As trainees learn transthoracic echocardiography scanning, periodic assessments provide important information for learners and teachers to monitor progression, identify areas for improvement, and, ultimately, to demonstrate achievement of the competence required for future intended practice. In the second part of this 2-part series on echocardiography scanning education, our group of echocardiographer and sonographer educators discusses the key elements in developing a structured approach for the assessment of technical scanning skills. We provide a sample assessment instrument that may be adapted to trainee skill level and local resources.
Background: Implantable loop recorders (ILRs) or insertable cardiac monitors (ICMs) are vital for long-term cardiac monitoring to diagnosed arrhythmias in patients with cryptogenic stroke, syncope, recurrent palpitations, and heart disease. Their increasing use raises demand on limited resources, worsened by the response to Covid-19, which led to deferring elective procedures such as ILR removal. We examined whether longer ILR dwell time was linked to more complications, assessed diagnostic yield, and compared explantation costs between a hospital and simulated community clinics. Methods: We retrospectively reviewed all ILR implantations at the London Health Sciences Centre from 2011 to 2025. We recorded patient characteristics, implantation and explantation dates, diagnostic findings, complications, and institutional cost data. We also modelled a simulated community-based explantation cost using local clinic resource estimates. Results: Among 772 patients, the mean device dwell time was 3.4 years; in the 355 patients (46%) who retained devices beyond the 3.25-year recommended replacement time the mean duration was 5.5 +/- 2.2 years. The overall complication rate was 8.0% (n = 62): patient-reported discomfort (3.9%), recording errors (1.8%), unsuitable device position (1.2%), excessive bleeding (0.5%), and infection (0.7%). On a time-to-event basis, the complication rate was highest in the first 180 days (4.13 per 100 device-years) and was not significantly higher beyond the recommended replacement time (incidence-rate ratio 1.36; P = 0.24). Symptom-rhythm correlation was established in 38.4% of patients. Explantation costs for ILR were approximately $490 per case in the hospital setting and $70 in the simulated community clinic, resulting in potential savings of 85.7%. Conclusions: Prolonged ILR dwell time didn't increase complication rates. Findings support deferred explantation and community removal to lower costs, but safety and feasibility need prospective studies.
Background: Artificial intelligence (AI)-assisted assessment of left ventricular ejection fraction (LVEF) has been increasingly adopted in clinical practice. In this study we aimed to assess the reliability and reproducibility of AI-assisted LVEF assessment in a diverse, real-world, multicentre setting. Methods: We conducted a retrospective multicentre study involving 354 cardiac magnetic resonance examinations. A standardized LVEF reassessment (M-LVEF) was performed using QMass 8.1 (Medis Medical Imaging, Leiden, Netherlands) and systematically compared with original report-derived LVEF values (R-LVEF) generated using vendor-specific AI tools. For interobserver reproducibility assessment, 3 operators independently analyzed 30 randomly selected cases using fully manual and AI-assisted modes, the latter also performed with QMass 8.1 software. Operator A performed 2 measurements in both modes for intraobserver analysis. Results: In overall and single-centre analyses, the consistency between R-LVEF and M-LVEF was good or excellent (intraclass correlation coefficient >= 0.86), but the 95% limits of agreement all exceeded +/- 5%. In 44.3% of cases > 5% differences between R-LVEF and M-LVEF were observed, and 17.5% showed > 10% differences. The AI-assisted method not only significantly reduced LVEF assessment time compared with manual analysis, but also showed superior reproducibility. This was evidenced by greater interobserver agreement among 3 operators (intraclass correlation coefficients, 0.968, 0.974, 0.984 for AI vs 0.913, 0.924, 0.971 for manual, respectively) and greater intraobserver reproducibility, all with significantly lower coefficients of variation (5.6%, 8.8%, 9.3% vs 2.5%, 3.8%, 4.4%, respectively; 3.5% vs 1.0% [P < 0.05). Conclusions: There are individual differences in AI-assisted LVEF assessment, but it has significant advantages in efficiency and reproducibility, making AI a worthwhile tool to facilitate cardiac magnetic resonance quantification.
This primer outlines alternative access strategies for transcatheter aortic valve replacement (TAVR) as its use extends to younger, lower-risk patients. Although transfemoral (TF) TAVR remains the standard, 5% to 10% of patients require alternative approaches. Such strategies include valve deployment via the carotid arteries, cavae, axillary/subclavian arteries, cardiac apex, and directly through the aorta. Careful computed tomography (CT)based planning, multidisciplinary heart-team discussion, and institutional expertise are essential. Durability data extend to 5 years with contemporary devices, whereas 10year results exist only for early-generation valves.
Background: In this we study aimed to develop and validate a dynamic prediction model for acute kidney injury (AKI) in heart failure (HF) patients. Methods: Using data from 7636 HF patients in the Medical Infor-mation Mart for Intensive Care IV (MIMIC-IV) version 3.1 database, we constructed a long short-term memory model with dynamic focal loss to handle class imbalance. We designed 2 prediction perspectives: for short-term prediction w used different data collection windows (6-72 hours) to dynamically predict the risk of AKI occurrence within subsequent specific time windows (12-72 hours); for long-term prediction we used data from specific time points after admission (12-72 hours) to predict the occurrence of AKI during the entire hospitalization. Results: The model showed robust performance across all prediction tasks (area under the receiver operating characteristic curve range, 0.80-0.94). Analysis of prediction lead time showed that the model could provide early warnings: the median lead times for predicting AKI occurrence within 12, 24, 48, and 72 hours were 9.57, 12.89, 18.77, and 27.25 hours, respectively. Feature importance analysis revealed that urine output, Sequential Organ Failure Assessment (SOFA) score, and systolic blood pressure played dominant roles in short-term prediction, whereas troponin T and history of cardiovascular surgery were more important in long-term prediction. Conclusions: The long short-term memory-based model proposed in this study captures dynamic physiological changes in HF patients and provides dynamic risk assessments for AKI with sufficient lead time.
