Effect of gabapentin on swallowing during and after chemoradiation for oropharyngeal squamous cell cancer.

The aim of this study was to examine the impact of gabapentin (neurontin) on swallowing and feeding tube use during chemoradiation (CRT) for oropharyngeal squamous cell carcinoma (OPSCC), and physiologic swallowing outcomes following completion of treatment. A total of 23 patients treated for OPSCC with concurrent CRT and prophylactically treated for pain using gabapentin were assessed. Historical controls were matched for T stage and primary site of disease. Timing of PEG use and removal were recorded. Video fluoroscopic swallowing studies were completed post-treatment to assess physiologic outcomes as well as penetration-aspiration scores (PAS). Functional oral intake scale (FOIS) scores were determined at the time of swallowing evaluation to assess diet level. Patients treated with gabapentin began using their PEG tubes later (3.7 vs. 2.29 weeks; P = 0.013) and had their PEG tubes removed earlier (7.29 vs. 32.56 weeks; P = 0.039) than the historical controls. A number of physiologic parameters were found to be less impacted in the gabapentin group, including oral bolus control (P = 0.01), epiglottic tilt (P = 0.0007), laryngeal elevation (P = 0.0017), and pharyngeal constriction (P = 0.002). PAS scores were significantly lower in the group treated with gabapentin (1.89 vs. 4; P = 0.0052). Patients receiving gabapentin had more advanced diet levels at the time of the initial swallowing study as evidenced by their FOIS scores (5.4 vs. 3.21; P = 0.0003). We conclude that patients using gabapentin for pain management during CRT appears to do well maintaining swallow function during treatment and have favorable post-treatment physiologic swallowing outcomes. Prospective evaluation is warranted.

Variations in Symptom Severity Patterns Among Oropharyngeal and Laryngeal Cancer Outpatients During Radiation Treatment: A Pilot Study.

BACKGROUND: Pain, difficulty swallowing, dry mouth, taste change, appetite loss, and mouth sores are associated with radiotherapy (RT). Knowledge of individual symptom patterns (ISPs) and differences among patients during RT can provide vital information for evaluation and delivery of patient-tailored care. OBJECTIVE: The objectives of this study were to identify and describe ISPs and symptom duration, resolution, and peak severity among outpatients with oropharyngeal and laryngeal cancer. METHODS: This was an analysis of data from a larger study using Visual Graphical Analysis to examine 21 outpatients' ISPs over RT. Individual symptom patterns for 6 severe symptoms were grouped by ISP and evaluated by a panel of experts. RESULTS: Three types of ISPs were identified. The majority of patients displayed a problematic ISP type for pain and difficulty swallowing. When the ISPs of a patient's 6 symptoms were displayed together, the presence of several ISP types was noted. A majority experienced at least 1 symptom pattern that was problematic, indicating the uniqueness of each patient's symptom experience. CONCLUSIONS: During cancer treatment, patients experience multiple symptoms that change in severity over a course of care. Visualizing the patterns of patient-reported symptoms provided information regarding 6 severe symptoms experienced by patients. IMPLICATIONS FOR PRACTICE: Identifying ISPs provided information about symptom variations among patients with oropharyngeal and laryngeal cancer, which can influence symptom management strategies used by providers. Study techniques used can be useful for nurses when managing patients' symptoms. In the future, information using real-time data visualized as patterns could be used to manage multiple symptoms experienced by patients during treatment.

A data-mining framework for large scale analysis of dose-outcome relationships in a database of irradiated head and neck cancer patients.

PURPOSE: To develop a hypothesis-generating framework for automatic extraction of dose-outcome relationships from an in-house, analytic oncology database. METHODS: Dose-volume histograms (DVH) and clinical outcomes have been routinely stored to the authors' database for 684 head and neck cancer patients treated from 2007 to 2014. Database queries were developed to extract outcomes that had been assessed for at least 100 patients, as well as DVH curves for organs-at-risk (OAR) that were contoured for at least 100 patients. DVH curves for paired OAR (e.g., left and right parotids) were automatically combined and included as additional structures for analysis. For each OAR-outcome combination, only patients with both OAR and outcome records were analyzed. DVH dose points, DVt, at a given normalized volume threshold Vt were stratified into two groups based on severity of toxicity outcomes after treatment completion. The probability of an outcome was modeled at each Vt = [0%, 1%, …, 100%] by logistic regression. Notable OAR-outcome combinations were defined as having statistically significant regression parameters (p < 0.05) and an odds ratio of at least 1.05 (5% increase in odds per Gy). RESULTS: A total of 57 individual and combined structures and 97 outcomes were queried from the database. Of all possible OAR-outcome combinations, 17% resulted in significant logistic regression fits (p < 0.05) having an odds ratio of at least 1.05. Further manual inspection revealed a number of reasonable models based on either reported literature or proximity between neighboring OARs. The data-mining algorithm confirmed the following well-known OAR-dose/outcome relationships: dysphagia/larynx, voice changes/larynx, esophagitis/esophagus, xerostomia/parotid glands, and mucositis/oral mucosa. Several surrogate relationships, defined as OAR not directly attributed to an outcome, were also observed, including esophagitis/larynx, mucositis/mandible, and xerostomia/mandible. CONCLUSIONS: Prospective collection of clinical data has enabled large-scale analysis of dose-outcome relationships. The current data-mining framework revealed both known and novel dosimetric and clinical relationships, underscoring the potential utility of this analytic approach in hypothesis generation. Multivariate models and advanced, 3D dosimetric features may be necessary to further evaluate the complex relationship between neighboring OAR and observed outcomes.