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

Bioelectronic Medicine

Research Article

Misagh Mansouri¹  Soroush Dehghan¹  Anahita Kyani¹  Michael Fishman²  Frank Huygen³  Ioannis Skaribas⁴  Derron Wilson⁵  Steven Falowski⁶  Denis Patterson⁷  David Schultz⁸  Gregory Moore⁹  Jan Willem Kallewaard^1,10  Robert Heros^1,11 

[1] Abbott, Austin, TX, USA;Center for Interventional Pain and Spine, Lancaster, PA, USA;Erasmus University Medical Center, Rotterdam, Netherlands;Expert Pain, Houston, TX, USA;Goodman Campbell Brain & Spine, Carmel, IN, USA;Neurosurgical Associates of Lancaster, Lancaster, PA, USA;Nevada Advanced Pain Specialists, Reno, NV, USA;Nura Clinics, Minneapolis, MN, USA;Pacific Sports and Spine, Eugene, OR, USA;Rijnstate Hospital, Arnhem, Netherlands;Spinal Diagnostics, Tualatin, OR, USA;

关键词: Chronic pain therapy;    Spinal cord stimulation;    Patient-reported outcomes;    Wearable technology;    Digital biomarkers for pain;    Dimensionality reduction;    Questionnaire burden;    Machine learning;    Predictive modeling;    PGIC;   

DOI  :  10.1186/s42234-023-00115-4

 received in 2023-05-08, accepted in 2023-06-06,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundNeurostimulation 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.MethodData 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).ResultsPrincipal 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.ConclusionsThe 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.

【 授权许可】

CC BY   
© The Author(s) 2023

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