Predicting acute odynophagia during lung cancer radiotherapy using observations derived from patient-centred nursing care

Abstract

During radiotherapy, lung cancer patients commonly experience pain while swallowing (odynophagia) of food and drink. Observations from patient-centred nursing practice have been used to generate predictive models for odynophagia needing prescription pain medication during external beam lung radiotherapy for non-small cell and small-cell lung cancer. Three multivariate logistic models were evaluated in repeat cross-validation: a manual-stepwise model and two supervised machine learning models. Overall predictive performance was good. Correct classification rates ranged from 0.82 to 0.84, and areas under the receiver operator curve ranged from 0.83 to 0.85. Model sensitivity (range: 0.92–0.97) was higher than model specificity (range: 0.58–0.63). Further validation of the models in clinical context is required. A predictive model for pain medication for odynophagia prior to commencement of radiotherapy would support Radiotherapy Technologists Nurses (RTNs) in directing nursing interventions towards patients at risk. Keywords: Patient-centred care, Predictive modelling, Odynophagia, Swallowing pain, Lung cancer, Acute oesophagitis