Construction of a Nomogram Model to Identify Atherosclerotic Cardiovascular Disease in Patients with Type 2 Diabetes Mellitus.

BACKGROUND: Approximately 50.0% of patients with type 2 diabetes mellitus (T2DM) experience macrovascular diseases, and nearly 80.0% of them succumb to macrovascular complications. Atherosclerotic cardiovascular disease (ASCVD) ranks among the most prevalent macrovascular complications in T2DM. In this study, we aim to develop a nomogram model for the early detection of ASCVD in T2DM patients, enabling us to provide valuable recommendations for the clinical prevention and management of macrovascular complications in this patient population.

METHODS: This retrospective analysis encompassed 2620 T2DM patients admitted between June 2015 and June 2021. The cohort comprised 1270 T2DM patients with coexisting ASCVD (referred to as the "ASCVD group") and 1350 individuals who did not experience ASCVD (the "non-ASCVD group"). We conducted a comparative assessment of their baseline characteristics and clinical data. A nomogram model for the identification of ASCVD in T2DM patients was constructed utilizing Logistic regression analysis and the R package. The model's performance was evaluated through receiver operating characteristic (ROC) curve analysis and calibration curves.

RESULTS: We developed a nomogram model for the identification of ASCVD in T2DM patients, incorporating ten variables: sex, age, hypertension, smoking history, low-density lipoprotein cholesterol/high-density lipoprotein cholesterol (LDL-C/HDL-C) ratio, alanine transaminase (ALT), adenosine deaminase (ADA), postprandial 2-hour C-peptide, monocyte count (MONO), and eosinophil count (EOS). ROC curves demonstrated that the area under the curve (AUC) of the nomogram model for identifying ASCVD in T2DM patients was 0.673 for the training dataset (with a cut-off value of 0.473, specificity of 0.629, and sensitivity of 0.637) and 0.655 for the validation dataset (with a cut-off value of 0.460, specificity of 0.605, and sensitivity of 0.675). The calibration curve indicated a substantial agreement between the predicted and observed cases of ASCVD in the training dataset and an acceptable level of agreement in the validation dataset.

CONCLUSIONS: The nomogram model effectively identifies ASCVD in T2DM patients, which can be instrumental in pinpointing the high-risk population for ASCVD among T2DM patients and facilitating timely clinical management.