Prediction model to predict critical @entity353 in @entity1 with XXXX during (chemo)radiotherapy.

@entity1 @entity135 @entity353 @entity138 @entity5
OBJECTIVES: @entity1 with @entity135 ( @entity135 ) frequently encounter @entity353 with multiple negative outcomes as a consequence. Adequate treatment is best achieved by early identification of @entity1 at risk for critical @entity353 . The objective of this study was to detect predictive factors for critical @entity353 in @entity1 with @entity135 receiving (chemo)radiotherapy ((C) @entity138 ). MATERIALS AND METHODS: In this cohort study, 910 @entity1 with @entity135 were included receiving @entity138 (  surgery/concurrent chemotherapy) with curative intent. Body weight was measured at the start and end of (C) @entity138 . Logistic regression and classification and regression tree (CART) analyses were used to analyse predictive factors for critical @entity353 (defined as >5%) during (C) @entity138 . Possible predictors included gender, age, WHO performance status, @entity5 location, TNM classification, treatment modality, @entity138 technique (three-dimensional conformal @entity138 ( @entity138 ) vs intensity-modulated @entity138 (IMRT)), total dose on the primary @entity5 and @entity138 on the elective or macroscopic lymph nodes. RESULTS: At the end of (C) @entity138 , mean @entity353 was 5.1  4.9%. Fifty percent of @entity1 had critical @entity353 during (C) @entity138 . The main predictors for critical @entity353 during (C) @entity138 by both logistic and CART analyses were @entity138 on the lymph nodes, higher @entity138 dose on the primary @entity5 , receiving @entity138 instead of IMRT, and younger age. CONCLUSION: Critical @entity353 during (C) @entity138 was prevalent in half of @entity135 @entity1 . To predict critical @entity353 , a practical prediction tree for adequate nutritional advice was developed, including the risk factors @entity138 to the neck, higher @entity138 dose, @entity138 , and younger age.
