JMIR Publications

ABSTRACT

Background:

Disturbed heart dynamics in depression seriously increases mortality risk. Heart rate variability (HRV) is a rich source of information for studying this dynamics.

Objective:

The objective of our study was to perform a meta-analytic review with a methodological commentary of application of nonlinear analysis of HRV and its possibility to address cardiovascular diseases in depression.

Methods:

We systematically searched online for papers on nonlinear analyses of HRV in depression, in line with PRISMA 2020 framework recommendations. We scrutinized chosen publications and performed random-effects meta-analysis, using jamovi/esci software where standardized effect sizes are corrected to yield the proof of practical utility of their results.

Results:

Twenty-six publications on the connection of nonlinear HRV measures and depression meeting our inclusion criteria are selected, examining total of 1537 patients diagnosed with depression and 1041 healthy controls (total sample 2578). Overall effect size (unbiased) is 1.03 (95% CI (0.703, 1.35), diamond ratio 3.60). We performed three more meta-analytic comparisons, demonstrating the overall effectivity of three subgroups of nonlinear analysis: DFA1 (overall effect size 0.364, CI (0.237, 0.491)), entropy-based measures (1.05, CI (0.572, 1.52)) and all other nonlinear measures (0.702, CI (0.422, 0.982)). The effectiveness of the applied methods of electrocardiogram (ECG) analysis are compared and discussed in the light of detection and prevention of depression-related cardiovascular risk. We compared the effect sizes of nonlinear and conventional time and spectral methods (found in the literature) and demonstrated that the first are larger. Conclusion: Early screening for cardiovascular abnormalities should be done routinely to prevent possible deleterious events in depressed patients, preferably by nonlinear methods of ECG analysis.