Development and Applicability Assessment of a TCM-based Risk Prediction Model for Major Adverse Cardiovascular and Cerebrovascular Events in Type 2 Diabetics with Stable Angina Pectoris

Abstract

Background Early treatment is crucial to the delay of the progression of type 2 diabetes mellitus with stable angina pectoris (T2DM-SAP) , which has poor prognosis, such as high rates of disability and mortality. As traditional Chinese medicine (TCM) has unique advantages in preventing diseases, developing a model with TCM and western medicine factors associated with major adverse cardiovascular and cerebrovascular events (MACCEs) incorporated may be a reliable tool that could be used to predict the risk of MACCEs in patients with T2DM-SAP. Objective To develop and assess the applicability of a risk prediction model for MACCEs in T2DM-SAP patients using identified risk factors associated with MACCEs in this group. Methods Participants were 674 inpatients with T2DM-SAP who received diagnostic and treatment services from The First Affiliated Hospital of Henan University of CM from 2012 to 2019. Through the hospital information system, electronic medical records and follow-up data of these patients were collected, including demographics, clinical characteristics, laboratory parameters, TCM symptoms and syndrome differentiation, and outcome (prevalence of MACCEs) . Patients were classified into a MACCEs group (n=190) and a non-MACCEs group (n=484) by prevalence of MACCEs. Independent risk factors for MACCEs in T2DM with SAP were identified using univariate and multivariate Logistic regression, and used to develop a nomogram-based predictive model. Then the model was internally validated using the bootstrap approach, and its predictive value was estimated using ROC analysis, C-index, calibration plot, Hosmer-Lemeshow test and decision curve analysis. Results Based on the multivariate Logistic regression analysis, the factors associated with MACCEs in T2DM-SAP patients (P<0.05) included age〔OR=1.033, 95%CI (1.014, 1.052) 〕, cerebrovascular disease history〔OR=3.799, 95%CI (2.529, 5.750) 〕, serum creatinine〔OR=1.005, 95%CI (1.002, 1.008) 〕, dark purple tongue〔OR=2.756, 95%CI (1.285, 5.935) 〕, decreased tongue coating〔OR=2.083, 95%CI (1.025, 4.166) 〕, thready pulse〔OR=5.822, 95%CI (1.867, 20.359) 〕, and obstruction of collateral channels caused by wind-phlegm〔OR=2.525, 95%CI (1.466, 4.387) 〕. The predictive model constructed using the above-mentioned factors showed moderate predictive power {C-index=0.769〔95%CI (0.729, 0.809) 〕, sensitivity=69.47%, specificity=75.00%} , indicating a good degree of distinction. The calibration plot showed the average absolute error between the predictive and actual adverse outcome risks was 0.011, with a C-index of 0.761 after fitting bias correction. The Hosmer-Lemeshow test showed a good calibration (χ2=6.004, P=0.647) . The decision curve analysis displayed a threshold probability of >30%, indicating that the model may be clinically beneficial. Conclusion The risk predictive model for MACCEs in T2DM-SAP patients was developed using the associated factors (including age, cerebrovascular disease history, serum creatinine, dark purple tongue, decreased tongue coating, thready pulse, and obstruction of collateral channels caused by wind-phlegm) identified by us, which has been proven to have good discrimination, calibration, and clinical effectiveness, and could be used as a tool for assessing the risk of MACCEs in patients with T2DM-SAP.

Keywords

• diabetes mellitus|stable angina pectoris|cardiovascular and cerebrovascular events|clinical predictive model|traditional chinese medical