Evidence map›Paper›PMID 34665369›Full record

ReviewNeurosurgical review2022

Prevalence of distinct types of hardware failures related to deep brain stimulation.

Ariana Moura Cabral, Adriano Alves Pereira, Marcus Fraga Vieira, Bruno Lima Pessôa, Adriano de Oliveira Andrade

Abstract readReview
PubMed Publisher
In one paragraph

Review in Neurosurgical review, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
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

10 citing papers in PubMed.

  1. Trial
  2. A guide to neuromodulation in drug-resistant epilepsy.Epileptic disorders : international epilepsy journal with videotape · 2026
    Review
  3. Article
  4. Article
  5. Review
  6. Mechanical Forces Guide Axon Growth through the Nigrostriatal Pathway in an Organotypic Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  7. Article
  8. Investigation of a Deep Brain Stimulator (DBS) System.Bioengineering (Basel, Switzerland) · 2023
    Article
  9. Article
  10. Review
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.

Ariana Moura CabralCentre for Innovation and Technology Assessment in Health, Faculty of Electrical Engineering, Postgraduate Program in Electrical and Biomedical Engineering, Federal University of Uberlândia, Campus Santa Mônica - Bloco 1E, Av. Joao Naves de Avila, 2121, Uberlandia, MG, 38408-100, Brazil.ORCID http://orcid.org/0000-0002-9804-353X
Adriano Alves PereiraCentre for Innovation and Technology Assessment in Health, Faculty of Electrical Engineering, Postgraduate Program in Electrical and Biomedical Engineering, Federal University of Uberlândia, Campus Santa Mônica - Bloco 1E, Av. Joao Naves de Avila, 2121, Uberlandia, MG, 38408-100, Brazil.ORCID http://orcid.org/0000-0002-1522-9989
Marcus Fraga VieiraBioengineering and Biomechanics Laboratory, Federal University of Goiás, Goiânia, Brazil.ORCID http://orcid.org/0000-0001-9096-1603
Bruno Lima PessôaPostgraduate Program in Neurology, Faculty of Medicine, Federal University of Fluminense, Niterói, Brazil.ORCID http://orcid.org/0000-0002-9772-7649
Adriano de Oliveira AndradeCentre for Innovation and Technology Assessment in Health, Faculty of Electrical Engineering, Postgraduate Program in Electrical and Biomedical Engineering, Federal University of Uberlândia, Campus Santa Mônica - Bloco 1E, Av. Joao Naves de Avila, 2121, Uberlandia, MG, 38408-100, Brazil. adriano@ufu.br.ORCID http://orcid.org/0000-0002-5689-6606

Funding

conselho nacional de desenvolvimento científico e tecnológico 304533/2020-3conselho nacional de desenvolvimento científico e tecnológico 304818/2018-6conselho nacional de desenvolvimento científico e tecnológico 58524/2020-9coordenação de aperfeiçoamento de pessoal de nível superior CAPES/COFECUB-88881.370894/2019-01coordenação de aperfeiçoamento de pessoal de nível superior CAPES/DFATD-88887.159028/2017-00national council for scientific and technological development (cnpq) 310911/2017-6
6 · The paper itself

Abstract

Deep brain stimulation (DBS) is an effective treatment of several types of neurological conditions, including Parkinson's disease, essential tremor, dystonia, and epilepsy. Despite technological progress in the past 10 years, the number of studies reporting side effects of DBS has increased, mainly due to hardware failures. This review investigated studies published between 2017 and 2021 to identify the prevalence of distinct types of hardware failures related to DBS. In total, fifteen studies were selected for the estimate of the prevalence of five distinct types of hardware failures: high impedance, fracture or failure of the lead or other parts of the implant, skin erosion and infection, lead malposition or migration, and implantable pulse generator (IPG) malfunction. The quality evaluation of the studies suggests a need to report results including populations from distinct regions of the world so that results can be generalized. The objective analysis of the prevalence of hardware failures showed that skin erosion and infection presented the highest prevalence in relation to other hardware failures. Despite the sophistication of the surgical technique of DBS over time, there is a considerable complication rate, about 7 per 100 individuals ([Formula: see text], in which CI is the confidence interval). Future research can also include correlation analysis with the aim of understanding the correlation between distinct hardware failures and variables such as gender, type of disorder, and age.

Indexed as

Deep Brain StimulationDystoniaElectrodes, ImplantedEquipment FailureHumansPrevalenceDeep brain stimulationHardware failuresMeta-analysis

Identifiers

What OpenQuestion holds

Textmetadata
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.