Variabilidade da frequência cardíaca como predição de mortalidade em pacientes portadores de insuficiência respiratória aguda submetidos à ventilação mecânica

Abstract

Introduction: Among the numerous techniques used for assessment; heart rate variability (HRV) has stood out as a simple and non-invasive measure of autonomic impulses, representative of one of the most promising quantitative markers of autonomic balance, revealing oscillations in the interval between consecutive heartbeats (known as R-R intervals). HRV analysis is also recognized as a diagnostic tool and investigated in various pathophysiological conditions in patients admitted to Intensive Care Units (ICUs), sedated, on mechanical ventilation (MV), and under stress stimuli. The application of HRV in three domains (time, frequency, and non-linear) aims to find prognostic mortality parameters in patients admitted with respiratory failure in the ICU. Objective: The aim was to verify mortality prediction through HRV, in the time, frequency, and non-linear domains in patients with acute respiratory failure undergoing mechanical ventilation. Material and Methods: It comprised assessing HRV in the three domains (time, frequency, and non-linearity domains) in patients admitted to the ICU with respiratory failure. Results: When comparing the general characteristics of patients with acute respiratory failure undergoing mechanical ventilation, significant differences were observed in HRV indices between patients who died and survivors regarding age (p = 0.023), mean arterial pressure (MAP) (p = 0.001), and fraction of inspired oxygen (FiO2) (p = 0.001). However, in the nonlinear analysis, the SampEn index did not show statistical significance (p = 0.1824). Considering multi-scale entropy, statistically significant differences were found for the following indices: MSE(2) (p = 0.0187), MSE(3) (p = 0.0095), MSE(4) (p = 0.0082), MSE(5) (p = 0.012), MSE(6) (p = 0.0222), MSE(13) (p = 0.0164), and MSE(19) (p = 0.0263). HRV indices that presented ROC curves with statistical significance were SDANN ms (ROC = 0.71), SDNN index ms (ROC = 0.78), MSE(3) (ROC = 0.70), MSE(4) (ROC = 0.71), MSE(5) (ROC = 0.70), and MSE(8) (ROC = 0.72). Analyzing the Poincaré plots, it was observed that patient 1, deceased, presented a tachogram with a variation in the range of 20 ms, SD1=1.0 ms, and SD2=1.5 ms, while patient 2, also deceased, had a tachogram with almost null variation, below 5 ms, SD1=0.9 ms, and SD2=1.3 ms. On the other hand, patient 1, who survived, showed a tachogram with a variation of about 100 ms, SD1=7.8 ms, and SD2=14.3 ms, while patient 2, also a survivor, had a variation of around 100 ms, SD1=13.1 ms, and SD2=18.5 ms. Conclusions: There was a significant loss of chaotic pattern in certain HRV indices in the group of patients who progressed to death. Additionally, the cutoff points of these parameters through the ROC curve allowed the early detection of patients at higher mortality risk. Although HRV parameters are not yet fully understood, their application in ICU monitoring will require further prospective studies addressing technical issues and identifying factors that may influence HRV analysis.