Patient's Clinical Presentation and CPPopt Availability: Any Association?

BACKGROUND: The 'optimal' CPP (CPPopt) concept is based on the vascular pressure reactivity index (PRx). The feasibility and effectiveness of CPPopt guided therapy in severe traumatic brain injury (TBI) patients is currently being investigated prospectively in the COGiTATE trial. At the moment there is no clear evidence that certain admission and treatment characteristics are associated with CPPopt availability (yield). OBJECTIVE: To test the relation between patients' admission and treatment characteristics and the average CPPopt yield. METHODS: Retrospective analysis of 230 patients from the CENTER-TBI high-resolution database with intracranial pressure (ICP) measured using an intraparenchymal probe. CPPopt was calculated using the algorithm set for the COGiTATE study. CPPopt yield was defined as the percentage of CPP monitored time (%) when CPPopt is available. The variables in the statistical model included age, admission Glasgow Coma Scale (GCS), gender, pupil response, hypoxia and hypotension at the scene, Marshall computed tomography (CT) score, decompressive craniectomy, injury severity score score and 24-h therapeutic intensity level (TIL) score. RESULTS: The median CPPopt yield was 80.7% (interquartile range 70.9-87.4%). None of the selected variables showed a significant statistical correlation with the CPPopt yield. CONCLUSION: In this retrospective multicenter study, none of the selected admission and treatment variables were related to the CPPopt yield.

Visualisation of the 'Optimal Cerebral Perfusion' Landscape in Severe Traumatic Brain Injury Patients.

OBJECTIVE: An 'optimal' cerebral perfusion pressure (CPPopt) can be defined as the point on the CPP scale corresponding to the greatest autoregulatory capacity. This can be established by examining the pressure reactivity index PRx-CPP relationship, which is approximately U-shaped but suffers from noise and missing data. In this paper, we present a method for plotting the whole PRx-CPP relationship curve against time in the form of a colour-coded map depicting the 'landscape' of that relationship extending back for several hours and to display this robustly at the bedside.This is a short version of a full paper recently published in Critical Care Medicine (2016) containing some new insights and details of a novel bedside implementation based on a presentation during Intracranial Pressure 2016 Symposium in Boston. METHODS: Recordings from routine monitoring of traumatic brain injury patients were processed using ICM+. Time-averaged means for arterial blood pressure, intracranial pressure, cerebral perfusion pressure (CPP) and pressure reactivity index (PRx) were calculated and stored with time resolution of 1 min. ICM+ functions have been extended to include not just an algorithm of automatic calculation of CPPopt but also the 'CPPopt landscape' chart. RESULTS: Examining the 'CPPopt landscape' allows the clinician to differentiate periods where the autoregulatory range is narrow and needs to be targeted from periods when the patient is generally haemodynamically stable, allowing for more relaxed CPP management. This information would not have been conveyed using the original visualisation approaches. CONCLUSIONS: We describe here a natural extension to the concept of autoregulatory assessment, providing the retrospective 'landscape' of the PRx-CPP relationship extending over the past several hours. We have incorporated such visualisation techniques online in ICM+. The proposed visualisation may facilitate clinical evaluation and use of autoregulation-guided therapy.

Deriving the PRx and CPPopt from 0.2-Hz Data: Establishing Generalizability to Bedmaster Users.

OBJECTIVE: The objective was to explore the validity of industry-parameterized vital signs in the generation of pressure reactivity index (PRx) and optimal cerebral perfusion pressure (CPPopt) values. MATERIALS AND METHODS: Ten patients with intracranial pressure (ICP) monitors from 2008 to 2013 in a tertiary care hospital were included. Arterial blood pressure (ABP) and ICP were sampled at 240 Hz (of waveform data) and 0.2 Hz (of parameterized data produced by heuristic industry proprietary algorithms). 240-Hz ABP were filtered for pulse pressure and diastolic ABP within the limits of 20-150 mmHg. The PRx was calculated as Pearson's correlation coefficient using 10-s averages of ICP and ABP over a 5-min moving window with 80% overlap. For ease of comparison, we used the naming convention of BMx for PRx values derived from 0.2-Hz data. A 5-min median cerebral perfusion pressure (CPP) trend was calculated, PRx or BMx values divided and averaged into CPP bins spanning 5 mmHg. The minimum Y value (PRx or BMx) of the parabolic function fit to the resulting XY plot of 4 h of data was obtained, and updated every 1 min. Pearson's R correlations were calculated for each patient. Linear mixed-effects models were used with a random intercept to assess the overall correlation between the PRx (outcome) and the BMx (fixed effect) or the CPPopt-PRx (outcome) and the CPPopt-BMx (fixed effect). RESULTS: The overall correlation between the PRx and BMx was 0.78 based on the linear mixed effects models (p < 0.0001), and the overall correlation for the CPPopt-PRx and CPPopt-BMx based on the linear mixed effects models was 0.76 (p < 0.0001). One patient had low correlation of CPPopts derived from the PRx vs the BMx; this patient had the least number of hours of CPPopt data to compare. CONCLUSIONS: The BMx shows promise in CPPopt derivation against the validated PRx measure. If further developed, it could expand the capability of centers to derive CPPopt goals for use in clinical trials.

Occurrence of CPPopt Values in Uncorrelated ICP and ABP Time Series.

OBJECTIVES: Optimal cerebral perfusion pressure (CPPopt) is a concept that uses the pressure reactivity (PRx)-CPP relationship over a given period to find a value of CPP at which PRx shows best autoregulation. It has been proposed that this relationship be modelled by a U-shaped curve, where the minimum is interpreted as being the CPP value that corresponds to the strongest autoregulation. Owing to the nature of the calculation and the signals involved in it, the occurrence of CPPopt curves generated by non-physiological variations of intracranial pressure (ICP) and arterial blood pressure (ABP), termed here "false positives", is possible. Such random occurrences would artificially increase the yield of CPPopt values and decrease the reliability of the methodology.In this work, we studied the probability of the random occurrence of false-positives and we compared the effect of the parameters used for CPPopt calculation on this probability. MATERIALS AND METHODS: To simulate the occurrence of false-positives, uncorrelated ICP and ABP time series were generated by destroying the relationship between the waves in real recordings. The CPPopt algorithm was then applied to these new series and the number of false-positives was counted for different values of the algorithm's parameters. RESULTS: The percentage of CPPopt curves generated from uncorrelated data was demonstrated to be 11.5%. CONCLUSION: This value can be minimised by tuning some of the calculation parameters, such as increasing the calculation window and increasing the minimum PRx span accepted on the curve.

Intracranial pressure for clinicians: it is not just a number.

BACKGROUND: Invasive intracranial pressure (ICP) monitoring is a standard practice in severe brain injury cases, where it allows to derive cerebral perfusion pressure (CPP); ICP-tracing can also provide additional information about intracranial dynamics, forecast episodes of intracranial hypertension and set targets for a tailored therapy to prevent secondary brain injury. Nevertheless, controversies about the advantages of an ICP clinical management are still debated. FINDINGS: This article reviews recent research on ICP to improve the understanding of the topic and uncover the hidden information in this signal that may be useful in clinical practice. Parameters derived from time-domain as well as frequency domain analysis include compensatory reserve, autoregulation estimation, pulse waveform analysis, and behavior of ICP in time. The possibility to predict the outcome and apply a tailored therapy using a personalised perfusion pressure target is also described. CONCLUSIONS: ICP is a crucial signal to monitor in severely brain injured patients; a bedside computer can empower standard monitoring giving new metrics that may aid in clinical management, establish a personalized therapy, and help to predict the outcome. Continuous collaboration between engineers and clinicians and application of new technologies to healthcare, is vital to improve the accuracy of current metrics and progress towards better care with individualized dynamic targets.

Feasibility of individualised severe traumatic brain injury management using an automated assessment of optimal cerebral perfusion pressure: the COGiTATE phase II study protocol.

INTRODUCTION: Individualising therapy is an important challenge for intensive care of patients with severe traumatic brain injury (TBI). Targeting a cerebral perfusion pressure (CPP) tailored to optimise cerebrovascular autoregulation has been suggested as an attractive strategy on the basis of a large body of retrospective observational data. The objective of this study is to prospectively assess the feasibility and safety of such a strategy compared with fixed thresholds which is the current standard of care from international consensus guidelines. METHODS AND ANALYSIS: CPPOpt Guided Therapy: Assessment of Target Effectiveness (COGiTATE) is a prospective, multicentre, non-blinded randomised, controlled trial coordinated from Maastricht University Medical Center, Maastricht (The Netherlands). The other original participating centres are Cambridge University NHS Foundation Trust, Cambridge (UK), and University Hospitals Leuven, Leuven (Belgium). Adult severe TBI patients requiring intracranial pressure monitoring are randomised within the first 24 hours of admission in neurocritical care unit. For the control arm, the CPP target is the Brain Trauma Foundation guidelines target (60-70 mm Hg); for the intervention group an automated CPP target is provided as the CPP at which the patient's cerebrovascular reactivity is best preserved (CPPopt). For a maximum of 5 days, attending clinicians review the CPP target 4-hourly. The main hypothesis of COGiTATE are: (1) in the intervention group the percentage of the monitored time with measured CPP within a range of 5 mm Hg above or below CPPopt will reach 36%; (2) the difference in between groups in daily therapy intensity level score will be lower or equal to 3. ETHICS AND DISSEMINATION: Ethical approval has been obtained for each participating centre. The results will be presented at international scientific conferences and in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT02982122.

Novel index for predicting mortality during the first 24 hours after traumatic brain injury.

OBJECTIVE: Failure of cerebral autoregulation and subsequent hypoperfusion is common during the acute phase of traumatic brain injury (TBI). The cerebrovascular pressure-reactivity index (PRx) indirectly reflects cerebral autoregulation and has been used to derive optimal cerebral perfusion pressure (CPP). This study provides a method for the use of a combination of PRx, CPP, and intracranial pressure (ICP) to better evaluate the extent of cerebral hypoperfusion during the first 24 hours after TBI, allowing for a more accurate prediction of mortality risk. METHODS: Continuous ICP and arterial blood pressure (ABP) signals acquired from 295 TBI patients during the first 24 hours after admission were retrospectively analyzed. The CPP at the lowest PRx was determined as the optimal CPP (CPPopt). The duration of a severe hypoperfusion event (dHP) was defined as the cumulative time that the PRx was > 0.2 and the CPP was < 70 mm Hg with the addition of intracranial hypertension (ICP > 20 or > 22 mm Hg). The outcome was determined as 6-month mortality. RESULTS: The cumulative duration of PRx > 0.2 and CPP < 70 mm Hg exhibited a significant association with mortality (p < 0.001). When utilized with basic clinical information available during the first 24 hours after admission (i.e., Glasgow Coma Scale score, age, and mean ICP), a dHP > 25 minutes yielded a significant predictive capacity for mortality (p < 0.05, area under the curve [AUC] = 0.75). The parameter was particularly predictive of mortality for patients with a mean ICP > 20 or > 22 mm Hg (AUC = 0.81 and 0.87, respectively). CONCLUSIONS: A short duration (25 minutes) of severe hypoperfusion, evaluated as lowered CPP during worsened cerebrovascular reactivity during the 1st day after TBI, is highly indicative of mortality.

Pressure autoregulation monitoring and cerebral perfusion pressure target recommendation in patients with severe traumatic brain injury based on minute-by-minute monitoring data.

OBJECT: In severe traumatic brain injury, a universal target for cerebral perfusion pressure (CPP) has been abandoned. Attempts to identify a dynamic CPP target based on the patient's cerebrovascular autoregulatory capacity have been promising so far. Bedside monitoring of pressure autoregulatory capacity has become possible by a number of methods, Czosnyka's pressure reactivity index (PRx) being the most frequently used. The PRx is calculated as the moving correlation coefficient between 40 consecutive 5-second averages of intracranial pressure (ICP) and mean arterial blood pressure (MABP) values. Plotting PRx against CPP produces a U-shaped curve in roughly two-thirds of monitoring time, with the bottom of this curve representing a CPP range corresponding with optimal autoregulatory capacity (CPPopt). In retrospective series, keeping CPP close to CPPopt corresponded with better outcomes. Monitoring of PRx requires high-frequency signal processing. The aim of the present study is to investigate how the processing of the information on cerebrovascular pressure reactivity that can be obtained from routine minute-by-minute ICP and MABP data can be enhanced to enable CPPopt recommendations that do not differ from those obtained by the PRx method, show the same associations with outcome, and can be generated in more than two-thirds of monitoring time. METHODS: The low-frequency autoregulation index (LAx) was defined as the moving minute-by-minute ICP/MABP correlation coefficient calculated over time intervals varying from 3 to 120 minutes. The CPPopt calculation was based on LAx-CPP plots and done for time windows between 1 and 24 hours and for each LAx type. The resulting matrix of CPPopts were then averaged in a weighted manner, with the weight based on the goodness of fit of a U-shape and the lower value of the LAx corresponding to the U-bottom, to result in a final CPPopt recommendation. The association between actual CPP/CPPopt and outcome was assessed in the multicenter Brain Monitoring with Information Technology Research Group (BrainIT) database (n = 180). In the Leuven-Tübingen database (60-Hz waveform data, n = 21), LAx- and PRx-based CPPopts were compared. RESULTS: In the BrainIT database, CPPopt recommendations were generated in 95% of monitoring time. Actual CPP being close to LAx-based CPPopt was associated with increased survival. In a multivariate model using the Corticosteroid Randomization After Significant Head Injury (CRASH) model as covariates, the average absolute difference between actual CPP and CPPopt was independently associated with increased mortality. In the high-frequency data set no significant difference was observed between PRx-based and LAx-based CPPopts. The new method issued a CPPopt recommendation in 97% of monitoring time, as opposed to 44% for PRx-based CPPopt. CONCLUSIONS: Minute-by-minute ICP/MABP data contain relevant information for autoregulation monitoring. In this study, the authors' new method based on minute-by-minute data resolution allowed for CPPopt calculation in nearly the entire monitoring time. This will facilitate the use of pressure reactivity monitoring in all ICUs.