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 po...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-06-01
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| Series: | Bioelectronic Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42234-023-00115-4 |
| _version_ | 1797795571075907584 |
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| author | Robert Heros Denis Patterson Frank Huygen Ioannis Skaribas David Schultz Derron Wilson Michael Fishman Steven Falowski Gregory Moore Jan Willem Kallewaard Soroush Dehghan Anahita Kyani Misagh Mansouri |
| author_facet | Robert Heros Denis Patterson Frank Huygen Ioannis Skaribas David Schultz Derron Wilson Michael Fishman Steven Falowski Gregory Moore Jan Willem Kallewaard Soroush Dehghan Anahita Kyani Misagh Mansouri |
| author_sort | Robert Heros |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T03:19:58Z |
| format | Article |
| id | doaj.art-3ff6602f0dc84719839054fe8c39a830 |
| institution | Directory Open Access Journal |
| issn | 2332-8886 |
| language | English |
| last_indexed | 2024-03-13T03:19:58Z |
| publishDate | 2023-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Bioelectronic Medicine |
| spelling | doaj.art-3ff6602f0dc84719839054fe8c39a8302023-06-25T11:23:19ZengBMCBioelectronic Medicine2332-88862023-06-019111310.1186/s42234-023-00115-4Objective wearable measures and subjective questionnaires for predicting response to neurostimulation in people with chronic painRobert Heros0Denis Patterson1Frank Huygen2Ioannis Skaribas3David Schultz4Derron Wilson5Michael Fishman6Steven Falowski7Gregory Moore8Jan Willem Kallewaard9Soroush Dehghan10Anahita Kyani11Misagh Mansouri12Spinal DiagnosticsNevada Advanced Pain SpecialistsErasmus University Medical CenterExpert PainNura ClinicsGoodman Campbell Brain & SpineCenter for Interventional Pain and SpineNeurosurgical Associates of LancasterPacific Sports and SpineRijnstate HospitalAbbottAbbottAbbottAbstract 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.https://doi.org/10.1186/s42234-023-00115-4Chronic pain therapySpinal cord stimulationPatient-reported outcomesWearable technologyDigital biomarkers for painDimensionality reduction |
| spellingShingle | Robert Heros Denis Patterson Frank Huygen Ioannis Skaribas David Schultz Derron Wilson Michael Fishman Steven Falowski Gregory Moore Jan Willem Kallewaard Soroush Dehghan Anahita Kyani Misagh Mansouri Objective wearable measures and subjective questionnaires for predicting response to neurostimulation in people with chronic pain Bioelectronic Medicine Chronic pain therapy Spinal cord stimulation Patient-reported outcomes Wearable technology Digital biomarkers for pain Dimensionality reduction |
| title | Objective wearable measures and subjective questionnaires for predicting response to neurostimulation in people with chronic pain |
| title_full | Objective wearable measures and subjective questionnaires for predicting response to neurostimulation in people with chronic pain |
| title_fullStr | Objective wearable measures and subjective questionnaires for predicting response to neurostimulation in people with chronic pain |
| title_full_unstemmed | Objective wearable measures and subjective questionnaires for predicting response to neurostimulation in people with chronic pain |
| title_short | Objective wearable measures and subjective questionnaires for predicting response to neurostimulation in people with chronic pain |
| title_sort | objective wearable measures and subjective questionnaires for predicting response to neurostimulation in people with chronic pain |
| topic | Chronic pain therapy Spinal cord stimulation Patient-reported outcomes Wearable technology Digital biomarkers for pain Dimensionality reduction |
| url | https://doi.org/10.1186/s42234-023-00115-4 |
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