Atrial fibrillation inpatient management patterns and clinical outcomes during the conflict in Syria: An observational cohort study.

BACKGROUND: Atrial fibrillation (AF) is the most common sustained arrhythmia worldwide. However, there is no data on AF inpatient management strategies and clinical outcomes in Syria. OBJECTIVES: The study aims were to review the inpatient management of patients with AF and assess cardiovascular (CV) mortality in a tertiary cardiology centre in Latakia, Syria. METHODS: A single-centre retrospective observational cohort study was conducted at Tishreen's University Hospital, Latakia, Syria, from June 2021 to June 2023. Patients ≥16 years of age presenting and being treated for AF as the primary diagnosis with or without a thromboembolic event were included. Medical records were examined for patients' demographics, laboratory results, treatment plans and inpatient details. Studied outcomes include inpatient all-cause and CV mortality, ischemic and bleeding events, and conversion to sinus rhythm (SR). RESULTS: The study included 596 patients. The median age was 58, and 61% were males. 121 patients (20.3%) were known to have AF. A rhythm control strategy was pursued in 39% of patients. Ischemic and bleeding events occurred in 62 (11%) and 12 (2%), respectively. CV and all-cause mortality occurred in 28 (4.7%) and 31 patients (5%), respectively. The presence of valvular heart disease (VHD) (adjusted odds ratio (aOR) = 9.1, 95% confidence interval (CI): 1.7 to 55.1, p < .001), thyroid disease (aOR: 9.7, 95% CI = 1.2 to 91.6, p < .001) and chronic obstructive pulmonary disease (COPD) (aOR: 82, 95% CI: 12.7 to 71, p < .001) were independent risk factors of increased CV inpatient mortality. CONCLUSION: Syrian inpatients admitted with AF in Latakia are relatively younger than those in other countries. Active thyroid disease, COPD and VHD were independent risk factors of inpatient CV mortality with AF.

The Student Grand Round: A Peer Teaching Initiative.

Introduction Oral presentation and public speaking skills are poorly emphasised in the medical school curriculum. The student grand round was created to tackle this deficiency by changing the way in which students are taught, from traditional lecture-based learning to interactive small-group peer-to-peer teaching. This approach encourages students to become responsible for their own learning, develop their public speaking and teaching skills, as well as identify and address gaps in their knowledge. Aims The primary aims of this study were to determine the understanding of students before and after peer teaching, including retention of concepts via quiz scores and confidence of students in giving SBAR (Situation, Background, Assessment, Recommendation) handovers. The secondary aim is to determine the place of student-led grand round teaching in the medical curriculum as a means of developing teaching skills and encouraging active learning. Methods A cohort of 21 third-year medical students from Leicester University attended a weekly peer teaching programme where students presented a case they had encountered during their clinical attachment. Peer teachers were required to research some background and pathophysiology regarding the topic and teach in an interactive manner and create discussion regarding the topic. The students then summarised the case and practised the skill of concise handovers using the SBAR format. Knowledge and understanding were assessed with an interactive quiz, and feedback via a survey was gathered before and after sessions. Each student engaged in case discussion and received input from a specialty registrar regarding their presentation skills, case knowledge, and SBAR handover. Results Individual and combined session analysis demonstrated a significant improvement in scores across understanding the topic and confidence in SBAR. Student recommendation for the session cumulatively was significant (p=0.02); however, comparison of medical student recommendations of individual sessions did not yield statistically significant results. There was a significant improvement in the overall quiz score (p=0.045), and average scores improved from 51% to 70% (p=0.043). There was a significant increase in the mean quiz result after the first two sessions (28-55% (p=0.002) and 56-85% (p=0.0001), respectively). Summary The student grand round is a promising teaching initiative that capitalises on peer teaching, a valuable learning theory that centres around students taking on the role of teachers to instruct their peers. Results from this study have shown that this method of collaborative teaching is effective in improving the understanding of medical topics, increases confidence in public speaking and precise handover skills, and therefore better prepares medical students for their career as future clinicians.

Artificial intelligence for ventricular arrhythmia capability using ambulatory electrocardiograms.

Aims: European and American clinical guidelines for implantable cardioverter defibrillators are insufficiently accurate for ventricular arrhythmia (VA) risk stratification, leading to significant morbidity and mortality. Artificial intelligence offers a novel risk stratification lens through which VA capability can be determined from the electrocardiogram (ECG) in normal cardiac rhythm. The aim of this study was to develop and test a deep neural network for VA risk stratification using routinely collected ambulatory ECGs. Methods and results: A multicentre case-control study was undertaken to assess VA-ResNet-50, our open source ResNet-50-based deep neural network. VA-ResNet-50 was designed to read pyramid samples of three-lead 24 h ambulatory ECGs to decide whether a heart is capable of VA based on the ECG alone. Consecutive adults with VA from East Midlands, UK, who had ambulatory ECGs as part of their NHS care between 2014 and 2022 were recruited and compared with all comer ambulatory electrograms without VA. Of 270 patients, 159 heterogeneous patients had a composite VA outcome. The mean time difference between the ECG and VA was 1.6 years (⅓ ambulatory ECG before VA). The deep neural network was able to classify ECGs for VA capability with an accuracy of 0.76 (95% confidence interval 0.66-0.87), F1 score of 0.79 (0.67-0.90), area under the receiver operator curve of 0.8 (0.67-0.91), and relative risk of 2.87 (1.41-5.81). Conclusion: Ambulatory ECGs confer risk signals for VA risk stratification when analysed using VA-ResNet-50. Pyramid sampling from the ambulatory ECGs is hypothesized to capture autonomic activity. We encourage groups to build on this open-source model. Question: Can artificial intelligence (AI) be used to predict whether a person is at risk of a lethal heart rhythm, based solely on an electrocardiogram (an electrical heart tracing)? Findings: In a study of 270 adults (of which 159 had lethal arrhythmias), the AI was correct in 4 out of every 5 cases. If the AI said a person was at risk, the risk of lethal event was three times higher than normal adults. Meaning: In this study, the AI performed better than current medical guidelines. The AI was able to accurately determine the risk of lethal arrhythmia from standard heart tracings for 80% of cases over a year away-a conceptual shift in what an AI model can see and predict. This method shows promise in better allocating implantable shock box pacemakers (implantable cardioverter defibrillators) that save lives.

Digitally enabled acute care for atrial fibrillation: conception, feasibility and early outcomes of an AF virtual ward.

BACKGROUND: Atrial fibrillation (AF) represents a growing healthcare challenge, mainly driven by acute hospitalisations. Virtual wards could be the way forward to manage acute AF patients through remote monitoring, especially with the rise in global access to digital telecommunication and the growing acceptance of telemedicine post-COVID-19. METHODS: An AF virtual ward was implemented as a proof-of-concept care model. Patients presenting acutely with AF or atrial flutter and rapid ventricular response to the hospital were onboarded to the virtual ward and managed at home through remote ECG-monitoring and 'virtual' ward rounds, after being given access to a single-lead ECG device, a blood pressure monitor and pulse oximeter with instructions to record daily ECGs, blood pressure, oxygen saturations and to complete an online AF symptom questionnaire. Data were uploaded to a digital platform for daily review by the clinical team. Primary outcomes included admission avoidance, readmission avoidance and patient satisfaction. Safety outcomes included unplanned discharge from the virtual ward, cardiovascular mortality and all-cause mortality. RESULTS: There were 50 admissions to the virtual ward between January and August 2022. Twenty-four of them avoided initial hospital admission as patients were directly enrolled to the virtual ward from outpatient settings. A further 25 readmissions were appropriately prevented during virtual surveillance. Patient satisfaction questionnaires yielded 100% positive responses among participants. There were three unplanned discharges from the virtual ward requiring hospitalisation. Mean heart rate on admission to the virtual ward and discharge was 122±26 and 82±27 bpm respectively. A rhythm control strategy was pursued in 82% (n=41) and 20% (n=10) required 3 or more remote pharmacological interventions. CONCLUSION: This is a first real-world experience of an AF virtual ward that heralds a potential means for reducing AF hospitalisations and the associated financial burden, without compromising on patients' care or safety.

COVID-19 inpatient treatments and outcomes during the conflict in Syria: an observational cohort study.

Background: During the COVID-19 pandemic, countries undergoing conflict have faced difficulties in mounting an effective health response. This observational cohort study describes the treatments and outcomes for inpatients with COVID-19 in the Syrian city of Latakia. Design and methods: A single-centre observational cohort study was conducted at Tishreen University Hospital, involving all patients over 18 admitted between October 1 and December 31, 2021 with a positive RT-PCR test for SARS-CoV-2. Clinical features, investigations, treatments, and outcomes were reported. Results: In total, 149 patients fitted the study criteria. Only one patient was double vaccinated against COVID-19. Oxygen supplementation was required in 87% (n = 130) of participants. Invasive mechanical ventilation was required in 4% (n = 5). Therapeutic anticoagulation was administered in 97.3% (n = 144). Intravenous dexamethasone was received by 97.3% (n = 145) of participants. All patients received empiric antibiotic treatment. In-hospital mortality was 48.4% (n = 72), while only 40.9% (n = 61) were discharged during the study period. Conclusion: The pandemic has placed a compromised Syrian healthcare system under more significant strain. This requires urgent international relief efforts from health agencies in order to aid the pandemic response.

Machine learning in sudden cardiac death risk prediction: a systematic review.

AIMS: Most patients who receive implantable cardioverter defibrillators (ICDs) for primary prevention do not receive therapy during the lifespan of the ICD, whilst up to 50% of sudden cardiac death (SCD) occur in individuals who are considered low risk by conventional criteria. Machine learning offers a novel approach to risk stratification for ICD assignment. METHODS AND RESULTS: Systematic search was performed in MEDLINE, Embase, Emcare, CINAHL, Cochrane Library, OpenGrey, MedrXiv, arXiv, Scopus, and Web of Science. Studies modelling SCD risk prediction within days to years using machine learning were eligible for inclusion. Transparency and quality of reporting (TRIPOD) and risk of bias (PROBAST) were assessed. A total of 4356 studies were screened with 11 meeting the inclusion criteria with heterogeneous populations, methods, and outcome measures preventing meta-analysis. The study size ranged from 122 to 124 097 participants. Input data sources included demographic, clinical, electrocardiogram, electrophysiological, imaging, and genetic data ranging from 4 to 72 variables per model. The most common outcome metric reported was the area under the receiver operator characteristic (n = 7) ranging between 0.71 and 0.96. In six studies comparing machine learning models and regression, machine learning improved performance in five. No studies adhered to a reporting standard. Five of the papers were at high risk of bias. CONCLUSION: Machine learning for SCD prediction has been under-applied and incorrectly implemented but is ripe for future investigation. It may have some incremental utility in predicting SCD over traditional models. The development of reporting standards for machine learning is required to improve the quality of evidence reporting in the field.