Nomogram spots malnutrition in cancer patients

A nutritional features-based clustering analysis is a viable method to identify malnutrition early in cancer patients, suggests a study.

This observation study included a total of 3,998 patients with cancer at two teaching hospitals in China. The authors performed hierarchical clustering to classify patients into well-nourished or malnourished clusters based on 17 features reflecting the phenotypic and aetiologic dimensions of malnutrition. They also analysed associations between the identified clusters and patient characteristics.

Finally, a nomogram for predicting malnutrition probability was made and independently validated to explore its clinical significance.

In cluster analysis, 2,736 (68.4 percent) patients were identified as well-nourished and 1,262 (31.6 percent) as malnourished, demonstrating significant agreement with the Patient-Generated Subjective Global Assessment and the Global Leadership Initiative on Malnutrition criteria (p<0.001 for both).

The malnourished cluster showed a negative association with nutritional status, physical status, quality of life, and short-term outcomes and was independently predictive of survival (hazard ratio, 1.38, 95 percent confidence interval [CI], 1.22–1.55; p<0.001).

The nomogram was developed by incorporating sex, body mass index, calf circumference, and weight loss percentages (within and beyond 6 months) and demonstrated high performance in the prediction of malnutrition (area under the curve, 0.972, 95 percent CI, 0.960–0.983).

Notably, the effectiveness and clinical usefulness of the tool were further demonstrated in the decision curve analysis and independent external validation.

“The derived nomogram shows effectiveness for the early identification of malnutrition in patients with cancer,” the authors said.