A predictive model for reactive tube feeding in head and neck cancer patients undergoing definitive (chemo) radiotherapy
Burnet, Neil G
Kota, Vamsi R
Lee, Lip W
Sykes, Andrew J
Thomson, David J
McPartlin, Andrew J
AffiliationThe Christie NHS Foundation Trust, Clinical Oncology, Proton Beam Therapy Centre, Manchester, UK
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AbstractAims: Careful management of a patient's nutritional status during and after treatment for head and neck squamous cell cancers (HNSCC) is crucial for optimal outcomes. The aim of this study was to develop a model for stratifying a patient's risk of requiring reactive enteral feeding through a nasogastric tube during radiotherapy for HNSCC, based on clinical and treatment-related factors. Materials and methods: A cohort of consecutive patients treated with definitive (chemo)radiotherapy for HNSCC between January 2016 and January 2018 was identified in the institutional electronic database for retrospective analysis. Patients requiring enteral feeding pretreatment were excluded. Clinical and treatment data were obtained from prospectively recorded electronic clinical notes and planning software. Results: Baseline patient characteristics and tumour-related parameters were captured for 225 patients. Based on the results of the univariate analysis and using a stepwise backwards selection process, clinical and dosimetric variables were selected to optimise a clinically predictive multivariate model, fitted using logistic regression. The parameters found to affect the probability, P, of requiring a nasogastric feeding tube for >4 weeks in our clinical multivariate model were: tumour site, tumour stage (early T0/1/2 stage versus advanced T3/T4 stage), chemotherapy drug (None versus any drug) and mean dose to the contralateral parotid gland. A scoring model using the regression coefficients of the selected variables in the clinical multivariate model achieved an area under the curve (AUC) of 0.745 (95% confidence interval 0.678-0.812), indicating good discriminative performance. Internal validation of the model involved splitting the dataset 80:20 into training and test datasets 10 times and assessing differences in AUC of the model fitted to these. Conclusions: We developed an easy-to-use prediction model based on both clinical and dosimetric parameters, which, once externally validated, can lead to more personalised treatment planning and inform clinical decision-making on the appropriateness of prophylactic versus reactive enteral feeding.
CitationGaito S, France A, Foden P, Abravan A, Burnet N, Garcez K, et al. A Predictive Model for Reactive Tube Feeding in Head and Neck Cancer Patients Undergoing Definitive (Chemo) Radiotherapy. Clinical Oncology . 2021 June.
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