Development and validation of a rapid-decision pathway to diagnose malnutrition in patients with lung cancer

2020 Objectives: Malnutrition is frequently developed and outcome-related in patients with lung cancer (LC). Making a rapid and accurate diagnosis of malnutrition is the major concern for dietitians and clinicians. Methods: We performed a multicenter, observational cohort study including 1219 patients with LC. Malnutrition was diagnosed using the Global Leadership Initiative on Malnutrition criteria, and the study population was randomly divided into a training group (n = 914) and a validation group (n = 305). A nomogram (to diagnose malnutrition) and two decision trees (to diagnose and grade malnutrition, respectively) were independently developed and tested. A random forest algorithm was used to calculate relative variable importance. Results: The Global Leadership Initiative on Malnutrition criteria identified 292 patients with malnutrition (24%). Sex, body mass index, weight loss within 6 mo, weight loss beyond 6 mo, calf circumference, and handgrip strength to weight ratio were screened for model development. The nomogram showed good discrimination with an area under the curve (AUC) of 0.982 (95% confidence interval, 0.969-0.995) and good calibration in the validation group. A decision curve analysis demonstrated that the nomogram was clinically useful. The diagnostic tree showed an accuracy of 0.98 (Kappa = 0.942; AUC = 0.978; 95% confidence interval, 0.964-0.992), and the classification tree showed an accuracy of 0.98 (Kappa = 0.955; AUC = 0.987) in the validation group. Weight loss within 6 mo contributed the largest importance to both trees. Conclusions: This study presents a rapid-decision pathway, including a set of tools that can be conveniently used to facilitate the diagnosis and severity grading of malnutrition in patients with LC. (c) 2020 Elsevier Inc. All rights reserved.

Automated summary

This study demonstrates the development of tools including nomograms and decision trees for rapid and accurate diagnosis and severity grading of malnutrition in patients with lung cancer.