PDF(Pubmed)
Abstract：
UNASSIGNED: Heart failure (HF) is a global health challenge affecting millions, with significant variations in patient characteristics and outcomes based on ejection fraction. This study aimed to differentiate between HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF) with respect to patient characteristics, risk factors, comorbidities, and clinical outcomes, incorporating advanced machine learning models for mortality prediction.
UNASSIGNED: The study included 1861 HF patients from 21 centers in Jordan, categorized into HFrEF (EF <40%) and HFpEF (EF ≥ 50%) groups. Data were collected from 2021 to 2023, and machine learning models were employed for mortality prediction.
UNASSIGNED: Among the participants, 29.7% had HFpEF and 70.3% HFrEF. Significant differences were noted in demographics and comorbidities, with a higher prevalence of males, younger age, smoking, and familial history of premature ASCVD in the HFrEF group. HFpEF patients were typically older, with higher rates of diabetes, hypertension, and obesity. Machine learning analysis, mainly using the Random Forest Classifier, demonstrated significant predictive capability for mortality with an accuracy of 0.9002 and an AUC of 0.7556. Other models, including Logistic Regression, SVM, and XGBoost, also showed promising results. Length of hospital stay, need for mechanical ventilation, and number of hospital admissions were the top predictors of mortality in our study.
UNASSIGNED: The study underscores the heterogeneity in patient profiles between HFrEF and HFpEF. Integrating machine learning models offers valuable insights into mortality risk prediction in HF patients, highlighting the potential of advanced analytics in improving patient care and outcomes.
UNASSIGNED: The study included 1861 HF patients from 21 centers in Jordan, categorized into HFrEF (EF <40%) and HFpEF (EF ≥ 50%) groups. Data were collected from 2021 to 2023, and machine learning models were employed for mortality prediction.
UNASSIGNED: Among the participants, 29.7% had HFpEF and 70.3% HFrEF. Significant differences were noted in demographics and comorbidities, with a higher prevalence of males, younger age, smoking, and familial history of premature ASCVD in the HFrEF group. HFpEF patients were typically older, with higher rates of diabetes, hypertension, and obesity. Machine learning analysis, mainly using the Random Forest Classifier, demonstrated significant predictive capability for mortality with an accuracy of 0.9002 and an AUC of 0.7556. Other models, including Logistic Regression, SVM, and XGBoost, also showed promising results. Length of hospital stay, need for mechanical ventilation, and number of hospital admissions were the top predictors of mortality in our study.
UNASSIGNED: The study underscores the heterogeneity in patient profiles between HFrEF and HFpEF. Integrating machine learning models offers valuable insights into mortality risk prediction in HF patients, highlighting the potential of advanced analytics in improving patient care and outcomes.
摘要：
心力衰竭（HF）是影响数百万人的全球健康挑战，根据射血分数，患者特征和结局存在显著差异。本研究旨在根据患者特征区分射血分数降低的HF（HFrEF）和射血分数保留的HF（HFpEF）。危险因素，合并症,和临床结果，结合先进的机器学习模型进行死亡率预测。
■该研究包括来自约旦21个中心的1861名HF患者，分为HFrEF（EF<40％）和HFpEF（EF≥50％）组。数据是从2021年到2023年收集的，机器学习模型用于死亡率预测。
■在参与者中，29.7%有HFpEF,70.3%有HFrEF。人口统计学和合并症方面存在显著差异，男性患病率较高，年龄较小,吸烟,HFrEF组有早熟ASCVD家族史。HFpEF患者通常年龄较大，糖尿病发病率较高，高血压,和肥胖。机器学习分析,主要使用随机森林分类器，表现出显著的死亡率预测能力,准确度为0.9002,AUC为0.7556.其他型号,包括Logistic回归，SVM,和XGBoost,也显示出有希望的结果。住院时间,需要机械通风，和住院人数是我们研究中死亡率的主要预测因素.
■该研究强调了HFrEF和HFpEF之间患者特征的异质性。整合机器学习模型为HF患者的死亡风险预测提供了有价值的见解，强调高级分析在改善患者护理和结果方面的潜力。
■该研究包括来自约旦21个中心的1861名HF患者，分为HFrEF（EF<40％）和HFpEF（EF≥50％）组。数据是从2021年到2023年收集的，机器学习模型用于死亡率预测。
■在参与者中，29.7%有HFpEF,70.3%有HFrEF。人口统计学和合并症方面存在显著差异，男性患病率较高，年龄较小,吸烟,HFrEF组有早熟ASCVD家族史。HFpEF患者通常年龄较大，糖尿病发病率较高，高血压,和肥胖。机器学习分析,主要使用随机森林分类器，表现出显著的死亡率预测能力,准确度为0.9002,AUC为0.7556.其他型号,包括Logistic回归，SVM,和XGBoost,也显示出有希望的结果。住院时间,需要机械通风，和住院人数是我们研究中死亡率的主要预测因素.
■该研究强调了HFrEF和HFpEF之间患者特征的异质性。整合机器学习模型为HF患者的死亡风险预测提供了有价值的见解，强调高级分析在改善患者护理和结果方面的潜力。
