Table 2_Risk prediction models for malnutrition in cancer patients: a systematic review and meta-analysis.docx

BackgroundAlthough numerous models have been developed in recent years to predict malnutrition in cancer patients, their methodological rigor and clinical applicability remain uncertain. The lack of systematic evaluation hampers their integration into routine oncology and nursing practice, where early identification of at-risk patients is crucial for optimizing nutritional interventions, enhancing treatment tolerance, and reducing morbidity and mortality.
ObjectiveThis systematic review aims to synthesize and critically evaluate existing risk prediction models for malnutrition in cancer patients, thereby providing evidence-based insights to inform model development and clinical implementation.
MethodsDatabases including PubMed, Embase, Web of Science, the Cochrane Library, and Scopus were systematically searched to identify studies on risk prediction models for malnutrition in cancer patients published from database inception to August 9, 2025. Data extracted from the included studies comprised study design, data sources, sample size, predictors, model development, and model performance. The methodological quality of each study was evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist, and a meta-analysis of the area under the curve (AUC) was performed using Stata version 15.0.
ResultA total of 13 studies encompassing 57 predictive models were included. In the model development domain, seven studies constructed models using logistic regression alone, whereas five studies combined logistic regression with machine learning techniques. The reported incidence of malnutrition ranged from 11.9 to 69.9%. The most frequently used predictors were body mass index (BMI), age, and sex. The AUC values ranged from 0.735 to 0.982, with a pooled AUC of 0.85 (95% CI: 0.79–0.92) for eight validated models, indicating good discriminative performance. All 13 studies were rated as having a high risk of bias, mainly due to inappropriate data sources and insufficient reporting within the analysis domain.
ConclusionCurrent models for predicting malnutrition in cancer patients remain in the exploratory phase. Although these models demonstrate good discriminatory performance, methodological shortcomings contribute to a high risk of bias. This systematic review underscores the need to integrate validated malnutrition prediction models into oncology and nursing practice. Such models can support clinicians and oncology nursing professionals in early screening and timely identification of high-risk patients, promote individualized nutritional interventions, and strengthen multidisciplinary collaboration among nurses, dietitians, and oncologists.
Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD420251128218, identifier: CRD420251128218.