Frontiers in nutrition · 2026
Nutritional risk score drives postoperative morbidity in colorectal cancer: findings from a LASSO-selected multivariable model in 1013 consecutive patients.
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Wang Lu, Wang Shun, Deng Liqin
Аннотация
BACKGROUND: Postoperative complications afflict approximately 40% of patients undergoing colorectal cancer (CRC) surgery and adversely affect long-term oncological outcomes. Reliable perioperative risk stratification tools that are both clinically interpretable and actionable remain limited.
METHODS: This retrospective cohort study enrolled 1,013 consecutive patients undergoing elective radical resection for histopathologically confirmed CRC at a single tertiary center. A nine-system composite complication endpoint was defined. Missing data were handled via multiple imputation by chained equations (MICE; m = 5). Candidate predictors were screened by univariable logistic regression and LASSO regularization; final predictors were entered into multivariable logistic regression with Rubin's rules pooling. Model performance was assessed by AUC, bootstrap internal validation (1,000 iterations), Hosmer-Lemeshow calibration, and decision curve analysis (DCA). A nomogram was constructed for individualized risk estimation.
RESULTS: Of 1,013 patients, 372 (36.7%) experienced ≥1 postoperative complication. LASSO selected eight independent predictors: NRS-2002 nutritional risk score (aOR 1.502, 95% CI 1.312-1.720), laparoscopic approach (aOR 0.457, 95% CI 0.297-0.702), intraoperative blood loss (aOR 1.002/mL), right colon tumor location (aOR 1.502), NLR (aOR 1.080/unit), operative time (aOR 1.002/min), hemoglobin, and total bilirubin. The model achieved an apparent AUC of 0.689 (bootstrap-corrected 0.680), satisfactory calibration (Hosmer-Lemeshow p = 0.709), and net clinical benefit across threshold probabilities of 10-90% on DCA. Sensitivity analyses confirmed robustness across four pre-specified scenarios (AUC range 0.666-0.715).
CONCLUSION: This LASSO-derived nomogram provides transparent, bedside-applicable risk stratification for postoperative composite complications in CRC surgery, identifying nutritional status as the dominant modifiable predictor and supporting targeted perioperative optimization.
METHODS: This retrospective cohort study enrolled 1,013 consecutive patients undergoing elective radical resection for histopathologically confirmed CRC at a single tertiary center. A nine-system composite complication endpoint was defined. Missing data were handled via multiple imputation by chained equations (MICE; m = 5). Candidate predictors were screened by univariable logistic regression and LASSO regularization; final predictors were entered into multivariable logistic regression with Rubin's rules pooling. Model performance was assessed by AUC, bootstrap internal validation (1,000 iterations), Hosmer-Lemeshow calibration, and decision curve analysis (DCA). A nomogram was constructed for individualized risk estimation.
RESULTS: Of 1,013 patients, 372 (36.7%) experienced ≥1 postoperative complication. LASSO selected eight independent predictors: NRS-2002 nutritional risk score (aOR 1.502, 95% CI 1.312-1.720), laparoscopic approach (aOR 0.457, 95% CI 0.297-0.702), intraoperative blood loss (aOR 1.002/mL), right colon tumor location (aOR 1.502), NLR (aOR 1.080/unit), operative time (aOR 1.002/min), hemoglobin, and total bilirubin. The model achieved an apparent AUC of 0.689 (bootstrap-corrected 0.680), satisfactory calibration (Hosmer-Lemeshow p = 0.709), and net clinical benefit across threshold probabilities of 10-90% on DCA. Sensitivity analyses confirmed robustness across four pre-specified scenarios (AUC range 0.666-0.715).
CONCLUSION: This LASSO-derived nomogram provides transparent, bedside-applicable risk stratification for postoperative composite complications in CRC surgery, identifying nutritional status as the dominant modifiable predictor and supporting targeted perioperative optimization.