Evidence map›Paper›PMID 35002310›Full record

ArticleJournal of pain research2021

A New Direction for Closed-Loop Spinal Cord Stimulation: Combining Contemporary Therapy Paradigms with Evoked Compound Action Potential Sensing.

Ricardo Vallejo, Krishnan Chakravarthy, Andrew Will, Karen Trutnau, David Dinsmoor

Open access · goldAbstract read
In one paragraph

Article in Journal of pain research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
5.4field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 41 citations in OpenAlex.

  1. Trial
  2. Review
  3. Electrophysiologic Characteristics of Epidural Spinal Signals in Preclinical Models of Spinal Cord Stimulation.Neuromodulation : journal of the International Neuromodulation Society · 2026
    Article
  4. Article
  5. Closed-loop SCS and sensing: now and in the future.Pain medicine (Malden, Mass.) · 2026
    Review
  6. Review
  7. Article
  8. Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Article
  14. Review
  15. Review
  16. Article
  17. Article
  18. Machine Learning in Pain Neuromodulation.Advances in experimental medicine and biology · 2024
    Review
  19. Machine Learning in Spinal Cord Stimulation for Chronic Pain.Operative neurosurgery (Hagerstown, Md.) · 2023
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 4 institutions in 1 country.

Ricardo VallejoMillennium Pain Center, Bloomington, IL, USA.
Krishnan ChakravarthyAnesthesiology and Pain Management, University of California San Diego, San Diego, CA, USA.
Andrew WillTwin Cities Pain Clinic, Edina, MN, USA.
Karen TrutnauTwin Cities Pain Clinic, Edina, MN, USA.
David DinsmoorNeuromodulation Research & Technology, Medtronic plc, Minneapolis, MN, USA.ORCID 0000-0003-2410-0084
Twin Cities Orthopedics · USMedtronic (United States) · USMillennium Pain Center · USUniversity of California San Diego · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spinal cord stimulation (SCS) utilizes the delivery of mild electrical pulses via epidural electrodes placed on the dorsal side of the spinal cord, typically to treat chronic pain. The first clinical use of SCS involved the delivery of paresthesia inducing, low-frequency waveforms to the neural targets corresponding to the painful areas. Contemporary SCS therapies now leverage novel therapeutic pathways to limit paresthesia and deliver superior clinical outcomes. Historically, SCS has largely been delivered with fixed stimulation parameters. This approach, referred to as open-loop (OL) SCS, does not account for the fluctuations in spacing-driven by postural changes and activity-between the electrodes and the cord. These fluctuations result in variability in the delivered dose and the volume of tissue activation (VTA) that manifests with each stimulation pulse. Inconsistent dosing may lead to suboptimal therapeutic efficacy and durability. To address this clinical need, closed-loop (CL) SCS systems have been developed to automatically adjust stimulation parameters to compensate for this variability. The evoked compound action potential (ECAP), a biopotential generated by the synchronous activation of dorsal column fibers, is indicative of the VTA resulting from the stimulation pulse. The ECAP may be utilized as a control signal in CL SCS systems to adjust stimulation parameters to reduce variability in the ECAP, and in turn, variability in the VTA. While investigational CL SCS systems with ECAP sensing have so far focused solely on managing paresthesia-based SCS, such systems must also incorporate the stimulation approaches that now define the contemporary clinical practice of SCS. Accordingly, we describe here a flexible, next-generation framework for neural responsive SCS that blends science-based methodologies for pain management with real-time CL control for biophysical variation. We conclude with a clinical example of such a system and the associated performance characteristics.

Indexed as

closed-loopevoked potentialsneuromodulationspinal cord stimulation

Identifiers

PMID35002310
PMCPMC8721159
OpenAlexW4200198621

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.