PurposeAccurate assessment of toxicity allows for timely delivery of supportive measures during radiation therapy for head and neck cancer. The current paradigm requires weekly evaluation of patients by a provider. The purpose of this study is to evaluate the feasibility of monitoring patient reported symptoms via mobile devices.Methods and materialsWe developed a mobile application for patients to report symptoms in 5 domains using validated questions. Patients were asked to report symptoms using a mobile device once daily during treatment or more often as needed. Clinicians reviewed patient-reported symptoms during weekly symptom management visits and patients completed surveys regarding perceptions of the utility of the mobile application. The primary outcome measure was patient compliance with mobile device reporting. Compliance is defined as number of days with a symptom report divided by number of days on study.ResultsThere were 921 symptom reports collected from 22 patients during treatment. Median reporting compliance was 71% (interquartile range, 45%-80%). Median number of reports submitted per patient was 34 (interquartile range, 21-53). Median number of reports submitted by patients per week was similar throughout radiation therapy and there was significant reporting during nonclinic hours. Patients reported high satisfaction with the use of mobile devices to report symptoms.ConclusionsA substantial percentage of patients used mobile devices to continuously report symptoms throughout a course of radiation therapy for head and neck cancer. Future studies should evaluate the impact of mobile device symptom reporting on improving patient outcomes.
change in external contour between the planning scan and daily treatment scan was >1 cm. The dosimetric impact was assessed and a replan generated if target volume coverage was inadequate or organs at risk dose exceeded tolerance. Patient demographics and tumor characteristics were recorded and compared between patients who were replanned and those that were not. Univariate and multivariate analyses were performed and factors found to be significant for replanning included in logistic regression and CART analysis. To assess the logistic regression and CART analysis with larger patient numbers, it was repeated on all patients who underwent a second planning scan, making the assumption that this scenario always necessitates replanning. Results: One hundred and ten patients were enrolled between October 2013 and December 2014. The majority were OPC (84.5%) and male (91.8%) and they were predominantly classified at the T2 (33.6%) and N2 (80%) stage. Twenty-one patients (19.1%) underwent a second planning scan, and of these, 5 (4.9%) patients underwent a replan. Nodal disease stage, pretreatment size of the largest node, diagnosis (P<.01), and initial weight (categorized in 2 groups) (P<.07) were identified as significant for inclusion in the logistic regression model predicting the need to replan. When the percentage of patients replanned was increased, nodal disease stage (PZ.06), pretreatment size of the largest node, diagnosis, initial weight, and percentage weight change (P<.01) were identified as significant for inclusion in the logistic regression model. Conclusion: Predictive modeling, using logistic regression and CART analysis, can be utilized in the identification of OPC or NPC patients more likely to require ART. This could facilitate the efficient implementation of ART resulting in the appropriate allocation of institutional resources.
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