[Analysis of risk factors and construction of a predictive model for the occurrence of heart failure with preserved ejection fraction in patients with type 2 diabetes mellitus complicated by coronary heart disease after percutaneous coronary intervention].

Objective: To explore the risk factors and construct a prediction model for heart failure with preserved ejection fraction (HFpEF) after percutaneous coronary intervention (PCI) in patients with type 2 diabetes mellitus (T2DM) and coronary heart disease(CHD). Methods: A retrospective analysis was conducted on the clinical data of 210 patients with T2DM complicated with CHD who underwent PCI at Tianjin Medical University Chu Hsien-I Memorial Hospital from January 2021 to June 2024. Among them, 136 were males and 74 were females, with the age of (66.4±9.1) years. Patients were divided into a training set and a validation set at a ratio of 7∶3 using the random number generator built into R software. Based on the occurrence of HFpEF within 12 months after PCI, patients in the training set were further categorized into the HFpEF group and the non-HFpEF group. Differences between the two groups were compared. The least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate logistic regression models were used to screen variables, and a nomogram of the prediction model was plotted. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were adopted to evaluate the predictive ability, calibration degree, and clinical applicability of the model. Results: The training set included 147 patients, with 38 in the HFpEF group and 109 in the non-HFpEF group. In the training set, patients in the HFpEF group had higher values than those in the non-HFpEF group in terms of age, body mass index (BMI), duration of diabetes, fasting blood glucose, glycosylated hemoglobin (HbA1c), triglyceride levels, triglyceride-glucose (TyG) index, N-terminal pro-B-type natriuretic peptide (NT-proBNP), and the ratio of mitral early diastolic flow velocity(E) to mitral annular early diastolic velocity(e'). Conversely, they exhibited lower values in estimated glomerular filtration rate (eGFR), left ventricular ejection fraction (LVEF), the proportion of complete revascularization, and the proportion of sodium-glucose cotransporter 2 inhibitor usage (all P<0.05). LASSO regression analysis screened out 8 variables, including age, BMI, TyG index, HbA1c, Ln(NT-proBNP), LVEF, E/e' ratio, and eGFR. Multivariate logistic regression analysis showed that advanced age (OR=1.070, 95%CI: 1.011-1.133), elevated TyG index (OR=8.560, 95%CI: 2.581-28.391), elevated Ln(NT-proBNP) (OR=3.880, 95%CI: 1.959-7.684), and elevated E/e' ratio (OR=1.480, 95%CI: 1.181-1.854) were risk factors for HFpEF after PCI in patients with T2DM complicated with CHD. A nomogram was constructed using the above parameters. In the training set, the AUC of the prediction model for HFpEF after PCI in patients with T2DM complicated with CHD was 0.886 (95%CI: 0.831-0.941), with a sensitivity of 84.2% and a specificity of 82.6%. In the validation set, the AUC was 0.871 (95%CI: 0.782-0.960), with the sensitivity of 81.3% and the specificity of 80.9%. The Hosmer-Lemeshow goodness-of-fit test indicated good calibration in both the training cohort (χ²=6.832, P=0.554) and the validation set (χ²=5.127, P=0.744). DCA demonstrated that the model achieved a high clinical net benefit when the threshold probability ranged from 10% to 90%. Conclusions: Advanced age, elevated TyG index, elevated Ln(NT-proBNP), and elevated E/e' ratio are risk factors for HFpEF in patients with T2DM complicated by CHD after PCI. The nomogram of the prediction model constructed based on these factors can intuitively evaluate the risk of HFpEF in patients with T2DM complicated by CHD after PCI.