The AUGIS survival predictor: prediction of long-term and conditional survival after esophagectomy using Random Survival Forests

OBJECTIVE: To develop a predictive model for overall survival after esophagectomy using pre/postoperative clinical data and machine learning. SUMMARY BACKGROUND DATA: For patients with esophageal cancer, accurately predicting long-term survival after esophagectomy is challenging. This study investigated survival prediction after esophagectomy using a Random Survival Forest (RSF) model derived from routine data from a large, well curated, national dataset. METHODS: Patients diagnosed with esophageal adenocarcinoma or squamous cell carcinoma between 2012 and 2018 in England and Wales who underwent an esophagectomy were included. Prediction models for overall survival were developed using the RSF method and Cox regression from 41 patient and disease characteristics. Calibration and discrimination (time dependent AUC) were validated internally using bootstrap resampling. RESULTS: The study analysed 6399 patients, with 2625 deaths during follow-up. Median follow-up was 41 months. Overall survival was 47.1% at 5 years. The final RSF model included 14 variables and had excellent discrimination with a 5-year tAUC of 83.9% (95%CI 82.6-84.9%), compared to 82.3% (95%CI 81.1-83.3%) for the Cox model. The most important variables were lymph node involvement, pT stage, CRM involvement (tumour at