ImportanceAccurate risk prediction of morbidity and mortality in patients with heart failure with preserved ejection fraction (HFpEF) may help clinicians risk stratify and inform care decisions.ObjectiveTo develop and validate a novel prediction model for clinical outcomes in patients with HFpEF using routinely collected variables and to compare it with a biomarker-driven approach.Design, Setting, and ParticipantsData were used from the Dapagliflozin Evaluation to Improve the Lives of Patients With Preserved Ejection Fraction Heart Failure (DELIVER) trial to derive the prediction model, and data from the Angiotensin Receptor Neprilysin Inhibition in Heart Failure With Preserved Ejection Fraction (PARAGON-HF) and the Irbesartan in Heart Failure With Preserved Ejection Fraction Study (I-PRESERVE) trials were used to validate it. The outcomes were the composite of HF hospitalization (HFH) or cardiovascular death, cardiovascular death, and all-cause death. A total of 30 baseline candidate variables were selected in a stepwise fashion using multivariable analyses to create the models. Data were analyzed from January 2023 to June 2023.ExposuresModels to estimate the 1-year and 2-year risk of cardiovascular death or hospitalization for heart failure, cardiovascular death, and all-cause death.ResultsData from 6263 individuals in the DELIVER trial were used to derive the prediction model and data from 4796 individuals in the PARAGON-HF trial and 4128 individuals in the I-PRESERVE trial were used to validate it. The final prediction model for the composite outcome included 11 variables: N-terminal pro–brain natriuretic peptide (NT-proBNP) level, HFH within the past 6 months, creatinine level, diabetes, geographic region, HF duration, treatment with a sodium-glucose cotransporter 2 inhibitor, chronic obstructive pulmonary disease, transient ischemic attack/stroke, any previous HFH, and heart rate. This model showed good discrimination (C statistic at 1 year, 0.73; 95% CI, 0.71-0.75) in both validation cohorts (C statistic at 1 year, 0.71; 95% CI, 0.69-0.74 in PARAGON-HF and 0.75; 95% CI, 0.73-0.78 in I-PRESERVE) and calibration. The model showed similar discrimination to a biomarker-driven model including high-sensitivity cardiac troponin T and significantly better discrimination than the Meta-Analysis Global Group in Chronic (MAGGIC) risk score (C statistic at 1 year, 0.60; 95% CI, 0.58-0.63; delta C statistic, 0.13; 95% CI, 0.10-0.15; P < .001) and NT-proBNP level alone (C statistic at 1 year, 0.66; 95% CI, 0.64-0.68; delta C statistic, 0.07; 95% CI, 0.05-0.08; P < .001). Models derived for the prediction of all-cause and cardiovascular death also performed well. An online calculator was created to allow calculation of an individual’s risk.Conclusions and RelevanceIn this prognostic study, a robust prediction model for clinical outcomes in HFpEF was developed and validated using routinely collected variables. The model performed better than NT-proBNP level alone. The model may help clinicians to identify high-risk patients and guide treatment decisions in HFpEF.

中文翻译：

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射血分数保留的心力衰竭死亡率和发病率的预后模型

重要性准确预测射血分数保留的心力衰竭 (HFpEF) 患者的发病率和死亡率可能有助于临床医生进行风险分层并为护理决策提供信息。 目的使用常规收集的变量开发和验证 HFpEF 患者临床结果的新型预测模型，并将其与生物标志物驱动的方法进行比较。设计、设置和参与者使用达格列净评估以改善射血分数保留的心力衰竭患者的生活 (DELIVER) 试验的数据来推导预测模型，并使用血管紧张素受体脑啡肽酶的数据射血分数保留的心力衰竭 (PARAGON-HF) 和厄贝沙坦治疗射血分数保留的心力衰竭研究 (I-PRESERVE) 试验用于对其进行验证。结果是心衰住院（HFH）或心血管死亡、心血管死亡和全因死亡的复合结果。使用多变量分析以逐步方式选择总共 30 个基线候选变量来创建模型。分析了 2023 年 1 月至 2023 年 6 月的数据。Exposures模型估计 1 年和 2 年心血管死亡或因心力衰竭、心血管死亡和全因死亡住院的风险。结果使用了来自 DELIVER 试验中 6263 名个体的数据为了得出预测模型，并使用 PARAGON-HF 试验中的 4796 名受试者和 I-PRESERVE 试验中的 4128 名受试者的数据来对其进行验证。综合结果的最终预测模型包括 11 个变量：N 端脑钠肽前体 (NT-proBNP) 水平、过去 6 个月内的 HFH、肌酐水平、糖尿病、地理区域、HF 持续时间、钠盐治疗葡萄糖协同转运蛋白 2 抑制剂、慢性阻塞性肺疾病、短暂性脑缺血发作/中风、任何既往 HFH 和心率。该模型在两个验证队列中都显示出良好的区分度（1 年时的 C 统计量，0.73；95% CI，0.71-0.75）（PARAGON-HF 中的 1 年时的 C 统计量，0.71；95% CI，0.69-0.74，以及 0.75；95） % CI，I-PRESERVE 中为 0.73-0.78）和校准。该模型显示出与包括高敏心肌肌钙蛋白 T 在内的生物标志物驱动模型相似的辨别力，并且比慢性荟萃分析全球组 (MAGGIC) 风险评分显着更好的辨别力（1 年时的 C 统计量，0.60；95% CI，0.58） -0.63；δ C 统计量，0.13；95% CI，0.10-0.15；磷< .001）和单独的 NT-proBNP 水平（1 年时的 C 统计量，0.66；95% CI，0.64-0.68；δ C 统计量，0.07；95% CI，0.05-0.08；磷< .001）。用于预测全因死亡和心血管死亡的模型也表现良好。创建了一个在线计算器来计算个人的风险。结论和相关性在这项预后研究中，开发了一个针对 HFpEF 临床结果的稳健预测模型，并使用常规收集的变量进行了验证。该模型的表现优于单独的 NT-proBNP 水平。该模型可以帮助临床医生识别 HFpEF 的高危患者并指导治疗决策。