High-frequency power of heart rate variability can predict the outcome of thoracic surgical patients with acute respiratory distress syndrome on admission to the intensive care unit: a prospective, single-centric, case-controlled study

Abstract

BackgroundThe morbidity and mortality of acute respiratory distress syndrome (ARDS) remains high, and the strategic focus of ARDS research has shifted toward identifying patients at high risk of mortality early in the course of illness. This study intended to identify the heart rate variability (HRV) measure that can predict the outcome of patients with ARDS on admission to the surgical intensive care unit (SICU).MethodsPatients who had lung or esophageal cancer surgery were included either in the ARDS group (n = 21) if they developed ARDS after surgery or in the control group (n = 11) if they did not. The ARDS patients were further stratified into survivors and non-survivors subgroups according to their outcomes. HRV measures of the patients were used for statistical analysis.ResultsThe mean RR interval (mRRI), high-frequency power (HFP) and product of low-/high-frequency power ratio tidal volume and tidal volume (LHR*VT) were significantly lower (p < 0.05), while the normalized HFP to VT ratio (nHFP/VT) was significantly higher in the ARDS patients (p = 0.011). The total power (TP), low-frequency power (LFP), HFP and HFP/VT were all significantly higher in the non-survived ARDS patients, whereas Richmond Agitation-Sedation Scale (RASS) was significantly lower in the non-survived ARDS patients. After adjustment for RASS, age and gender, firth logistic regression analysis identified the HFP, TP as the significant independent predictors of mortality for ARDS patients.ConclusionsThe vagal modulation of thoracic surgical patients with ARDS was enhanced as compared to that of non-ARDS patients, and the non-survived ARDS patients had higher vagal activity than those of survived ARDS patients. The vagal modulation-related parameters such as TP and HFP were independent predictors of mortality in patients with ARDS on admission to the SICU, and the HFP was found to be the best predictor of mortality for those ARDS patients. Increased vagal modulation might be an indicator for poor prognosis in critically ill patients following thoracic surgery.

Heart Rate Variability Using The Analgesia Nociception Index as A Predictor of Illness Severity and Mortality in Critically Ill Patients with COVID-19.

“…Also, Huang CT et al [14] concluded that spectral analysis of HRV in 101 patients admitted to the intensive care unit undergoing mechanical ventilation could predict the success or failure of removal of said support and that patients extubated with a lower HRV and a lower TP had a higher risk of reintubation after 72 hours. Chen IC et al [15] demonstrated, as in our study, that lower energy (total power) and higher vagal tone (HF) were independent predictors of mortality in patients with adult respiratory distress syndrome (ARDS).…”

“…They also show that, in particular, low autonomic activity with depletion of the sympathetic component (LF) and proportionally greater parasympathetic tone (HF) can predict the risk of developing respiratory distress (ARDS) in patients on mechanical ventilation, the success or failure of removal of this support, and mortality in these patients [14,15].…”

The use of Ventilator Parameters in the Prediction of Development and Outcome of Acute Respiratory Distress Syndrome in Postoperative Patients

“…However, only three variables can be included in the multivariable analysis based on the rule of 10 events per variable (EPV-10), as in our previous study. 11 The candidate of mechanical ventilation measurements, included in the multivariable analysis, were the significant variables in the univariate analysis of differences between the two groups. Since these mechanical ventilation measurements were highly correlated with one another, principal component analysis (PCA) was performed to reduce the correlated variables to a small set of variables.…”

“…The reason have been presented in our previous article. 11 In order to resolve this issue, the PCA selection method, EPV-10 principle and Firth logistic regression of penalty likelihood approach were chosen in our investigation so that our findings could be substantiated.…”

“…Patient selection criteria in this study have been presented in our previous article. [11] Eleven patients in the non-ARDS group and 21 patients in the ARDS group were analyzed in this study.…”