Department of Cardiology, Shaoxing People's Hospital, Shaoxing Hospital of Zhejiang University, Shaoxing, China.
The First Clinical Medical College, Wenzhou Medical University, Wenzhou, China.
Front Public Health. 2022 Sep 6;10:947204. doi: 10.3389/fpubh.2022.947204. eCollection 2022.
In recent years, the prevalence of type 2 diabetes mellitus (T2DM) has increased annually. The major complication of T2DM is cardiovascular disease (CVD). CVD is the main cause of death in T2DM patients, particularly those with comorbid acute coronary syndrome (ACS). Although risk prediction models using multivariate logistic regression are available to assess the probability of new-onset ACS development in T2DM patients, none have been established using machine learning (ML).
Between January 2019 and January 2020, we enrolled 521 T2DM patients with new-onset ACS or no ACS from our institution's medical information recording system and divided them into a training dataset and a testing dataset. Seven ML algorithms were used to establish models to assess the probability of ACS coupled with 5-cross validation.
We established a nomogram to assess the probability of newly diagnosed ACS in T2DM patients with an area under the curve (AUC) of 0.80 in the testing dataset and identified some key features: family history of CVD, history of smoking and drinking, aspartate aminotransferase level, age, neutrophil count, and Killip grade, which accelerated the development of ACS in patients with T2DM. The AUC values of the seven ML models were 0.70-0.96, and random forest model had the best performance (accuracy, 0.89; AUC, 0.96; recall, 0.83; precision, 0.91; F1 score, 0.87).
ML algorithms, especially random forest model (AUC, 0.961), had higher performance than conventional logistic regression (AUC, 0.801) for assessing new-onset ACS probability in T2DM patients with excellent clinical and diagnostic value.
近年来，2 型糖尿病（T2DM）的患病率呈逐年上升趋势。T2DM 的主要并发症是心血管疾病（CVD）。CVD 是 T2DM 患者死亡的主要原因，尤其是合并急性冠状动脉综合征（ACS）的患者。尽管使用多变量逻辑回归的风险预测模型可用于评估 T2DM 患者新发 ACS 发生的概率，但尚未使用机器学习（ML）建立模型。
本研究纳入了 2019 年 1 月至 2020 年 1 月期间来自我院医疗信息记录系统的 521 例新发 ACS 或无 ACS 的 T2DM 患者，将其分为训练数据集和测试数据集。使用 7 种 ML 算法建立模型来评估 ACS 发生的概率，并进行了 5 次交叉验证。
我们建立了一个列线图来评估 T2DM 患者新发 ACS 的概率，在测试数据集中的 AUC 为 0.80，并确定了一些关键特征：CVD 家族史、吸烟和饮酒史、天冬氨酸转氨酶水平、年龄、中性粒细胞计数和 Killip 分级，这些特征加速了 T2DM 患者 ACS 的发展。7 种 ML 模型的 AUC 值为 0.70-0.96，随机森林模型的性能最佳（准确率为 0.89；AUC 为 0.96；召回率为 0.83；精密度为 0.91；F1 评分为 0.87）。
ML 算法，尤其是随机森林模型（AUC，0.961），在评估 T2DM 患者新发 ACS 概率方面的性能优于传统逻辑回归（AUC，0.801），具有出色的临床和诊断价值。
