Occipital peripheral nerve stimulation in the management of chronic intractable occipital neuralgia in a patient with neurofibromatosis type 1: a case report

<p>Abstract</p> <p>Introduction</p> <p>Occipital peripheral nerve stimulation is an interventional pain management therapy that provides beneficial results in the treatment of refractory chronic occipital neuralgia. Herein we present a first-of-its-kind case study of a patient with neurofibromatosis type 1 and bilateral occipital neuralgia treated with occipital peripheral nerve stimulation.</p> <p>Case presentation</p> <p>A 42-year-old Caucasian woman presented with bilateral occipital neuralgia refractory to various conventional treatments, and she was referred for possible treatment with occipital peripheral nerve stimulation. She was found to be a suitable candidate for the procedure, and she underwent implantation of two octapolar stimulating leads and a rechargeable, programmable, implantable generator. The intensity, severity, and frequency of her symptoms resolved by more than 80%, but an infection developed at the implantation site two months after the procedure that required explantation and reimplantation of new stimulating leads three months later. To date she continues to experience symptom resolution of more than 60%.</p> <p>Conclusion</p> <p>These results demonstrate the significance of peripheral nerve stimulation in the management of refractory occipital neuralgias in patients with neurofibromatosis type 1 and the possible role of neurofibromata in the development of occipital neuralgia in these patients.</p

Objective wearable measures correlate with self-reported chronic pain levels in people with spinal cord stimulation systems

Abstract Spinal Cord Stimulation (SCS) is a well-established therapy for treating chronic pain. However, perceived treatment response to SCS therapy may vary among people with chronic pain due to diverse needs and backgrounds. Patient Reported Outcomes (PROs) from standard survey questions do not provide the full picture of what has happened to a patient since their last visit, and digital PROs require patients to visit an app or otherwise regularly engage with software. This study aims to assess the feasibility of using digital biomarkers collected from wearables during SCS treatment to predict pain and PRO outcomes. Twenty participants with chronic pain were recruited and implanted with SCS. During the six months of the study, activity and physiological metrics were collected and data from 15 participants was used to develop a machine learning pipeline to objectively predict pain levels and categories of PRO measures. The model reached an accuracy of 0.768 ± 0.012 in predicting the pain intensity of mild, moderate, and severe. Feature importance analysis showed that digital biomarkers from the smartwatch such as heart rate, heart rate variability, step count, and stand time can contribute to modeling different aspects of pain. The results of the study suggest that wearable biomarkers can be used to predict therapy outcomes in people with chronic pain, enabling continuous, real-time monitoring of patients during the use of implanted therapies

Objective wearable measures and subjective questionnaires for predicting response to neurostimulation in people with chronic pain

Abstract Background Neurostimulation is an effective therapy for treating and management of refractory chronic pain. However, the complex nature of pain and infrequent in-clinic visits, determining subject’s long-term response to the therapy remains difficult. Frequent measurement of pain in this population can help with early diagnosis, disease progression monitoring, and evaluating long-term therapeutic efficacy. This paper compares the utilization of the common subjective patient-reported outcomes with objective measures captured through a wearable device for predicting the response to neurostimulation therapy. Method Data is from the ongoing international prospective post-market REALITY clinical study, which collects long-term patient-reported outcomes from 557 subjects implanted by Spinal Cord Stimulator (SCS) or Dorsal Root Ganglia (DRG) neurostimulators. The REALITY sub-study was designed for collecting additional wearables data on a subset of 20 participants implanted with SCS devices for up to six months post implantation. We first implemented a combination of dimensionality reduction algorithms and correlation analyses to explore the mathematical relationships between objective wearable data and subjective patient-reported outcomes. We then developed machine learning models to predict therapy outcome based on the subject’s response to the numerical rating scale (NRS) or patient global impression of change (PGIC). Results Principal component analysis showed that psychological aspects of pain were associated with heart rate variability, while movement-related measures were strongly associated with patient-reported outcomes related to physical function and social role participation. Our machine learning models using objective wearable data predicted PGIC and NRS outcomes with high accuracy without subjective data. The prediction accuracy was higher for PGIC compared with the NRS using subjective-only measures primarily driven by the patient satisfaction feature. Similarly, the PGIC questions reflect an overall change since the study onset and could be a better predictor of long-term neurostimulation therapy outcome. Conclusions The significance of this study is to introduce a novel use of wearable data collected from a subset of patients to capture multi-dimensional aspects of pain and compare the prediction power with the subjective data from a larger data set. The discovery of pain digital biomarkers could result in a better understanding of the patient’s response to therapy and their general well-being