Izraiq Mahmoud, Almousa Eyas, Hammoudeh Suhail, Sudqi Mazen, Ahmed Yaman B, Abu-Dhaim Omran A, Mughrabi Sabbagh Abdel-Latif, Khraim Karam I, Toubasi Ahmad A, Al-Kasasbeh Abdullah, Rawashdeh Sukaina, Abu-Hantash Hadi
Cardiology Section, Internal Medicine Department, Specialty Hospital, Amman, Jordan.
Department of Cardiology, Istishari Hospital, Amman, Jordan.
Int J Gen Med. 2024 May 18;17:2253-2264. doi: 10.2147/IJGM.S465169. eCollection 2024.
Heart failure (HF) is a common final pathway of various insults to the heart, primarily from risk factors including diabetes mellitus (DM) type 2. This study analyzed the clinical characteristics of HF in a Jordanian population with a particular emphasis on the relationship between DM and HF.
This prospective study used the Jordanian Heart Failure Registry (JoHFR) data. Patients with HF were characterized by DM status and HF type: HF with preserved ejection fraction (HFpEF) or HF with reduced ejection fraction (HFrEF). Demographics, clinical presentations, and treatment outcomes were collected. Statistical analyses and machine learning techniques were carried out for the prediction of mortality among HF patients: Recursive Feature Elimination with Cross-Validation (RFECV) and Synthetic Minority Over-sampling Technique with Edited Nearest Neighbors (SMOTEENN) were employed.
A total of 2007 patients with HF were included. Notable differences between diabetic and non-diabetic patients are apparent. Diabetic patients were predominantly male, older, and obese (p < 0.001 for all). A higher incidence of HFpEF was observed in the diabetes cohort (p = 0.006). Also, diabetic patients had significantly higher levels of cholesterol (p = 0.008) and LDL (p = 0.003), reduced hemoglobin levels (p < 0.001), and more severe renal impairment (eGFR; p = 0.006). Machine learning models, particularly the Random Forest Classifier, highlighted its superiority in mortality prediction, with an accuracy of 90.02% and AUC of 80.51%. Predictors of mortality included creatinine levels >115 µmol/L, length of hospital stay, and need for mechanical ventilation.
This study underscores notable differences in clinical characteristics and outcomes between diabetic and non-diabetic heart failure patients in Jordan. Diabetic patients had higher prevalence of HFpEF and poorer health indicators such as elevated cholesterol, LDL, and impaired kidney function. High creatinine levels, longer hospital stays, and the need for mechanical ventilation were key predictors of mortality.
心力衰竭（HF）是各种心脏损伤的常见最终结局，主要源于包括2型糖尿病（DM）在内的危险因素。本研究分析了约旦人群中HF的临床特征，特别关注DM与HF之间的关系。
这项前瞻性研究使用了约旦心力衰竭登记处（JoHFR）的数据。HF患者根据DM状态和HF类型进行特征描述：射血分数保留的HF（HFpEF）或射血分数降低的HF（HFrEF）。收集了人口统计学、临床表现和治疗结果。对HF患者的死亡率预测进行了统计分析和机器学习技术：采用了带交叉验证的递归特征消除（RFECV）和带编辑最近邻的合成少数过采样技术（SMOTEENN）。
共纳入2007例HF患者。糖尿病患者和非糖尿病患者之间存在明显差异。糖尿病患者以男性居多，年龄较大且肥胖（所有p<0.001）。糖尿病队列中HFpEF的发病率较高（p=0.006）。此外，糖尿病患者的胆固醇（p=0.008）和低密度脂蛋白（LDL）水平显著更高，血红蛋白水平降低（p<0.001），肾功能损害更严重（估算肾小球滤过率；p=0.006）。机器学习模型，尤其是随机森林分类器，在死亡率预测方面表现出优越性，准确率为90.02%，曲线下面积为80.51%。死亡率的预测因素包括肌酐水平>115µmol/L、住院时间和机械通气需求。
本研究强调了约旦糖尿病和非糖尿病心力衰竭患者在临床特征和结局方面的显著差异。糖尿病患者HFpEF的患病率较高，且健康指标较差，如胆固醇、LDL升高和肾功能受损。高肌酐水平、较长的住院时间和机械通气需求是死亡率的关键预测因素。
