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评估伴有长期动脉粥样硬化性心血管疾病(ASCVD)危险因素的2型糖尿病患者的死亡风险:纳入冠状动脉钙化(CAC)评分的包容性博爱预测评分系统。

Assessing mortality risk in Type 2 Diabetes patients with prolonged ASCVD risk factors: the inclusive Poh-Ai predictive scoring system with CAC Score integration.

作者信息

Lei Meng-Huan, Hsu Yu-Chen, Chung Sheng-Liang, Chen Chao-Chin, Chen Wei-Cheng, Chen Wan-Ming, Jao An-Tzu, Hsiao Ju-Feng, Hsu Jen-Te, Wu Szu-Yuan

机构信息

Division of Cardiology, Department of Internal Medicine, Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, No. 83, Nanchang St., Luodong Township, Yilan County, 265, Taiwan.

Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City, Taiwan.

出版信息

Diabetol Metab Syndr. 2024 May 19;16(1):104. doi: 10.1186/s13098-024-01341-9.

Abstract

PURPOSE

To enhance the predictive risk model for all-cause mortality in individuals with Type 2 Diabetes (T2DM) and prolonged Atherosclerotic Cardiovascular Disease (ASCVD) risk factors. Despite the utility of the Coronary Artery Calcium (CAC) score in assessing cardiovascular risk, its capacity to predict all-cause mortality remains limited.

METHODS

A retrospective cohort study included 1929 asymptomatic T2DM patients with ASCVD risk factors, aged 40-80. Variables encompassed demographic attributes, clinical parameters, CAC scores, comorbidities, and medication usage. Factors predicting all-cause mortality were selected to create a predictive scoring system. By using stepwise selection in a multivariate Cox proportional hazards model, we divided the patients into three risk groups.

RESULTS

In our analysis of all-cause mortality in T2DM patients with extended ASCVD risk factors over 5 years, we identified significant risk factors, their adjusted hazard ratios (aHR), and scores: e.g., CAC score > 1000 (aHR: 1.57, score: 2), CAC score 401-1000 (aHR: 2.05, score: 2), and more. These factors strongly predict all-cause mortality, with varying risk groups (e.g., very low-risk: 2.0%, very high-risk: 24.0%). Significant differences in 5-year overall survival rates were observed among these groups (log-rank test < 0.001).

CONCLUSION

The Poh-Ai Predictive Scoring System excels in forecasting mortality and cardiovascular events in individuals with Type 2 Diabetes Mellitus and extended ASCVD risk factors.

摘要

目的

增强2型糖尿病(T2DM)患者及具有长期动脉粥样硬化性心血管疾病(ASCVD)危险因素个体的全因死亡率预测风险模型。尽管冠状动脉钙化(CAC)评分在评估心血管风险方面具有实用性,但其预测全因死亡率的能力仍然有限。

方法

一项回顾性队列研究纳入了1929名年龄在40 - 80岁、有ASCVD危险因素的无症状T2DM患者。变量包括人口统计学特征、临床参数、CAC评分、合并症和用药情况。选择预测全因死亡率的因素以创建一个预测评分系统。通过在多变量Cox比例风险模型中采用逐步选择法,我们将患者分为三个风险组。

结果

在我们对具有延长的ASCVD危险因素的T2DM患者5年全因死亡率的分析中,我们确定了显著的危险因素、其调整后的风险比(aHR)和评分:例如,CAC评分>1000(aHR:1.57,评分:2),CAC评分401 - 1000(aHR:2.05,评分:2)等等。这些因素能有力地预测全因死亡率,不同风险组(例如,极低风险:2.0%,极高风险:24.0%)情况各异。这些组之间观察到5年总生存率存在显著差异(对数秩检验<0.001)。

结论

Poh - Ai预测评分系统在预测2型糖尿病和具有延长的ASCVD危险因素个体的死亡率和心血管事件方面表现出色。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7f7/11103845/9a18d99ccad2/13098_2024_1341_Fig1_HTML.jpg

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