Racheal Louise Johnson

Patient-initiated follow-up of early endometrial cancer: a potential to improve post-treatment cardiovascular risk?

Abstract Purpose Is patient-initiated follow-up, post-surgical treatment of early endometrial cancer safe and can it be used holistically to improve cardiovascular health? What are the cost implications of this model of follow-up? Methods Retrospective data of 98 patients discharged to patient-initiated scheme since 2012. Service evaluation by anonymous patient feedback including physical health effects of the programme including weight loss. Financial cost was compared to traditional hospital-based follow-up over five years. Results No evidence of recurrence over 54 months median follow-up in low-risk endometrioid endometrial cancer. Patient feedback indicates that the exercise course helped women reduce their BMI. Over one third women felt happier and one fifth felt more confident and had a better ability to cope with stress. Total of 91% patients would recommend this model of follow-up to friends or family in the same circumstance. European Society for Medical Oncology guidance suggests the number of hospital-based follow-up appointments required for this cohort would cost £109,760. Calculations in this paper examine the cost of patient-initiated follow-up and reflect an overall saving of around 96.5%. Conclusion This service evaluation supports the claim that patient-initiated follow-up represents a safe alternative to the traditional hospital-based protocol. There is a potential for additional services to be offered to encourage and promote a healthy lifestyle linked to improving quality of life and cardiovascular survival following surgery for endometrial cancer. Implications for cancer survivors Cardiovascular morbidity is the most common cause of death in endometrial cancer survivors. Incorporating an exercise course as part of routine follow-up can help reduce this risk. The friendships formed by this communal follow-up can contribute towards emotional health and recovery. This holistic approach should be incorporated into novel follow-up strategies to help reduce patient BMI and reduce cardiovascular risk.

Stratification of Length of Stay Prediction following Surgical Cytoreduction in Advanced High-Grade Serous Ovarian Cancer Patients Using Artificial Intelligence; the Leeds L-AI-OS Score

(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.