PDF(Pubmed)
Abstract：
UNASSIGNED: The diagnosis of heart failure with preserved ejection fraction (HFpEF) in the clinical setting remains challenging, especially in patients with obesity.
UNASSIGNED: This study aimed to identify novel predictors of HFpEF well suited for patients with obesity.
UNASSIGNED: 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/m2) 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.
UNASSIGNED: 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; P < 0.001).
UNASSIGNED: 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.
UNASSIGNED: This study aimed to identify novel predictors of HFpEF well suited for patients with obesity.
UNASSIGNED: 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/m2) 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.
UNASSIGNED: 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; P < 0.001).
UNASSIGNED: 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.
摘要：
射血分数保留心力衰竭（HFpEF）的临床诊断仍然具有挑战性，尤其是肥胖患者。
■本研究旨在确定适合肥胖患者的HFpEF的新预测因子。
我们对一组特征明确的HFpEF肥胖患者（n=404；平均体重指数[BMI]36.6kg/m2）和对照组（n=67）进行了回顾性分析。我们使用机器学习算法GradientBoostingMachine来分析各种参数与HFpEF诊断的关联，并随后创建了用于诊断的多变量逻辑模型。
■梯度提升机确定的BMI，估计肾小球滤过率，左心室质量指数,左心房与左心室容积比是HFpEF的最强预测因子。这些变量用于建立模型，该模型鉴定HFpEF的灵敏度为0.83，特异性为0.82，曲线下面积（AUC）为0.88。具有乐观调整的AUC的模型的内部验证显示AUC为0.87。在研究的队列中，新评分优于H2FPEF评分(AUC:0.88vs0.74;P<0.001)。
■在患有肥胖症的HFpEF队列中，BMI,估计肾小球滤过率，左心室质量指数,左心房与左心室容积比与HFpEF的鉴定最相关，基于这些变量的评分（HFpEF-JH评分）优于当前使用的H2PEF评分。进一步验证这个新颖的分数是必要的，因为它可能有助于提高HFpEF的诊断准确性，特别是肥胖患者。
■本研究旨在确定适合肥胖患者的HFpEF的新预测因子。
我们对一组特征明确的HFpEF肥胖患者（n=404；平均体重指数[BMI]36.6kg/m2）和对照组（n=67）进行了回顾性分析。我们使用机器学习算法GradientBoostingMachine来分析各种参数与HFpEF诊断的关联，并随后创建了用于诊断的多变量逻辑模型。
■梯度提升机确定的BMI，估计肾小球滤过率，左心室质量指数,左心房与左心室容积比是HFpEF的最强预测因子。这些变量用于建立模型，该模型鉴定HFpEF的灵敏度为0.83，特异性为0.82，曲线下面积（AUC）为0.88。具有乐观调整的AUC的模型的内部验证显示AUC为0.87。在研究的队列中，新评分优于H2FPEF评分(AUC:0.88vs0.74;P<0.001)。
■在患有肥胖症的HFpEF队列中，BMI,估计肾小球滤过率，左心室质量指数,左心房与左心室容积比与HFpEF的鉴定最相关，基于这些变量的评分（HFpEF-JH评分）优于当前使用的H2PEF评分。进一步验证这个新颖的分数是必要的，因为它可能有助于提高HFpEF的诊断准确性，特别是肥胖患者。
