Is the heart rate variability monitoring using the analgesia nociception index a predictor of illness severity and mortality in critically ill patients with COVID-19? A pilot study

Abstract

Introduction: The analysis of heart rate variability (HRV) has proven to be an important tool for the management of autonomous nerve system in both surgical and critically ill patients. We conducted this study to show the different spectral frequency and time domain parameters of HRV as a prospective predictor for critically ill patients, and in particular for COVID-19 patients who are on mechanical ventilation. The hypothesis is that most severely ill COVID-19 patients have a depletion of the sympathetic nervous system and a predominance of parasympathetic activity reflecting the remaining compensatory anti-inflammatory response.

Materials and methods: A single-center, prospective, observational pilot study which included COVID-19 patients admitted to the Surgical Intensive Care Unit was conducted. The normalized high-frequency component (HFnu), i.e. ANIm, and the standard deviation of RR intervals (SDNN), i.e. Energy, were recorded using the analgesia nociception index monitor (ANI). To estimate the severity and mortality we used the SOFA score and the date of discharge or date of death.

Results: A total of fourteen patients were finally included in the study. ANIm were higher in the non-survivor group (p = 0.003) and were correlated with higher IL-6 levels (p = 0.020). Energy was inversely correlated with SOFA (p = 0.039) and fewer survival days (p = 0.046). A limit value at 80 of ANIm, predicted mortalities with a sensitivity of 100% and specificity of 85.7%. In the case of Energy, a limit value of 0.41 ms predicted mortality with all predictive values of 71.4%.

Conclusion: A low autonomic nervous system activity, i.e. low SDNN or Energy, and a predominance of the parasympathetic system, i.e. low HFnu or ANIm, due to the sympathetic depletion in COVID-19 patients are associated with a worse prognosis, higher mortality, and higher IL-6 levels.

Fig 1. STROBE patient flow diagram.

Fig 2. Box plot (left) and ROC curves for ANIm (right).

Box plot represents the median values of ANIm in both groups. ROC curve demonstrates the ability of ANI to discriminate the mortality with an AUC = 0.980 at an ANIm threshold of 80 (sensitivity 100%, specificity 85.7%, positive predictive value 87.5%, negative predictive value 100%). ANIm, median analgesia nociception index; 0 death, survivor group; 1 death, non-survivor group.

Fig 3. Box plot (left) and ROC curves for Energy (right).

Box plot represents the median values of Energy in both groups. ROC curve demonstrates the ability of Energy to discriminate the mortality with an AUC = 0.694 at a threshold of 0.41 ms (sensitivity 71.4%, specificity 71.4%, positive predictive value 71.4%, negative predictive value 71.4%). 0 death, survivor group; 1 death, non-survivor group.

Fig 4. Sub-analysis: Box plot (left) and ROC curves for ANIm (right) in the RASS -4 / -5 patients.

Box plot represents the median values of ANIm in both groups. ROC curve demonstrates the ability of ANI to discriminate the mortality with an AUC = 1 at an ANIm threshold of 80 (sensitivity 100%, specificity 100%, positive predictive value 100%, negative predictive value 100%). ANIm, median analgesia nociception index; 0 death, survivor group; 1 death, non-survivor group.

Fig 5. Sub-analysis: Box plot (left) and ROC curves for Energy (right) in the RASS -4 / -5 patients.

Box plot represents the median values of Energy in both groups. ROC curve demonstrates the ability of Energy to discriminate the mortality with an AUC = 0.750 at a threshold of 0.41 (sensitivity 71.4%, specificity 75%, positive predictive value 83.3%, negative predictive value 60%). 0 death, survivor group; 1 death, non-survivor group.