Edinburgh Royal Infirmary

Mitral valve replacement (MVR) in patients with complex mitral valve (MV) pathology often necessitates excision of the anterior mitral leaflet (AML), potentially compromising left ventricular (LV) geometry and function. We describe a customised full chordal-sparing mitral valve replacement (FCS-MVRpl) technique that preserves the AML and subvalvular apparatus, aiming to maintain LV architecture and improve postoperative outcomes in patients unsuitable for MV repair. Between 2014 and 2023, 161 consecutive patients underwent FCS-MVRpl. MV pathology included degenerative (n = 94, 57.1%), functional (n = 58, 36.0%), infective (n = 9, 5.6%), and rheumatic (n = 2, 1.2%) disease. Among these, 93 patients (57.8%) exhibited complex MV pathology: massive annular calcification (n = 59, 36.6%), mitral annular disjunction (MAD; n = 25, 15.5%), and posterior annular abscesses due to endocarditis (n = 9, 5.6%). The AML was fully mobilised and translocated, preserving the entire chordal apparatus. In cases with excess leaflet tissue, partial excision of the free edge was performed to facilitate prosthesis seating while maintaining chordal integrity. At 12 months, freedom from procedural complications was 98.6% ± 0.97%, with only two adverse events (1.2%) directly attributable to the technique. Freedom from heart failure-related rehospitalisation was 94.2% ± 1.9%. Multivariable Cox regression identified reduced preoperative left ventricular ejection fraction (LVEF) as a predictor of treatment failure (HR: 0.95; 95% CI: 0.92–0.99; p = 0.015). NYHA functional class improved in the majority of patients: 112 (81.2%) were classified as Class I at follow-up, while 11 (8.0%) experienced deterioration. FCS-MVRpl is a safe and effective alternative for patients with complex MV pathology who are not candidates for repair. Preservation of the AML and subvalvular apparatus supports LV geometry and functional recovery. The technique is particularly beneficial in cases of posterior annular calcification, Barlow disease with excess leaflet tissue, and infective endocarditis involving the posterior annulus. However, it is contraindicated in cases of circumferential MV calcification. Further prospective studies are warranted to validate long-term outcomes and broader applicability.

Robotic-assisted systems are being increasingly integrated into orthopaedic surgery, offering potential improvements in precision, reproducibility, and patient-reported outcome measures (PROMs) for unicompartmental knee arthroplasty (UKA), total knee arthroplasty (TKA), and total hip arthroplasty (THA). A review and summary of the early value assessment (EVA) undertaken by the National Institute for Health and Care Excellence (NICE) to evaluate the clinical effectiveness, cost-effectiveness, and implementation considerations of these technologies is presented. The EVA includes a review of the literature, an evidence gap analysis, and an evidence generation plan. Evidence from 28 studies and two registries suggested that robotic-assisted surgery was noninferior to conventional approaches in key outcomes, including PROMs, complications, hospital stay, and revisions, but it was associated with enhanced implant alignment. Short-term PROMs showed modest improvements, although most differences fell below currently defined clinically meaningful thresholds. Revision data were limited in follow-up, and ergonomic benefits for surgeons were uncertain. Economic evaluations suggested potential cost-effectiveness, particularly for THA, but assumptions regarding utilities, resource use, procedure volume, and implant costs introduced substantial uncertainty. The EVA highlighted the importance of training, equitable access, and national coordination for implementation. Evidence gaps were identified in long-term revision rates, health-related quality of life outcomes, productivity, sub-group effects, surgical team benefits, and learning curves. NICE recommended conditional adoption of six robotic platforms (ApolloKnee (Corin, UK), CORI (Smith & Nephew, UK), Mako (Stryker, USA), ROSA (Zimmer Biomet, USA), SkyWalker (MicroPort, China), and VELYS (DePuy Synthes, USA)) over a three-year evidence generation period, requiring annual reporting and adherence to regulatory standards. While robotic-assisted arthroplasty offers technical advantages, definitive clinical and economic benefits remain unproven. Surgeons, healthcare systems, and industry partners will need to work collaboratively to provide the required evidence in the next three years. A UK robotic registry could help address the evidence gaps more rapidly than individual centres by capturing large-scale national data.

BackgroundDiagnosing acute heart failure in the Emergency Department remains challenging. CoDE-HF (Collaboration for the Diagnosis and Evaluation of Heart Failure) is a previously developed machine learning algorithm that predicts an individualised probability of acute heart failure using NT-proBNP as a continuous variable in combination with routinely available clinical variables.(1)PurposeTo externally validate the CoDE-HF algorithm in a prospective cohort of patients presenting with acute breathlessness.MethodsPatients presenting to the Emergency Department with acute breathlessness in whom acute heart failure was suspected were enrolled in this study. CoDE-HF integrates NT-proBNP with age, estimated glomerular filtration rate, haemoglobin, body mass index, heart rate, systolic blood pressure, peripheral oedema, chronic obstructive pulmonary disease and ischaemic heart disease. Diagnosis was adjudicated by two independent cardiologists, with disagreements resolved by a third. Performance was assessed using the area under the receiver operating characteristic curve (AUC), Brier score, and diagnostic accuracy at pre-specified thresholds.ResultsOverall, 1,030 patients (mean age 73±14, 49% female) were included in this study, of whom, 29% (294/1,030) had a prior diagnosis of heart failure. Acute heart failure was adjudicated in 374 of 1,030 patients (36%). In patients without prior heart failure, the NT-proBNP rule-out threshold of 300 pg/mL identified 32% as low probability, with sensitivity 95.9% (91.9–97.9) and negative predictive value (NPV) 96.6% (93.3–98.3). The age-specific rule-in thresholds identified 47% as high probability, with specificity 65.4% (61.3–69.3) and positive predictive value (PPV) 45.8% (40.6–51.1). In the same group, CoDE-HF had an AUC of 0.839 (95% CI 0.809–0.869) and Brier score of 0.152. A rule-out threshold of 5.7 identified 30% as low probability, with sensitivity 98.4% (95.3–99.5) and NPV 98.6% (95.7–99.5). A rule-in threshold of 69.2 identified 19% as high probability, with specificity 90.1% (87.2–92.3) and PPV 61.7% (53.4–69.3).In patients with prior heart failure, the NT-proBNP age-specific rule-in thresholds identified 75% as high probability, with specificity 33.6% (25.5–42.8) and PPV 65.9% (59.4–71.9). The CoDE-HF rule-in threshold of 85.0 identified 36% as high probability, with specificity 80.5% (72.2–86.8) and PPV 80.2% (71.7–86.6).ConclusionsIn this prospective cohort, CoDE-HF provided improved, individualised diagnostic accuracy, especially in patients without prior heart failure. It offers a consistent, data-driven tool to support clinical decision-making.

BackgroundClinical debriefing (CD) positively impacts individuals, teams and systems and has been shown to improve patient outcomes and staff wellbeing. Although there is a growing evidence base supporting CD, it has not been routinely adopted by many healthcare organisations. Despite the work environment being an important component of transfer of learning, there has been minimal focus on how it influences implementation and maintenance of CD in practice. The overall aim of this study was to explore the work environment barriers and enablers influencing the transfer of clinical debriefing skills from simulation to clinical practice.MethodsFollowing ethical approval, medical registrars who had participated in a simulation course involving a within-scenario CD were invited to participate in semi-structured interviews. These utilised Burke and Hutchins’ evaluation model as the initial conceptual framework and took place at least two months post-course. Interviews explored participants’ experiences of transferring learning related to CD from simulation to the clinical workplace, and were transcribed verbatim and dual coded using template analysis.ResultsFifteen medical registrars participated in interviews between January and May 2025. The work environment influences from Burke and Hutchins’ evaluation model resonated as important factors affecting adoption of CD. With the addition of subthemes generated inductively from the data, the model provided a framework for identification and articulation of the barriers and enablers to CD in the workplace. The most striking finding was participants’ sense of personal responsibility to engage with CD. In addition, participants identified the requirement for cultural change to enable CD.ConclusionsWork environment influences represent both barriers and enablers of CD in relation to the transfer of learning from simulation to clinical practice. Personal responsibility and workplace culture are important drivers of CD, and attention should be paid to the influence of both constructs in this context. Recommendations for practice, based on our findings, are designed to enable educators and organisations to promote the adoption of CD in their own settings. This will help to bridge the gap and make CD the norm, not the exception.Supplementary InformationThe online version contains supplementary material available at 10.1186/s41077-025-00405-8.

BackgroundWhole-slide imaging enables the digitization of entire histological slides at a high resolution, allowing pathologists and researchers to analyze tissue samples digitally rather than through traditional microscopy. This technology has become increasingly valuable in pathology for research, education, and clinical diagnostics. Endometrial biopsy is very common, often being undertaken to exclude noncancerous disease. This means that most cases do not contain cancer, and the challenge is to accurately and efficiently exclude serious pathology rather than simply make a diagnosis of malignancy. A well-curated, expert-annotated, endometrial whole-slide dataset covering a spread of cancer and noncancer diagnoses will support machine learning applications in automated diagnosis, facilitate research into the pathology of endometrial cancer, and serve as an educational resource for medical professionals.ResultsWe introduce a newly constructed, large-scale dataset of endometrial biopsy specimens, comprising 2,909 whole-slide images in iSyntax format, each accompanied by a corresponding annotation file in JSON format. Each whole-slide image is labeled with a primary class label representing its final diagnosis and a subcategory label providing further details within that diagnostic class. These class labels are critical for machine learning applications, as they enable the development of artificial intelligence models capable of distinguishing between different types of endometrial abnormalities, improving automated classification, and guiding clinical decision-making.ConclusionsConstructing and curating a high-quality endometrial whole-slide dataset requires significant effort to ensure accurate annotations, data integrity, and patient privacy protection. However, the availability of a well-annotated dataset with detailed class labels is crucial for advancing digital pathology. Such a resource can enhance diagnostic accuracy, support personalized treatment strategies, and ultimately improve outcomes for patients with endometrial cancer and other endometrial conditions.