Objectives: This study aims to systematically evaluate the quality and reliability of YouTube videos on cardiac rehabilitation, addressing a gap in the literature regarding the assessment of online health resources in this field. Design and setting: The study is a cross-sectional analysis. This research was conducted entirely online, utilizing the YouTube platform for data collection. Main measures: The videos were assessed for educational quality and reliability using modified versions of the DISCERN, Journal of the American Medical Association (JAMA), and Global Quality Scale (GQS) benchmarks. Specific data points such as upload date, length, uploader and narrator identity, and engagement metrics (views, likes, and dislikes) were also collected. Results: Out of 300 videos initially reviewed, 140 met the inclusion criteria. The majority of videos were of low quality (67.9%), with medium (12.9%) and high-quality (19.3%) content being less common. Videos were predominantly uploaded by academic, university, or hospital sources (63.6%) and narrated by non-physician health professionals (41.4%). The content mainly provided general information about cardiac rehabilitation. Conclusions: The study revealed a concerning predominance of low-quality YouTube content on cardiac rehabilitation, underscoring the necessity for healthcare professionals and academic institutions to enhance the quality of online resources.

Background: Precise pre-procedural localisation of ventricular ectopic (VE) foci shortens mapping time, reduces fluoroscopy, and improves ablation success. Large language models such as ChatGPT offer instant, free-text clinical support; however, their accuracy in ECG-based VE localisation is unknown. Methods: In this single-centre pilot study, we assessed the diagnostic accuracy of ChatGPT in 50 consecutive adults (average age: 43 ± 14 years; 58% women) scheduled for first-time VE ablation. ChatGPT served as the index test, and invasive electroanatomical mapping during the ablation served as the reference standard. A blinded electrophysiologist converted each index 12-lead ECG into a structured textual description of QRS morphology. ChatGPT-4o (temperature 0.2) was then tasked with assigning one of five anatomical origins (RVOT, LVOT, papillary muscle, fascicular, and epicardial). Predictions were compared with electro-anatomical mapping during catheter ablation, and agreement was measured using Cohen’s κ (κ). Results: Electro-anatomical mapping identified 30 RVOT, 11 LVOT, 4 papillary, 1 fascicular, and 4 epicardial foci. ChatGPT correctly localised 17/50 cases (34%), yielding an overall Cohen’s κ of −0.02 (95% CI –0.18 to 0.14). Sensitivity/specificity was 40%/55% for the RVOT and 36%/62% for the LVOT; no fascicular or epicardial origins were correctly predicted. The performance of ChatGPT did not differ based on the presence of structural heart disease ( p = 0.43). The duration of the procedure and the acute ablation success rate (96%) were unaffected by the accuracy of ChatGPT. Conclusion: Freetext querying of ChatGPT failed to provide clinically meaningful VE localisation, performing no better than chance and markedly below published ECG-based algorithms. This likely reflects the model’s lack of domain-specific training and its reliance on purely text-based reasoning without direct access to ECG signals. Current general-purpose language models should not be relied upon for procedural planning in VE ablation; future work must integrate multimodal training and domain-specific optimisation before LLMs can augment electrophysiology practice.

Ramadan fasting involves abstaining from food and drink from dawn to sunset, yet its impact on blood pressure (BP) and kidney function in newly diagnosed hypertensive patients remains unclear. This retrospective study examined 200 newly diagnosed hypertensive patients from Konya, Turkey, during Ramadan 2023. Half of the patients (n = 100) observed daily fasting throughout Ramadan, while the other half (n = 100) did not. All patients received a diuretic‐containing regimen consisting of an angiotensin‐converting enzyme inhibitor or angiotensin receptor blocker combined with hydrochlorothiazide. Baseline clinical and laboratory data, including serum creatinine and estimated glomerular filtration rate (eGFR), were compared with those obtained at a one‐month follow‐up (end of Ramadan). Both fasting and non‐fasting groups exhibited significant reductions in systolic and diastolic BP from baseline to the first month, with no statistically significant difference in final BP between the two groups. Kidney function, as indicated by creatinine levels and eGFR, remained stable in both groups, suggesting that Ramadan fasting did not adversely affect renal parameters. Modest improvements in lipid profiles were also observed in both cohorts. These findings indicate that, among newly diagnosed hypertensive patients on diuretic‐containing therapy, Ramadan fasting may be safe if accompanied by individualized clinical advice. However, larger and more prolonged studies are warranted to validate these results and explore potential variations in other hypertensive populations.

Background: Naples Prognostic Score (NPS), a composite index incorporating inflammatory and nutritional markers, has emerged as a potential prognostic tool in various cardiovascular conditions; however, no meta‐analysis has yet pooled the available evidence to comprehensively assess its prognostic utility. Objectives: To evaluate the association of NPS with clinical outcomes, including all‐cause mortality, in‐hospital mortality, no‐reflow (NR) phenomenon, and left ventricular ejection fraction (LVEF), in acute coronary syndrome (ACS) patients undergoing percutaneous coronary intervention (PCI). Methods: MEDLINE, Cochrane, and EMBASE databases were searched for studies comparing high and low NPS groups in ACS patients undergoing PCI. Random‐effects models were used to pool risk ratios (RR) for binary outcomes and mean differences (MD) for continuous outcomes. Heterogeneity was assessed with I² statistics. Statistical analyses were performed using Review Manager 5.4, and R, version 4.2.2. Results: We included seven studies comprising 13 268 patients, with 5628 (42.4%) patients in the low NPS group. Low NPS was significantly associated with decreased all‐cause mortality (RR: 0.42; 95% CI: 0.32–0.55; I² = 48%) and decreased incidence of NR (RR: 0.60; 95% CI: 0.40–0.88; I² = 83%). Patients with low NPS also had higher LVEF (MD: −2.69%; 95% CI: −3.41 to −1.97; I² = 99%). No significant difference was observed in in‐hospital mortality (RR: 0.54; 95% CI: 0.28–1.05; I² = 94%). Conclusion: In ACS patients undergoing PCI, elevated NPS was associated with worse clinical outcomes. These findings support the use of NPS as a practical, biomarker‐based tool for risk stratification in this population.

Objective: This retrospective, modeled study evaluates the accuracy of ChatGPT (GPT-4)-based warfarin dose adjustments compared to clinician recommendations at the Cardiology Clinic of Konya City Hospital, focusing on patients with international normalized ratio (INR) values outside the therapeutic range (2–3). We hypothesized that ChatGPT could provide reliable, consistent dose guidance. Methods: We reviewed the records of warfarin-treated patients from 1 June 2022 through 24 November 2024. Clinical data used by physicians (e.g. baseline INR, warfarin indication, comorbidities, and current dose) were provided to ChatGPT to generate hypothetical weekly dose recommendations. ChatGPT's impact on INR normalization was modeled using standard dose–response assumptions and compared with actual outcomes under physician-guided therapy. Results: A total of 180 patients met the inclusion criteria. ChatGPT's recommended doses were within ±1 mg/week of physician prescriptions in 74% of cases and within ±2 mg/week in 84%. The mean physician dose was 28.0 ± 6.1 mg/week versus ChatGPT's 27.5 ± 5.9 mg/week ( p = .12). Seventy-two percent of patients achieved therapeutic INR under physician-managed dosing, while the model suggested a 69% success rate for ChatGPT-guided dosing ( p = .15). Real-world adverse events were infrequent under physician management (1.1% major bleeding, 0.6% thrombotic events). Conclusion: In this retrospective, exploratory analysis with modeled outcomes, ChatGPT's weekly dose suggestions showed high concordance with clinician dosing. These findings are hypothesis-generating and do not establish clinical efficacy or safety; prospective, physician-supervised trials—potentially integrated with home INR monitoring—are required for validation.