress. For the assessment of the severity of depression symptoms, 17 version of Hamilton depression scale was used to evaluate the baseline ventricular late potential, and DMS lab3.0 ECG platform late potential analysis system was used to determine the assessment (CardioScan 12 NET version). The first depression patients with positive ventricular late potential were followed up for 3 years. The changes of the severity of ventricular late potential and depression symptoms were investigated, and the correlation with the subsequent course of arrhythmia was investigated.SPSS 20.0 software package was used for statistical distraction, chi square test was used for count data, independent samples t test was used for normal distribution measurement data, Mann-Whitney U test was used for non-normal distribution count data, and logistic regression method was used to calculate relative risk( RR). Results:According to the 3-year follow-up of 400 first-episode depression patients, 22.25% (89/400) had malignant arrhythmia. The incidence of malignant arrhythmia was 39.46% (58/147) in ventricular late potential positive group and 12.25% (31/253) in ventricular late potential negative group, and the difference was statistically significant(χ 2=9.578, P<0.01). Logistic regression analysis showed that positive ventricular late potential at baseline (compared with negative ventricular late potential at baseline, RR=10.78, 95% CI=8.34-13.80), having a family history of arrhythmia (compared with no family history of arrhythmia, RR=5.23, 95% CI=2.41-9.85), had a higher severity of depression at baseline (compared with lower severity of depression at baseline, RR=1.73, 95% CI=1.25-2.85), poor first-time efficacy and more repeated hospitalizations (compared with good first-time efficacy and less hospitalizations, RR=1.11, 95% CI=1.04-1.17), and age of onset< 20 (compared with age of onset≥20, RR=1.07, 95% CI=1.02-1.93) were the risk factors of malignant arrhythmia in patients with first-episode depression(all P<0.05). Conclusion:The incidence of arrhythmia is very high in those patients with baseline positive late ventricular potential. Positive late ventricular potential, family history of arrhythmia, younger onset age and poor therapeutic effect were the relative risk of arrhythmia in the patients with depression.