Mao Lin Shuang, Geng Liang, Wang Yi Xuan, Qi Yang, Wang Min Hui, Ding Feng Hua, Dai Yang, Lu Lin, Zhang Qi, Shen Wei Feng, Shen Ying
Department of Cardiovascular Medicine, School of Medicine, Rui Jin Hospital, Shanghai Jiao Tong University, Shanghai, 200025, China.
Department of Cardiovascular Medicine, Shanghai Eastern Hospital, Tongji University School of Medicine, Shanghai, 200120, China.
BMC Cardiovasc Disord. 2025 Apr 2;25(1):250. doi: 10.1186/s12872-025-04687-8.
This study sought to develop and externally validate a score that predicts the probability for poor coronary collateralization (CC) in stable angina patients with type 2 diabetes mellitus (T2DM).
Clinical and laboratory variables were collected on admission in 1022 T2DM patients with chronic total occlusion (CTO). Coronary collaterals with Rentrop score 0 or 1 were considered as poor CC. Multivariable logistic regression analysis was used to identify independent predictors for poor CC. The external validation cohort comprised 234 T2DM patients with CTO selected randomly from an independent external center.
Eight predictors were independently associated with poor CC and applied to construct the risk model. A score incorporating these factors predicted poor CC, ranging from 7% when all factors were absent to 97% when ≥ 7 factors were present. Internal validation showed an AUC of 0.748 (95%CI, 0.695-0.795) and the external validation had an AUC of 0.754 (95%CI, 0.694-0.808). A cumulative predictive score was developed by summing points assigned to each factor based on its regression coefficient. Smoking and neutrophil > 6.5 × 10⁹/L were assigned 3 points, female gender, hypercholesterolemia, and eGFR < 60 mL/min/1.73 m² were assigned 2 points, age > 65 years, hypertension, and HbA1c > 6.5% were assigned 1 point. The optimal cutoff score was 4 for predicting poor CC with sensitivity 75.4% and specificity 64.1%.
We have demonstrated a risk score based on clinical and laboratory characteristics providing an easy-to-use tool to predict poor CC in T2DM patients with stable coronary artery disease.
NCT06054126 Date of registration: September 19th, 2023.
本研究旨在开发并外部验证一种评分系统,用于预测2型糖尿病(T2DM)稳定型心绞痛患者冠状动脉侧支循环不良(CC)的概率。
收集了1022例慢性完全闭塞(CTO)的T2DM患者入院时的临床和实验室变量。Rentrop评分0或1的冠状动脉侧支被视为CC不良。采用多变量逻辑回归分析确定CC不良的独立预测因素。外部验证队列包括从独立外部中心随机选取的234例CTO的T2DM患者。
八个预测因素与CC不良独立相关,并用于构建风险模型。包含这些因素的评分可预测CC不良,当所有因素均不存在时为7%,当存在≥7个因素时为97%。内部验证显示曲线下面积(AUC)为0.748(95%可信区间,0.695 - 0.795),外部验证的AUC为0.754(95%可信区间,0.694 - 0.808)。通过根据每个因素的回归系数分配分数并求和来制定累积预测评分。吸烟和中性粒细胞>6.5×10⁹/L各得3分,女性、高胆固醇血症和估算肾小球滤过率(eGFR)<60 mL/min/1.73 m²各得2分,年龄>65岁、高血压和糖化血红蛋白(HbA1c)>6.5%各得1分。预测CC不良的最佳截断评分为4分,敏感性为75.4%,特异性为64.1%。
我们展示了一种基于临床和实验室特征的风险评分,为预测T2DM稳定型冠心病患者CC不良提供了一种易于使用的工具。
NCT06054126 注册日期:2023年9月19日