Establishment and Validation of Clinical Prediction Model for 1-year MACEs Risk After PCI in CHD Patients with Blood Stasis Syndrome
10.13422/j.cnki.syfjx.20231594
- VernacularTitle:冠心病血瘀证PCI术后1年MACEs风险临床预测模型的建立与验证
- Author:
Shiyi TAO
1
;
Lintong YU
1
;
Deshuang YANG
2
;
Gaoyu ZHANG
1
;
Lanxin ZHANG
3
;
Zihan WANG
1
;
Jiarong FAN
1
;
Li HUANG
2
;
Mingjing SHAO
2
Author Information
1. Beijing University of Chinese Medicine，Beijing 100029，China
2. China-Japan Friendship Hospital，Beijing 100029，China
3. Guang'anmen Hospital，China Academy of Chinese Medical Sciences，Beijing 100053，China
- Publication Type:Journal Article
- Keywords:
coronary heart disease;
percutaneous coronary intervention;
clinical prediction model;
nomogram;
prognosis;
correlation
- From:
Chinese Journal of Experimental Traditional Medical Formulae
2023;29(20):69-80
- CountryChina
- Language:Chinese
-
Abstract:
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.
