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NUTRISCORE: A Novel Nutrition-Based Nomogram to Predict Severe Neutropenia After Adjuvant Chemotherapy in Gastric
Chun Gao1, Li Zhu1, Yi Xin Tong1
1Huazhong University of Science and Technology, Wuhan, People's Republic of China.
Background:
To develop and validate a clinically applicable nomogram (NUTRISCORE) predicting severe neutropenia (Grade 3/4) across cycles 2 to 8 of adjuvant chemotherapy in gastric cancer.
Methods:
This retrospective cohort study analyzed 391 patients receiving postoperative adjuvant chemotherapy (CAPEOX/XELOX/SOX) following curative laparoscopic D2 gastrectomy. Cohorts were stratified into derivation (n = 318; 1518 cycles) and validation (n = 73; 356 cycles) sets. Generalized Estimating Equations (GEE) were used to account for within-patient correlation of multiple cycles. Multivariable logistic regression identified independent predictors, with subsequent nomogram construction using R. Model performance was quantified through AUC-ROC and decision curve analysis (DCA).
Results:
Four predictors emerged: age > 60 years (OR = 2.1, P < .01), prealbumin ≤ 180 mg/L (OR = 3.4, P = .002), PNI < 45 (OR = 2.8, P = .008), and prior cycle neutropenia (OR =4.7, P < .001). The nomogram achieved AUCs of 0.825 (95% CI 0.789-0.860) and 0.810 (0.783-0.835) in derivation and validation cohorts, with DCA confirming clinical utility across threshold probabilities (4%-65% in derivation, 3%-63% in validation), supporting its use for guiding G-CSF prophylaxis at clinically relevant risk thresholds (>15%).
Conclusions:
We establish a nutrition-centric predictive tool for severe neutropenia, demonstrating robust accuracy in stratifying chemotherapy toxicity risk. This model directly informs personalized prophylactic strategies, warranting prospective multicenter validation.
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