Ninewells Hospital is a large teaching hospital, based on the western edge of Dundee, Scotland. It is internationally renowned for introducing laparoscopic surgery to the UK as well as being a leading centre in developing fields such as the management of cancer, medical genetics and robotic surgery. Within the UK, it is also a major NHS facility for psychosurgery. The medical school was ranked first in the UK in 2009. The hospital has nursing and research links with the University of Dundee and is managed by NHS Tayside.
BACKGROUND: The distinction between frailty and ageing is becoming increasingly recognised, however, the clinical implication of this in head and neck cancers has not been fully explored. In this review we evaluate the prognostic value of frailty, assessed through established screening tools, in predicting the long-term survival outcomes among patients with head and neck cancer. METHODS: We performed a systematic review in accordance with the PRISMA guidelines. The search was conducted in Ovid MEDLINE, Ovid Embase, PubMed, Cochrane library and ClinicalTrials.gov databases. RESULTS: Five studies met the inclusion criteria for this review, encompassing 1131 patients. Of these four studies employed a retrospective design, while one was prospective. Three of the five studies demonstrated a significant correlation between frailty and long-term survival outcomes. In contrast, two of the five studies specifically examined long-term disease free survival (DFS), but neither found a significant association between frailty and long-term DFS. CONCLUSION: Frailty is prevalent amongst patients with head and neck cancer and its assessment using validated screening tools may hold significant prognostic value. Currently, frailty assessment is most valuable as a guide in patient/treatment selection and enabling shared decision making. However, further research is needed to determine the most effective frailty screening tool and to clarify its specific role in this patient population.
INTRODUCTION:We aimed to assess diagnostic performance and accuracy for local staging of CEM combined with DBT (CE + DBT) compared to digital mammography (DM) and MRI. METHODS:Female patients aged 18-70 years with clinical or sonographic suspicion of breast cancer had CE + DBT and breast MRI prior to treatment. Diagnostic accuracy, ability to identify additional disease foci, and disease extent estimations were compared for all imaging techniques. Histopathology was the reference standard. RESULTS:Eighty-seven participants were recruited; 80 included in the study; 69 had cancer. DBT had greater diagnostic sensitivity than DM, but lowest specificity. CEM and CE + DBT showed 100% sensitivity. Specificity was lower for CE + DBT than CEM alone. MRI sensitivity was higher than DM or DBT, but lower than CEM or CE + DBT. MRI specificity was lower than CE-DBT, both separately and in combination. Thirty-nine patients were included in exploratory subset analysis regarding identifying additional foci. DM missed the most cases (8/10); DBT had higher sensitivity but lower specificity; MRI s sensitivity but with more false positives; CEM had the greatest overall accuracy. Thirty patients were included in unifocal disease extent analysis. MRI, CEM, and CE-DBT all demonstrated strong correlation with pathological size, with MRI showing closest agreement. CONCLUSIONS:Combined CE + DBT is feasible within a one-stop clinic appointment and may obviate the need for MRI. CEM demonstrated comparable accuracy to MRI, both for diagnostic accuracy and in local staging. We showed no clear benefit to combining morphological information derived from DBT with functional data of CEM compared to CEM alone.
BACKGROUND:Insertable loop recorder (ILR) services are increasingly constrained by high volumes of transmitted episodes, many of which are false, clinically irrelevant, or non-actionable. Cloud-based artificial intelligence (AI) algorithms have the potential to suppress false atrial fibrillation (AF) and pause alerts while preserving clinically relevant events. We present the first real-world impact of an AI algorithm activated across a fixed multicentre ILR cohort. METHODS:We performed a retrospective, multicentre before-and-after cohort analysis of consecutive patients. Eligible patients had continuous ILR monitoring for 12 months before and 12 months after service-wide activation ("switch-on") of the Medtronic AccuRhythm AI platform (Medtronic plc, Galway, Ireland), enabling within-patient comparison. All clinician-facing transmitted alerts were counted in each period. Secondary analyses examined the concentration of alert burden across patients and estimated workflow impact using published time-and-motion data for remote transmission review. Differences in alert counts were tested using paired t-tests, reporting mean paired differences with 95% confidence intervals (CI). Results: The cohort included 445 patients (Reveal LINQ n=438; LINQ II n=7); 440 (99%) were implanted for syncope (mean age 67±15 years; 49.6% male). Total transmitted alert volume fell from 4,261 pre-AI to 2,509 post-AI (29% reduction). The mean paired within-patient change was -3.94 alerts (95% CI -7.5 to -0.4; p<0.05). Alerts were highly concentrated: 7% of patients generated >90% of alerts, and 104 patients had intermittent disconnection from remote monitoring. Applying established workflow timings (11-13 minutes per remote transmission review) translated the reduction into 185-218 hours of physiologist review time released annually: approximately four hours per week of "virtual physiologist" capacity. CONCLUSIONS:In routine practice, AI activation in a multicentre observational study was associated with a statistically significant reduction in ILR alert burden and a clinically meaningful release of staff capacity. Parallel management of high-alert patients and connectivity optimisation may further amplify the operational benefit.
