# Brain-heart interactions are associated with mortality and acute encephalopathy in ICU patients with severe COVID-19

Bertrand Hermann, Sarah Benganem, Estelle Pruvost-Robieux, Tarek

Sharshar, Martine Gavaret, Alain Cariou, Jean-Luc Diehl, Diego

Candia-Rivera

► To cite this version:

Bertrand Hermann, Sarah Benganem, Estelle Pruvost-Robieux, Tarek Sharshar, Martine Gavaret, et al. Brain-heart interactions are associated with mortality and acute encephalopathy in ICU patients with severe COVID-19. Clinical Neurophysiology, 2025, 175, pp.2110745. 10.1016/j.clinph.2025.2110745. hal-05090462

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# Brain-heart interactions are associated with mortality and acute encephalopathy in ICU patients with severe COVID-19

Bertrand Hermann<sup>a,b,c,*</sup> <sup>□</sup>, Sarah Benganem<sup>a,d</sup>, Estelle Pruvost-Robieux<sup>a,e □</sup>, Tarek Sharshar<sup>a,b</sup>, Martine Gavaret<sup>a,e □</sup>, Alain Cariou<sup>d,f</sup>, Jean-Luc Diehl<sup>c,g □</sup>, Diego Candia-Rivera<sup>h,* □</sup>

## ARTICLE INFO

**Keywords:**

Brain-heart interaction

Autonomic dysfunction

Heart rate variability

COVID-19

Acute encephalopathy

SARS-CoV-2

## ABSTRACT

**Objective:** Research in critical care has revealed the significance of autonomic dysfunctions, and more recently of brain-heart interactions, as valuable biomarkers for evaluating patients' physiological status. These biomarkers provide insights into consciousness levels, severity, and outcomes. This study aims to determine the potential of these biomarkers in predicting the mortality and neurological outcome of severe COVID-19 patients.

**Methods:** We examined severe COVID-19 patients who required mechanical ventilation and observed them both during sedation and after sedation cessation. Standard EEG and ECG recordings were conducted at bedside, from which 5 min of continuous data were analyzed. Using a synthetic data generation model, we evaluated bidirectional brain-heart interactions from EEG power and heartbeat dynamics series.

**Results:** Our findings indicate that brain-heart interactions, especially involving cardiac parasympathetic activity, indicate the degree of patients' severity. We observed correlations with acute encephalopathy duration (coma and delirium), particularly evident in top-down markers (from brain to heart) while bottom-up signaling (from heart to brain) correlated with ICU mortality. Additionally, we noted stronger modulation of brain-heart interactions in milder patients when comparing sedation versus non-sedation conditions, compared to those in more severe states.

**Conclusions:** Our results imply that autonomic dysfunctions, as measured through brain-heart interactions, can track the pathophysiology of comatose states following SARS-CoV-2 infection.

**Significance:** These findings highlight the potential of brain-heart interactions as an integrated marker for autonomic function in critical care, offering a more accurate assessment of patient severity and outcomes compared to isolated cardiac or brain measures.

**Abbreviations:** ACE2, Angiotensin-converting enzyme 2; AE, Acute encephalopathy; ARDS, Acute respiratory distress syndrome; AUC, Area under the curve; CAM, Confusion Assessment Method; COVID-19, Coronavirus disease 2019; CPI, Cardiac parasympathetic index; CSI, Cardiac sympathetic index; ECG, Electrocardiogram; EEG, Electroencephalogram; FOUR, Full outline of unresponsiveness; HR, Heart rate; HRV, Heart rate variability; ICU, Intensive care unit; SARS-CoV-2, Severe acute respiratory syndrome coronavirus 2; SOFA, Sequential organ failure assessment; RASS, Richmond agitation sedation scale.

* Corresponding author at: Université Paris Cité, Inserm UMR-S 1266, Institute of Psychiatry and Neurosciences of Paris, F-75014 Paris, France.

** Corresponding author at: Sorbonne Université, Paris Brain Institute (ICM), INRIA Paris, CNRS UMR 7225, INSERM U1127, AP-HP Hôpital de la Pitié-Salpetrière, Paris, France.

E-mail addresses: bertrand.hermann@aphp.fr (B. Hermann), diego.candia.r@ug.uchile.cl (D. Candia-Rivera).

<footnote><sup>a</sup> Université Paris Cité, Inserm UMR-S 1266, Institute of Psychiatry and Neurosciences of Paris F-75014 Paris, France</footnote>

<footnote><sup>b</sup> GHU Paris Psychiatrie Neurosciences, Service Hospitalo-universitaire de Neuro-anesthésie Réanimation, F-75014 Paris, France</footnote>

<footnote><sup>c</sup> Service Médecine Intensive - Réanimation, AP-HP, Centre, Hopital Européen Georges Pompidou, F-75015 Paris, France</footnote>

<footnote><sup>d</sup> Service Médecine Intensive - Réanimation, AP-HP, Centre, Hopital Cochin, F-75014 Paris, France</footnote>

<footnote><sup>e</sup> Neurophysiology and Epileptology Department, GHU Paris Psychiatrie et Neurosciences, Paris, France</footnote>

<footnote><sup>f</sup> Paris-Cardiovascular-Research-Center, INSERM U970, Paris, France</footnote>

<footnote><sup>g</sup> Université Paris Cité, INSERM, Innovative Therapies in Haemostasis, Paris, France</footnote>

<footnote><sup>h</sup> Sorbonne Université, Paris Brain Institute (ICM), INRIA, CNRS UMR 7225, INSERM U1127, AP-HP Hôpital de la Pitié-Salpetrière, Paris, France</footnote>
# 1. Introduction

The spectrum of neurological manifestations in hospitalized patients ranges from mild headaches to severe presentations such as seizures, encephalitis or acute demyelinating neuropathies (Mao et al., 2020). Studies show that up to 49 % of hospitalized COVID-19 patients experience acute encephalopathy, and 17 % progress to a comatose state. Both conditions significantly increase the risk of in-hospital death, with their underlying mechanisms remaining unclear, including those detectable during the early stages of hospitalization (Chou et al., 2021).

The underlying mechanisms behind these neurological manifestations remain poorly understood. Among the numerous neurological complications in COVID-19 patients, non-specific acute encephalopathy with prolonged coma and delirium emerges as a significant concern. Indeed, non-specific systemic insults, including inflammation, vasculopathy, and hypoxemia are common and further complicate the treatments (Newcombe et al., 2021; Scott-Solomon et al., 2021). Exploring potential risk factors for acute encephalopathy reveals insights into the evolving pathology, where the transition from mild to severe often encompasses stages of delirium progressing towards coma (Helms et al., 2020; Pun et al., 2021).

The autonomic nervous system plays a crucial role in mediating the effects of brain complications on the heart, involving neural, endocrine, and immune mechanisms, such as through the hypothalamus-pituitary-adrenal axis (Chen et al., 2017). Dysautonomia appears to be a significant feature of many critical care conditions, notably in sepsis (Carrara et al., 2021). COVID-19 can include direct influences on cardiovascular systems, including dysregulation of ACE2 enzyme, microvascular dysfunction, hypoxemia and cardiotoxicity (Abha Mishra et al., 2023). The infection might disrupt as well neurovascular coupling, altering cerebral blood flow and the subsequent adjustments in brain activity according to perfusion levels, ultimately affecting cellular homeostasis, unbalancing energy supply and demand (Kim et al., 2016). While cardiac metrics have proven valuable in various critical care contexts (Buchman et al., 2002), qualitative and quantitative markers of brain function tend to provide more accurate assessments. Brain-heart interactions could play a crucial role in prognostication, as neurological disorders can lead to a range of cardiovascular changes, from benign electrographic alterations to severe myocardial injury and cardiac death (Tahsili-Fahadan and Geocadin, 2017). Some mechanisms can operate through the neurocardiac axis and neurogenic stunned myocardium, though their specific mechanisms and clinical impact remain underexplored (El-Menyar et al., 2017). Few studies have investigated the potential link between brain and cardiac oscillations in neuro-prognostication. For example, patients with severe post-cardiac arrest brain injury exhibit bidirectional brain-heart interactions that correlate with the severity of the brain injury and the patients' neurological outcomes at 3 months (Hermann et al., 2024).

Considering this evidence, our present study investigates the relationship between COVID-19 complications and brain-heart interactions. We focused on adults admitted in the intensive care unit (ICU) for severe COVID-19 with Acute Respiratory Distress Syndrome (ARDS) requiring invasive mechanical ventilation and deep sedation. Our investigation aims to investigate how brain-heart interactions can elucidate the physiological state from an early stage. We examined electroencephalogram (EEG) and electrocardiogram (ECG) recordings from patients within the first 12-72 h following neuromuscular blocking agent cessation (T1), and between 3 to 7 days after sedation weaning (T2). We aimed at contrasting the brain-heart interactions at these two timepoints, as a function of the patient complications, and to investigate if early brain-heart interactions were associated with ICU outcome (survival and acute encephalopathy-free days at day 28). To achieve this, we conducted an analysis of brain-heart interactions by modeling interactions between EEG and cardiac time series (Candia-Rivera, 2023). This study seeks to explore two key questions regarding brain-heart interactions in severe COVID-19 ICU patients. First, it aims to examine

whether early brain-heart interactions, potentially indicative of dysautonomia, hold prognostic value for patient outcomes. Second, it investigates how these interactions evolve during the ICU stay and potentially track the neurological/behavioral status of patients.

# 2. Materials and methods

## 2.1. Population

This was an ancillary analysis of the Brainstem-CoV-ICU study (Benghanem et al., 2022). Briefly, the Brainstem-CoV-ICU study was a bicentric prospective observational study investigating the prevalence and prognostic significance of an early clinical and standard EEG assessment in ICU COVID-19 patients. Adult patients included were admitted in the ICU for a severe, proven SARS-CoV-2 pneumonia, leading to an Acute Respiratory Distress Syndrome (ARDS) and requiring invasive mechanical ventilation with initial deep sedation (Richmond Agitation-Sedation Scale < -3 for at least 12 h). Patients with previous central neurologic disorders were excluded.

IRB approval was granted by Comité de Protection des Personnes 'Ile-de-France II' (n°ID RCB: 2020-01559-30) and the study was a priori registered on clinicaltrials.gov: NCT04527198. A written informed consent was obtained from each of the patients or their relatives.

## 2.2. Design and data collection

Detailed neurological assessments and EEG recordings were performed at two timepoints during the ICU stay (Fig. 1A). The first timepoint (T1) was under sedation, 12-72 h following neuromuscular blocking agents' withdrawal if any, and the second timepoint (T2) was 3-7 days after definite sedation withdrawal. At each timepoint, concomitant organ failures (based on the Sequential Organ Failure Assessment score, SOFA) and sedative/opioid exposure (duration, infusion rates and cumulative doses at time of assessments) were collected.

Neurological status was assessed by the Richmond Agitation-Sedation Scale (RASS) every 4 h and by the Confusion Assessment Method for the ICU (CAM-ICU) twice daily during the 28-days following T1 or up to ICU discharge, whichever came first. Acute encephalopathy was defined as a state of coma (RASS < -3) or delirium (positive CAM-ICU in patients with RASS ≥ -3). Additionally, we defined awakening as two consecutive RASS ≥ -2 and delayed awakening as the absence of awakening 3 days after sedation discontinuation (Pun et al., 2021). Last, vital status at 28 days and at ICU discharge was also collected.

## 2.3. EEG/ECG recordings and preprocessing

Standard video-EEG recordings were acquired with 11 scalp electrodes (10-20 international system: Fp1, C3, T3, O1, Fp2, C4, T4, O2, Fz, Cz and Pz) and 1 ECG lead for 20 min. Impedances were kept below 5 kΩ. Sampling rate was 256 Hz.

The ECG signals were filtered using a band-pass filter from 0.5 to 20 Hz. EEG signals were filtered using a band-pass filter from 0.5 to 45 Hz. Butterworth filters of order 6 and 8 were applied for ECG and EEG filtering, respectively. Notch filters were employed at 50 Hz and 100 Hz. The 11 scalp electrodes were re-referenced to an average reference. This approach was chosen to balance computational simplicity for potential future clinical use while addressing the biases in brain-heart interaction metrics that arise when using univariate references, such as mastoids or midline central electrodes (Candia-Rivera et al., 2021b). While alternative methods for low-density EEG settings have been proposed (Cataldo et al., 2022), we opted for a univariate approach in this study. Thus, after preprocessing, the signals from all electrodes were averaged after the final brain-heart interaction metric computation to avoid spatial biases in the EEG analysis.

Independent component analysis (ICA) was then applied to the EEG
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Computation of brain–heart interactions through a synthetic data generation model. (A) Severe COVID-19 patients admitted in the ICU. (B) Data acquisition was performed under sedation, 12–72 h following neuromuscular blocking agents’ withdrawal (T1) and 3–7 following sedation weaning (T2). (C) Bidirectional brain–heart interactions between the brain within the band of interest (1–12 Hz) and cardiac sympathetic (CSI) and parasympathetic indices (CPI). The model estimates the functional interplay between the brain and the heart by assuming a closed-loop circuit, in which ongoing EEG power modulates autonomic activity, while in turn, ongoing autonomic activity (CSI and CPI) modulates EEG activity.</figcaption></figure>

signals, following a high pass filtering at 1 Hz. The resulting components were visually examined for significant artifacts, such as eye movements, blinks, or cardiac-field artifacts. The artifact-free EEG was then compared to the original recording to evaluate the quality of artifact removal and preservation of brain EEG signals. Subsequently, the reconstructed recordings underwent visual inspection, and periods containing residual artifacts were manually excluded. To ensure a robust estimation of brain–heart interplay, the resulting first 5-minute period of consecutive (i.e., continuous) data without EEG and ECG artifacts was retained for further analysis. Patients with less than 5 min of continuous EEG/ECG data were discarded.

EEG power spectrum densities were computed using a short-time Fourier transform with a Hamming taper using a sliding window procedure (2 s segments with 50 % overlap). The 2 s window allows for capturing oscillations from 1 Hz. While it may not fully encompass the entire range of delta oscillations, it represents a compromise by generating power series comparable to those derived from heart-related data, which adequately capture the physiological dynamics of interest (Hermann et al., 2024). In the same line, we integrated the EEG power series within 1–12 Hz. This broadband integration was guided by the characteristic slow-frequency fluctuations observed in critical care patients (Frohlich et al., 2021). It also builds on our previous findings, which showed that differences in patient severity and prognosis are primarily reflected in the delta-to-alpha frequency range (Hermann et al., 2024). Finally, in case of residual noise, power series from individual EEG channels were denoised using Wavelet thresholding method (Jansen, 2001), with a threshold defined automatically, based on signal’s length and variance (Gabard-Durnam et al., 2018).

## 2.4. Brain-heart interaction modeling

Bidirectional brain–heart interactions were computed using a

synthetic data generation model (Candia-Rivera, 2023), between EEG power series within the 1–12 Hz band, hereafter called broadband power, and Cardiac Sympathetic (CSI) and parasympathetic indices (CPI). The objective of this brain–heart interaction modeling is to capture directed, co-varying patterns of activity, which can potentially indicate maladaptive mechanisms in critical care patients and distinct physiological responses to anesthesia (Fabus et al., 2024; Purdon et al., 2013). The modeling specifically uses heart-derived time series as an approximate proxy for the sympathetic and parasympathetic nervous systems, capturing slow and fast changes in heart rate variability (HRV), respectively and independently of the background heart rate (Candia-Rivera et al., 2025). These dynamics are particularly of interest, based on previous research suggesting that dysautonomia or brainstem reflexes can serve as indicators of the patient status (Benghanem et al., 2020; Sharshar et al., 2011).

The synthetic data generation framework quantifies time-resolved relationships between ongoing fluctuations in brain and cardiac time series. On the heart side, the framework aims to model the stimulations to the sinoatrial node that causes the heartbeat generation. On the brain side, the framework models the fluctuations on EEG power on time as an autoregressive process, with an external input (CSI or CPI). The outputs of the model are time-varying coefficients accounting for the brain–heart interactions for each combination of EEG power series, CSI or CPI, and ascending or descending directionality, as depicted in Fig. 1B and C. The computation of brain–heart interplay coefficients was done over EEG power series (mV²) and CSI and CPI series (arbitrary units) resampled at 1 Hz, using a 15 s sliding time window with 1 s step. To quantify the overall strength of the brain–heart coupling, absolute values of the resulting time-varying coefficients were averaged among channels and on time using the median over the 5 min time series and log-transformed (arbitrary units).
## 2.5. Exposures and outcomes

Main exposures were the median brain-heart interplay coefficients from and towards both cardiac sympathetic and parasympathetic indices, that is four metrics in total: brain → CSI, brain → CPI, CSI → brain and CPI → brain.

The two main outcomes were ICU survival and the burden of acute encephalopathy, expressed as the time to recovery from the acute encephalopathy, within the 28 days from T1 (days-free of coma and delirium for at least 48 h). Other outcomes were delayed awakening and neurological status at T2. For further analyses, brain-heart coupling values were log10 scaled.

## 2.6. Statistical analyses

Continuous variables were expressed as medians and interquartile ranges. Paired and unpaired comparisons were performed using Wilcoxon-Mann-Whitney tests. Qualitative variables were expressed as number and percentages and compared using chi-square or Fisher test as appropriate.

To assess the multiple variables associated with the patients and their potential interactions, we used multiple linear regression models of the logarithm of the hazard on the variables (multivariable Cox). This allowed us to investigate the association of brain-heart interplay metrics and both the cumulative incidence of survival and of acute encephalopathy recovery at day 28 from T1. Besides the four brain-heart metrics, univariable models were built with non-neurological SOFA, RASS score, sedative/analgesic instantaneous rate, cumulative dose (in midazolam equivalent, assuming that 10 mg of propofol would be equal to 1 mg of midazolam) and duration at T1. P-values were computed with the likelihood ratio test. In univariable analyses, the level of significance was corrected according to the Bonferroni rule for multiple comparisons and set at $α = 0.05/4 = 0.0125$ to account for the joint assessment of 4 brain-heart metrics. An exploratory multivariable analysis with adjustment on variables with unadjusted p-values < 0.2 on univariable analysis was performed. Additionally, as sedation might be associated with both mortality and encephalopathy, and influence brain-heart interactions, multivariable models adjusted on sedation-related variables were performed. P-values of multivariable analyses were not corrected for multiple comparisons.

Lastly, evolution of brain-heart metrics over time (from T1 to T2) according to neurological status at T1 was modeled with aligned rank transformed for non-parametric factorial analysis of variance. Mixed ANOVA with the timing as within-subject factor and neurological status as between-subject factor was performed on aligned rank transformed data (package ARTool).

Statistical analyses were performed using R Software, R version 4.3.3 (2024-02-29); https://cran.r-project.org/.

# 3. Results

## 3.1. Population

From April to December 2020, out of the 52 patients included in the Brainstem-CoV-ICU study, 49 could be included in this study (3 patients were discarded due to less than 5 min of electrophysiological recordings of sufficient quality, see flowchart in supplementary figure 1). Patients were mostly men (82%) aged 68 [56–73] years. Median SAPS2 and SOFA at ICU admission were 48 [35–67] and 5 [4–10]. ARDS severity was moderate in 55% and severe in 45% with 88% of patients treated with dexamethasone. Population characteristics are presented in Table 1.

The first assessment was performed in median 4 [3–6] days after ICU admission in comatose patients under deep sedation. Details about neurological and sedative status at T1 are presented in Table 2. There were no significant correlations between any of the brain-heart metrics

<table><caption>**Table 1**
Population and ICU outcome.</caption><thead><tr><th>Characteristic</th><th>N = 49</th></tr></thead><tbody><tr><td>Age (years)</td><td>68 [56–73]</td></tr><tr><td>Female sex (%)</td><td>9 (18)</td></tr><tr><td>BMI (kg/m<sup>2</sup>)</td><td>27.3 [25.0–29.6]</td></tr><tr><td>Obesity (BMI ≥ 30 kg/m<sup>2</sup>)</td><td>13 (27)</td></tr><tr><td>MacCabe score (%)</td><td></td></tr><tr><td>1</td><td>39 (80)</td></tr><tr><td>2</td><td>10 (20)</td></tr><tr><td>SAPS2 at ICU admission</td><td>48 [35–67]</td></tr><tr><td>SOFA at ICU admission</td><td>5 [4–10]</td></tr><tr><td>ARDS severity (%)</td><td></td></tr><tr><td>Moderate (100 &lt; PaO<sub>2</sub>/FiO<sub>2</sub> ≤ 200)</td><td>27 (55)</td></tr><tr><td>Severe (PaO<sub>2</sub>/FiO<sub>2</sub> ≤ 100)</td><td>22 (45)</td></tr><tr><td>Minimal PaO<sub>2</sub>/FiO<sub>2</sub></td><td>106 [85–119]</td></tr><tr><td>Awakening (%)</td><td></td></tr><tr><td>Yes</td><td>40 (82)</td></tr><tr><td>Delay (days)</td><td>3 [0–11]</td></tr><tr><td>Delayed</td><td>26 (65)</td></tr><tr><td>Acute encephalopathy duration (days)</td><td>16 [11–29]</td></tr><tr><td>Coma incidence (%)</td><td>49 (100)</td></tr><tr><td>Coma duration (days)</td><td>12 [7–25]</td></tr><tr><td>Delirium incidence (%)</td><td>30 (75)</td></tr><tr><td>Delirium duration (days)</td><td>5 [3–8]</td></tr><tr><td>Coma/Delirium-free days at day 28 from T1</td><td>14 [0–25]</td></tr><tr><td>ICU mortality</td><td>12 (24)</td></tr></tbody></table>

<table><caption>**Table 2**
Neurological and sedative status at T1 and T2 assessments.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Assessment timing</th></tr><tr><th>T1, N = 49</th><th>T2, N = 36</th></tr></thead><tbody><tr><td>ICU admission to assessment delay (days)</td><td>4 [3–6]</td><td>18 [10–25]</td></tr><tr><td>NMB agent weaning to evaluation delay (hours)</td><td>26 [22–44]</td><td>194 [138–264]</td></tr><tr><td>Evaluation to sedation weaning delay (days)</td><td>-6 [-12–-1]</td><td>3.0 [3.0–4.25]</td></tr><tr><td>Sedative molecule (%)</td><td></td><td></td></tr><tr><td>Midazolam</td><td>49 (100)</td><td>0 (0)</td></tr><tr><td>Propofol</td><td>6 (12)</td><td>0 (0)</td></tr><tr><td>Sedation rate (mg/kg/h)</td><td>0.11 [0.07–0.23]</td><td>NA</td></tr><tr><td>Analgesic rate (mg/kg/h)</td><td>0.17 [0.09–0.23]</td><td>NA</td></tr><tr><td>Sedative duration (days)</td><td>4 [3–7]</td><td>10 [6–18]</td></tr><tr><td>Sedative cumulative dose (mg/kg)</td><td>13 [8–21]</td><td>32 [23–64]</td></tr><tr><td>Analgesic cumulative dose (mg/kg)</td><td>18 [11–30]</td><td>52 [31–77]</td></tr><tr><td>Non neurological SOFA</td><td>6 [3–8]</td><td>3 [2–6]</td></tr><tr><td>RASS</td><td>-4 [-5–-4]</td><td>0 [-3–0]</td></tr><tr><td>FOUR score</td><td>5 [5–7]</td><td>13 [9–14]</td></tr><tr><td>Acute encephalopathy (%)</td><td>49 (100)</td><td>20 (56)</td></tr><tr><td>Coma</td><td>49 (100)</td><td>7 (19)</td></tr><tr><td>Delirium</td><td>0 (0)</td><td>13 (36)</td></tr></tbody></table>

and either non-neurological SOFA score or the RASS score. This time-point was designed to capture early brain-heart patterns associated with patient's outcome.

## 3.2. Early heart → brain interactions are associated with mortality

Twelve patients (24%) died during their ICU stay. All brain-heart interactions metrics measured at T1 showed differences between patients who died vs. those who survived, with those indexing heart → brain being significantly different after multiple comparisons correction. Death was associated with stronger brain → heart coupling and lower heart → brain coupling (Fig. 2). Notably, individual brain and heart metrics did not significantly distinguish vital status as brain-heart interaction metrics did (Supplementary figure 2). Only two patients who ultimately died in the ICU survived until T2 and the brain-heart metrics of these two patients showed no overlap with those who survived
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Brain-heart metrics according to vital status at ICU discharge (see Supplementary Fig. 2 to visualize brain and heart metrics separately, and Supplementary Fig. 3 to visualize the distributions of the subgroup of patients who developed acute encephalopathy). P-values correspond to the unpaired Wilcoxon test, with significance defined by the Bonferroni rule for multiple comparisons at α = 0.0125 (bold). Brain: EEG power integrated within 1–12 Hz, CSI: cardiac sympathetic index, CPI: cardiac parasympathetic index.</figcaption></figure>

(Supplementary figure 3).

To refine this association, we ran time-to-event analyses investigating the association of brain–heart metrics with the cumulative incidence of death during the 28 days from T1 follow-up using Cox models. In univariable analyses, both heart → brain metrics, CSI → brain and CPI → brain, were significantly associated with the cumulative incidence of death (crude HR 0.21 [0.05–0.85], uncorrected p = 0.022 and HR = 0.24 [0.07–0.77], uncorrected p = 0.016 respectively), but these associations did not resist to multiple comparison correction (p > 0.0125) (Supplementary Table 1). However, on multivariable analyses CPI → brain remained independently associated with mortality in all models, with lower coupling values being associated with higher cumulative incidence of death (Table 3). This effect seemed also independent of sedation regimen as the association persisted after adjustment on either instantaneous rate, cumulative dose or duration of sedative and analgesic at T1 (Supplementary Table 2).

Univariable and multivariable Cox regressions models. In univariable analyses P-values correspond to likelihood ratio test, with significance defined by the Bonferroni rule for multiple comparisons at α = 0.0125 (in bold). In multivariable analyses, hazard ratios were adjusted on variables with unadjusted p-values < 0.2 on univariable analysis, that is RASS, non-neurological SOFA and sedative rate for mortality and RASS and sedative

<caption>**Table 3**</caption>

Cox regression models for associations of brain–heart metrics with mortality and encephalopathy at day 28.

<table><thead><tr><th colspan="5">Mortality at day 28 from T1</th></tr><tr><th rowspan="2">Independent Variables</th><th colspan="2">Univariable</th><th colspan="2">Multivariable</th></tr><tr><th>HR [95 % CI]</th><th>p uncorrected</th><th>aHR [95 % CI]</th><th>p uncorrected</th></tr></thead><tbody><tr><td>CSI → Brain</td><td>0.21<br>[0.05–0.85]</td><td>0.022</td><td>0.24<br>[0.06–1.06]</td><td>0.060</td></tr><tr><td>CPI → Brain</td><td>0.24<br>[0.07–0.77]</td><td>0.016</td><td>0.24<br>[0.07–0.82]</td><td>0.022</td></tr></tbody></table><table><thead><tr><th colspan="5">Recovery from coma and delirium at day 28 from T1</th></tr><tr><th rowspan="2">Independent Variables</th><th colspan="2">Univariable</th><th colspan="2">Multivariable</th></tr><tr><th>HR [95 % CI]</th><th>p uncorrected</th><th>aHR [95 % CI]</th><th>p uncorrected</th></tr></thead><tbody><tr><td>Brain → CPI</td><td>0.38<br>[0.18–0.8]</td><td>0.005</td><td>0.43<br>[0.2–0.95]</td><td>0.036</td></tr><tr><td>CSI → Brain</td><td>2.76<br>[1.18–6.42]</td><td>0.016</td><td>2.35<br>[0.95–5.81]</td><td>0.064</td></tr><tr><td>CPI → Brain</td><td>2.32<br>[1.09–4.93]</td><td>0.025</td><td>1.78<br>[0.8–3.96]</td><td>0.155</td></tr></tbody></table>

rate for encephalopathy (uncorrected p-values).

### 3.3. Early brain → heart interactions are also associated with acute encephalopathy

Median acute encephalopathy duration was 16 [12–29] days, with median duration of coma of 12 [7–25] and a median duration of delirium of 5 [3–8] days in the 30 (75%) patients that developed delirium (Table 2).

As for the mortality outcome, we assessed the association of brain–heart metrics with coma- and delirium-free days, using Cox regression models. In univariable analyses, both heart → brain metrics were significantly associated with the cumulative incidence of acute encephalopathy recovery within 28 days from T1 (HR 2.76 [1.18–6.42], uncorrected p = 0.016 for CSI → brain and HR 2.32 [1.09–4.93], uncorrected p = 0.025 for CPI → brain), as well as brain → CPI (HR = 0.38 [0.18–0.80], p = 0.005). After correction for multiple comparisons, only brain → CPI remained associated with the cumulative incidence of acute encephalopathy recovery (p < 0.0125) (Supplementary Table 1). In multivariable analyses, only brain → CPI remained significantly associated with acute encephalopathy, with higher coupling being associated with lower coma/delirium-free days at day 28 (Table 3). Again, this association was independent of sedative/analgesic (Supplementary Table 2).

Lastly, 9 patients died before sedation could be stopped and delayed awakening was observed in 26 (65%) of the remaining 40 patients. Brain → CPI at T1 was also significantly higher in patients with delayed awakening (9.66 [9.31–9.99] vs. 9.05 [8.87–9.10], p = 0.001).

### 3.4. Brain-heart metrics outperform individual brain and heart metrics

Finally, we examined whether heart and brain metrics alone could explain some of the relationships found in brain–heart interactions. EEG power was significantly associated with ICU mortality (AUC = 0.75 [0.56–0.93]) but with lower AUC than heart → brain metrics (CPI → brain AUC = 0.79 [0.6–0.98] and CSI → brain AUC = 0.77 [0.59–0.96]). CSI and CPI metrics derived from the ECG were not associated with ICU mortality (Fig. 3A, Supplementary Table 3). EEG power was also associated with delayed awakening (AUC = 0.69 [0.51–0.87]), but again with a lower AUC than brain → CPI (AUC = 0.8 [0.63–0.97]). Again, CPI and CSI were not associated with delayed awakening (Fig. 3B, Supplementary Table 4). Lastly, a significant correlation with acute encephalopathy duration was only found with brain → CPI (rho = 0.44, p = 0.004) while CSI, CPI and EEG power were not (Fig. 3C).
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Relationships between ECG metrics (CSI and CPI, yellow), EEG metric (EEG power, green) and brain–heart metrics (red) at T1 and different outcomes. (A) ICU mortality, (B) delayed awakening and (C) acute encephalopathy (AE) duration. For ICU mortality and delayed awakening, area under the ROC curve (AUC) and corresponding 95% confidence interval (95% CI) are represented. Correlation with AE duration was computed using spearman correlation coefficients (rho). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

**3.5. Acute encephalopathy modulates the evolution of brain–heart interactions**

At T2, that is 3–7 days after sedative withdrawal, 36 (73%) patients could be assessed, 13 were excluded because 7 died before, EEG was not performed in 3 of them, and insufficient data quality in other 3 of them (see flowchart in Supplementary figure 1). Regarding neurological status, 20 (56%) patients still suffered from acute encephalopathy, being either comatose (19%) or delirious (36%) (Table 2). This second timepoint was designed to identify different brain–heart patterns associated with the neurological status of patients, in the absence of sedative lasting effects. Besides, no significant difference was found in sedation duration, nor in sedative and analgesic cumulative dose between patients with and without acute encephalopathy at T2 (Supplementary Table 5). We thus investigated the changes over time (from T1 to T2, within-subject factor) according to neurological status at T2 (acute encephalopathy vs. non-acute-encephalopathy, between-subject) using an aligned rank transformed mixed ANOVA (from T1 to T2) in patients in whom EEG/ECG data were available at both timepoints (T1 ∩ T2, see flowchart in

Supplementary figure 1 and Supplementary Table 6).

For brain → heart metrics, only a significant main effect of timing was found (F(1, 31) = 14.6, p < 0.001 for brain → CSI, F(1,31) = 20.9, p < 0.001 for brain → CPI), with increasing coupling values over time. While in the opposite direction, a significant timing × neurological status interaction was found for both CSI → brain (F(1, 31) = 8.1, p = 0.008) and CPI → brain (F(1, 31) = 7.6, p = 0.01). Post-hoc comparisons showed that a significant reduction of heart → brain coupling from T1 to T2 was only observed in patients without acute encephalopathy at T2 (T1 vs. T2: CSI → brain, p = 0.0001; CPI → brain, p = 0.0003), as compared to patients with acute encephalopathy (CSI → brain, p = 0.3512; CPI → brain, p = 0.6152), as shown in Table 4 and Fig. 4.

When dissociating comatose from delirious patients, we still found a significant time × neurological status (comatose, delirious, no acute encephalopathy) for CSI → brain (F(2,30) = 5.7356, p = 0.008) and CPI → brain (F(2,30) = 5.2165, p = 0.01), with post-hoc tests showing significant differences from T1 to T2 only for patients without acute encephalopathy (p = 0.0003 and p = 0.0007 respectively) and not for either comatose or delirious patients (Supplementary Table 7 and
<caption>**Table 4**</caption>

Aligned Rank Transformed ANOVA analyses of the evolution of brain-heart metrics over time (from T1 to T2) according to the presence/absence of acute encephalopathy at T2.

<table><thead><tr><th rowspan="2">Effect<br>values</th><th colspan="2">Timing</th><th colspan="2">State (at T2)</th><th colspan="2">Interaction: Timing<br>× State</th></tr><tr><th>F(1,31)</th><th>p uncor.</th><th>F<br>(1,31)</th><th>p uncor.</th><th>F<br>(1,31)</th><th>p uncor.</th></tr></thead><tbody><tr><td>brain → CSI</td><td>14.6359</td><td><strong>0.0006</strong></td><td>1.8907</td><td>0.1790</td><td>1.2887</td><td>0.2650</td></tr><tr><td>brain → CPI</td><td>20.9259</td><td><strong>&lt;0.0001</strong></td><td>1.7288</td><td>0.1982</td><td>3.9497</td><td>0.0558</td></tr><tr><td>CSI → brain</td><td>21.8315</td><td><strong>&lt;0.0001</strong></td><td>4.2738</td><td>0.0471</td><td>8.1019</td><td><strong>0.0078</strong></td></tr><tr><td>CPI → brain</td><td>18.6688</td><td><strong>0.0001</strong></td><td>3.9892</td><td>0.0546</td><td>7.5864</td><td><strong>0.0097</strong></td></tr></tbody></table>

F statistic and the corresponding uncorrected p-values are presented for the main effect of timing (T1/T2), neurological status at T2 (acute encephalopathy vs. no acute encephalopathy) and the timing × neurological status interaction. As there are four brain-heart metrics, p-values significant after correction for four multiple comparisons following the Bonferroni rule ($\alpha = 0.05/4 = 0.0125$) are highlighted in bold.

supplementary figure 4).

## 4. Discussion

In our prospective study, we investigated brain-heart interactions within a cohort of patients hospitalized in the ICU due to severe COVID-19 pneumonia. We uncovered compelling insights into the relationship between these interactions and both patient neurological status and prognosis. Notably, our findings revealed a significant association between brain-heart interactions within the first days of admission to ICU and mortality, and the burden of acute encephalopathy at day 28.

<figure><img src="image_7.png" /></figure>

### 4.1. Brain-heart interactions inform multisystem dysfunctions

We observed that bidirectional brain-heart interactions related to patient's outcomes, where more severe patients were associated with aberrant interactions: a higher brain → heart (hyper coupling) and lower heart → brain interactions. These results suggest that severe COVID-19 pneumonia leads to systemic changes, including dysautonomia, causing the heart to respond abnormally to most brain changes, but also resulting in reduced central integration of interoceptive signals. Considering that the cardiovascular system maintains a closed-loop communication with cortical areas, including structures such as the insular cortex, anterior cingulate gyrus and brainstem autonomic centers (Nagai et al., 2010), their impairment can lead to changes in specific cardiac rhythms, blood pressure, disrupted levels of brain natriuretic peptide, catecholamines, glycemia and breathing patterns. The latter could contribute to respiratory and weaning failure due to maladaptive central respiratory drive (Esnault et al., 2020; González-Duarte and Norcliffe-Kaufmann, 2020; Manganelli et al., 2020).

Central to our understanding of severe COVID-19 manifestations is the role of the brainstem, serving as a critical hub for motor function, sensory processing, arousal, respiratory control, autonomic regulation, and neuroimmune mechanisms (Benghanem et al., 2020). Brainstem reflexes, while non-specific, hold high prognostic value, making them valuable tools in clinical assessment (Kandelman et al., 2020; Rohaut et al., 2017; Sharshar et al., 2011). The regulatory function linked to the autonomic nervous system suggests a potential connection with the pathophysiology of COVID-19 (Rua et al., 2024). The dysfunctions in autonomic mechanisms appear to be a significant feature in many critical care conditions, notably in sepsis (Carrara et al., 2021). Recently, we showed that brain-heart interactions in post-cardiac arrest hypoxic ischemic encephalopathy signal the severity and prognosis (Hermann

<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Evolution of brain-heart metrics (from T1 to T2) according to neurological status at T2 (with or without acute encephalopathy). P-values correspond to the ANOVA analysis with respect to the timing (T1 or T2), state at T2 (AE or no AE), and their interaction. CSI: cardiac sympathetic index, CPI: cardiac parasympathetic index. AE: acute encephalopathy.</figcaption></figure>
et al., 2024). While most studies have primarily focused on assessing cardiac autonomic dysfunction through measures of HRV (Benghanem et al., 2024; Endoh et al., 2019), the assessment of bidirectional brain-heart interactions proved useful in further characterizing the patients. Indeed, these findings are in line with recent efforts to stratify post-comatose patients based on their wakefulness and responsiveness (Candia-Rivera and Machado, 2023), in which a preserved brain-heart communication and its variability were indicative of the presence and level of consciousness.

## 4.2. Bidirectional interactions differ based on etiology

Our findings on the relationship between aberrant brain-heart interactions and patients' severity are in line with our previous work, in which we found that similar patterns also indicated poor neurological outcome after cardiac arrest (Hermann et al., 2024). However, in the present study, heart → brain provided stronger associations with mortality, while in cardiac arrest patients it was primarily the brain → heart metrics that showed a stronger relationship with the severity and outcomes. Although these differences might arise from varying patients' etiologies, specific pathophysiological mechanisms are likely responsible for them. For instance, from differences in the regions affected in both types of brain injury. In cardiac arrest patients, there are extensive cortical lesions caused by the post-anoxic injury, with relatively spared brainstem (Endisch et al., 2020). Instead, infections involve brainstem responses due to neuroinflammation, such as those well-reported in sepsis (Becher et al., 2017; Benghanem et al., 2020; Sharshar et al., 2003), but also in COVID-19 (Matschke et al., 2020; Thakur et al., 2021; von Weyhern et al., 2020).

These results would also indicate that both directions of the brain-heart interactions might assess several components of the autonomic nervous system (from brainstem centers to limbic and neocortical higher-order areas involved in autonomic control) which are differentially involved in vital and neurological outcome. Nonetheless, this top-down hyper coupling with reduced bottom-up signaling, seems to be a hallmark of both hypoxic ischemic brain injury and COVID-19 associated encephalopathy. Indeed, in turn, brain → heart interactions were mainly associated with acute encephalopathy.

## 4.3. Acute cognitive impairments influence the progression of brain-heart interactions

Longitudinal follow-up of patients who survived until sedation weaning showed that brain-heart interactions were able to track patients' neurological status during the recovery phase, indicative of the presence of acute encephalopathy (coma and delirium). Indeed, patients with absence of acute encephalopathy, indicative of a milder state, presented a stronger reduction of their heart → brain interactions at T2. We did not find different trajectories between coma and delirium, but our cohort might have been underpowered for this analysis. Non-specific acute encephalopathies with prolonged coma and delirium are common neurological complications in severe COVID-19 infection (Helms et al., 2020; Pun et al., 2021), and could relate to the degree of hypoxemia (Waldrop et al., 2022). While some early qualitative EEG patterns (i.e. discontinuous and nonreactive background) have been linked to mortality and duration of acute encephalopathy (Benghanem et al., 2022), altered brain-heart interactions appears as a key element in COVID-19 pathophysiology fostering the development of severe multiorgan failure and endothelial injury (Lionetti et al., 2021).

While the relationship between the progression of brain-heart interactions and the presence of acute encephalopathy after sedation weaning seems to appear from a strong modulation at the brain level, cortical hypersynchrony is typically observed in general anesthesia (Supp et al., 2011), which may extend to the heart. When patients experience deviations from these trends, as reported using functional connectivity, it may indicate the presence of significant physiological

failures incompatible with consciousness and its recovery (Duclos et al., 2022). Interestingly, while high brain-heart coupling in the early phase (while sedated) was indicative of more severe states, during the recovery (after sedation weaning), a higher coupling was associated with milder states, illustrating the complex and dynamic nature of the autonomic response to systemic dysfunctions. This further illustrates how brain-heart interactions can index maladaptive mechanisms responding to sedation, as such reported from EEG-only markers of connectivity, aperiodicity, complexity and criticality (Duclos et al., 2022; Maschke et al., 2024; Maschke et al., 2023). As opposed to the previous studies in which propofol was used, patients in our cohort were all sedated with midazolam with additional propofol in only a few patients. This did not allow us to study the differential effects on brain-heart interaction of these two drugs whose impact on brain activity differ (Numan et al., 2019). Nevertheless, the presence or absence of these dynamical shifts underscore the evolving interplay between adaptive and maladaptive responses throughout the phases of critical care.

Finally, our results may lead to future research bridging the gap with the reported brain-heart interactions informing about disorders of consciousness (Candia-Rivera et al., 2023, 2021a; Candia-Rivera and Machado, 2023; Pérez et al., 2021; Raimondo et al., 2017). Intriguingly, a preserved brain-heart communication and its variability can serve as a marker of presence and level of consciousness, respectively (Candia-Rivera and Machado, 2023). In light of those results, our study underscores the importance of measuring the directionality of altered brain-heart interactions to better inform acute cognitive impairments across different etiologies.

## 4.4. Predominant dysregulations in the parasympathetic nervous system

We found that the relationship between aberrant brain-heart interactions and the patients' severity was predominant from and towards the parasympathetic system. Sympathetic activity is generally considered a better marker for assessing the severity of a patient's condition in critical care (Hilz et al., 2019). For instance, elevated sympathetic activity, such as elevated heart rate or blood pressure, is often associated with acute stress responses, such as in cases of trauma or severe infections (Baguley et al., 2006; Shashikumar et al., 2017). On the other hand, parasympathetic activity, may not show such clear and immediate changes in response to severe physiological status (Hilz et al., 2019), although vagal reflexes play a significant role in balancing physiological responses and relaxation, but also in regulating respiratory rate and immune response. Our results indicate that brain-heart interactions, particularly involving parasympathetic activity, reveal clearer differences in patient states and outcomes. This finding suggests that specific pathways, potentially disrupted in severe COVID-19, are at play. Associations between delirium and changes in cardiac markers indexing parasympathetic activity have been reported, although not all markers show this relationship (Amit Patel et al., 2024). During severe COVID-19, several mechanisms can affect vagal reflexes, such as hypoxia (Lemes and Zoccal, 2014), as a mechanism to maintain oxygen homeostasis. Furthermore, intubation, can trigger vagal reflexes leading to bradycardia (Jones et al., 2012). Additionally, inflammatory responses can also be mediated by or trigger posterior vagal activations, such as the case of cytokine storms leading to systemic organ failure (De Virgiliis and Di Giovanni, 2020). Indeed, a recent case-control study found that COVID-19 ARDS was associated with marked bradycardia and severe HRV impairment, suggesting sympathovagal imbalance with vagal overtone as compared to non-COVID-19 ARDS (Dumargne et al., 2024). Similar findings were made in another study comparing COVID-19 and all-cause sepsis (Kamaleswaran et al., 2021). Among COVID-19 patients, lower HRV indices associated with vagal modulation are generally associated with more severe disease (Pan et al., 2021) and mortality (Mol et al., 2021), consistent with our findings.

It is important to note that the sympathetic and parasympathetic dynamics measured by CSI and CPI are approximations (Candia-Rivera
et al., 2025), as a direct neurogram would provide a more precise
assessment of these systems. Using HRV as a proxy for sympathetic tone
has faced criticism due to biases highlighted in various studies
(Goldstein et al., 2011; Reyes del Paso et al., 2013). For example, strong
parasympathetic activation can influence markers intended to measure
sympathetic dynamics, but the reverse is not likely to occur (Acharya
et al., 2006). Since our results indicate stronger effects involving para-
sympathetic dynamics, it is unlikely that these effects arise from sym-
pathetic activation. In any case, while the exact pathways of the
mechanisms involved in these interactions as well as their relations with
the etiology and/or severity of the disease require more research, the
pronounced effects on parasympathetic activity underscore the need for
comprehensive monitoring of both sympathetic and parasympathetic
functions.

4.5. *Limitations statement*

The main limitations of our study include the relatively small sample
size, potential confounding effects from sedation, and variability in
patient severity and management, such as the use of medications like
vasopressors that could affect brain or heart rhythms. Despite these
limitations, the significant associations between brain-heart in-
teractions and both mortality and acute encephalopathy remained after
adjusting for sedation and non-neurological SOFA scores for mortality.
Additionally, the absence of a non-COVID-19 ARDS control group limits
our ability to determine whether our findings are specific to COVID-19
compared to other causes of ARDS, whether septic or non-infectious.

Another important limitation of our study is the lack of EEG spatial
analysis, with brain-heart interaction metrics averaged across all EEG
channels. This decision was made because the study used standard
clinical EEG recordings, which are low-density and noisy. While this
approach is practical for potential clinical translation, as high-density
EEG is not typically used in ICU settings, future studies should explore
brain-heart interactions with high-density EEG. This would allow for
the identification of spatial patterns and specific brain regions involved,
enabling the development of targeted interventions such as those using
brain stimulation.

5. Conclusions

Our study demonstrates that bilateral brain-heart interactions in
severe COVID-19 patients admitted to the ICU are compelling markers
indicative of both mortality risk and acute encephalopathy in the ICU.
These interactions evolve dynamically with the progression of the dis-
ease, with more pronounced dynamic changes correlating with milder
conditions. Our findings highlight the importance of continuous moni-
toring of bilateral brain-heart interactions to understand these dynamics
better and to develop tailored prognostic and therapeutic strategies
based on the patients' systemic conditions.

CRediT authorship contribution statement

**Bertrand Hermann:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Visualization, Writing – original draft, Writing – review & editing.
**Sarah Benghanem:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Writing – review & editing.
**Estelle Pruvost-Robieux:** Data curation, Writing – review & editing.
**Tarek Sharshar:** Conceptualization, Methodology, Supervision, Writing – review & editing.
**Martine Gavaret:** Data curation, Writing – review & editing.
**Alain Cariou:** Conceptualization, Investigation, Writing – review & editing.
**Jean-Luc Diehl:** Conceptualization, Investigation, Writing – review & editing.
**Diego Candia-Rivera:** Conceptualization, Formal analysis, Investigation, Methodology, Software, Supervision, Visualization, Writing – original draft, Writing – review & editing.

Funding

This study received funding from the Département Médico-Uni-
versitaire Urgences- Réanimation of Assistance Publique – Hôpitaux de
Paris Centre- Université de Paris.

Declaration of competing interest

The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.
org/10.1016/j.clinph.2025.2110745.

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