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Brain tissue oxygen monitoring in traumatic brain injury: part I—To what extent does PbtO2 reflect global cerebral physiology?

Matters Arising to this article was published on 20 November 2023

Abstract

Background

The primary aim was to explore the association of global cerebral physiological variables including intracranial pressure (ICP), cerebrovascular reactivity (PRx), cerebral perfusion pressure (CPP), and deviation from the PRx-based optimal CPP value (∆CPPopt; actual CPP-CPPopt) in relation to brain tissue oxygenation (pbtO2) in traumatic brain injury (TBI).

Methods

A total of 425 TBI patients with ICP- and pbtO2 monitoring for at least 12 h, who had been treated at the neurocritical care unit, Addenbrooke’s Hospital, Cambridge, UK, between 2002 and 2022 were included. Generalized additive models (GAMs) and linear mixed effect models were used to explore the association of ICP, PRx, CPP, and CPPopt in relation to pbtO2. PbtO2 < 20 mmHg, ICP > 20 mmHg, PRx > 0.30, CPP < 60 mmHg, and ∆CPPopt < − 5 mmHg were considered as cerebral insults.

Results

PbtO2 < 20 mmHg occurred in median during 17% of the monitoring time and in less than 5% in combination with ICP > 20 mmHg, PRx > 0.30, CPP < 60 mmHg, or ∆CPPopt < − 5 mmHg. In GAM analyses, pbtO2 remained around 25 mmHg over a large range of ICP ([0;50] mmHg) and PRx [− 1;1], but deteriorated below 20 mmHg for extremely low CPP below 30 mmHg and ∆CPPopt below − 30 mmHg. In linear mixed effect models, ICP, CPP, PRx, and ∆CPPopt were significantly associated with pbtO2, but the fixed effects could only explain a very small extent of the pbtO2 variation.

Conclusions

PbtO2 below 20 mmHg was relatively frequent and often occurred in the absence of disturbances in ICP, PRx, CPP, and ∆CPPopt. There were significant, but weak associations between the global cerebral physiological variables and pbtO2, suggesting that hypoxic pbtO2 is often a complex and independent pathophysiological event. Thus, other variables may be more crucial to explain pbtO2 and, likewise, pbtO2 may not be a suitable outcome measure to determine whether global cerebral blood flow optimization such as CPPopt therapy is successful.

Introduction

Traditional neurocritical care (NCC) for patients with traumatic brain injury (TBI) has focused on monitoring and treatment of intracranial pressure (ICP) and cerebral perfusion pressure (CPP) to avoid brain herniation and ischaemia [1,2,3,4]. However, it has been realized that these patients also exhibit disrupted cerebral pressure autoregulation, oxygen diffusion limitations, and mitochondrial dysfunction that contribute to energy failure and secondary brain injury [1, 5,6,7,8,9]. Current research aims at utilizing multimodality monitoring techniques and advanced software to diagnose these pathophysiological events in order to better guide clinical management [10].

A leading approach to detect cerebral pressure autoregulatory disturbances is based on the pressure reactivity index (PRx), which is assessed as the moving correlation coefficient between slow changes in arterial blood pressure (ABP) and ICP. Negative and positive PRx-values indicate preserved and impaired autoregulation, respectively [11]. PRx often varies with CPP in a U-shaped way over a certain buffer of time, and the CPP with the lowest PRx has been denoted as the optimal CPP (CPPopt) [12, 13]. CPPopt differs between TBI patients and changes over time for the individual patient [12,13,14,15]. CPPopt is hypothesized to be a better CBF surrogate than fixed, absolute CPP targets [16, 17]. Consistently, in observational TBI studies, CPP close to CPPopt has been associated with better brain tissue oxygenation (pbtO2) [18, 19], energy metabolism [20], and favourable outcome [12,13,14, 21, 22]. A phase II-trial recently demonstrated that CPPopt-guided therapy is safe and feasible in TBI [15], but a phase III-trial is needed to determine whether targeting CPPopt translates into improved cerebral physiology and functional outcome.

PbtO2, which is measured with a focal probe in the brain, is a downstream variable to CBF that has received great interest as a potential treatment target in TBI [23]. Low pbtO2 is associated with unfavourable outcome [24,25,26,27,28] and a phase II-trial recently demonstrated that integrated management based on pbtO2 together with ICP was safe, feasible, and with a trend towards lower mortality compared with ICP-management alone [29]. There are now three ongoing randomized controlled trials (BOOST-3, OXY-TC, and BONANZA) aimed at evaluating if pbtO2-directed therapy adds any clinical value in terms of outcomes [23]. Current pbtO2-integrated protocols are tailored based on the combination of ICP-, CPP-, and pbtO2-insults. In cases with low pbtO2 in the absence of high ICP and low CPP, therapy is aimed at increasing cerebral oxygen delivery by augmentation of CBF and the arterial oxygen content [23, 30]. Interestingly, although cerebral ischaemia is one reason for brain tissue hypoxia in TBI, retrospective studies indicate that low pbtO2 typically occurs despite well-targeted global cerebral physiological variables (ICP and CPP) [27, 31]. One explanation could be that absolute CPP is a poor CBF surrogate and deviation from CPPopt towards lower CPP might be more sensitive to ischaemic hypoxia [18, 19, 32]. Another explanation is that cerebral ischaemia and hypoxia are spatially distributed throughout the brain and only overlap to some extent after TBI [33]. Thus, global CBF and its descriptors may be poor surrogates of focal brain tissue hypoxia. Instead, capillary flow disturbances and oxygen diffusion limitations may explain the impeded oxygen delivery in these cases of low pbtO2 in the absence of focal ischaemia [6,7,8].

Altogether, CPPopt may be a better global CBF surrogate than absolute CPP and could be a means to fine-tune CBF optimization, particularly in case of hypoxic values of pbtO2. At the same time, pbtO2 is a downstream variable to CBF and could be used as a target to determine whether CPPopt therapy is successful and within which range CPPopt should be targeted. However, the relative contribution of CPP/CPPopt/CBF to pbtO2 may be limited as low pbtO2 may often reflect non-perfusion-related disturbances in TBI. To better understand these uncertainties, we performed a two-part study on the role of pbtO2 monitoring in TBI. Here we present part I of our study, where we aimed to explore the influence of ICP, PRx, CPP, and deviation from CPPopt on pbtO2 in a large cohort of TBI patients with pbtO2 and high-frequency ICP and ABP monitoring data. Our hypothesis was that the global cerebral physiological variables described above would be associated with pbtO2, but only explain a small part of its variations. In part II, we explored the prognostic value of pbtO2 in TBI patients, and this will be presented in a separate manuscript.

Materials and methods

Patients and study design

In this observational, single-centre study, 781 de-identified records of high-resolution neuromonitoring data of TBI patients admitted in the NCC unit of the Addenbrooke’s Hospital (Cambridge, UK) between 2002 and 2022, were screened from the Brain Physics Lab research database. All patients had ABP and ICP data, 461 of these patients were also monitored with pbtO2, of which 36 records had less than 12 h of data and these were excluded. Thus, the final population included those 425 TBI patients with ICP, ABP, and pbtO2 data for more than 12 h, and they were admitted between 2006 and 2022. Ten-second averages of pre-processed cleaned ABP, ICP, and pbtO2 data were accessed. The following clinical descriptors were also retrieved: age, sex, Glasgow Coma Scale (GCS), pupillary reactivity, decompressive craniectomy, and Glasgow outcome scale (GOS).

Management protocol

The management protocol has been described in detail in previous studies [2, 34]. The protocol went through slight revisions during the study period of 20 years [2]. CPP was targeted above 60 mmHg, ICP below 20 and 22 mmHg, pbtO2 above 20 mmHg, partial pressure of carbon dioxide within 4.5–5 kPa, and arterial glucose within 6–8 mmol/L. PRx and CPPopt were introduced at the bedside in 1999 and 2012, respectively, and were primarily used as monitoring variables. However, CPPopt could also be targeted at the discretion of the responsible clinician. Unconscious patients (GCS < 9) were intubated, mechanically ventilated, and sedated. ICP-treatments included head elevation, sedation, muscle paralysis, cerebrospinal fluid drainage via an external ventricular drainage, osmotic agents, induced hypothermia, barbiturate coma, and decompressive craniectomy. CPP was maintained at first hand by intravenous fluids and at second hand with vasopressors/inotropes. PbtO2 was mainly maintained above 20 mmHg by reducing ICP and increasing CPP. Additional efforts to keep pbtO2 above the designated target were allowed based on the treating physician's discretion. This could involve raising the fraction of inspired oxygen (FiO2) levels or implementing a moderate upward adjustment of CPP.

Clinical outcome

Clinical outcome was evaluated according to the GOS at 6 months post-injury, by clinical assessment or via telephone interviews by trained staff. GOS ranges from 1 (death) to 5 (good recovery) [35, 36]. Outcome was dichotomized as favourable and unfavourable outcome (GOS 4–5 and 1–3, respectively) as well as survival and mortality (GOS 2–5 and 1, respectively).

Physiological measurements

ICP (Codman ICP MicroSensor, Codman & Shurtleff, Raynham, Massachusetts) and pbtO2 (Licox, Integra Neurosciences, Andover, UK), were monitored with intraparenchymal probes in the non-dominant frontal lobe. Direct placement of the pbtO2 probe into lesions was avoided. ICP could also be monitored by external ventricular drain (EVD), ABP was monitored in the radial or femoral artery (Baxter Healthcare, Deerfield, Illinois) with a zero calibration at the level of the right atrium (2002–2015) and at the foramen of Monroe (2015–2022). Physiological data were streamed in real time from the monitors into the ICM + software running on a laptop at the bedside (ICM + software, Cambridge Enterprises, University of Cambridge, UK) [https://icmplus.neurosurg.cam.ac.uk], which provided data integration and storage at sampling rate from 50 to 250 Hz, and de-identification for the Brain Physics Lab research database.

Data processing

ICM+ software was used for data pre-processing prior to storage into the database according to local protocols, briefly outlined here. The data were first curated manually for large artefacts. Further, automated artefact mark-up was applied. PbtO2 values below 5 mmHg or above 100 mmHg were rejected. ABP artefacts were removed automatically in case of absence of detected pulse, or when diastolic values fell to below a threshold of 15 mmHg. Automatic ICP artefacts removal used low amplitude values (< 0.04 mmHg) to reject artefactual periods, which corresponded to either noise or short recording periods during which EVD was kept open (for those recordings where ICP was measured via EVD). 95% Spectral edge frequency above 10 Hz was used to reject periods of non-physiological measurements of ICP. All recorded signals were subsequently down-sampled to 0.1 Hz by coarse graining using 10 s, non-overlapping averages.

The 10-s physiological data were accessed from the database and were further manually scrutinized and cleaned of large artefacts for the scope of this study. PRx was calculated as the moving Pearson correlation coefficient of 30 consecutive 10-s average values of ABP and ICP and updated every minute [11, 37]. Minute-by-minute values of CPPopt were calculated as the CPP with the concurrently lowest PRx, using the multi-window weighted algorithm based on a data buffer of 2 to 8 h [38]. ∆CPPopt was defined as the difference between actual CPP and CPPopt.

Measurements

The physiological variables were analysed over the entire NCC monitoring period. The percentage of good monitoring time (GMT, i.e. the remaining coverage of data after cleaning as described above) with pbtO2 < 20 mmHg, ICP > 20 and 22 mmHg, PRx > 0.00 and 0.30, CPP < 60 mmHg, and ∆CPPopt < -5 and − 10 mmHg, were analysed. In addition, the combination of pbtO2 < 20 mmHg together with ICP > 20/22 mmHg, PRx > 0.00/0.30, CPP < 60 mmHg, or ∆CPPopt < − 5/− 10 mmHg, respectively, of the GMT were calculated. The pbtO2-threshold at 20 mmHg was chosen in accordance with the current randomized controlled trials [23]. The thresholds of ICP at 20 and 22 mmHg and CPP at 60 mmHg were chosen in accordance with our management protocol and international guidelines [39]. This PRx-threshold at 0.00 indicates the transition from negative to positive ABP/ICP correlation, while a threshold close to 0.30 has previously been strongly associated with unfavourable outcome [40] and been suggested as the approximate threshold for identifying limits of autoregulation [41]. ∆CPPopt < − 5 mmHg was previously suggested as the autoregulatory threshold for hypoperfusion [12], while ∆CPPopt < − 10 mmHg was considered a severe insult.

The data were summarized both as minute-by-minute and hourly values for the statistical analyses. The hourly values were also sub-classified into sequenced blocks for every coherent period of pbtO2 monitoring without a data gap. For the hourly data, separate variables of negative/positive ∆CPPopt below/above 0 were calculated. Every minute-value of ∆CPPopt > 0 was counted as 0 and every ∆CPPopt < 0 was counted as a positive mmHg (e.g. CPP at 75 mmHg and CPPopt at 80 mmHg yielded a positive value at 5 mmHg) when hourly values of negative ∆CPPopt were calculated. Similarly, every minute-value of ∆CPPopt < 0 was counted as 0 and every ∆CPPopt > 0 was counted as a positive mmHg when hourly values of positive ∆CPPopt was calculated. This was done to reduce the risk that ∆CPPopt was averaged to 0 in patients with high blood pressure variability with both hypo- and hyperperfusion [42].

Statistical analysis

The statistical analyses were conducted in RStudio software (version 2022.12.0) [43]. Descriptive data of demographics, treatment variables, clinical outcome, and physiological variables were described as number (proportion) and median (interquartile range (IQR)). The association between pbtO2 in relation to ICP, PRx, CPP, and ∆CPPopt, respectively, was analysed with generalized additive models (GAMs) with cubic spline and according to the restricted maximum likelihood, based on minute-by-minute data. This approach allowed us to explore large amounts of data points and to capture the shape of the relationship between two variables without choosing a particular parametric form a priori. This means that we could create a regression line that allowed to reveal nonlinear associations between the variables. Sub-analyses of these GAMs for ICP, CPP, and ∆CPPopt in relation to pbtO2 were conducted based on filtered data when autoregulation was intact or impaired autoregulation (PRx below or above 0.30). Similar GAMs were computed for the subgroup of patients who were not treated with decompressive craniectomy.

Linear mixed effect models (R package lme4) based on hourly monitoring data with pbtO2 as the dependent variable and the global physiology-related variables as the independent variables were also done. This approach allowed us to adjust for potential patient-specific and temporal effects. Two separate models were used. One was based on absolute CPP-values and included ICP, PRx (above/below 0.30), CPP, the interaction between ICP and CPP with PRx, and time from start of monitoring as fixed effects and patients, time from monitoring, and the specific monitoring sequence as random effects. Stepwise backward model reduction was done to exclude variables that were not significant (p > 0.05). A similar model was done with negative/positive ∆CPPopt instead of CPP as an independent variable. Model assumptions were evaluated and data transformation was applied as appropriate to meet the linearity assumption. Conditional and marginal R squared (R package performance v 0.1.0.0) were assessed in accordance with Nakagawa et al. [44]. A p-value below 0.05 was considered statistically significant.

Results

Demography, clinical variables, treatments, and outcome

In this study with 425 TBI patients (Table 1), median age was 35 (25–55) years and the male/female ratio was 260/65 (80/20%). At presentation, GCS was in median 7 (IQR 4–10) and pupillary reactivity was preserved bilaterally in 150 (84%), unilaterally unreactive in 20 (11%), and bilaterally unreactive in 8 (4%) cases. Seventy-three (25%) patients were treated with decompressive craniectomy. At 6 months, median GOS was 4 (IQR 2–4), 124/115 (52/48%) recovered favourably/unfavourably, and 58/181(24/76%) had deceased/survived. Clinical and outcome data were unavailable in some cases, as outlined in the table legend.

Table 1 Demographics, admission variables, treatments, and clinical outcome

Cerebral physiological variables and secondary insults during neurocritical care

The median values of the cerebral physiological variables during NCC are described in Table 2. ICP insults above 20 mmHg occurred during 5 (IQR 2–20) % of the GMT, PRx-insults above 0.30 occurred during 23 (IQR 15–35) % of the GMT, CPP-insults below 60 mmHg occurred during 3 (IRQ 1–7) % of GMT, ∆CPPopt below − 5 mmHg during 28 (IQR 20–36) % of GMT, and pbtO2 < 20 mmHg during 17 (IQR 2–49) % of GMT. PbtO2 below 20 mmHg in combination with either ICP above 20/22 mmHg, PRx above 0.30, CPP below 60 mmHg, or ∆CPPopt below − 5/− 10 mmHg occurred in less than 5% of GMT. The number of days with available data of ICP and pbtO2 was in median 6 (IQR 3–10) and 4 (IQR 2–5), respectively.

Table 2 Physiological variables and burden of secondary insults during neurocritical care

PbtO2 in relation to global cerebral physiology-related variables

In GAM analyses, pbtO2 remained within 20 to 30 mmHg over the range of ICP between 0 to 50 mmHg and PRx between − 1 and + 1 (Fig. 1). PbtO2 plateaued at 25 mmHg for CPP above 60–70 mmHg, gradually decreased for lower CPP-values, and even went below 20 mmHg when CPP was below 30 mmHg. Similarly, pbtO2 plateaued at 25 mmHg around ∆CPPopt of − 10 mmHg and higher, but went below 20 mmHg for ∆CPPopt below − 30 mmHg. Furthermore, GAMs (Fig. 2) with regression lines separated depending on if they were based on minute-values when pressure autoregulation was intact or impaired (PRx below or above 0.30) were conducted. As illustrated, pbtO2 was generally minimally lower over the ICP-range when pressure autoregulation was impaired. In addition, pbtO2 was slightly lower for CPP below 60–70 mmHg when pressure autoregulation was impaired, but minimally higher for higher CPP-values. Similarly, pbtO2 was slightly lower for negative ∆CPPopt when pressure autoregulation was impaired, but minimally higher for positive ∆CPPopt. Similar GAMs were done for patients who had not undergone decompressive craniectomy (Additional files: 1 and 2), which showed slight differences in the regression lines at the extreme values of the global cerebral physiological variables as compared to the entire cohort.

Fig. 1
figure 1

PbtO2 in relation to cerebral physiological variables—a GAM analysis. In these GAM analyses, pbtO2 remained within 20–30 mmHg over the range of ICP between 0 to 50 mmHg and of PRx between − 1 and + 1. PbtO2 plateaued at 25 mmHg for CPP above 60–70 mmHg, gradually decreased for lower CPP-values, and even went below 20 mmHg when CPP was below 30 mmHg. Similarly, pbtO2 plateaued around 25 mmHg for ∆CPPopt of − 10 mmHg and higher, but went below 20 mmHg for ∆CPPopt below − 30 mmHg. CPP cerebral perfusion pressure, GAM generalized additive model, ICP intracranial pressure, PbtO2 partial brain tissue oxygenation, PRx pressure reactivity index

Fig. 2
figure 2

PbtO2 in relation to cerebral physiological variables depending on the concurrent pressure or oxygen reactivity status—a GAM analysis. Impaired/intact reactivity was defined as PRx above/below 0.30. As illustrated, pbtO2 was generally minimally lower over the ICP-range when pressure autoregulation was impaired. In addition, pbtO2 was slightly lower for CPP below 60–70 mmHg when pressure autoregulation was impaired, but minimally higher for higher CPP-values. Similarly, pbtO2 was slightly lower for negative ∆CPPopt when pressure autoregulation was impaired, but minimally higher for positive ∆CPPopt. CPP cerebral perfusion pressure. GAM generalized additive model, ICP intracranial pressure, PbtO2 partial brain tissue oxygenation, PRx pressure reactivity index

In a linear mixed effect model based on absolute CPP (Table 3), higher ICP, intact pressure autoregulation, and higher CPP were associated with higher pbtO2. The association between CPP and pbtO2 was attenuated if pressure autoregulation was intact. In a similar linear mixed model where ∆CPPopt had replaced absolute CPP, higher ICP, impaired autoregulation, and absence of negative ∆CPPopt were associated with higher pbtO2. The association between negative ∆CPPopt and pbtO2 was attenuated if pressure autoregulation was intact. Conditional squared and marginal R squared suggested that only 3.5% (CPP model) and 1.5% (CPPopt model) of the variance was explained by the fixed effects, while majority of the variance was explained by the random effects.

Table 3 Global physiology-related variables in relation to pbtO2—linear mixed effect model analyses

Discussion

In this observational study including 425 TBI patients, the main findings were that low pbtO2 was fairly common and these events often occurred in the absence of disturbances in ICP, PRx, CPP, and ∆CPPopt. Although these global cerebral physiological variables were indeed associated with pbtO2 in linear mixed effect models, even large changes in these variables only had a modest effect on pbtO2. Otherwise, ICP and CPP were typically controlled and such insults were rare during NCC. This could explain their limited association with low pbtO2, whereas the relative contribution of non-perfusion-related pathophysiological mechanisms such as hypoxemia, capillary dysfunction, and oxygen diffusion limitations might have had a greater impact on pbtO2. The weak associations between the global cerebral physiological variables and pbtO2 implies that other parameters may be more crucial to affect pbtO2. It also indicates that pbtO2 may not be a suitable outcome measure to determine whether global cerebral blood flow optimization such as CPPopt therapy is successful.

First, we found that pbtO2-insults below 20 mmHg were relatively common and occurred, in median, during 17% of the GMT. Interestingly, ICP-, PRx-, CPP-, and ∆CPPopt-insults were relatively infrequent during these periods of low pbtO2, indicating that the latter was often an independent pathophysiological event. This observation corroborates findings in previous smaller studies [27, 31].

Second, when pbtO2 was explored in relation to ICP, PRx, CPP, and ∆CPPopt, we found variability in pbtO2 to be weakly associated with these variables in the GAMs and linear mixed effect models. However, pbtO2 essentially remained stable within large ranges of these variables. A larger pbtO2 deterioration with at least 5 mmHg to values below 20 mmHg was chiefly seen at extremely low values of CPP and ∆CPPopt, i.e. when the entire brain was likely to be affected by ischaemic hypoxia. The GAMs showed that pbtO2 increased modestly between ICP 5–10 mmHg and thereafter slightly declined, whereas higher ICP was associated with small increases in pbtO2 in the linear mixed models. This discrepancy could be explained by the limitations of the linear assumptions in the linear mixed model. However, the main message from both the GAM and linear mixed models is still that great changes in the global cerebral physiological variables only led to small changes in pbtO2.

Third, PRx influenced the associations of particularly CPP/∆CPPopt and pbtO2, but, again, only with small magnitudes. Particularly, impaired pressure reactivity increased the susceptibility for ischaemic hypoxia when CPP went below 60–70 mmHg. This was expected, since impaired pressure reactivity at the lower limit of autoregulation implies a reduced capacity to maintain CBF and brain tissue oxygenation. Although CBF was expected to remain stable even for very low CPP when pressure autoregulation was intact thanks to compensatory vasodilation, we did see slight deteriorations in pbtO2 for very low CPP even when PRx was low. This challenges the traditional view of pressure autoregulation to some extent, but is consistent with a recent experimental pig study, which demonstrated that CBF starts to deteriorate slightly before the entire vasodilatory reserve/pressure autoregulation is exhausted [45]. Thus, cerebral ischaemia and hypoxia could still occur for low CPP even when PRx is relatively low. For higher CPP-values, pbtO2 remained relatively constant with a tendency towards slightly lower values as CPP reached above 100 mmHg. This was in contrast to a smaller, exploratory TBI study showing that pbtO2 continues to increase when CPP reaches very high levels, consistent with the idea that higher CPP yields higher CBF, oxygen delivery, and ultimately pbtO2 [19]. Although this makes sense, there are several general physiological as well as TBI pathophysiological mechanisms that may alter the association between high CPP and pbtO2. Particularly, even when CBF starts to increase after CPP has exceeded the upper breakpoint of autoregulation, pbtO2 will only increase to some extent until it is saturated as the vascular and tissue oxygen gradient gradually decreases [8, 46]. In addition, TBI typically leads to capillary dysfunction due to astrocyte swelling and microvascular thrombosis, which increases capillary transit time heterogeneity and to various degrees impedes focal oxygen delivery and extraction [8, 46]. The extent of capillary flow disturbances modulates the effect of CPP and CBF on pbtO2 and higher focal CBF may even decrease oxygen delivery when the capillaries are severely injured [8, 46]. Furthermore, high CPP may increase brain oedema, which could increase oxygen diffusion limitations and thereby decrease pbtO2 [30, 47]. Thus, many factors may contribute to the relation between CPP/CBF in the higher range and pbtO2 [48]. Lastly, for the autoregulatory perfusion variable ∆CPPopt, pbtO2 deteriorated when ∆CPPopt went to very negative values below − 20 to − 30 mmHg, but pbtO2 was otherwise relatively stable at 25 mmHg for ∆CPPopt above such low values. PbtO2 was slightly lower for any given negative ∆CPPopt when pressure autoregulation was impaired (high PRx). Again, this supports the experimental findings that ischaemia may begin while the cerebral vessels are to some extent still reactive [45]. This also highlights the importance of the CPPopt curve shape and the role of absolute PRx-values in CPPopt therapy [21].

Altogether, pbtO2 was primarily sensitive to global cerebral physiological variables when the latter were severely deranged, i.e. when the entire brain was affected by severe ischaemic hypoxia, but otherwise to a very limited extent. This was expected for several reasons. Within individual TBI patients, there is a great spatial CBF heterogeneity as ischaemia and hyperaemia can co-exist in different brain regions at the same time [48]. In addition, brain tissue hypoxia is also usually spatially distributed in the brain for individual TBI patients, but only overlaps with ischaemic areas to some extent [33]. In addition, pbtO2 is a sum measure of focal CBF, arterial oxygen content, oxygen diffusion, energy requirement (seizures, sedation), and mitochondrial function (capacity for oxygen utilization), so global cerebral physiological variables may only explain variations in pbtO2 to some degree [5, 33]. Since ICP, PRx, CPP, and ∆CPPopt are all global variables, whereas pbtO2 is focally limited to a small brain area it would be expected to mainly correlate with the global variables when the entire brain is affected.

Methodological considerations

The main strength of this manuscript was the large cohort size (n = 425) of TBI patients with high-frequency data of ICP and pbtO2. We also applied advanced statistical approaches to explore potential nonlinear associations (GAM analyses) as well as time- and patient-related factors (linear mixed effect models) in the physiological analyses of global cerebral physiological variables in relation to pbtO2.

There were also some limitations. First, demographic, clinical, imaging, and outcome data could not be retrieved in some cases. Particularly, this inhibited us from proceeding with classifications based on pbtO2 probe location (peri-lesional and normal-appearing brain). The proximity for the pbtO2 probe to any focal lesion might have affected the association between cerebral physiology and pbtO2 [26, 49]. However, in the clinical management, placement of the pbtO2 probes close to lesions was avoided. We also addressed the role of the probe location in the linear mixed effect models by taking into account patients (and inherently their probe location) as a random factor. Still, this could not be done in the GAMs and more detailed data of the probe locations would have made our analyses more granular and clearer. Third, the reliability of PRx and thus CPPopt post-craniectomy has been questioned. Preliminary studies indicate that the reliability of these variables may be preserved after craniectomy [50] and we therefore decided to keep these patients in the main analyses, but also explored excluding them in the GAMs. The reliability of PRx and CPPopt has also been questioned for EVD based ICP measurements, particularly if the protocol management requires regular opening and closing of the EVD. Since we could not clearly identify all the patients that had EVD, we attempted to address this issue with careful signal processing. Fourth, this was a single-centre study with a selection of those patients who received both ICP- and pbtO2 monitoring. This limits the external validity of our findings. Fifth, in addition to perfusion-related variables and probe location, many other variables that were not investigated here might have influenced pbtO2, such as arterial oxygen content (haemoglobin and pO2) and cerebral energy metabolism (temperature, seizures). All of these were also to various degrees influenced by a complex NCC management including e.g. red blood cell transfusions, ventilatory settings, fever management, and sedation. The relative importance of these variables on pbtO2 should be further explored in future studies. In addition, the patients were treated and optimized at an NCC, particularly according to an ICP-/CPP-oriented protocol. It is possible that the association between the global cerebral physiological variables and pbtO2 would have been different if the patients had been treated according to another regime or if the variables had been less strictly optimized. Most likely, the attentive NCC management attenuated the association between the global cerebral physiological variables and pbtO2 in our analyses (GAMs and linear mixed effect models).

Conclusions

PbtO2 below 20 mmHg was relatively common after moderate to severe TBI. This typically occurred despite adequate ICP, PRx, CPP, and ∆CPPopt. Although these global cerebral physiological variables were associated with pbtO2, this only explained a small part of the variation in brain tissue oxygenation. This could be viewed in light of previous studies showing great spatial CBF heterogeneity between brain regions within patients and limited overlap between focal ischaemia and hypoxia. This is of great importance since ICP, PRx, CPP, and ∆CPPopt are global cerebral physiological variables, whereas pbtO2 is measured focally. Considering the weak associations between perfusion-related variables and pbtO2, our findings suggest that pbtO2 may overall be a poor indicator of global CBF homeostasis, except for extremely low CPP/negative ∆CPPopt when the entire brain is affected. Consequently, pbtO2 might not be a suitable outcome measure to determine whether global CBF optimization such as CPPopt-guided therapy is successful, although it may convey other pathophysiological information such as focal disturbances in capillary blood flow and oxygen diffusion limitations. To understand the potential clinical value of pbtO2 better, the prognostic role of exhibiting pbtO2 below certain critical thresholds was investigated in the part II study.

Availability of data and materials

Data are available upon reasonable request.

Abbreviations

ABP:

Arterial blood pressure

AIC:

Akaike information criterion

CBF:

Cerebral blood flow

CPP:

Cerebral perfusion pressure

CPPopt:

Optimal CPP

GAM:

Generalized additive model

GMT:

Good monitoring time

GOS:

Glasgow outcome scale

ICP:

Intracranial pressure

IQR:

Interquartile range

NCC:

Neurocritical care

PbtO2 :

Partial brain tissue oxygenation

PRx:

Pressure reactivity index

SE:

Standard error

TBI:

Traumatic brain injury

References

  1. Svedung Wettervik TM, Lewén A, Enblad P. Fine tuning of traumatic brain injury management in neurointensive care-indicative observations and future perspectives. Front Neurol. 2021;12:638132.

    PubMed  PubMed Central  Google Scholar 

  2. Donnelly J, Czosnyka M, Adams H, Cardim D, Kolias AG, Zeiler FA, Lavinio A, Aries M, Robba C, Smielewski P, et al. Twenty-five years of intracranial pressure monitoring after severe traumatic brain injury: a retrospective, single-center analysis. Neurosurgery. 2019;85(1):E75-e82.

    PubMed  Google Scholar 

  3. Elf K, Nilsson P, Enblad P. Outcome after traumatic brain injury improved by an organized secondary insult program and standardized neurointensive care. Crit Care Med. 2002;30(9):2129–34.

    PubMed  Google Scholar 

  4. Hawryluk GWJ, Aguilera S, Buki A, Bulger E, Citerio G, Cooper DJ, Arrastia RD, Diringer M, Figaji A, Gao G, et al. A management algorithm for patients with intracranial pressure monitoring: the seattle international severe traumatic brain injury consensus conference (SIBICC). Intensive Care Med. 2019;45(12):1783–94.

    PubMed  PubMed Central  Google Scholar 

  5. Lazaridis C. Cerebral oxidative metabolism failure in traumatic brain injury: “Brain shock.” J Crit Care. 2017;37:230–3.

    CAS  PubMed  Google Scholar 

  6. Menon DK, Coles JP, Gupta AK, Fryer TD, Smielewski P, Chatfield DA, Aigbirhio F, Skepper JN, Minhas PS, Hutchinson PJ. Diffusion limited oxygen delivery following head injury. Crit Care Med. 2004;32(6):1384–90.

    PubMed  Google Scholar 

  7. Rosenthal G, Hemphill JC 3rd, Sorani M, Martin C, Morabito D, Obrist WD, Manley GT. Brain tissue oxygen tension is more indicative of oxygen diffusion than oxygen delivery and metabolism in patients with traumatic brain injury. Crit Care Med. 2008;36(6):1917–24.

    CAS  PubMed  Google Scholar 

  8. Østergaard L, Engedal TS, Aamand R, Mikkelsen R, Iversen NK, Anzabi M, Næss-Schmidt ET, Drasbek KR, Bay V, Blicher JU, et al. Capillary transit time heterogeneity and flow-metabolism coupling after traumatic brain injury. J Cereb Blood Flow Metab: Off J Int Soc Cereb Blood Flow Metab. 2014;34(10):1585–98.

    Google Scholar 

  9. Guilfoyle MR, Helmy A, Donnelly J, Stovell MG, Timofeev I, Pickard JD, Czosnyka M, Smielewski P, Menon DK, Carpenter KLH, et al. Characterising the dynamics of cerebral metabolic dysfunction following traumatic brain injury: a microdialysis study in 619 patients. PLoS ONE. 2021;16(12):e0260291.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Khellaf A, Khan DZ, Helmy A. Recent advances in traumatic brain injury. J Neurol. 2019;266(11):2878–89.

    PubMed  PubMed Central  Google Scholar 

  11. Czosnyka M, Smielewski P, Kirkpatrick P, Laing RJ, Menon D, Pickard JD. Continuous assessment of the cerebral vasomotor reactivity in head injury. Neurosurgery. 1997;41(1):11–9.

    CAS  PubMed  Google Scholar 

  12. Aries MJ, Czosnyka M, Budohoski KP, Steiner LA, Lavinio A, Kolias AG, Hutchinson PJ, Brady KM, Menon DK, Pickard JD, et al. Continuous determination of optimal cerebral perfusion pressure in traumatic brain injury. Crit Care Med. 2012;40(8):2456–63.

    PubMed  Google Scholar 

  13. Steiner LA, Czosnyka M, Piechnik SK, Smielewski P, Chatfield D, Menon DK, Pickard JD. Continuous monitoring of cerebrovascular pressure reactivity allows determination of optimal cerebral perfusion pressure in patients with traumatic brain injury. Crit Care Med. 2002;30(4):733–8.

    PubMed  Google Scholar 

  14. Svedung Wettervik TM, Howells T, Enblad P, Lewén A. Temporal neurophysiological dynamics in traumatic brain injury: role of pressure reactivity and optimal cerebral perfusion pressure for predicting outcome. J Neurotrauma. 2019;36(11):1818–27.

    PubMed  Google Scholar 

  15. Tas J, Beqiri E, van Kaam RC, Czosnyka M, Donnelly J, Haeren RH, van der Horst ICC, Hutchinson PJ, van Kuijk SMJ, Liberti AL, et al. Targeting autoregulation-guided cerebral perfusion pressure after traumatic brain injury (COGiTATE): a feasibility randomized controlled clinical trial. J Neurotrauma. 2021;38(20):2790–800.

    PubMed  Google Scholar 

  16. Zeiler FA, Aries M, Czosnyka M, Smielewski P. Cerebral autoregulation monitoring in traumatic brain injury: an overview of recent advances in personalized medicine. J Neurotrauma. 2022;39(21–22):1477–94.

    PubMed  Google Scholar 

  17. Wettervik TS, Fahlström M, Enblad P, Lewén A. Cerebral pressure autoregulation in brain injury and disorders: a review on monitoring, management, and future directions. World Neurosurg. 2021;158:118–31.

    Google Scholar 

  18. Jaeger M, Dengl M, Meixensberger J, Schuhmann MU. Effects of cerebrovascular pressure reactivity-guided optimization of cerebral perfusion pressure on brain tissue oxygenation after traumatic brain injury. Crit Care Med. 2010;38(5):1343–7.

    PubMed  Google Scholar 

  19. Gomez A, Sekhon M, Griesdale D, Froese L, Yang E, Thelin EP, Raj R, Aries M, Gallagher C, Bernard F, et al. Cerebrovascular pressure reactivity and brain tissue oxygen monitoring provide complementary information regarding the lower and upper limits of cerebral blood flow control in traumatic brain injury: a CAnadian High Resolution-TBI (CAHR-TBI) cohort study. Intensive Care Med Exp. 2022;10(1):54.

    PubMed  PubMed Central  Google Scholar 

  20. Wettervik TS, Howells T, Hillered L, Rostami E, Lewén A, Enblad P. Autoregulatory or fixed cerebral perfusion pressure targets in traumatic brain injury: determining which is better in an energy metabolic perspective. J Neurotrauma. 2021;38(14):1969–78.

    Google Scholar 

  21. Svedung Wettervik T, Hånell A, Howells T, Lewén A, Enblad P. Autoregulatory management in traumatic brain injury: the role of absolute PRx-values and CPPopt curve shape. J Neurotrauma. 2023. https://doi.org/10.1089/neu.2023.0017.

    Article  PubMed  Google Scholar 

  22. Wettervik TS, Hånell A, Howells T, Engström ER, Lewén A, Enblad P. Autoregulatory cerebral perfusion pressure insults in traumatic brain injury and aneurysmal subarachnoid hemorrhage: the role of insult intensity and duration on clinical outcome. J Neurosurg Anesthesiol. 2023. https://doi.org/10.1097/ana.0000000000000922.

    Article  Google Scholar 

  23. Leach MR, Shutter LA. How much oxygen for the injured brain: can invasive parenchymal catheters help? Curr Opin Crit Care. 2021;27(2):95–102.

    PubMed  PubMed Central  Google Scholar 

  24. Oddo M, Levine JM, Mackenzie L, Frangos S, Feihl F, Kasner SE, Katsnelson M, Pukenas B, Macmurtrie E, Maloney-Wilensky E, et al. Brain hypoxia is associated with short-term outcome after severe traumatic brain injury independently of intracranial hypertension and low cerebral perfusion pressure. Neurosurgery. 2011;69(5):1037–45 (discussion 1045).

    PubMed  Google Scholar 

  25. van den Brink WA, van Santbrink H, Steyerberg EW, Avezaat CJ, Suazo JA, Hogesteeger C, Jansen WJ, Kloos LM, Vermeulen J, Maas AI. Brain oxygen tension in severe head injury. Neurosurgery. 2000;46(4):868–76 (discussion 876-868).

    PubMed  Google Scholar 

  26. Ponce LL, Pillai S, Cruz J, Li X, Julia H, Gopinath S, Robertson CS. Position of probe determines prognostic information of brain tissue PO2 in severe traumatic brain injury. Neurosurgery. 2012;70(6):1492–502 (discussion 1502-1493).

    PubMed  Google Scholar 

  27. Chang JJ, Youn TS, Benson D, Mattick H, Andrade N, Harper CR, Moore CB, Madden CJ, Diaz-Arrastia RR. Physiologic and functional outcome correlates of brain tissue hypoxia in traumatic brain injury. Crit Care Med. 2009;37(1):283–90.

    CAS  PubMed  Google Scholar 

  28. Eriksson EA, Barletta JF, Figueroa BE, Bonnell BW, Sloffer CA, Vanderkolk WE, McAllen KJ, Ott M. The first 72 h of brain tissue oxygenation predicts patient survival with traumatic brain injury. J Trauma Acute Care Surg. 2012;72(5):1345–9.

    PubMed  Google Scholar 

  29. Okonkwo DO, Shutter LA, Moore C, Temkin NR, Puccio AM, Madden CJ, Andaluz N, Chesnut RM, Bullock MR, Grant GA, et al. Brain oxygen optimization in severe traumatic brain injury phase-II: a phase II randomized trial. Crit Care Med. 2017;45(11):1907–14.

    PubMed  PubMed Central  Google Scholar 

  30. Kohler K, Nallapareddy S, Ercole A. In silico model of critical cerebral oxygenation after traumatic brain injury: implications for rescuing hypoxic tissue. J Neurotrauma. 2019;36(13):2109–16.

    PubMed  Google Scholar 

  31. Zeiler FA, Beqiri E, Cabeleira M, Hutchinson PJ, Stocchetti N, Menon DK, Czosnyka M, Smielewski P, Ercole A. Brain tissue oxygen and cerebrovascular reactivity in traumatic brain injury: a collaborative European neurotrauma effectiveness research in traumatic brain injury exploratory analysis of insult burden. J Neurotrauma. 2020;37(17):1854–63.

    PubMed  PubMed Central  Google Scholar 

  32. Svedung Wettervik T, Engquist H, Hånell A, Howells T, Rostami E, Ronne-Engström E, Lewén A, Enblad P. Cerebral blood flow and oxygen delivery in aneurysmal subarachnoid hemorrhage: relation to neurointensive care targets. Neurocrit Care. 2022;37(1):281–92.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Veenith TV, Carter EL, Geeraerts T, Grossac J, Newcombe VF, Outtrim J, Gee GS, Lupson V, Smith R, Aigbirhio FI. Pathophysiologic mechanisms of cerebral ischemia and diffusion hypoxia in traumatic brain injury. JAMA Neurol. 2016;73(5):542–50.

    PubMed  Google Scholar 

  34. Menon DK, Ercole A. Critical care management of traumatic brain injury. Handb Clin Neurol. 2017;140:239–74.

    CAS  PubMed  Google Scholar 

  35. Teasdale GM, Pettigrew LE, Wilson JT, Murray G, Jennett B. Analyzing outcome of treatment of severe head injury: a review and update on advancing the use of the Glasgow outcome scale. J Neurotrauma. 1998;15(8):587–97.

    CAS  PubMed  Google Scholar 

  36. Wilson JL, Pettigrew LE, Teasdale GM. Structured interviews for the Glasgow outcome scale and the extended Glasgow outcome scale: guidelines for their use. J Neurotrauma. 1998;15(8):573–85.

    CAS  PubMed  Google Scholar 

  37. Zeiler FA, Donnelly J, Calviello L, Smielewski P, Menon DK, Czosnyka M. Pressure Autoregulation measurement techniques in adult traumatic brain injury, part II: a scoping review of continuous methods. J Neurotrauma. 2017;34(23):3224–37.

    PubMed  Google Scholar 

  38. Beqiri E, Ercole A, Aries MJH, Placek MM, Tas J, Czosnyka M, Stocchetti N, Smielewski P. Towards autoregulation-oriented management after traumatic brain injury: increasing the reliability and stability of the CPPopt algorithm. J Clin Monitor Comput. 2023. https://doi.org/10.1007/s10877-023-01009-1.

    Article  Google Scholar 

  39. Carney N, Totten AM, O’Reilly C, Ullman JS, Hawryluk GW, Bell MJ, Bratton SL, Chesnut R, Harris OA, Kissoon N, et al. Guidelines for the management of severe traumatic brain injury, fourth edition. Neurosurgery. 2017;80(1):6–15.

    PubMed  Google Scholar 

  40. Zeiler FA, Ercole A, Cabeleira M, Zoerle T, Stocchetti N, Menon DK, Smielewski P, Czosnyka M. Univariate comparison of performance of different cerebrovascular reactivity indices for outcome association in adult TBI: a CENTER-TBI study. Acta Neurochir. 2019;161(6):1217–27.

    PubMed  Google Scholar 

  41. Donnelly J, Czosnyka M, Adams H, Robba C, Steiner LA, Cardim D, Cabella B, Liu X, Ercole A, Hutchinson PJ. Individualizing thresholds of cerebral perfusion pressure using estimated limits of autoregulation. Crit Care Med. 2017;45(9):1464–71.

    PubMed  PubMed Central  Google Scholar 

  42. Svedung Wettervik T, Howells T, Lewén A, Enblad P. Blood pressure variability and optimal cerebral perfusion pressure-new therapeutic targets in traumatic brain injury. Neurosurgery. 2020;86(3):E300-e309.

    PubMed  Google Scholar 

  43. R: A language and environment for statistical computing

  44. Nakagawa S, Johnson PCD, Schielzeth H. The coefficient of determination R(2) and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J R Soc Interface. 2017;14(134):20170213.

    PubMed  PubMed Central  Google Scholar 

  45. Klein SP, De Sloovere V, Meyfroidt G, Depreitere B. Differential hemodynamic response of pial arterioles contributes to a quadriphasic cerebral autoregulation physiology. J Am Heart Assoc. 2022;11(1):e022943.

    PubMed  Google Scholar 

  46. Østergaard L. Blood flow, capillary transit times, and tissue oxygenation: the centennial of capillary recruitment. J Appl Physiol (Bethesda, Md: 1985). 2020;129(6):1413–21.

    Google Scholar 

  47. Grande PO. Critical evaluation of the lund concept for treatment of severe traumatic head injury, 25 years after its introduction. Front Neurol. 2017;8:315.

    PubMed  PubMed Central  Google Scholar 

  48. Launey Y, Fryer TD, Hong YT, Steiner LA, Nortje J, Veenith TV, Hutchinson PJ, Ercole A, Gupta AK, Aigbirhio FI, et al. Spatial and temporal pattern of ischemia and abnormal vascular function following traumatic brain injury. JAMA Neurol. 2019;77(3):339–49.

    PubMed Central  Google Scholar 

  49. Longhi L, Pagan F, Valeriani V, Magnoni S, Zanier ER, Conte V, Branca V, Stocchetti N. Monitoring brain tissue oxygen tension in brain-injured patients reveals hypoxic episodes in normal-appearing and in peri-focal tissue. Intensive Care Med. 2007;33(12):2136–42.

    PubMed  Google Scholar 

  50. Zeiler FA, Aries M, Cabeleira M, van Essen TA, Stocchetti N, Menon DK, Timofeev I, Czosnyka M, Smielewski P, Hutchinson P, et al. Statistical cerebrovascular reactivity signal properties after secondary decompressive craniectomy in traumatic brain injury: a CENTER-TBI pilot analysis. J Neurotrauma. 2020;37(11):1306–14.

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We express our gratitude to the personnel at the NCC, Addenbrooke’s Hospital (Cambridge, UK), for meticulous patient care. We also thank dr Anders Hånell for providing insightful thoughts on the data analysis.

Funding

Open access funding provided by Uppsala University. Erta Beqiri is supported by the Medical Research Council (Grant No.: MR N013433-1) and by the Gates Cambridge Scholarship. Stefan Yu Bögli is supported by the Swiss National Science Foundation (Grant No.: 210839).

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TSW, EB, and PS contributed to conceptualization, methodology, formal analysis, and writing—original draft preparation. PH and PS contributed to resources. TSW and EB contriubted to data curation. All authors contributed to writing—review and editing.

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Correspondence to Teodor Svedung Wettervik.

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The Brain Physics Lab research database was approved by the Research Ethics Committee (REC 23/YH/0085). Informed consent was waived in this retrospective, observational study.

Competing interests

Peter Smielewski receives part of the licensing fees for ICM+ software, licensed by Cambridge Enterprise Ltd, University of Cambridge, Cambridge.

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Supplementary Information

Additional file 1.

PbtO2 in relation to cerebral physiological variables for patients who were not treated with decompressive craniectomy – a GAM analysis.

Additional file 2.

PbtO2 in relation to cerebral physiological variables depending on the concurrent pressure or oxygen reactivity status for patients who were not treated with decompressive craniectomy – a GAM analysis.

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Svedung Wettervik, T., Beqiri, E., Bögli, S.Y. et al. Brain tissue oxygen monitoring in traumatic brain injury: part I—To what extent does PbtO2 reflect global cerebral physiology?. Crit Care 27, 339 (2023). https://doi.org/10.1186/s13054-023-04627-y

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