Bermea Kevin C, Lovell Jana P, Hays Allison G, Goerlich Erin, Vungarala Soumya, Jani Vivek, Shah Sanjiv J, Sharma Kavita, Adamo Luigi
Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
JACC Adv. 2024 Jun 7;3(7):101040. doi: 10.1016/j.jacadv.2024.101040. eCollection 2024 Jul.
The diagnosis of heart failure with preserved ejection fraction (HFpEF) in the clinical setting remains challenging, especially in patients with obesity.
This study aimed to identify novel predictors of HFpEF well suited for patients with obesity.
We performed a retrospective analysis of a well-characterized cohort of patients with obesity with HFpEF (n = 404; mean body mass index [BMI] 36.6 kg/m) and controls (n = 67). We used the machine learning algorithm Gradient Boosting Machine to analyze the association of various parameters with the diagnosis of HFpEF and subsequently created a multivariate logistic model for the diagnosis.
Gradient Boosting Machine identified BMI, estimated glomerular filtration rate, left ventricular mass index, and left atrial to left ventricular volume ratio as the strongest predictors of HFpEF. These variables were used to build a model that identified HFpEF with a sensitivity of 0.83, a specificity of 0.82, and an area under the curve (AUC) of 0.88. Internal validation of the model with optimism-adjusted AUC showed an AUC of 0.87. Within the studied cohort, the novel score outperformed the H2FPEF score (AUC: 0.88 vs 0.74; < 0.001).
In a HFpEF cohort with obesity, BMI, estimated glomerular filtration rate, left ventricular mass index, and left atrial to left ventricular volume ratio most correlated with the identification of HFpEF, and a score based on these variables (HFpEF-JH score) outperformed the currently used H2PEF score. Further validation of this novel score is warranted, as it may facilitate improved diagnostic accuracy of HFpEF, particularly in patients with obesity.
在临床环境中，射血分数保留的心力衰竭（HFpEF）的诊断仍然具有挑战性，尤其是在肥胖患者中。
本研究旨在确定非常适合肥胖患者的HFpEF新预测指标。
我们对一组特征明确的肥胖HFpEF患者（n = 404；平均体重指数[BMI] 36.6 kg/m）和对照组（n = 67）进行了回顾性分析。我们使用机器学习算法梯度提升机来分析各种参数与HFpEF诊断之间的关联，并随后创建了一个用于诊断的多变量逻辑模型。
梯度提升机确定BMI、估计肾小球滤过率、左心室质量指数和左心房与左心室容积比是HFpEF的最强预测指标。这些变量被用于构建一个模型，该模型识别HFpEF的灵敏度为0.83，特异度为0.82，曲线下面积（AUC）为0.88。对该模型进行乐观度调整后的AUC内部验证显示AUC为0.87。在研究队列中，新评分优于H2FPEF评分（AUC：0.88对0.74；P < 0.001）。
在肥胖的HFpEF队列中，BMI、估计肾小球滤过率、左心室质量指数和左心房与左心室容积比与HFpEF的识别最相关，基于这些变量的评分（HFpEF-JH评分）优于目前使用的H2PEF评分。由于该新评分可能有助于提高HFpEF的诊断准确性，特别是在肥胖患者中，因此有必要对其进行进一步验证。
