Machine Learning-based Correlation Study between Perioperative Immunonutritional Index and Postoperative Anastomotic Leakage in Patients with Gastric Cancer

Backgrounds: The immunonutritional index showed great potential for predicting postoperative complications in various malignant diseases, while risk assessment based on machine learning (ML) methods is becoming popular in clinical practice. Early detection and prevention for postoperative anastomotic leakage (AL) play an important role in prognosis improvement among patients with gastric cancer (GC). Methods: This retrospective study included 297 patients with gastric cancer receiving gastrectomy between 2018 and 2021 in general surgery department of Xinhua Hospital. Perioperative clinical variables were collected to evaluate the predictive value for postoperative AL with 5 ML models. Then, AUROC was applied to identify the optimal perioperative clinical index and ML model for predicting postoperative AL. Results: The incidence of postoperative AL was 6.1% (n=18). After the training of 5 ML classification models, we found that immunonutritional index had significantly better classification ability than inflammatory or nutritional index alone separately (AUROC=0.87 vs. 0.83, P=0.01; AUROC=0.87 vs. 0.68, P<0.01). Next, we found that support vector machine (SVM), one of the ML methods, with selected immunonutritional index showed significantly greater classification ability than optimal univariant parameter [CRP on postoperative day 4 (AUROC=0.89 vs.0.86, P=0.02)]. Also, statistical analysis revealed multiple variables with significant relevance to postoperative AL, including serum CRP and albumin on postoperative day 4, NLR and SII etc. Conclusion: This study showed that perioperative immunonutritional index could act as an indicator for postoperative AL. Also, ML methods could significantly enhance the classification ability, and therefore, could be applied as a powerful tool for postoperative risk assessment for patients with GC.