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

Abstract

(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.

RoBERTa-Assisted Outcome Prediction in Ovarian Cancer Cytoreductive Surgery Using Operative Notes

“…While survival is the ultimate treatment outcome, prediction of residual disease is a key issue in the advanced EOC trajectory. This disease quantification can valuably complement our previous work using AI to predict EOC-specific surgical outcomes 13,17,[25][26][27] and validate the paradigm shift towards complete clearance to improve the survival outcomes of these patients. 13,35 The use of language in medicine is often underestimated not that all Gynaecologic Oncology Surgeons speak the same language.…”

“…Most of these discrete and engineered data predictors have been interrogated in our previous studies. 13,17,25–27…”

“…Most of these discrete and engineered data predictors have been interrogated in our previous studies. 13,17,[25][26][27] The hyperparameters of the XGBoost model were selected by using an exhaustive grid hyperparameter search. The grid search also implemented cross-validation.…”

Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)

“…In one study, 111 patients with EOC were examined by transvaginal ultrasonography, and the characteristics of ultrasonography were extracted to establish a comprehensive PFS prediction model combined with clinical variables ( 90 ). Additionally, AI can predict the length of postoperative hospital stay for patients with HGSOC ( 91 ), which may contribute to the individualized treatment and management plans in clinical practice.…”