Background and Objectives: The electrocardiogram (ECG)–based STEMI/NSTEMI classification determines the urgency of invasive management in acute myocardial infarction. However, it often underestimates the presence of acute coronary occlusion (ACO) in patients presenting with non-ST-elevation myocardial infarction (NSTEMI). Artificial intelligence (AI)-assisted ECG interpretation has emerged as a potential tool to improve early recognition of ACO. This study aimed to determine the prevalence of ACO among NSTEMI patients, to compare clinical characteristics between patients with and without ACO and to explore the potential role of AI in earlier recognition of ACO. Materials and Methods: All consecutive NSTEMI patients undergoing coronary angiography between September 2022 and December 2024 were included. Contrary to other studies that included TIMI flow grades 0–1, 0–2, or 0–3, ACO in our study was defined strictly as a culprit lesion with TIMI flow grade 0 at index coronary angiography. Clinical characteristics were compared between ACO and non-ACO patients. Admission 12-lead ECGs from ACO patients were retrospectively analysed using a clinically validated AI-based ECG interpretation model and classified according to the urgency of invasive management. Results: Among 520 NSTEMI patients, 49 (9.4%) had angiographically confirmed ACO. Within the non-ACO group, 7.0% of patients had TIMI flow grade 1 on index coronary angiography (6.3% of the total population). Therefore, 15.7% of the study population had TIMI flow grade 0/1. ACO patients were younger (60.9 ± 12.8 vs. 66.3 ± 12.0 years, p = 0.0065). Clinical characteristics did not differ between the groups, except for dyslipidemia, which was more prevalent in non-ACO patients (38.8% vs. 53.9%, p = 0.043). Revascularisation rates were higher in the ACO group (93.9% vs. 82.2%, p = 0.037). Culprit vessel distribution differed markedly between the groups (p < 0.0001). In multivariable logistic regression analysis, age was independently associated with ACO (OR 0.96, 95% CI 0.93–0.99, p = 0.007). AI-assisted ECG analysis was performed in 42 ACO patients; 57.1% were classified as requiring immediate invasive management. Conclusions: A significant proportion of NSTEMI patients have ACO. AI-assisted ECG interpretation may support earlier identification of ACO, although its clinical impact requires further validation. Future studies are warranted to confirm these findings.

Aims: This study aims to evaluate the predictive and discriminatory value of peak apical rotation for left ventricular (LV) thrombus formation following anterior myocardial infarction (MI). Methods and results: In a multicentre prospective observational study, peak apical rotation was quantified post hoc in 270 patients presenting with anterior MI using speckle-tracking echocardiography. LV thrombus was identified by echocardiography or cardiac magnetic resonance during index admission or follow-up. Associations were evaluated using multivariable regression models and discrimination assessed using receiver operating characteristic analysis. LV thrombus was identified in 33% of the eligible patients. Peak apical rotation was significantly lower in patients with thrombus (5.0° vs. 9.8°, P < 0.001). After multivariate adjustment, higher apical rotation was independently protective against thrombus (OR 0.45, P < 0.001), corresponding to an approximately two-fold increase in thrombus odds for every 1° reduction in rotation. Admission apical rotation also independently predicted delayed thrombus development (0.46, P < 0.001). Apical rotation demonstrated superior discrimination for thrombus compared to LV ejection fraction (LVEF) (AUC: 0.91 vs. 0.62, P < 0.001), maintaining accuracy within the LVEF ≤ 40% subgroup (AUC: 0.89 vs. 0.48, P < 0.001). Conclusion: Impaired peak apical rotation independently predicts and discriminates LV thrombus following anterior MI, outperforming global systolic parameters. Prospective validation is required to establish apical rotation as a tool for risk stratification.

BackgroundBystanders to out-of-hospital cardiac arrest (OHCA) are at significant risk of psychological distress, yet no standardised support pathway exists for them in the UK. RescQ™ is a digital-first bystander support pathway embedded within regional prehospital and cardiac arrest care services.AimTo evaluate the feasibility of embedding RescQ™ into routine emergency medical services (EMS) practice leading to appropriate contact with bystanders involved in OHCA. Secondary aims were to describe digital engagement, demand for liaison officer (LO) support, identification of younger bystanders, and EMS clinician acceptability.MethodsProspective one-year regional service evaluation of RescQ™ across Essex, UK. Bystanders involved in eligible OHCA events were registered by EMS clinicians and provided with immediate access to secure digital resources. Bystanders requiring additional support could seek LO contact. ResultsDuring the one-year evaluation, out of 432 eligible OHCAs, 298 bystanders from 220 OHCA events were identified, and 260 eligible adult bystanders were registered, representing 50.9% of eligible incident capture. Website engagement was high, with 96.2% of registered bystanders accessing the digital resources at least once. Among identified bystanders, 30/298 (10.1%) were aged under 18 years. Approximately 10% of registered bystanders required additional, personalised LO support. EMS feedback supported the acceptability of the pathway, while identifying practical barriers to registration during high-pressure emergency scenes.ConclusionRescQTM provides a feasible and scalable model for identifying and supporting bystanders after OHCA, achieving meaningful incident capture, high digital engagement and identification of a younger subgroup requiring additional support. These findings support wider implementation and further evaluation of a digital-first support model for bystanders after cardiac arrest.