Development and validation of a clinical prediction model for detecting coronary heart disease in middle-aged and elderly people: a diagnostic study

Abstract

Objective: To develop and validate a multivariate prediction model to estimate the risk of coronary heart disease (CHD) in middle-aged and elderly people and to provide a feasible method for early screening and diagnosis in middle-aged and elderly CHD patients.

Methods: This study was a single-center, retrospective, case-control study. Admission data of 932 consecutive patients with suspected CHD were retrospectively assessed from September 1, 2020 to December 31, 2021 in the Department of Integrative Cardiology at China-Japan Friendship Hospital. A total of 839 eligible patients were included in this study, and 588 patients were assigned to the derivation set and 251 as the validation set at a 7:3 ratio. Clinical characteristics of included patients were compared between derivation set and validation set by univariate analysis. The least absolute shrinkage and selection operator (Lasso) regression analysis method was performed to avoid collinearity and identify key potential predictors. Multivariate logistic regression analysis was used to construct a clinical prediction model with identified predictors for clinical practice. Bootstrap validation was used to test performance and eventually we obtained the actual model. And the Hosmer-Lemeshow test was carried out to evaluate the goodness-fit of the constructed model. The area under curve (AUC) of receiver operating characteristic (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were plotted and utilized with validation set to comprehensively evaluate the predictive accuracy and clinical value of the model.

Results: A total of eight indicators were identified as risk factors for the development of CHD in middle-aged and elderly people by univariate analysis. Of these candidate predictors, four key parameters were defined to be significantly related to CHD by Lasso regression analysis, including age (OR 1.034, 95% CI 1.002 ~ 1.067, P = 0.040), hemoglobin A1c (OR 1.380, 95% CI 1.078 ~ 1.768, P = 0.011), ankle-brachial index (OR 0.078, 95% CI 0.012 ~ 0.522, P = 0.009), and brachial artery flow-mediated vasodilatation (OR 0.848, 95% CI 0.726 ~ 0.990, P = 0.037). The Hosmer-Lemeshow test showed a good calibration performance of the clinical prediction model (derivation set, χ² = 7.865, P = 0.447; validation set, χ² = 11.132, P = 0.194). The ROCs of the nomogram in the derivation set and validation set were 0.722 and 0.783, respectively, suggesting excellent predictive power and suitable performance. The clinical prediction model presented a greater net benefit and clinical impact based on DCA and CIC analysis.

Conclusion: Overall, the development and validation of the multivariate model combined the laboratory and clinical parameters of patients with CHD, which could be beneficial to the individualized prediction of middle-aged and elderly people, and helped to facilitate clinical assessments and decisions during treatment and management of CHD.

Keywords: Clinical prediction model; Coronary heart disease; Diagnostic study; Nomogram; Risk factor.

Fig. 1

Flowchart of the detailed selection process

Fig. 2

The process of selecting key potential predictors by Lasso regression analysis. A Coefficients profile of selected predictors using Lasso regression analysis; B Using all the sample and candidate predictors, we employ Lasso to select the primitive predictors by cross-validation method

Fig. 3

Nomogram predicting CHD in middle-aged and elderly people

Fig. 4

Apparent performance of the prediction model in the derivation set and validation set. A1 Calibration curve of the multivariate prediction model in the derivation set. B1 ROC curve of the multivariate prediction model in the derivation set. C1 DCA of the model in the derivation set: Y-axis represents the net benefit. The red solid line represents the CHD prediction model, the thin solid line is the hypothesis that all patients get achievement of CHD and receive treatment, and the thick one is the assumption that no patients have CHD and none receive treatment. D1 CIC of the model in the derivation set. The yellow solid line represents the number of high-risk patients and the blue dotted line is the number of high-risk patients with events in the 1000 patients.A1Calibration curve of the multivariate prediction model in the validation set. (B2) ROC curve of the multivariate prediction model in the validation set. C1 DCA of the multivariate prediction model in the validation set. C1 CIC of the multivariate prediction model in the validation set