NUTRIGETNE: A Prediction Tool for Malnutrition and Sarcopenia in GEP-NEN Patients

399 patients with gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) were analyzed to develop a clinical prediction tool. The model, built using LASSO regression on a 4:1 training-validation split, identifies malnutrition and sarcopenia risk factors. This diagnostic nomogram was created by Maria Argente-Pla and published on figshare in May 2026.

Use Cases

• Predicting malnutrition risk in GEP-NEN patients based on tumor differentiation and symptoms like nausea/vomiting
• Assessing sarcopenia risk based on patient-reported difficulties in activities of daily living and long walks
• Developing clinical screening tools using the derived nomogram for high-risk case identification

Strengths

• Model developed on a cohort of 399 patients, providing a substantive sample size
• Predictive factors are quantified with odds ratios and confidence intervals, such as poor tumor differentiation (OR: 4.4) for malnutrition
• External validation was performed, with reported accuracy of 65.3% for malnutrition and 71.2% for sarcopenia

Limitations

• Column-level documentation is absent; field semantics must be inferred after download
• Row count for the underlying data is unknown, which may limit suitability assessment
• The dataset is very small (13.4 KB), suggesting limited scope or aggregated summary data

Provenance

Source

Results from the NUTRIGETNE (GETNE-S2109) study, authored by Maria Argente-Pla.

Collection Method

Patients were randomized into training and validation sets to build a LASSO model, corrected by sex, age, and BMI.

Freshness

Last updated 2026-05-26 04:37:20

Geography

Data collection focused on patients receiving anticancer treatment in Spain.