Analysis of influencing factors and construction of risk prediction model for cardio-cerebrovascular complications in patients with type 2 diabetes mellitus
10.3760/cma.j.cn114015-20240613-00439
- VernacularTitle:2型糖尿病患者发生心脑血管并发症的影响因素分析及风险预测模型构建
- Author:
Min RUI
1
;
Jianjie WANG
1
;
Zhigang LING
1
Author Information
1. 黄山市人民医院药剂科，黄山 245000
- Publication Type:Journal Article
- Keywords:
Diabetes mellitus, type 2;
Cardiovascular diseases;
Cerebrovascular disorders;
Medication literacy;
Beliefs about medications;
Medication adherence;
Cardio-
- From:
Adverse Drug Reactions Journal
2025;27(3):162-168
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To understand the influencing factors for cardio-cerebrovascular complications in patients with T2DM and construct a nomogram risk prediction.Methods:The study design was a prospective observational study, and the subjects were selected from hospitalized patients with T2DM admitted to Huangshan City People′s Hospital from May 2022 to April 2023. Data on patients' gender, age, body mass index, alcohol consumption, smoking status, family history of cardio-cerebrovascular diseases, insulin use, duration of diabetes, blood pressure, and routine laboratory test results were collected using the hospital electronic medical record system. At discharge, patients were assessed using the T2DM-Specific Medication Belief Scale (total score range: 10-50), Medication Literacy Assessment Scale (total score range: 0-7), and Morisky Medication Adherence Scale (total score range: 0-8). Patients were followed up by telephone for 6 months after discharge and divided into 2 groups based on the occurrence of cardio-cerebrovascular complications. Logistic regression analysis was performed using SPSS 26.0 software to identify influencing factors for cardio-cerebrovascular complications in T2DM patients. A nomogram prediction model was constructed using R 4.1.0 software, and internal validation of the model was conducted using the Bootstrap method.Results:A total of 294 T2DM patients were included in the analysis. The medication belief score was (32.6±5.6) score, the medication literacy score was (4.2±0.5) score, and the medication adherence score was (6.1±0.8) score. During the 6-month follow-up, a total of 43 patients (14.6%) experienced cardio-cerebrovascular complications, including of coronary heart disease (23 cases), heart failure (12 cases), and stroke (8 cases). Compared to patients without cardio-cerebrovascular complications, patients with complications had higher body mass index, glycosylated hemoglobin A1c (HbA1c), D-dimer, and uric acid levels, as well as lower medication belief scores, medication literacy scores, and medication adherence scores (all P<0.05). Binary logistic regression analysis showed that HbA1c, D-dimer, uric acid, medication belief, medication literacy, and medication adherence were influencing factors for cardio-cerebrovascular complications in T2DM patients. Accordingly, a nomogram prediction model was established. Internal validation results of the model showed that the concordance index was 0.958, the area under the receiver operating characteristic curve was 0.824, and the calibration curve was close to the ideal curve. Conclusions:The current status of medication belief, medication literacy, and medication adherence in T2DM patients was not ideal. High levels of HbA1c, D-dimer, and uric acid, as well as poor medication belief, medication literacy, and medication adherence were risk factors for cardio-cerebrovascular complications in T2DM patients. The nomogram model, which integrated multiple influencing factors, had high value in predicting the risks.
