Evidence map›Paper›PMID 30038486›Full record

ReviewBiomarker insights2018

Heart Rate Variability as a Biomarker for Predicting Stroke, Post-stroke Complications and Functionality.

Ty Lees, Fatima Shad-Kaneez, Ann M Simpson, Najah T Nassif, Yiguang Lin, Sara Lal

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Biomarker insights, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
59citing papers in PubMed, 4 pooled it
–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

59 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Trial
  7. Trial
  8. Heart Meets Brain: Insights into Neurocardiac Pathophysiology.Pathophysiology : the official journal of the International Society for Pathophysiology · 2026
    Review
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Dysautonomia and activity in the early stroke recovery period.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024
    Review
  19. Review
  20. Observational
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

6 authors.

Ty LeesNeuroscience Research Unit, School of Life Sciences, University of Technology Sydney, Broadway, NSW, Australia.
Fatima Shad-KaneezSchool of Life Sciences, University of Technology Sydney, Broadway, NSW, Australia.
Ann M SimpsonSchool of Life Sciences, University of Technology Sydney, Broadway, NSW, Australia.
Najah T NassifSchool of Life Sciences, University of Technology Sydney, Broadway, NSW, Australia.
Yiguang LinSchool of Life Sciences, University of Technology Sydney, Broadway, NSW, Australia.
Sara LalNeuroscience Research Unit, School of Life Sciences, University of Technology Sydney, Broadway, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHeart rate variability (HRV) is a non-invasive measure of the function of the autonomic nervous system, and its dynamic nature may provide a means through which stroke and its associated complications may be predicted, monitored, and managed.

objectiveThe objective of this review is to identify and provide a critique on the most recent uses of HRV in stroke diagnosis/management and highlight areas that warrant further research.

methodsThe MEDLINE, CINAHL, and OVID MEDLINE databases were canvassed using a systematic search strategy, for articles investigating the use of HRV in stroke diagnosis and management. Initial paper selections were based on title alone, and final paper inclusion was informed by a full-text critical appraisal.

resultsThe systematic search returned 98 records, of which 51 were unique. Following screening, 22 records were included in the final systematic review. The included papers provided some information regarding predicting incident stroke, which largely seems to be best predicted by time- and frequency-domain HRV parameters. Furthermore, post-stroke complications and functionality are similarly predicted by time- and frequency-domain parameters, as well as non-linear parameters in some instances.

conclusionsCurrent research provides good evidence that HRV parameters may have utility as a biomarker for stroke and for post-stroke complications and/or functionality. Future research would benefit from the integration of non-linear, and novel parameters, the hybridisation of HRV parameters, and the expansion of the utilisation of predictive regression and hazard modelling.

Indexed as

biomarkerHeart Rate VariabilityHRVStroke

Identifiers

PMID30038486
PMCPMC6052496

What Socratic 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 Socratic graph.