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PubMed2026

Feasibility of implementation, diagnostic accuracy, and end-user impact of an electronic health record (EHR)-based ureteral stent tracking tool in a pediatric population.

INTRODUCTION & OBJECTIVES: Ureteral stent tracking systems have reduced stent retention in adults, but their accuracy and impact in pediatrics have been minimally explored. With low event rates in children, such tools may yield high false positives, raising questions on balancing event prevention with provider burden. We aimed to evaluate the feasibility, diagnostic accuracy, and end-user impact of an Electronic Surveillance Tool for Evaluating Nephroureteral stent Tracking (eSTENT) at our institution. STUDY DESIGN: eSTENT, implemented in 1/2024, flags ureteral stents at risk for retention based on implant documentation, expected explant date, and explant documentation. Monthly reports are generated for stents missing explant documentation. We retrospectively evaluated the diagnostic performance of eSTENT from 1/2024-8/2025 at our pediatric hospital. A usability survey including a validated 1-7 implementation score (higher = easier implementation) was distributed to pediatric urologists and operating room nurses. RESULTS: Of 172 cases with ureteral stent placement, eSTENT flagged 28 events (16%) in 24 patients. Of these, 26 represented documentation gaps where explant had been appropriate. Two flags had no documentation of explant, representing near miss events that were identified. No retained stents occurred, consistent with high sensitivity and modest specificity. There were no flags in the last 6 months of the study period. Survey response rate was 100% for surgeons and 55% for nurses. Before eSTENT, stents were not routinely tracked. All surgeons and 93% of nurses reported no added burden, despite occasional misidentification of retained stents. Three surgeons found eSTENT beneficial, four were neutral, and free-text responses generally cited eSTENT's "fail safe" nature as positive. Nurses suggested improvements, including user support and integrated documentation reminders. The average implementation score among both groups was 6/7, indicating easy adoption. DISCUSSION: While the impact of stent tracking tools in adult literature has been positive, our study emphasizes the feasibility of broader adoption at a pediatric hospital. Integration of eSTENT may avoid the potentially devastating consequences of a retained stent. Prioritizing sensitivity over specificity appears acceptable for a "never event" in patient safety. Our study is limited by the retrospective nature of data collection and survey bias. CONCLUSIONS: Though no stents were retained in the study period, eSTENT appropriately flagged two cases without added burden to most end-users. Further optimization is warranted, but adoption in pediatric centers may enhance care reliability.

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PubMed2026

Structured robotic colorectal training in a non-tertiary NHS hospital: a 502-case consecutive cohort implementation study.

Robotic-assisted colorectal surgery has expanded rapidly across NHS practice in the UK. Structured unit-wide training pathways are essential for safe technology adoption, yet published outcome data from non-tertiary hospitals remain limited. This study describes the implementation and feasibility of a unit-wide robotic colorectal program at a high-volume non-tertiary hospital, reporting outcomes across 502 consecutive resections performed by eight consultant surgeons and presenting these in the context of nationally published benchmarks. A retrospective cohort study of 502 consecutive robotic colorectal resections performed at York Teaching Hospital between May 2022 and December 2025. Eight consultant surgeons (A-H) participated in a structured four-phase training pathway incorporating simulation training, proctored cases, complexity-based case progression, and formal credentialing. Primary outcomes were 30-day mortality, unplanned return to theatre (RTT), and anastomotic leak (AL). Anastomotic leak was calculated using only patients who underwent anastomosis as the denominator. Procedure-stratified and individual surgeon outcomes with 95% confidence intervals were reported. Risk-adjusted cumulative sum (RA-CUSUM) analysis was performed to evaluate learning curves. Outcomes are presented descriptively alongside nationally published reference data; no formal statistical comparison against national benchmarks was performed. 502 robotic colorectal resections were performed. Mean patient age was 70.0 ± 11.3 years; 58.4% were male. Median ASA grade was III. The indication was malignancy in 89.2% of cases. Length of stay was non-normally distributed and is therefore reported using median and interquartile range in the revised analysis. Key outcomes: - 30-day mortality: 1.0% (5/502; 95% CI 0.4-2.3%) - Unplanned return to theatre (RTT): 5.2% (26/502; 95% CI 3.6-7.5%) - Anastomotic leak (AL): 3.3% (15/450; 95% CI 2.0-5.5%; denominator = patients with anastomosis) - 30-day unplanned readmission: 5.0% (25/502; 95% CI 3.4-7.2%) - Conversion to open surgery: 3.6% (18/502; 95% CI 2.3-5.6%) - Lymph node yield ≥12: 91.3% of cancer resections - R0 resection rate: 95.1% of cancer resections All primary outcomes fell within or below the published reference ranges used for descriptive context. RA-CUSUM trajectories were heterogeneous: no surgeon crossed the predefined upper control limit, but several curves showed later upward movement. Accordingly, the analysis is interpreted as safety surveillance rather than evidence of uniform performance improvement. RA-CUSUM monitoring showed that no surgeon crossed the predefined upper control limit; however, heterogeneous trajectories precluded a claim of uniform performance improvement.

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PubMed2026

Early Worsening of Diabetic Retinopathy Following Initiation of Hybrid Closed-Loop/Automated Insulin Delivery Systems in Type 1 Diabetes: A Systematic Review and Structured Study-Level Synthesis.

BACKGROUND: Hybrid closed-loop (HCL) systems achieve rapid, algorithm-driven improvements in glycaemia in type 1 diabetes (T1D). Paradoxically, rapid improvement in glycaemic control is associated with early worsening of diabetic retinopathy (EWDR), a phenomenon established in the intensive insulin therapy era. Whether HCL initiation carries a clinically meaningful EWDR risk is unknown. No systematic review has previously addressed this question. METHODS: A systematic review and structured quantitative synthesis was performed using study-level estimates only (PROSPERO CRD:420261391951). MEDLINE, SCOPUS and Web of Science were searched to 14th May 2026. Studies reporting retinal outcomes in people with T1D initiating any HCL system were eligible. Two reviewers independently screened studies and extracted data. Risk of bias was assessed using ROBINS-I and certainty of evidence using the GRADE framework. EWDR incidence was summarised using study-level proportions, and comparative studies were summarised using study-specific risk ratios for HCL versus control therapy. Given substantial heterogeneity in EWDR definitions, retinal assessment timing, follow-up duration, and comparator groups, no pooled or meta-analytic estimates were derived. RESULTS: Eight studies (n = 1487 participants; 860 HCL users) were included; all were observational and six were retrospective. EWDR varied markedly with the timing of retinal assessment. In studies assessing the retina within ≤ 12 months of HCL initiation, EWDR rates ranged from 8.9% to 26.5%. Studies with longer follow-up reported lower rates of retinal worsening or incident DR, 6.7% at 24 months and 6.1% over a mean follow-up of 4.9 years, suggesting that these studies may capture background DR progression rather than true early worsening. Three comparative studies included 177 HCL users and 315 controls; EWDR study-specific risk ratios were directionally inconsistent, ranging from 0.32 to 1.51, and were therefore not pooled. The most consistently identified risk factors were higher baseline HbA1c and older age. The magnitude of HbA1c reduction was not a consistent predictor of EWDR in the HCL context, in contrast to pre-HCL era evidence. Risk of bias ranged from moderate to critical and certainty of evidence was very low for all outcomes. CONCLUSIONS: Study-defined retinal worsening was reported in a minority of participants. The current evidence base is dominated by retrospective studies, variable retinal assessment timing, and inconsistent EWDR definitions. Well-designed prospective studies with protocol-specified retinal surveillance anchored to HCL initiation are required to generate reliable incidence estimates, identify risk factors, determine visual consequences, and inform standardised screening guidance.

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PubMed2026

Acute mild cold exposure with shivering reduces 24 h glucose levels in individuals with type 2 diabetes but not prediabetes.

AIMS/HYPOTHESIS: Repeated cold exposure with shivering has been proposed as a potential strategy to enhance glucose metabolism by increasing energy expenditure and substrate utilisation. However, acute effects/benefits of cold-induced shivering on glucose homeostasis in metabolically compromised individuals are unknown. Here, we aimed to determine whether cold exposure at two different intensities improves 24 h glucose homeostasis in individuals with prediabetes and type 2 diabetes. METHODS: In a randomised crossover trial conducted in the South Limburg/Maastricht region of the Netherlands, men and postmenopausal women with prediabetes (n=12) and stable type 2 diabetes (n=12), aged 40-75 years, body mass index ≥27 and ≤35 kg/m2, non-smoking and sedentary, underwent two whole-body cold exposure sessions using a water-perfused suit. Session order was randomised using an online randomisation tool (randomizer.org); participants were masked to the cold exposure intensity received, but investigators were not. Sessions were designed to elicit ~1.5-fold (mild, 15°C) and ~2.5-fold (moderate, 4°C) increases in resting metabolic rate (RMR). Continuous glucose monitoring assessed interstitial glucose concentrations over 24 h periods before and after each intervention, with controlled diet and activity. Shivering was confirmed via indirect calorimetry and electromyography. RESULTS: In both study groups and periods, RMR increased significantly vs baseline (p<0.001 for all). In prediabetes, the increase in the final 1 h of cold was 1.53 × RMR in mild and 1.94 × RMR in moderate cold. In type 2 diabetes, the increase was 1.57 × RMR and 2.09 × RMR in the final 1 h of mild and moderate cold, respectively. In prediabetes, neither mild nor moderate cold exposure altered mean 24 h glucose levels. In contrast, after mild cold exposure the type 2 diabetes group exhibited a significant reduction in mean 24 h glucose levels (-0.6 ± 0.5 mmol/l, p=0.003) and fasting glucose (-0.6 ± 0.8 mmol/l, p=0.019), as well as an increase in time in normal range (+8.8 ± 10.3%, p=0.013) and reduced time in hyperglycaemia (-10.9 ± 12.9%, p=0.014). Moderate cold did not significantly affect any of the glucose outcomes in type 2 diabetes. Baseline fasting glucose, age and ALT levels were predictors of the glucose-lowering response, suggesting greater benefits in individuals who have higher baseline glucose levels, are younger and/or have more optimal liver health, i.e. lower ALT. CONCLUSIONS/INTERPRETATION: Acute mild cold exposure with shivering reduced 24 h glucose levels in individuals with type 2 diabetes. No changes were observed in prediabetes. The observed effects appear to depend on baseline metabolic status rather than acute substrate utilisation during cold exposure. These findings support the potential of cold exposure as an adjunct non-pharmacological therapy for type 2 diabetes, although further mechanistic studies and validation in larger cohorts are warranted. TRIAL REGISTRATION: ClinicalTrials.gov NCT05576025 FUNDING: Dutch Organisation for Knowledge and Innovation in Health, Healthcare and Well-being (ZonMw): 09120012010062.

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PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

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