Heart Rate Variability as a Biomarker for Predicting Stroke, Post-stroke Complications and Functionality. Issue 13 (17th July 2018)

• Record Type: Journal Article
• Authors: Lees, Ty; Shad-Kaneez, Fatima; Simpson, Ann M; Nassif, Najah T; Lin, Yiguang; Lal, Sara
• Is Part Of: Biomarker insights. Volume 2018:Issue 13(2018)
• Publication Date: 2018-07-17
• Subjects: Stroke -- Heart Rate Variability -- HRV -- biomarker; Biochemical markers -- Periodicals; 572.43
• DOI: 10.1177/1177271918786931
• Languages: English
• ISSNs: 1177-2719

Abstract

Background: Heart 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. Objective: The 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. Methods: The 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. Results: The 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. Conclusions: Current 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.