1 INTRODUCTION

The association between obesity (body mass index > 30kg/m2) and intracranial hypertension (ICH) has been widely described.1-3 Elevation in the intracranial pressure (ICP) may reduce intracranial compliance (ICC), what is the equilibrium among intracranial content (brain, blood volume and cerebrospinal fluid),4 impacting cerebral perfusion and cellular metabolism.5 Several mechanisms linking obesity with chronic ICH have been proposed, mainly as disturbances of cerebrospinal fluid circulation,6, 7 dysregulation of the metabolic neuroendocrine axis,8 compression of thoracic and abdominal organs impairing cerebral venous return,9 sleep apnea leading to cerebral hemodynamics disorders (CHD)6, 10, 11 and brain temperature elevation.12 Additionally to genetic and epigenetic determinants,13 these factors may also play a role in increasing risks of neurodegenerative diseases (NDD) development in this population.

ICH prevalence among general population has been not widely studied to the date, especially because techniques to assess ICP require skull opening for catheter introduction, what is ethically not recommended. Nevertheless, 90–95% of patients with idiopathic intracranial hypertension (IIH) symptoms have obesity.14 Hence, the hypothesis of obesity coexisting with a lifetime regimen of ICH and consequently ICC impairment (ICCI) if obesity is untreated15 becomes suitable.

At the current COVID-19 pandemic, obesity has been considered a prognostic risk factor, with particular monitoring and earlier respiratory support recommended for these patients.16-18 Considering this background explained above, for obese patients with COVID-19 severe respiratory syndromes, the hypothesis of ICCI and CHDbeing higher when compared with lean patients is feasible. The objective of the present study was to evaluate the prevalence of ICCI and CHD in correlation with short-term clinical outcomes as death and mechanical ventilation weaning (MVW) in severe COVID-19 among patients with and without obesity.

2 METHODS 2.1 Study design

A single center, observational and prospective study in six intensive care units (ICUs) of Hospital das Clínicas, São Paulo University, Brazil, from May to June 2020 was conducted. All methods were performed in accordance with the relevant guidelines and regulations, informed consent was obtained from all legally authorized representatives (LAR)/next of kin instead of the patients because of illness severity.

The study included consecutive COVID-19 severely ill patients for the observation of ICCI and CHD in this population although the observation of high prevalence of obesity among the included patients motivated this present analysis. All patients included had confirmed COVID-19 by real-time reverse transcription–polymerase chain reaction positive testing and were included within the first 72 h since the initiation of invasive mechanical ventilation. Exclusion criteria included the absence of LAR consent, the absence of temporal acoustic window for TCD assessment, patients unable to undergo ICC monitoring due to lesions and/or skin infections in the sensor application region and patients with head circumference smaller than 47 cm. The study protocol was according to the Standards for Reporting of Diagnostic Accuracy Studies (STARD) statement (Supplemental Table).

Eligible subjects were identified by the ICU teams (SYW, SF, BT, EB and LMSM). As patients were included consecutively, inclusion of patients with and without obesity was spontaneous. Two assessments of CVH and ICC were performed: the first during the first 3 days from intubation and the second up to 72 h after extubation or tracheotomy without administration of sedatives; for patients who died while intubated only the first evaluation was considered. Clinical parameters, such as systemic arterial pressure, fluid balance, use of sedatives, PaO2 and PaCO2, hemoglobin and body temperature, were concomitantly recorded. One operator, without knowledge of the individual clinical features, performed all evaluations. Data on demographic characteristics, Simplified Acute Physiologic Score (SAPS) 3, use of intravenous sedatives, vasopressors and other physiological and laboratory data were also collected.

2.2 Intracranial compliance monitoring technique

ICC was evaluated non-invasively by assessing cranial deformation using a specific device (B4C; Brain4care Corp., São Carlos). The B4C sensor consists of a support for a sensor bar that detects local cranial bone deformations using specific sensors. The detection of these deformations is obtained by a cantilever bar modeled through finite element calculations. Voltage meters are attached to this bar for deformation detection. Non-invasive contact with the skull is obtained by adequate pressure directly into the scalp by means of a pin. The system is positioned in the frontotemporal region, around 3 centimeters over the first third of the orbitomeatal line; consequently, avoiding temporal superficial artery main branches and temporal muscle, providing contact of the sensor with an area of thin skin and skull, whereas slight pressure is applied to the adjustable band until optimal signal is detected.

Variations in ICP cause deformations in the cranial bone, which are detected by the sensor bar. The device filters, amplifies and scans the sensor signal and sends the data to a mobile device. The method is completely non-invasive and painless. In addition, it does not interfere with any routine monitoring. The waveform obtained is equivalent to ICP waveform obtained using invasive techniques, such intraparenchymal probes or external ventricular derivation,19 and the relation between its different components provides information on ICC.20 In particular, each cardiac beat corresponds to an ICP waveform composed of three peaks: arterial pulsation (P1); cerebral venous flow, which is secondary to cyclic fluctuations of arterial blood volume, reflecting intracranial compliance (P2); the aortic valve closure (P3; Figure 1).21

Intracranial pressure waves morphology in accordance with cerebral compliance

The B4C analytics system verified all data collected by the sensor, that is, ICP pulse waves morphology parameters such as the P2/P1 ratio. For this study, all calculations were performed using the mean pulse of the ICP, calculated by identifying and extracting all ICP pulses, excluding possible artifacts. The mean pulse was used to calculate the amplitudes of the P1 and P2 peaks, which were obtained by detecting the highest point of these peaks and subtracting the base value of the ICP pulse. The P2/P1 ratio was calculated by dividing the amplitude of these two points. In case of P2 > P1, ICC was defined as “abnormal”.

2.3 Cerebrovascular hemodynamics assessment

Conventional transcranial Doppler [(TCD) EZ-DOP, DWL Compumetrics, Singen, Germany] was used to assess CHD.22 A complete evaluation of right and left cerebral hemispheres and the brainstem arteries was performed prior to the study to discard focal stenosis, using Doppler colored technique with low frequency probe (2MHz) and scanning every 1 mm of arterial extension, through the temporal, orbital, suboccipital, retro-mastoid and submandibular windows. Hemodynamic parameters of interest were mean flow velocities in the middle cerebral arteries (mCBFV), peak systolic and final diastolic velocities, because the MCA supplies approximately 80% of cerebral blood flow. Abnormal mCBFV was identified by values < 40 or ≥ 100 cm/sec.

Using TCD, elevation of ICP was suspected when pulsatility index (PI) ≥ 1.2 (i.e., “abnormal” PI).23,25 PI was calculated by the following formula: PI = Sv-Dv/Mv (Sv: systolic velocity, Dv: diastolic velocity and Mv: mean flow velocity). Moreover, TCD allows calculation of estimated CPP (eCPP) and ICP (eICP),24 which are significantly correlated with invasive ICP measurements.22, 25 Abnormal eICP was considered if > 20 mmHg; abnormal eCPP if ≤ 45 or ≥ 75 mmHg.

2.4 Outcomes

As a wide range of variables are involved in the prognosis of COVID-19,26-29 our analyses were limited to the prevalence and predictive values of ICC and CVH disturbances on early unfavorable outcome (UO); between patients included consecutively and distributed in two groups according to presence or absence of obesity. UO was a composite endpoint including either absence of weaning from mechanical ventilation (MV) or death on day 7 after inclusion in the study.

CHD and ICC impairment were identified using the different combination of TCD and B4C values; in particular, P2/P1 ratio, mCBFV, eICP, PI and eCPP were categorized and an arbitrary score was developed to describe different degrees of these alterations (Table 1). For each variable, severity was defined by a CHD/ICCI score from 1 to 4. As such, the sum of the severity score for each variable gave a score ranging from a minimum of 5 to a maximum of 20. The score was then classified as: “normal”, that is, five points, which suggested no abnormalities; “mild CHD/ICCI abnormalities”, that is, Six to seven points, which was associated with minor disturbances in one or two variables; “moderate CVH/ICC abnormalities”, that is, Eight to nine points; and “severe CHD/ICCI”, that is, 10 points.

TABLE 1. Thresholds for P2/P1 ratio, mCBFV, PI, eICP and eCPP. Progressive points were in accordance with the worst results Points P2/P1 mCBFV PI eICP eICP Score (sum of Each) 1 ≤1 40 to 70 <1.2 <15 50 to 75 5 no CVH/ICCI 2 1.01 to 1.19 71 to 99 ≥1.2 15-20 ≥75 6-7 mild CVH/ICCI 3 ≥1.2 ≥100 ≥1.3 21-25 ≤50 8-9 moderate CVH/ICCI 4 ≥1.4 <40 ≥1.4 >25 <40 ≥10 severe CVH/ICCI Abbreviations: CVH/ICCI, cerebrovascular hemodynamics and intracranial compliance impairment; eCPP, estimated cerebral perfusion pressure; eICP, estimated intracranial pressure; PI, pulsatility index. 2.5 Sample size

A pilot study was performed. Using the upper confidence interval for the population variance approach to the sample size calculation, a pilot sample size between 20 and 40 was chosen, corresponding to standardized effect sizes of 0.4 and 0.7 (for 90% power based on a standard sample size calculation).30 Thus, considering the risk of early deaths and lack of second TCD and B4C assessment, 50 patients were enrolled to test our hypothesis.

2.6 Statistical analysis

The 50 patients included were separated in two groups according whether the BMI was over or under 30 kg/m2. Descriptive statistics were computed for all study variables. Categorical variables are presented as count (%), while continuous variables are presented as mean (± standard deviation) or median (25th–75th percentiles), according to their distribution, which was assessed through skewness and kurtosis values, as well as graphical methods. Differences between groups were assessed using a χ-square or Fisher's exact test for categorical variables, t-Student test for normally distributed continuous variables and Mann-Whitney tests for asymmetrically distributed continuous variables.

Multivariable adjustment with multiple logistic regression was used to verify the independent association between obesity and ICCI with results expressed in odds ratios and their respective 95% Confidence Intervals (CI). The model was specified a priori to adjust for disease respiratory severity and overall severity. The former model included age and PaO2/FiO2 ratio and the latter included age, PaO2/FiO2 ratio, ICU admission SOFA score laboratory parameters (creatinine, bilirubin and platelets) and d-dimer. Except for age, all other covariates were log transformed to ensure the normality of the residuals. Multiple imputation was used to handle missing data.

Final p values under 0.05 were considered statistically significant. All analyses were performed using the software Statistical Package for Social Sciences (IBM SPSS Statistics for Windows, version 24.0. Armonk, NY: IBM Corp.). This clinical trial (CT) study protocol was approved by the local Ethics Committee, in 19 April 2020 and registered under number NCT04429477 (available at clinicaltrials.gov).

3 RESULTS 3.1 Sample features

Overall COVID-19 admissions between May and June were 2813, whereas ICU admissions in this period were 1579 in our institution, with 552 (34.9%) deaths (institution reference for moderate-severe cases). Among eligible subjects, drop-outs were 1 because of LAR refusal, and 23 because of transference from another institution over the inclusion period. TCD evaluations on CVH were performed for the entire sample, with no absence of temporal acoustic windows found.

Overall group's features are described in Table 2. 30 (60%) patients died during hospitalization, among these, 21 (42%) died in the first 4 to 29 (13 days average) days of ICU therapy, still under ventilatory support being no TCDand B4C reassessed. There were no statistical differences between survivor and non-survivor groups, except for higher age and lower PaO2/FiO2 among deceased subjects. 29 patients reached reassessment because of MVW or tracheostomy with sedation interruption. Average length of hospitalization was 51 (4–67) days for deceased and 30 (9–70) for survivors. Seven needed tracheostomy and eight underwent re-intubation. Respiratory rate was over 16 bpm and oxygen saturation under 94% for 48 and 31 patients, respectively. 28 days posterior to RSW, only 8 (40% of survivors) patients reached hospital discharge.

TABLE 2. Sample characteristics according to obesity status Variable General (50) Obesity p value No (27) Yes (23) Age (mean ± SD) 55.9 ± 16.6 58.4 ± 18.3 53 ± 14.1 0.253 Female 22 (44) 11 (40.7) 11 (47.8) 0.615 Unfavorable outcome 33 (66) 12 (44.4) 16 (69.6) 0.075 Altered parameter P2/P1 ratio 31 (62) 13 (48.1) 18 (78.3) 0.029 mCBFV 33 (66) 18 (66.7) 15 (65.2) 0.914 PI 33 (66) 17 (63) 16 (69.6) 0.623 eICP 18 (36) 3 (33.3) 9 (39.1) 0.670 eCPP 28 (56) 17 (63) 11 (47.8) 0.283 Chronic kidney injury 9 (18) 3 (11.1) 6 (26.1) 0.270 Smoking 16 (32) 7 (25.9) 9 (39.1) 0.318 Cardiovascular disease 10 (20) 5 (18.5) 5 (21.7) >0.999 Diabetes 17 (34) 7 (25.9) 10 (43.5) 0.192 Hypertension 28 (56) 13 (48.1) 15 (65.2) 0.226 Cancer 7 (14) 5 (18.5) 2 (8.7) 0.430 PaO2/2FiO2 ratio 142 (125 – 182) 149 (133 – 182) 137 (97 – 201) 0.384 Admission laboratory parameters (median and quartiles) Creatinine 1.1 (0.7 – 2.6) 1.1 (0.7 – 2.3) 0.9 (0.7 – 2.8) 0.946 Bilirubin 0.4 (0.2 – 0.5) 0.4 (0.3 – 0.5) 0.3 (0.2 – 0.6) 0.633 Platelets 226 (150 – 320) 227 (148 – 348) 216 (163 – 313) 0.820 D-dimer 2622 (1250 – 6058) 3219 (1166 – 10,234) 2390 (1416 – 5629) 0.763 Length of stay (median and quartiles) 23 (13 – 30) 25 (12 – 30) 20 (14 – 30) 0.977 Abbreviations: eCPP, estimated cerebral perfusion pressure; eICP, estimated intracranial pressure; mCBFV, middle cerebral artery highest mean velocity; PI, pulsatility index; SD, standard deviation.

Twenty-three patients were obese (Table 2). There was no difference between obese and non-obese patients regarding age, gender, comorbidities, PaO2/2FiO2 ratio and ICU admission laboratory parameters (creatinine, bilirubin, platelets and d-dimer). ICCI was more frequent in obese patients (78.3 vs. 48.1%, p = 0.029), although the TCD parameters (mCBFV, PI, eICP, eCPP) were similar between groups. Obese patients tended to present more unfavorable outcomes (69.6 vs. 44,4%, p = 0.075).

Besides being more present among obese patients, in the univariate analysis, ICCI was associated with admission creatinine and bilirubin as well as mCBFV, PI and eICP (Table 3). Unfavorable outcomes occurred more frequently among those with ICCI (74.2 vs. 26.3%, p = 0.001).

TABLE 3. Sample characteristics according to intracranial compliance status Variable Intracranial Compliance Impairment p value No (19) Yes (31) Age (mean ± SD) 51.4 ± 19.6 58.6 ± 14.1 0.137 Female 6 (31.6) 16 (51.6) 0.166 Obesity 5 (26.3) 18 (58.1) 0.029 Unfavorable outcome 5 (26.3) 23 (74.2) 0.001 Altered parameter mCBFV 5 (26.3) 28 (90.3) <0.001 PI 5 (26.3) 28 (90.3) <0.001 eICP 4 (21.1) 14 (45.2) 0.085 eCPP 10 (52.6) 18 (58.1) 0.707 Chronic kidney injury 3 (15.8) 6 (19.4) >0.999 Smoking 4 (21.1) 12 (38.7) 0.194 Cardiovascular disease 3 (15.8) 7 (22.6) 0.722 Diabetes 7 (36.8) 10 (33.2) 0.740 Hypertension 8 (42.1) 20 (64.5) 0.121 Cancer 2 (10.5) 5 (16.1) 0.695 PaO2/FiO2 ratio 145 (125 – 216) 142 (115 – 173) 0.394 Admission laboratory parameters (median and quartiles) Creatinine 1.3 (0.8 – 3.0) 0.9 (0.7 – 1.7) 0.089 Bilirubin 0.6 (0.4 – 1.3) 0.3 (0.2 – 0.4) 0.006 Platelets 169 (120 – 315) 249 (178 – 329) 0.136 D-dimer 2338 (779 – 5470) 3219 (1457 – 7343) 0.250 Length of stay (median and quartiles) 26 (12 – 37) 20 (14 – 30) 0.555 Abbreviations: eCPP, estimated cerebral perfusion pressure; eICP, estimated intracranial pressure; mCBFV, middle cerebral artery highest mean velocity; SD, standard deviation.

In the multivariable analysis, obesity maintained independent association with ICCI after adjustment for respiratory disease severity (Table 4, model 1, OR 5.47, 95% CI 1.35 - 22.18, p = 0.017) and overall severity (Table 4, model 2, OR 12.35, 95% CI 1.57–97.36, p = 0.017). Concerning early outcomes, ICCI and admission d-dimer were associated with unsuccessful MVW/death (Table 5). Moreover, older patients tended to have a higher risk of ICCI. Figure 2 depicts the estimated probability of an altered intracranial compliance according to obesity status and age after multivariable adjustment.

TABLE 4. Multivariable analysis for the predictors of intracranial compliance impairment Variable Coef. SE Wald OR 95% CI p value Model 1 Age (per year) 0.04 0.02 2.86 1.04 0.99 – 1.09 0.091 Obesity 1.70 0.71 5.66 5.47 1.35 - 22.18 0.017 PaO2/FiO2 (Log) −1.41 2.48 0.32 0.25 0.01 – 31.59 0.571 Model 2 Age (per year) 0.066 0.037 - 1.07 0.99 – 1.15 0.076 Obesity 2.514 1.049 - 12.35 1.57 – 97.36 0.017 PaO2/FiO2 (Log) -0.269 2.934 - 0.76 0.01 – 246.83 0.927 Admission lab (Log) Creatinine −2.200 2.533 - 0.11 0.01 – 19.96 0.393 Bilirubin 0.047 1.703 - 1.05 0.03 – 35.22 0.978 Platelets 3.493 2.465 - 32.90 0.25 – 4375.69 0.160 D-dimer 1.250 0.831 - 3.492 0.68 – 17.87 0.133 Abbreviations: CI, confidence interval; Coef., coefficient; SE, standard error; OR, odds ratio. TABLE 5. Multivariable analysis for the predictors of unfavorable outcome Variable Coef. SE OR 95% CI p value Age (per year) 0.05 0.03 1.06 1.00 – 1,12 0.070 Obesity 1.49 0.93 4.44 0.71 – 27,55 0.110 ICCI 2.02 0.89 7.52 1.31 – 43,12 0.024 Admission d-dimer (Log) 1.75 0.88 5.74 1.02 – 32,29 0.047 SOFA score 0.06 0.25 1.06 0.65 – 1,72 0.816 Abbreviations: Coef., Coefficient; SE, Standard error; OR, Odds ratio; CI, Confidence interval; ICCI, intracranial compliance impairment.

Estimated probability of intracranial compliance impairment according to obesity status and age (adjusted for PaO2/FiO2 and laboratory parameters after multiple imputation)

4 DISCUSSION

In the present study, ICCI was significantly more present among patients with obesity. CHD prevalence was not different between groups, probably because our entire sample was composed by SARS patients, included in early stages of respiratory function depression and mechanical ventilation support, when CHD is commonly observed.31 Survival and mechanical ventilatory support successful weaning were also significantly higher among non-obese subjects.

Despite of our exploratory study design, prevalence of obesity (46%) in our sample was considerably higher than Brazilian overall population obesity rate (20.7%),32 but similar to previous studies reported, which have noticed increased risk of hospitalization, severe disease and invasive mechanical ventilation in COVID-19.18, 33, 34 Therefore, obesity was pointed as a cli