Department of Health Policy and Management, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, United States.
Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, United States.
Diabetes Obes Metab. 2022 Nov;24(11):2203-2211. doi: 10.1111/dom.14806. Epub 2022 Aug 8.
Early identification and prediction of incident heart failure (HF) is important because of severe morbidity and mortality. This study aimed to predict onset of HF among patients with diabetes.
A time-varying Cox model was derived from ACCORD clinical trial to predict the risk of incident HF, defined by hospitalization for HF (HHF). External validation was performed on patient-level data from the Harmony Outcome trial and Chronic Renal Insufficiency Cohort (CRIC) study. The model was transformed into an integer-based scoring algorithm for 10-year risk evaluation. A stepwise algorithm identified and selected predictors from demographic characteristics, physical examination, laboratory results, medical history, medication and health care utilization, to develop a risk prediction model. The main outcome was incident HF, defined by HHF. The C statistic and Brier score were used to assess model performance.
In total, 9649 patients with diabetes free of HF were used, with median follow-up of 4 years and 299 incident hospitalization of HF events. The model identified several predictors for the 10-year HF incidence risk score 'DM-CURE': socio-Demographic [education, age at type 2 diabetes (T2DM) diagnosis], Metabolic (glycated haemoglobin, systolic blood pressure, body mass index, high-density lipoproteins), diabetes-related Complications (myocardial infarction, revascularization, cardiovascular medications, neuropathy, hypertension duration, albuminuria, urine albumin-to-creatinine ratio, End Stage Kidney Disease), and health care Utilization (all-cause hospitalization, emergency room visits) for Risk Evaluation. Among them, the strongest impact factors for future HF were age at T2DM diagnosis, health care utilization and cardiovascular disease-related variables. The model showed good discrimination (C statistic: 0.838, 95% CI: 0.821-0.855) and calibration (Brier score: 0.006, 95% CI: 0.006-0.007) in the ACCORD data and good performance in the validation data (Harmony: C statistic: 0.881, 95% CI: 0.863-0.899; CRIC: C statistic: 0.813, 95% CI: 0.794-0.833). The 10-year risk of incident HF increased in a graded fashion, from ≤1% in quintile 1 (score ≤14), 1%-5% in quintile 2 (score 15-23), 5%-10% in quintile 3 (score 24-27), 10%-20% in quintile 4 (score 28-33) and ≥20% in quintile 5 (score >33).
The DM-CURE model and score were useful for population risk stratification of incident HHF among patients with T2DM and can be easily applied in clinical practice.
由于发病率和死亡率高，早期识别和预测心力衰竭（HF）的发生非常重要。本研究旨在预测糖尿病患者发生 HF 的情况。
采用 ACCORD 临床试验中的时变 Cox 模型预测 HF 事件的发生风险，HF 事件定义为因 HF 住院（HHF）。采用来自 Harmony 试验和慢性肾脏病队列（CRIC）研究的患者水平数据进行外部验证。该模型被转化为基于整数的 10 年风险评估评分算法。逐步算法从人口统计学特征、体格检查、实验室结果、既往病史、药物和医疗保健利用中识别和选择预测因素，以开发风险预测模型。主要结局是因 HHF 而发生的 HF。使用 C 统计量和 Brier 评分评估模型性能。
共纳入 9649 例无 HF 的糖尿病患者，中位随访时间为 4 年，有 299 例发生 HHF 事件。该模型确定了用于 10 年 HF 发生风险评分“DM-CURE”的几个预测因素：社会人口统计学[教育程度、2 型糖尿病（T2DM）诊断时的年龄]、代谢[糖化血红蛋白、收缩压、体重指数、高密度脂蛋白]、糖尿病相关并发症[心肌梗死、血运重建、心血管药物、神经病、高血压持续时间、白蛋白尿、尿白蛋白与肌酐比值、终末期肾病]和医疗保健利用（全因住院、急诊就诊）用于风险评估。其中，对未来 HF 影响最大的因素是 T2DM 诊断时的年龄、医疗保健利用和心血管疾病相关变量。该模型在 ACCORD 数据中显示出良好的区分度（C 统计量：0.838，95%CI：0.821-0.855）和校准度（Brier 评分：0.006，95%CI：0.006-0.007），在验证数据中表现良好（Harmony：C 统计量：0.881，95%CI：0.863-0.899；CRIC：C 统计量：0.813，95%CI：0.794-0.833）。HF 事件的 10 年发生风险呈梯度增加，从第 1 五分位数（评分≤14）的≤1%，第 2 五分位数（评分 15-23）的 1%-5%，第 3 五分位数（评分 24-27）的 5%-10%，第 4 五分位数（评分 28-33）的 10%-20%，到第 5 五分位数（评分>33）的≥20%。
DM-CURE 模型和评分可用于 T2DM 患者 HHF 事件的人群风险分层，易于在临床实践中应用。
