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Summary: Background: The burden of heart failure (HF) is high globally, but information on its burden in the Eastern Mediterranean Region (EMR) is limited. This study provides a systematic analysis of the burden and underlying causes of HF in the EMR, including at the country level, between 1990 and 2019. Methods: We used the 2019 Global Burden of Disease (GBD) data for estimates of prevalence, years lived with disability (YLDs), and underlying causes of HF in the EMR. Age-standardised prevalence, YLDs, and underlying causes of HF were compared by 5-year age groups (considering 15 years old and more), sex (male and female), and countries. Findings: In contrast with the decreasing trend of HF burden globally, EMR showed an increasing trend. Globally, the HF age-standardised prevalence and YLDs decreased by 7.06% (95% UI: −7.22%, −6.9%) and 6.82% (95% UI: −6.98%, −6.66%) respectively, from 1990 to 2019. The HF age-standardised prevalence and YLDs in the EMR in 2019 were 706.43 (95% UI: 558.22–887.87) and 63.46 (95% UI: 39.82–92.59) per 100,000 persons, representing an increase of 8.07% (95% UI: 7.9%, 8.24%) and 8.79% (95% UI: 8.61%, 8.97%) from 1990, respectively. Amongst EMR countries, the age-standardised prevalence and YLDs were highest in Kuwait, while Pakistan consistently had the lowest HF burden. The dramatic increase of the age-standardised prevalence and YLDs were seen in Oman (28.79%; 95% UI: 28.51%, 29.07% and 29.56%; 95% UI: 29.28%, 29.84%), while Bahrain witnessed a reduction over the period shown (−9.66%; 95% UI: −9.84%, −9.48% and−9.14%; 95% UI: −9.32%, −8.96%). There were significant country-specific differences in trends of HF burden from 1990 to 2019. Males had relatively higher rates than females in all age groups. Among all causes of HF in 2019, ischemic heart disease accounted for the highest age-standardised prevalence and YLDs, followed by hypertensive heart disease. Interpretation: The burden of HF in the EMR was higher than the global, with increasing age-standardised prevalence and YLDs in countries of the region. A more comprehensive approach is needed to prevent underlying causes and improve medical care to control the burden of HF in the region. Funding: None.

Objective Cardiac rehabilitation (CR) is a comprehensive model of secondary preventive care. There is a wide variety in implementation characteristics globally, and hence quality control is paramount. Thus, the International Council of Cardiovascular Prevention and Rehabilitation was urged to develop a CR registry. The purpose of this study was to test the perceived usability of the International Cardiac Rehabilitation Registry (ICRR) to optimise it.Design This was a qualitative study, comprising virtual usability tests using a think-aloud method to elicit feedback on the ICRR, while end-users were entering patient data, followed by semistructured interviews.Setting Ultimately, 12 tests were conducted with CR staff (67% female) in low-resource settings from a variety of disciplines in all regions of the world but Europe before saturation was achieved.Primary outcome measure Participants completed the System Usability Scale. Interviews were transcribed verbatim except to preserve anonymity, and coded using NVIVO by two researchers independently. The Unified Theory of Acceptance and Use of Technology 2 informed analysis.Results The ICRR was established as easy to use, relevant, efficient, with easy learnability, operability, perceived usefulness, positive perceptions of output quality and high end-user satisfaction. System usability was 83.75, or ‘excellent’ and rated ‘A’. Four major themes were deduced from the interviews: (1) ease of approvals, adoption and implementation; (2) benefits for programmes, (3) variables and their definitions, as well as (4) patient report and follow-up assessment. Based on participant observation and utterances, suggestions for changes to the ICRR were implemented, including to the programme survey, on-boarding processes, navigational instructions, inclusion of programme logos, direction on handling unavailable data and optimising data completeness, as well as policies for authorship and programme certification.Conclusions With usability of the ICRR optimised, pilot testing shall ensue.

aim,1 Mohammed AL-Ithawi,1 Obada Mohammad Mansour,1 Anoud Hamati,1 Farah A Khraisat,2 Hadi Abu-Hantash41Cardiology Section, Internal Medicine Department, Specialty Hospital, Amman, Jordan; 2Cardiology Section, Internal Medicine Department, Jordan University Hospital, Amman, Jordan; 3Cardiology Section, Internal Medicine Department, King Abdullah University Hospital, Irbid, Jordan; 4Department of Cardiology, Amman Surgical Hospital, Amman, Jordan Correspondence: Mahmoud Izraiq, Email [email protected] Background: 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. Methodology: 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. Results: 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. Conclusion: 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. [ABSTRACT FROM AUTHOR]

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]

During the COVID-19 pandemic, the individual might feel isolated, and his psychological capital (PsyCap) is negatively influenced. Most research on peer support has focused on its contribution to employee well-being when the peers are in the same workplace. Scant research targets the times of lockdowns and social distancing. This paper investigates how peer support influences employee PsyCap during the COVID-19 pandemic. By conducting a questionnaire with N = 42 employees and three online focus groups with N = 15 employees out of N = 350 employees working at an international organization operating in Egypt as a bilateral development partner funded by the German government, the paper concludes that peer support has a positive influence on employee PsyCap, especially self-efficacy, hope, and optimism. [ABSTRACT FROM AUTHOR]