Application research of different nutritional scoring predictive models in evaluating the prognosis of pancreatic cancer patients after pancreaticoduodenectomy

LI Hong-tao; HUANG Zi-jian; SU Tie; LI Nan-nan; ZHANG Wen-bin; WANG Gang

doi:10.19538/j.cjps.issn1005-2208.2025.06.13

Abstract：Objective To evaluate the predictive performance of distinct nutritional scores for overall survival(OS)in pancreatic cancer patients after pancreaticoduodenectomy(PD)and establish a personalized prognostic model.Methods A retrospective cohort study was performed on 188 pancreatic cancer patients who underwent PD at the First Affiliated Hospital of Harbin Medical University,between December 2013 and December 2022.Independent prognostic factors for OS were identified through univariate and multivariate Cox regression analyses.Four predictive models were constructed by integrating each nutritional score(NRI,NPS,PNI,and CONUT)with identified independent predictors.Model performance was compared using the concordance index(C-index),calibration curves,decision curve analysis(DCA),and time-dependent ROC curves.A nomogram was ultimately developed based on the optimal nutritional score.Results Multivariate analysis confirmed lymph node metastasis(HR=1.299,95％CI 0.926～1.824,P=0.047),tumor diameter ≥2.5 cm(HR=1.411,95％CI 1.010～1.972,P=0.043),and poor tumor differentiation(HR=1.494,95％CI 1.069～2.089,P=0.019)as independent risk factors for poor prognosis after PD.Comparative assessment of the four models revealed superior predictive capability for NRI and NPS models compared to PNI and CONUT models,as evidenced by DCA and calibration curves.While the NRI(C-index=0.687,95％CI 0.663～0.712)and NPS(C-index=0.684,95％CI 0.660～0.707)models demonstrated equivalent discriminatory power,time-dependent ROC analysis indicated comparable accuracy and stability,with NPS exhibiting enhanced long-term predictive performance.A nomogram incorporating NPS and independent prognostic risk factors was established,with a cutoff score of 80.6.Conclusion The NPS-derived predictive model enables accurate survival risk stratification for pancreatic cancer patients after PD,providing critical insights for nutritional risk-adapted management and tailored clinical interventions.