Achieving complete surgical cytoreduction in advanced stage high grade serous ovarian cancer (HGSOC) patients warrants an availability of Critical Care Unit (CCU) beds. Machine Learning (ML) could be helpful in monitoring CCU admissions to improve standards of care. We aimed to improve the accuracy of predicting CCU admission in HGSOC patients by ML algorithms and developed an ML-based predictive score. A cohort of 291 advanced stage HGSOC patients with fully curated data was selected. Several linear and non-linear distances, and quadratic discriminant ML methods, were employed to derive prediction information for CCU admission. When all the variables were included in the model, the prediction accuracies were higher for linear discriminant (0.90) and quadratic discriminant (0.93) methods compared with conventional logistic regression (0.84). Feature selection identified pre-treatment albumin, surgical complexity score, estimated blood loss, operative time, and bowel resection with stoma as the most significant prediction features. The real-time prediction accuracy of the Graphical User Interface CCU calculator reached 95%. Limited, potentially modifiable, mostly intra-operative factors contributing to CCU admission were identified and suggest areas for targeted interventions. The accurate quantification of CCU admission patterns is critical information when counseling patients about peri-operative risks related to their cytoreductive surgery.

(1) Background: Length of stay (LOS) has been suggested as a marker of the effectiveness of short-term care. Artificial Intelligence (AI) technologies could help monitor hospital stays. We developed an AI-based novel predictive LOS score for advanced-stage high-grade serous ovarian cancer (HGSOC) patients following cytoreductive surgery and refined factors significantly affecting LOS. (2) Methods: Machine learning and deep learning methods using artificial neural networks (ANN) were used together with conventional logistic regression to predict continuous and binary LOS outcomes for HGSOC patients. The models were evaluated in a post-hoc internal validation set and a Graphical User Interface (GUI) was developed to demonstrate the clinical feasibility of sophisticated LOS predictions. (3) Results: For binary LOS predictions at differential time points, the accuracy ranged between 70–98%. Feature selection identified surgical complexity, pre-surgery albumin, blood loss, operative time, bowel resection with stoma formation, and severe postoperative complications (CD3–5) as independent LOS predictors. For the GUI numerical LOS score, the ANN model was a good estimator for the standard deviation of the LOS distribution by ± two days. (4) Conclusions: We demonstrated the development and application of both quantitative and qualitative AI models to predict LOS in advanced-stage EOC patients following their cytoreduction. Accurate identification of potentially modifiable factors delaying hospital discharge can further inform services performing root cause analysis of LOS.

Research nurses describe how some patients report anxiety and feelings of guilt after discharge from hospital Since the early days of the COVID-19 pandemic, the UK government has prioritised research into effective treatments. Studies were under way in record time. And as two research nurses – one normally working in oncology and the second a senior nurse manager with cardiology research experience – we have responded to the crisis by contributing to the set up and delivery of COVID-19 studies in our trust, Leeds Teaching Hospitals NHS Trust.

Background: In recent years, telemedicine use in cancer care has grown rapidly to support long-term cancer survivorship care needs. While remote care offers many benefits such as reduced travel burden for patients and increased clinic capacity for those with complex needs, challenges with adoption remain. Existing systematic reviews on telemedicine focus on barriers and facilitators of implementation. This systematic review will use the RE-AIM framework to evaluate telemedicine in cancer follow-up, aiming to identify components for sustainable implementation. It will also explore nurse-led care delivery and Patient-Reported Outcome Measures in these interventions. Methods: Electronic databases will be systematically searched for publications in English that describe telemedicine interventions in cancer follow-up care of at least 1 year. The database searches [MEDLINE (Ovid), CINAHL (EBESCO), Web of Science, Embase (Ovid), PsycINFO (Ovid), Cochrane Central Register of Controlled Trials, and IEEE Xplore] will be supplemented by searching the reference lists of relevant full texts. Title and abstract screening will be conducted by two independent reviewers and discrepancies resolved through discussion with senior authors. Data extraction and narrative synthesis will be guided by the five dimensions of the RE-AIM framework: Reach, Effectiveness, Adoption, Implementation, and Maintenance. Discussion: This systematic review aims to collate the evidence regarding telemedicine interventions in cancer follow-up care. Findings will contribute to the development of a toolkit for a new, nurse-led model of germ cell cancer follow-up. Systematic review registration: PROSPERO CRD42024589524