Evidence map›Paper›PMID 42230735›Full record

ArticleScientific reports2026

A nomogram for risk stratification of protein-energy malnutrition in pediatric cancer patients: development and internal validation.

Hong Zhang, Ka Yan Ho, Janelle Yorke, Ting Mao, Gulisumuhan Abulaiti, Shuwan Dong

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Hong ZhangDepartment of Pediatrics, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830000, Xinjiang, China.
Ka Yan HoSchool of Nursing, The Hong Kong Polytechnic University, HKSAR, Hung Hom, China.
Janelle YorkeSchool of Nursing, The Hong Kong Polytechnic University, HKSAR, Hung Hom, China.
Ting MaoSchool of Nursing, The Hong Kong Polytechnic University, HKSAR, Hung Hom, China.
Gulisumuhan AbulaitiDepartment of Pediatrics, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830000, Xinjiang, China.
Shuwan DongDepartment of Pediatrics, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830000, Xinjiang, China. dongshuwan2025@163.com.

Funding

Natural Science Foundation of Xinjiang Uygur Autonomous Region 2023D01C108
6 · The paper itself

Abstract

To develop and internally validate a nomogram for predicting the risk of protein-energy malnutrition (PEM) in pediatric cancer patients and to evaluate its clinical utility. A total of 375 pediatric cancer patients admitted to a tertiary hospital in Xinjiang (January-May 2025) were enrolled. Nutritional status was assessed using the WHO BMI-for-age Z-score. Candidate variables (P < 0.01 in univariate analysis) were entered into the least absolute shrinkage and selection operator (LASSO) model, and multivariate logistic regression was applied to construct the prediction model. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration plots with the Hosmer-Lemeshow test, and decision curve analysis (DCA). Six readily obtainable variables were retained in the final model. Protective factors included being an only child (OR = 0.278), time since diagnosis of 0-6 months (OR = 0.264) or > 12 months (OR = 0.351), caregiver education of high school or above (OR = 0.406), and household per-capita income ≥ 1,000 yuan (OR = 0.255). Rural residence (OR = 2.643) and ≥ 6 chemotherapy cycles (OR = 2.259) were risk factors for PEM. The nomogram demonstrated robust discrimination in both the modeling (AUC = 0.823, 95% CI: 0.767-0.868) and validation (AUC = 0.779, 95% CI: 0.685-0.859) cohorts. Calibration curves showed close agreement between predicted and observed probabilities, and Hosmer-Lemeshow tests indicated good fit (all P > 0.05). DCA confirmed meaningful net clinical benefit across a wide range of threshold probabilities. The nomogram provides an interpretable and clinically practical tool for early identification of PEM risk in pediatric cancer patients, supporting targeted nutritional screening and timely intervention.

Indexed as

NeoplasmsNomogramsProtein-Energy MalnutritionAdolescentChildChild, PreschoolFemaleHumansInfantMaleNutritional StatusRisk AssessmentRisk FactorsROC CurveNomogramPediatric cancerPrediction modelProtein-energy malnutrition

Identifiers

PMID42230735
PMCPMC13463097

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