Impact of Diabetes Mellitus on Heart Failure Patients: Insights from a Comprehensive Analysis and Machine Learning Model Using the Jordanian Heart Failure Registry.

aim,¹ Abdel-Latif Mughrabi Sabbagh,¹ Karam I Khraim,¹ Ahmad A Toubasi,⁴ Abdullah Al-Kasasbeh,³ Sukaina Rawashdeh,³ Hadi Abu-Hantash⁵¹Cardiology Section, Internal Medicine Department, Specialty Hospital, Amman, Jordan; ²Department of Cardiology, Istishari Hospital, Amman, Jordan; ³Cardiology Section, Internal Medicine Department, King Abdullah University Hospital, Irbid, Jordan; ⁴Cardiology Section, Internal Medicine Department, Jordan University Hospital, Amman, Jordan; ⁵Department of Cardiology, Amman Surgical Hospital, Amman, Jordan Correspondence: Mahmoud Izraiq, Cardiology Section, Internal Medicine Department, Specialty Hospital, Amman, Jordan, Tel +962795652260, Email [email protected] Background: 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. Methods: 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. Results: 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. Conclusion: 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. [ABSTRACT FROM AUTHOR]