Utilizing machine learning approaches to investigate the relationship between cystatin C and serious complications in esophageal cancer patients after esophagectomy

The purpose of this study is to investigate the relationship between preoperative cystatin C levels and the risk of serious postoperative complications in esophageal cancer (EC) patients, utilizing advanced machine learning (ML) methodologies.

We conducted an observational cohort study, involving 524 EC patients from December 2014 to July 2022. ML models, including logistic regression (LR) and multilayer perceptron (MLP), were applied to investigate the relationship between cystatin C and the serious postoperative complications. The predictive value of cystatin C was evaluated using receiver operating characteristic (ROC) analysis. Based on a restricted cubic spline (RCS) method, the potential nonlinear association was scrutinized.

The morbidity of serious postoperative complications was 8.78%. Bleeding volume, operating time, NRS2002 score, PONS score, and cystatin C were significantly associated with serious postoperative complications. The MLP model demonstrated superior predictive accuracy (AUC = 0.775, 95% CI: 0.701–0.849) compared to the LR model (AUC = 0.714, 95% CI: 0.630–0.798) and cystatin C alone (AUC = 0.612, 95% CI: 0.526–0.699). High cystatin C level independently predicted serious postoperative complications in EC patients. A positive and linear association was found between cystatin C and serious complications.

This research uncovers a notable correlation between cystatin C and the severe complications in EC patients after esophagectomy. Employing ML techniques offers a robust method for forecasting patient outcomes and emphasizes the potential of cystatin C as a predictive biomarker in medical practice.