36 Predicting Long-Term Survival and Time-to-Recurrence After Oesophagectomy in Patients with Oesophageal Cancer. (28th February 2022)

• Record Type: Journal Article
• Authors: Gujjuri, R.; Clarke, J.; Elliot, J.; Reynolds, J.; Markar, S.
• Is Part Of: British journal of surgery. Volume 109(2022)Supplement 1
• Publication Date: 2022-02-28
• Subjects: Surgery -- Periodicals; 617.005
• DOI: 10.1093/bjs/znac041.001
• Languages: English
• ISSNs: 0007-1323

Abstract

Abstract: Aim: Long-term survival after oesophagectomy remains poor, with recurrence a feared common outcome. Prediction tools can identify high-risk patients and optimise treatment decisions based on their prognostic factors. This study developed a prediction model to predict long-term survival and time-to-recurrence following surgery for oesophageal cancer. Method: Patients undergoing curative surgery from the European iNvestigation of SUrveillance After Resection for Esophageal Cancer study were included. Prediction models were developed for overall survival (OS) and disease-free survival (DFS) using Cox proportional hazards (CPH) and Random Survival Forest (RSF). Model performance was evaluated using discrimination (time-dependent area under the curve (tAUC)) and calibration (visual comparison of predicted and observed survival probabilities). Results: This study included 4719 patients with an OS of 47.7% and DFS of 48.4% at 5 years. Sixteen variables were included. CPH and RSF demonstrated good discrimination with a tAUC of 78.2% (95% CI 77.4–79.1%) and 77.1% (95% CI 76.1–78.1%) for OS and a tAUC of 79.4% (95% CI 78.5–80.2%) and 78.6% (95% CI 77.5–79.5%) respectively for DFS at 5 years. CPH showed good agreement between predicted and observed probabilities in all quintiles. RSF showed good agreement for patients with survival probabilities between 20–80%. Conclusions: This study demonstrated that a statistical model can accurately predict long-term survival and time-to-recurrence after oesophagectomy. Identification of patient groups at risk of recurrence and poor long-term survival can improve patient outcomes by optimising treatment methods and surveillance strategies. Future work evaluating prediction-based decisions against standard decision-making is required to understand the clinical utility derived from prognostic model use.