Introduction: Cardiovascular disease is the first cause of death globally with myocardial infarction as the main event. Heart rate variability (HRV) has been associated with an increased risk of mortality post-myocardial infarction. However, which indices of heart rate variability are the best predictors for total and cardiac mortality post-myocardial infarction remains unclear. Predict mortality risk by these indices, is important as HRV is a low-cost, low-invasive, and easy biomarker to interpret. Hypothesis: Indices evaluating heart rate variability are differentially associated with cardiac and total mortality, but to date, there is no evidence about which is the best for these outcomes Methods: PubMed, Google Scholar, Embase, Cochrane databases, and grey literature were searched for studies with HRV as a predictive mortality marker. Two authors independently selected papers and extracted data. Disagreements were solved with a third author. Clinical and statistical heterogeneity was assessed. Forest and funnel plot graphs were made. HRV indices included were SDNN, SDANN, HRV index, Total power, RMSSD, pNN50, LF, HF, ULF, VLF, and LF/HF. Sensitivity analysis, cumulative and regression meta-analysis were performed. Stata 16 was used for statistical analysis. Results: From 338 articles found, 29 were included. Low values of SDNN, HRV index, HF, ULF, and VLF showed statistically significant association with cardiac mortality, but HRV index had the highest association RR: 5.10 (CI95% 3.92 - 6.65, I 2 of 22%). For overall mortality, SDNN, HRV index, LF, VLF y ULF had statistically significant association, but HRV index was the best index, RR:3.60 (CI95% 2.52- 5.15, I 2 0%). Both associations had high consistency in subgroup analysis, with quality of articles, year of publication, and different cut-off points. Conclusions: the best index associated with cardiac and total mortality post-myocardial infarction is low values of HRV index.
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