Development and validation of a LASSO-based nomogram for predicting anastomotic leakage in elderly patients after laparoscopic gastrectomy.

BACKGROUND

Anastomotic leakage (AL), a major postoperative complication following laparoscopic gastrectomy (LG), presents a critical diagnostic challenge in elderly patients, often resulting in life-threatening outcomes. This study aimed to develop and validate a risk prediction model to facilitate the early identification of AL in this population.

METHODS

Retrospective data from 884 elderly patients diagnosed with gastric cancer who underwent LG were analyzed. The patients were randomly divided into training and validation cohorts in a 7:3 ratio. Clinically relevant predictors of AL were identified using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analyses. A nomogram model was subsequently developed using these predictors. Model performance was evaluated and validated using the area under the curve (AUC) for discrimination, the Hosmer-Lemeshow test and calibration curve for accuracy, and decision curve analysis (DCA) for clinical applicability.

RESULTS

The incidence rate of AL in the cohort was 13.6% (120/884). Five variables emerged as independent predictors of AL, including age, American Society of Anesthesiologists (ASA), diabetes, intraoperative blood loss, and prognostic nutritional index (PNI). The nomogram exhibited robust predictive accuracy, with AUC values of 0.870 [95% confidence interval (CI): 0.826-0.913] and 0.890 (95% CI: 0.828-0.952) in the training and validation cohorts, respectively. Calibration curves demonstrated a strong concordance between predicted and observed outcomes. DCA further indicated favorable clinical utility across a wide range of risk thresholds.

CONCLUSIONS

This study developed a LASSO-derived nomogram that incorporates five routinely assessed perioperative variables (age, ASA score, diabetes, intraoperative blood loss, and PNI) as a reliable tool for predicting AL risk in elderly patients undergoing LG. The model demonstrated satisfactory accuracy, discrimination, and clinical efficacy, thus enabling early risk identification to guide targeted preventive interventions.