ArticleAsia Pacific journal of clinical nutrition2024
A nomogram for predicting nutritional risk before gastric cancer surgery.
Article in Asia Pacific journal of clinical nutrition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk prediction models for malnutrition in patients with cancer: a systematic review.Frontiers in nutrition · 2026Pooled it
- Predicting Nutritional Risk in Elderly Patients With Community-Acquired Pressure Injury: A Noninvasive Model Integrating Age, Intake, BMI, and Braden Score.International wound journal · 2026Article
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7 authors.
Funding
Abstract
BACKGROUND AND
objectivesGastric cancer (GC) is the fourth leading cause of cancer death worldwide. Patients with GC have higher nutritional risk. This study aimed to construct a nomogram model for predicting preoperative nutritional risk in patients with GC in order to assess preoperative nutritional risk in patients more precisely. METHODS AND STUDY
designPatients diagnosed with GC and undergoing surgical treatment were included in this study. Data was collected through clinical information, laboratory testing, and radiomics-derived characteristics. Least absolute shrinkage selection operator (LASSO) regression analysis and multi-variable logistic regression were employed to construct a clinical prediction model, which takes the form of a logistic nomogram. The effectiveness of the nomogram model was evaluated using receiver operat-ing characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).
resultsA total of three predictors, namely body mass index (BMI), hemoglobin (Hb) and radiomics characteristic score (Radscore) were identified by LASSO regression analysis from a total of 21 variables studied. The model constructed using these three predictors displayed medium prediction ability. The area under the ROC curve was 0.895 (95% CI 0.844-0.945) in the training set, with a cutoff value of 0.651, precision of 0.957, and sensitivity of 0.718. In the validation set, it was 0.880 (95% CI 0.806-0.954), with a cutoff value of 0.655, precision of 0.930, and sensitivity of 0.698. DCA also confirmed the clinical benefit of the combined model.
conclusionsThis simple and dependable nomogram model for clinical prediction can assist physicians in assessing preoperative nutritional risk in GC patients in a time-efficient and accurate manner to facilitate early identification and diagnosis.
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