P621 - DEVELOPMENT AND INTERNAL VALIDATION OF A PREDICTION MODEL FOR THE MALNUTRITION IN LUNG CANCER PATIENTS BASED ON MODIFIED PATIENT-GENERATED SUBJECTIVE GLOBAL ASSESSMENT

P621

DEVELOPMENT AND INTERNAL VALIDATION OF A PREDICTION MODEL FOR THE MALNUTRITION IN LUNG CANCER PATIENTS BASED ON MODIFIED PATIENT-GENERATED SUBJECTIVE GLOBAL ASSESSMENT

C. XIAO^1,*, P. SHI¹, N. Lin¹

¹Department of Clinical Nutrition, The General Hospital of the Western Theater Command, Chengdu, China

Rationale: The study aimed to establish a new predictive model based on mPG-SGA  to diagnose malnutrition among Chinese lung cancer (LC) patients.

Methods: The study is a prospective cohort of 354 LC patients. Blood test indexes, baseline characteristics and human body composition (BIA) were collected . The data set was splitted as training and testing sets in a 7:3 ratio. Univariate and multivariate logistic regression analysis were taken to select meaningful variables, which was further combined with mPG-SGA to build the model. The discrimination of the model was evaluated using R², Brier score, C-index, goodness-of -fit, net reclassification index. The model was assessed for clinical utility by decision curve analysis (DCA).

Results: The median age of LC patients was 64 y (56, 70), and 261 (73.73%) were male. There were 313 (88.42%) patients with advanced clinical stage. Malnutrition was detected using GLIM and mPG-SGA tools with prevalence of 70.34% and 57.91% , respectively. Hemoglobin was finally selected to combine with mPG-SGA as a new predictive model. The C-index was 0.686, 95% CI (0.610-0.761) in the training set and 0.6197, 95% CI (0.491-0.7483) in the testing set. Goodness-of -fit test revealed that X-squared = 1.0237, df = 2, p-value = 0.5994 in the training set and X-squared = 0.19973, df = 2, p-value = 0.905 in the testing set. Compared with GLIM, NRI for new model was 0.4052, 95% CI (0.1226 - 0.6878), p-value: 0.00495. The prediction model produces greater net clinical benefit in both two sets as revealed by the clinical decision curve.

Conclusion: The mPG-SGA combined with hemoglobin provides nearly equivalent power to predict the survival of LC patients as the GLIM, indicating that this new model has the potential to be an alternative replacement for nutritional assessment among LC patients.

Disclosure of Interest: None declared