Cardiology Section, Internal Medicine Department, Specialty Hospital, Amman 12344, Jordan.
Cardiology Section, Internal Medicine Department, King Abdullah University Hospital, Irbid 22110, Jordan.
Medicina (Kaunas). 2024 May 19;60(5):831. doi: 10.3390/medicina60050831.
Heart failure (HF) is a prevalent and debilitating condition that imposes a significant burden on healthcare systems and adversely affects the quality of life of patients worldwide. Comorbidities such as chronic kidney disease (CKD), arterial hypertension, and diabetes mellitus (DM) are common among HF patients, as they share similar risk factors. This study aimed to identify the prognostic significance of multiple factors and their correlation with disease prognosis and outcomes in a Jordanian cohort. Materials Data from the Jordanian Heart Failure Registry (JoHFR) were analyzed, encompassing medical records from acute and chronic HF patients attending public and private cardiology clinics and hospitals across Jordan. An online form was utilized for data collection, focusing on three kidney function tests, estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN), and creatinine levels, with the eGFR calculated using the Cockcroft-Gault formula. We also built six machine learning models to predict mortality in our cohort. From the JoHFR, 2151 HF patients were included, with 644, 1799, and 1927 records analyzed for eGFR, BUN, and creatinine levels, respectively. Age negatively impacted all measures ( ≤ 0.001), while smokers surprisingly showed better results than non-smokers ( ≤ 0.001). Males had more normal eGFR levels compared to females ( = 0.002). Comorbidities such as hypertension, diabetes, arrhythmias, and implanted devices were inversely related to eGFR (all with -values <0.05). Higher BUN levels were associated with chronic HF, dyslipidemia, and ASCVD ( ≤ 0.001). Higher creatinine levels were linked to hypertension, diabetes, dyslipidemia, arrhythmias, and previous HF history (all with -values <0.05). Low eGFR levels were associated with increased mechanical ventilation needs ( = 0.049) and mortality ( ≤ 0.001), while BUN levels did not significantly affect these outcomes. Machine learning analysis employing the Random Forest Classifier revealed that length of hospital stay and creatinine >115 were the most significant predictors of mortality. The classifier achieved an accuracy of 90.02% with an AUC of 80.51%, indicating its efficacy in predictive modeling. This study reveals the intricate relationship among kidney function tests, comorbidities, and clinical outcomes in HF patients in Jordan, highlighting the importance of kidney function as a predictive tool. Integrating machine learning models into clinical practice may enhance the predictive accuracy of patient outcomes, thereby supporting a more personalized approach to managing HF and related kidney dysfunction. Further research is necessary to validate these findings and to develop innovative treatment strategies for the CKD population within the HF cohort.
心力衰竭（HF）是一种普遍且虚弱的病症，给医疗体系带来了巨大负担，并对全球患者的生活质量产生了不利影响。慢性肾脏病（CKD）、动脉高血压和糖尿病（DM）等合并症在 HF 患者中很常见，因为它们具有相似的危险因素。本研究旨在确定多种因素的预后意义及其与疾病预后和结局的相关性，研究对象为约旦队列。
分析了来自约旦心力衰竭注册中心（JoHFR）的数据，这些数据来自约旦公立和私立心脏病学诊所和医院的急性和慢性 HF 患者的医疗记录。使用在线表格收集数据，重点关注三个肾功能测试，即估算肾小球滤过率（eGFR）、血尿素氮（BUN）和肌酐水平，其中 eGFR 使用 Cockcroft-Gault 公式计算。我们还构建了六个机器学习模型来预测我们队列中的死亡率。
从 JoHFR 中纳入了 2151 例 HF 患者，分别对 eGFR、BUN 和肌酐水平的 644、1799 和 1927 份记录进行了分析。年龄对所有指标均有负面影响（≤0.001），而令人惊讶的是，吸烟者的结果优于非吸烟者（≤0.001）。男性的 eGFR 水平比女性更高（=0.002）。高血压、糖尿病、心律失常和植入设备等合并症与 eGFR 呈负相关（所有-值<0.05）。较高的 BUN 水平与慢性 HF、血脂异常和 ASCVD 相关（≤0.001）。较高的肌酐水平与高血压、糖尿病、血脂异常、心律失常和既往 HF 病史相关（所有-值<0.05）。较低的 eGFR 水平与机械通气需求增加（=0.049）和死亡率相关（≤0.001），而 BUN 水平对这些结局没有显著影响。使用随机森林分类器的机器学习分析表明，住院时间和肌酐>115 是死亡率的最重要预测因素。该分类器的准确性为 90.02%，AUC 为 80.51%，表明其在预测建模中的有效性。
本研究揭示了约旦 HF 患者肾功能测试、合并症和临床结局之间的复杂关系，强调了肾功能作为预测工具的重要性。将机器学习模型纳入临床实践可能会提高患者结局的预测准确性，从而支持对 HF 及其相关肾功能障碍患者进行更个性化的治疗。需要进一步研究来验证这些发现，并为 HF 队列中的 CKD 人群开发创新的治疗策略。
