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Abstract Swallowing is an ensemble of voluntary and autonomic processes key to maintaining our body’s homeostatic balance. Abnormal swallowing (dysphagia) can cause dehydration, malnutrition, aspiration pneumonia, weight loss, anxiety, or even mortality—especially in older adults—by airway obstruction. To prevent or mitigate these outcomes, it is imperative to regularly assess swallowing ability in those who are at risk of developing dysphagia and those already diagnosed with it. However, current diagnostic tools such as endoscopy, manometry, and videofluoroscopy require access to clinical experts to interpret the results. These results are often sampled from a limited examination timeframe of swallowing activity in a controlled environment. Additionally, there is some risk of periprocedural complications associated with these methods. In contrast, the field of epidermal sensors is finding non-invasive and minimally obtrusive ways to examine swallowing function and dysfunction. In this review, we summarize the current state of wearable devices that are aimed at monitoring swallowing function and detecting its abnormalities. We pay particular attention to the materials and design parameters that enable their operation. We examine a compilation of both proof-of-concept studies (which focus mainly on the engineering of the device) and studies whose aims are biomedical (which may involve larger cohorts of subjects, including patients). Furthermore, we briefly discuss the methods of signal acquisition and device assessment in relevant wearable sensors. Finally, we examine the need to increase adherence and engagement of patients with such devices and discuss enhancements to the design of such epidermal sensors that may encourage greater enthusiasm for at-home and long-term monitoring. 

PURPOSE: Identify Oropharyngeal cancer (OPC) patients at high-risk of developing long-term severe radiation-associated symptoms using dose volume histograms for organs-at-risk, via unsupervised clustering. MATERIAL AND METHODS: All patients were treated using radiation therapy for OPC. Dose-volume histograms of organs-at-risk were extracted from patients’ treatment plans. Symptom ratings were collected via the MD Anderson Symptom Inventory (MDASI) given weekly during, and 6 months post-treatment. Drymouth, trouble swallowing, mucus, and vocal dysfunction were selected for analysis in this study. Patient stratifications were obtained by applying Bayesian Mixture Models with three components to patient’s dose histograms for relevant organs. The clusters with the highest total mean doses were translated into dose thresholds using rule mining. Patient stratifications were compared against Tumor staging information using multivariate likelihood ratio tests. Model performance for prediction of moderate/severe symptoms at 6 months was compared against normal tissue complication probability (NTCP) models using cross-validation. RESULTS: A total of 349 patients were included for long-term symptom prediction. High-risk clusters were significantly correlated with outcomes for severe late drymouth (p <.0001, OR = 2.94), swallow (p = .002, OR = 5.13), mucus (p = .001, OR = 3.18), and voice (p = .009, OR = 8.99). Simplified clusters were also correlated with late severe symptoms for drymouth (p <.001, OR = 2.77), swallow (p = .01, OR = 3.63), mucus (p = .01, OR = 2.37), and voice (p <.001, OR = 19.75). Proposed cluster stratifications show better performance than NTCP models for severe drymouth (AUC.598 vs.559, MCC.143 vs.062), swallow (AUC.631 vs.561, MCC.20 vs -.030), mucus (AUC.596 vs.492, MCC.164 vs -.041), and voice (AUC.681 vs.555, MCC.181 vs -.019). Simplified dose thresholds also show better performance than baseline models for predicting late severe ratings for all symptoms. CONCLUSION: Our results show that leveraging the 3-D dose histograms from radiation therapy plan improves stratification of patients according to their risk of experiencing long-term severe radiation associated symptoms, beyond existing NTPC models. Our rule-based method can approximate our stratifications with minimal loss of accuracy and can proactively identify risk factors for radiation-associated toxicity.