Čukić Milena, Savić Danka, Sidorova Julia
Empa Materials Science and Technology, Empa Swiss Federal Institute, St Gallen, Switzerland.
Vinča Institute for Nuclear Physics, Laboratory of Theoretical and Condensed Matter Physics 020/2, Vinca Institute, University of Belgrade, Belgrade, Serbia.
JMIR Ment Health. 2023 Jan 17;10:e40342. doi: 10.2196/40342.
Disturbed heart dynamics in depression seriously increases mortality risk. Heart rate variability (HRV) is a rich source of information for studying this dynamics. This paper is a meta-analytic review with methodological commentary of the application of nonlinear analysis of HRV and its possibility to address cardiovascular diseases in depression.
This paper aimed to appeal for the introduction of cardiological screening to patients with depression, because it is still far from established practice. The other (main) objective of the paper was to show that nonlinear methods in HRV analysis give better results than standard ones.
We systematically searched on the web for papers on nonlinear analyses of HRV in depression, in line with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 framework recommendations. We scrutinized the chosen publications and performed random-effects meta-analysis, using the esci module in jamovi software where standardized effect sizes (ESs) are corrected to yield the proof of the practical utility of their results.
In all, 26 publications on the connection of nonlinear HRV measures and depression meeting our inclusion criteria were selected, examining a total of 1537 patients diagnosed with depression and 1041 healthy controls (N=2578). The overall ES (unbiased) was 1.03 (95% CI 0.703-1.35; diamond ratio 3.60). We performed 3 more meta-analytic comparisons, demonstrating the overall effectiveness of 3 groups of nonlinear analysis: detrended fluctuation analysis (overall ES 0.364, 95% CI 0.237-0.491), entropy-based measures (overall ES 1.05, 95% CI 0.572-1.52), and all other nonlinear measures (overall ES 0.702, 95% CI 0.422-0.982). The effectiveness of the applied methods of electrocardiogram analysis was compared and discussed in the light of detection and prevention of depression-related cardiovascular risk.
We compared the ESs of nonlinear and conventional time and spectral methods (found in the literature) and demonstrated that those of the former are larger, which recommends their use for the early screening of cardiovascular abnormalities in patients with depression to prevent possible deleterious events.
抑郁症患者心脏动力学紊乱会严重增加死亡风险。心率变异性（HRV）是研究这种动力学的丰富信息来源。本文是一篇荟萃分析综述，并对HRV非线性分析的应用及其解决抑郁症患者心血管疾病问题的可能性进行了方法学评论。
本文旨在呼吁对抑郁症患者进行心脏筛查，因为目前这一做法仍远未成为既定实践。本文的另一个（主要）目的是表明HRV分析中的非线性方法比标准方法能产生更好的结果。
我们按照PRISMA（系统评价和荟萃分析的首选报告项目）2020框架建议，在网上系统搜索关于抑郁症患者HRV非线性分析的论文。我们仔细审查了所选出版物，并使用jamovi软件中的esci模块进行随机效应荟萃分析，其中标准化效应量（ESs）经过校正，以证明其结果的实际效用。
总共筛选出26篇关于非线性HRV测量与抑郁症关联且符合我们纳入标准的出版物，共检查了1537名被诊断为抑郁症的患者和1041名健康对照者（N = 2578）。总体ES（无偏）为1.03（95%置信区间0.703 - 1.35；菱形比3.60）。我们又进行了3次荟萃分析比较，证明了三组非线性分析的总体有效性：去趋势波动分析（总体ES 0.364，95%置信区间0.237 - 0.491）、基于熵的测量（总体ES 1.05，95%置信区间0.572 - 1.52）以及所有其他非线性测量（总体ES 0.702，95%置信区间0.422 - 0.982）。根据抑郁症相关心血管风险的检测和预防，对所应用的心电图分析方法的有效性进行了比较和讨论。
我们比较了非线性方法与传统时间和频谱方法（在文献中找到）的ESs，结果表明前者的ESs更大，这建议将其用于抑郁症患者心血管异常的早期筛查，以预防可能的有害事件。
