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首页> 外文期刊>Endocrinology, Diabetes & Metabolism >Predictive scores for identifying patients with type 2 diabetes mellitus at risk of acute myocardial infarction and sudden cardiac death
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Predictive scores for identifying patients with type 2 diabetes mellitus at risk of acute myocardial infarction and sudden cardiac death

机译：用于鉴定2型糖尿病患者的预测分数，急性心肌梗死的风险和突然心脏死亡

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Introduction The present study evaluated the application of incorporating non-linear J/U-shaped relationships between mean HbA1c and cholesterol levels into risk scores for predicting acute myocardial infarction (AMI) and non-AMI-related sudden cardiac death (SCD) respectively, amongst patients with type 2 diabetes mellitus. Methods This was a territory-wide cohort study of patients with type 2 diabetes mellitus above the age 40 and free from prior AMI and SCD, with or without prescriptions of anti-diabetic agents between January 1st, 2009 to December 31st, 2009 at government-funded hospitals and clinics in Hong Kong. Patients recruited were followed up until 31 December 2019 or their date of death. Risk scores were developed for predicting incident AMI and non-AMI-related SCD. The performance of conditional inference survival forest (CISF) model compared to that of random survival forests (RSF) model and multivariate Cox model. Results This study included 261?308 patients (age?=?66.0?±?11.8?years old, male?=?47.6%, follow-up duration?=?3552?±?1201?days, diabetes duration?=?4.77?±?2.29?years). Mean HbA1c and low high-density lipoprotein-cholesterol (HDL-C) were significant predictors of AMI on multivariate Cox regression. Mean HbA1c was linearly associated with AMI, whilst HDL-C was inversely associated with AMI. Mean HbA1c and total cholesterol were significant multivariate predictors with a J-shaped relationship with non-AMI-related SCD. The AMI and SCD risk scores had an area under the curve (AUC) of 0.666 (95% confidence interval (CI)?=?[0.662, 0.669]) and 0.677 (95% CI?=?[0.673, 0.682]), respectively. CISF significantly improves prediction performance of both outcomes compared to RSF and multivariate Cox models. Conclusion A holistic combination of demographic, clinical and laboratory indices can be used for the risk stratification of patients with type 2 diabetes mellitus for AMI and SCD.
机译：引言本研究评估了将平均HBA1C和胆固醇水平之间的非线性J / U形关系掺入风险评分中，以预测急性心肌梗死（AMI）和非AMI相关的突发性心脏死亡（SCD）。患有2型糖尿病的患者。方法这是一个全港队列的糖尿病患者患者40岁以上的患者，并且在2009年1月1日至2009年12月31日在政府的抗糖尿病药剂的情况下，有或没有处于抗糖尿病药剂的患者。香港资助的医院和诊所。招聘的患者随访于2019年12月31日或其死亡日期。为预测事件AMI和非AMI相关的SCD而开发了风险分数。条件推断生存森林（CISF）模型的性能与随机生存林（RSF）模型和多元COX模型相比。结果本研究包括261名患者（年龄？=？66.0？±11.8？岁，男性？= 47.6％，随访持续时间？=？3552？±±1201？天，糖尿病持续时间？= 4.77 ？±2.29？年）。平均HBA1C和低密度脂蛋白 - 胆固醇（HDL-C）是在多元COX回归上的AMI的显着预测因子。平均HBA1C与AMI线性相关，而HDL-C与AMI相反。平均HBA1C和总胆固醇是具有与非AMI相关的SCD的J形关系的显着多元预测因子。 AMI和SCD风险得分在0.666（95％置信区间（CI）（CI）（CI）（CI）的曲线（AUC）下有一个区域（95％，0.669]）和0.677（95％CI？=？[0.673,0.682]），分别。与RSF和多元COX模型相比，CISF显着提高了结果的预测性能。结论人口统计学，临床和实验室指数的整体组合可用于AMI和SCD型糖尿病患者的风险分层。

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