Development and Validation of a Nomogram to Predict the Future Risk of Cardiovascular Disease.

ABSTRACT

Background: Early identification of individuals at a high risk of cardiovascular disease (CVD) is crucial. This study aimed to construct a nomogram for CVD risk prediction in the general population. Methods: This retrospective study analyzed the data between January 2012 and September 2020 at the Physical Examination Center of the Second Affiliated Hospital of Nanjing Medical University (randomized 73 to the training and validation cohorts). The outcome was the occurrence of CVD events, which were defined as sudden cardiac death or any death related to myocardial infarction, acute exacerbation of heart failure, or stroke. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression were applied to screen the significant variables related to CVD. Results: Among the 537 patients, 54 had CVD (10.1%). The median cardiac myosin-binding protein-C (cMyBP-C) level in the CVD group was higher than in the no-CVD group (42.25 pg/mL VS 25.00 pg/mL, p = 0.001). After LASSO selection and multivariable analysis, cMyBP-C (Odds ratio [OR] = 1.004, 95% CI [CI, confidence interval] 1.000-1.008, p = 0.035), age (OR = 1.023, 95% CI 0.999-1.048, p = 0.062), diastolic blood pressure (OR = 1.025, 95% CI 0.995-1.058, p = 0.103), cigarettes per day (OR = 1.066, 95% CI 1.021-1.113, p = 0.003), and family history of CVD (OR = 2.219, 95% CI 1.003-4.893, p = 0.047) were associated with future CVD events (p < 0.200). The model, including cMyBP-C, age, diastolic blood pressure, cigarettes per day, and family history of CVD, displayed a high predictive ability with an area under the curve (AUC) of 0.816 (95% CI 0.714-0.918) in the training cohort (specificity and negative predictive value of 0.92 and 0.96) and 0.774 (95% CI 0.703-0.845) in the validation cohort. Conclusions: A nomogram based on cMyBP-C, age, diastolic blood pressure, cigarettes per day, and family history of CVD was constructed. The model displayed a high predictive ability.