Exploration of the relationship between partial pressure of brain tissue oxygen and intracranial pressure.

Introduction: Partial pressure of brain tissue oxygen (PbtO2) has been shown to be a safe an effective monitoring modality to compliment intracranial pressure (ICP) monitoring. It is related to metabolic activity, disease severity and mortality. Research question: Understanding the complex relationship between PbtO2 and ICP for patients with traumatic brain injury will enable better clinical decision making beyond simple threshold treatment strategies. Material and methods: Patients with PbtO2 monitoring were identified from the BrainIT database, a multi-centre dataset, containing minute by minute PbtO2 and ICP readings. Missing data was imputed and a multi-level log-normal regression model with a compound symmetry correlation structure was built. This accounted for any increased correlation due to the repeated measurements. The model was adjusted for mean arterial pressure and the partial pressure of carbon dioxide. Non-linearity was assessed using analysis of deviance and trends using expected marginal means. Results: 11 subjects with over 82,000 readings were included. They had a median age of 38 (IQR: 37-47), 73% were male, a median length of stay of 11.8 (IQR: 6.6-19.7) days and a median extended Glasgow outcome scale of 7.00 (IQR: 5-8).There is a statistically significant (p < 0.001) non-linear effect of ICP on PbtO2. With an overall increase in PbtO2 of 5.2% (95% CI 4%-6.4%, p < 0.001) for a 10 mmHg increase in ICP below 22 mmHg and a decrease of 5.5% (95% CI 2.7%-8.3%, p=<0.001) in PbtO2 for a 10 mmHg increase in ICP above 22 mmHg. As well as a decrease of 40.9% (95% CI 2.3%-64.3%, p = 0.040) in PbtO2 per day in the intensive care unit. Discussion and conclusion: This model demonstrates that there is a significant non-linear relationship between ICP and PbtO2, however, this is a small heterogeneous cohort and further validation will be required.

Visualization of the Intracranial Pressure and Time Burden in Childhood Brain Trauma: What We Have Learned One Decade on With KidsBrainIT.

To validate the intracranial pressure (ICP) dose-response visualization plot for the first time in a novel prospectively collected pediatric traumatic brain injury (pTBI) data set from the multi-center, multi-national KidsBrainIT consortium. Prospectively collected minute-by-minute ICP and mean arterial blood pressure time series of 104 pTBI patients were categorized in ICP intensity-duration episodes. These episodes were correlated with the 6-month Glasgow Outcome Score (GOS) and displayed in a color-coded ICP dose-response plot. The influence of cerebrovascular reactivity and cerebral perfusion pressure (CPP) were investigated. The generated ICP dose-response plot on the novel data set was similar to the previously published pediatric plot. This study confirmed that higher ICP episodes were tolerated for a shorter duration of time, with an approximately exponential decay curve delineating the positive and negative association zones. ICP above 20 mm Hg for any duration in time was associated with poor outcome in our patients. Cerebrovascular reactivity state did not influence their respective transition curves above 10 mm Hg ICP. CPP below 50 mm Hg was not tolerated, regardless of ICP and duration, and was associated with worse outcome. The ICP dose-response plot was reproduced in a novel and independent pTBI data set. ICP above 20 mm Hg and CPP below 50 mm Hg for any duration in time were associated with worse outcome. This highlighted a pressing need to reduce pediatric ICP therapeutic thresholds used at the bedside.

Decompressive craniectomy as a second/third-tier intervention in traumatic brain injury: A multicenter observational study.

OBJECTIVES: RESCUEicp studied decompressive craniectomy (DC) applied as third-tier option in severe traumatic brain injury (TBI) patients in a randomized controlled setting and demonstrated a decrease in mortality with similar rates of favorable outcome in the DC group compared to the medical management group. In many centers, DC is being used in combination with other second/third-tier therapies. The aim of the present study is to investigate outcomes from DC in a prospective non-RCT context. METHODS: This is a prospective observational study of 2 patient cohorts: one from the University Hospitals Leuven (2008-2016) and one from the Brain-IT study, a European multicenter database (2003-2005). In thirty-seven patients with refractory elevated intracranial pressure who underwent DC as a second/third-tier intervention, patient, injury and management variables including physiological monitoring data and administration of thiopental were analysed, as well as Extended Glasgow Outcome score (GOSE) at 6 months. RESULTS: In the current cohorts, patients were older than in the surgical RESCUEicp cohort (mean 39.6 vs. 32.3; p < 0.001), had higher Glasgow Motor Score on admission (GMS < 3 in 24.3% vs. 53.0%; p = 0.003) and 37.8% received thiopental (vs. 9.4%; p < 0.001). Other variables were not significantly different. GOSE distribution was: death 24.3%; vegetative 2.7%; lower severe disability 10.8%; upper severe disability 13.5%; lower moderate disability 5.4%; upper moderate disability 2.7%, lower good recovery 35.1%; and upper good recovery 5.4%. The outcome was unfavorable in 51.4% and favorable in 48.6%, as opposed to 72.6% and 27.4% respectively in RESCUEicp (p = 0.02). CONCLUSION: Outcomes in DC patients from two prospective cohorts reflecting everyday practice were better than in RESCUEicp surgical patients. Mortality was similar, but fewer patients remained vegetative or severely disabled and more patients had a good recovery. Although patients were older and injury severity was lower, a potential partial explanation may be in the pragmatic use of DC in combination with other second/third-tier therapies in real-life cohorts. The findings underscore that DC maintains an important role in managing severe TBI.

Development and External Validation of a Machine Learning Model for the Early Prediction of Doses of Harmful Intracranial Pressure in Patients with Severe Traumatic Brain Injury.

Treatment and prevention of elevated intracranial pressure (ICP) is crucial in patients with severe traumatic brain injury (TBI). Elevated ICP is associated with secondary brain injury, and both intensity and duration of an episode of intracranial hypertension, often referred to as "ICP dose," are associated with worse outcomes. Prediction of such harmful episodes of ICP dose could allow for a more proactive and preventive management of TBI, with potential implications on patients' outcomes. The goal of this study was to develop and validate a machine-learning (ML) model to predict potentially harmful ICP doses in patients with severe TBI. The prediction target was defined based on previous studies and included a broad range of doses of elevated ICP that have been associated with poor long-term neurological outcomes. The ML models were used, with minute-by-minute ICP and mean arterial blood pressure signals as inputs. Harmful ICP episodes were predicted with a 30 min forewarning. Models were developed in a multi-center dataset of 290 adult patients with severe TBI and externally validated on 264 patients from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) dataset. The external validation of the prediction model on the CENTER-TBI dataset demonstrated good discrimination and calibration (area under the curve: 0.94, accuracy: 0.89, precision: 0.87, sensitivity: 0.78, specificity: 0.94, calibration-in-the-large: 0.03, calibration slope: 0.93). The proposed prediction model provides accurate and timely predictions of harmful doses of ICP on the development and external validation dataset. A future interventional study is needed to assess whether early intervention on the basis of ICP dose predictions will result in improved outcomes.

Demystifying the Black Box: The Importance of Interpretability of Predictive Models in Neurocritical Care.

Neurocritical care patients are a complex patient population, and to aid clinical decision-making, many models and scoring systems have previously been developed. More recently, techniques from the field of machine learning have been applied to neurocritical care patient data to develop models with high levels of predictive accuracy. However, although these recent models appear clinically promising, their interpretability has often not been considered and they tend to be black box models, making it extremely difficult to understand how the model came to its conclusion. Interpretable machine learning methods have the potential to provide the means to overcome some of these issues but are largely unexplored within the neurocritical care domain. This article examines existing models used in neurocritical care from the perspective of interpretability. Further, the use of interpretable machine learning will be explored, in particular the potential benefits and drawbacks that the techniques may have when applied to neurocritical care data. Finding a solution to the lack of model explanation, transparency, and accountability is important because these issues have the potential to contribute to model trust and clinical acceptance, and, increasingly, regulation is stipulating a right to explanation for decisions made by models and algorithms. To ensure that the prospective gains from sophisticated predictive models to neurocritical care provision can be realized, it is imperative that interpretability of these models is fully considered.

Use of Direct Intracranial Pressure and Brain Tissue Oxygen Monitoring in Perioperative Management of Patients with Moyamoya Disease.

Intracranial pressure monitoring and brain tissue oxygen monitoring are commonly used in head injury for goal-directed therapies, but there may be more indications for its use. Moyamoya disease involves progressive stenosis of the arterial circulation and formation of collateral vessels that are at risk of hemorrhage. The risk of ischemic events during revascularization surgery and postoperatively is high. Impaired cerebral autoregulation may be one of the factors that are implicated. We present our experience with monitoring of cerebral oxygenation and autoregulation in the pathological hemisphere during the perioperative period in four patients with moyamoya disease.

Evaluation of Software for Automated Measurement of Adherence to ICP-Monitoring Threshold Guideline.

Challenges inherent in clinical guideline development include a long time lag between the key results and incorporation into best practice and the qualitative nature of adherence measurement, meaning it will have no directly measurable impact. To address these issues, a framework has been developed to automatically measure adherence by clinicians in neurological intensive care units to the Brain Trauma Foundation's intracranial pressure (ICP)-monitoring guidelines for severe traumatic brain injury (TBI).The framework processes physiological and treatment data taken from the bedside, standardises the data as a set of process models, then compares these models against similar process models constructed from published guidelines. A similarity metric (i.e. adherence measure) between the two models is calculated, composed of duration and scale of non-adherence.In a pilot clinical validation test, the framework was applied to physiological/treatment data from three TBI patients exhibiting ICP secondary insults at a local neuro-centre where clinical experts coded key clinical interventions/decisions about patient management.The framework identified non-adherence with respect to drug administration in one patient, with a spike in non-adherence due to an inappropriately high dosage; a second patient showed a high severity of guideline non-adherence; and a third patient showed non-adherence due to a low number of associated events and treatment annotations.

Time Series Analysis and Prediction of Intracranial Pressure Using Time-Varying Dynamic Linear Models.

Intracranial pressure (ICP) monitoring is a key clinical tool in the assessment and treatment of patients in a neuro-intensive care unit (neuro-ICU). As such, a deeper understanding of how an individual patient's ICP can be influenced by therapeutic interventions could improve clinical decision-making. A pilot application of a time-varying dynamic linear model was conducted using the BrainIT dataset, a multi-centre European dataset containing temporaneous treatment and vital-sign recordings. The study included 106 patients with a minimum of 27 h of ICP monitoring. The model was trained on the first 24 h of each patient's ICU stay, and then the next 2 h of ICP was forecast. The algorithm enabled switching between three interventional states: analgesia, osmotic therapy and paralysis, with the inclusion of arterial blood pressure, age and gender as exogenous regressors. The overall median absolute error was 2.98 (2.41-5.24) mmHg calculated using all 106 2-h forecasts. This is a novel technique which shows some promise for forecasting ICP with an adequate accuracy of approximately 3 mmHg. Further optimisation is required for the algorithm to become a usable clinical tool.

Telemetric Intracranial Pressure: A Snapshot Does not Give the Full Story.

Telemetric intracranial pressure (ICP) monitors are useful tools in the management of complex hydrocephalus and idiopathic intracranial hypertension (IIH). Clinicians may use them as a "snapshot" screening tool to assess shunt function or ICP. We compared "snapshot" telemetric ICP recordings with extended, in-patient periods of monitoring to determine whether this practice is safe and useful for clinical decision making.

Assessment of Outliers and Detection of Artifactual Network Segments Using Univariate and Multivariate Dispersion Entropy on Physiological Signals.

Network physiology has emerged as a promising paradigm for the extraction of clinically relevant information from physiological signals by moving from univariate to multivariate analysis, allowing for the inspection of interdependencies between organ systems. However, for its successful implementation, the disruptive effects of artifactual outliers, which are a common occurrence in physiological recordings, have to be studied, quantified, and addressed. Within the scope of this study, we utilize Dispersion Entropy (DisEn) to initially quantify the capacity of outlier samples to disrupt the values of univariate and multivariate features extracted with DisEn from physiological network segments consisting of synchronised, electroencephalogram, nasal respiratory, blood pressure, and electrocardiogram signals. The DisEn algorithm is selected due to its efficient computation and good performance in the detection of changes in signals for both univariate and multivariate time-series. The extracted features are then utilised for the training and testing of a logistic regression classifier in univariate and multivariate configurations in an effort to partially automate the detection of artifactual network segments. Our results indicate that outlier samples cause significant disruption in the values of extracted features with multivariate features displaying a certain level of robustness based on the number of signals formulating the network segments from which they are extracted. Furthermore, the deployed classifiers achieve noteworthy performance, where the percentage of correct network segment classification surpasses 95% in a number of experimental setups, with the effectiveness of each configuration being affected by the signal in which outliers are located. Finally, due to the increase in the number of features extracted within the framework of network physiology and the observed impact of artifactual samples in the accuracy of their values, the implementation of algorithmic steps capable of effective feature selection is highlighted as an important area for future research.

Indirect effects of the COVID-19 pandemic on paediatric healthcare use and severe disease: a retrospective national cohort study.

OBJECTIVES: To determine the indirect consequences of the COVID-19 pandemic on paediatric healthcare utilisation and severe disease at a national level following lockdown on 23 March 2020. DESIGN: National retrospective cohort study. SETTING: Emergency childhood primary and secondary care providers across Scotland; two national paediatric intensive care units (PICUs); statutory death records. PARTICIPANTS: 273 455 unscheduled primary care attendances; 462 437 emergency department attendances; 54 076 emergency hospital admissions; 413 PICU unplanned emergency admissions requiring invasive mechanical ventilation; and 415 deaths during the lockdown study period and equivalent dates in previous years. MAIN OUTCOME MEASURES: Rates of emergency care consultations, attendances and admissions; clinical severity scores on presentation to PICU; rates and causes of childhood death. For all data sets, rates during the lockdown period were compared with mean or aggregated rates for the equivalent dates in 2016-2019. RESULTS: The rates of emergency presentations to primary and secondary care fell during lockdown in comparison to previous years. Emergency PICU admissions for children requiring invasive mechanical ventilation also fell as a proportion of cases for the entire population, with an OR of 0.52 for likelihood of admission during lockdown (95% CI 0.37 to 0.73), compared with the equivalent period in previous years. Clinical severity scores did not suggest children were presenting with more advanced disease. The greatest reduction in PICU admissions was for diseases of the respiratory system; those for injury, poisoning or other external causes were equivalent to previous years. Mortality during lockdown did not change significantly compared with 2016-2019. CONCLUSIONS: National lockdown led to a reduction in paediatric emergency care utilisation, without associated evidence of severe harm.

Augmentation of Dispersion Entropy for Handling Missing and Outlier Samples in Physiological Signal Monitoring.

Entropy quantification algorithms are becoming a prominent tool for the physiological monitoring of individuals through the effective measurement of irregularity in biological signals. However, to ensure their effective adaptation in monitoring applications, the performance of these algorithms needs to be robust when analysing time-series containing missing and outlier samples, which are common occurrence in physiological monitoring setups such as wearable devices and intensive care units. This paper focuses on augmenting Dispersion Entropy (DisEn) by introducing novel variations of the algorithm for improved performance in such applications. The original algorithm and its variations are tested under different experimental setups that are replicated across heart rate interval, electroencephalogram, and respiratory impedance time-series. Our results indicate that the algorithmic variations of DisEn achieve considerable improvements in performance while our analysis signifies that, in consensus with previous research, outlier samples can have a major impact in the performance of entropy quantification algorithms. Consequently, the presented variations can aid the implementation of DisEn to physiological monitoring applications through the mitigation of the disruptive effect of missing and outlier samples.

Transcranial bioimpedance measurement in horses: a pilot study.

OBJECTIVE: This pilot study aimed to evaluate the feasibility of transcranial bioimpedance (TCBI) measurement and variability of TCBI values in healthy conscious horses and to study effects of body position and time on TCBI in anaesthetized horses. STUDY DESIGN: Prospective, observational study. ANIMALS: A total of four research horses and 16 client-owned horses presented for surgery. METHODS: After establishing optimal electrode position using computed tomography scans of cadaver heads, TCBI [described using impedance at zero frequency, R0, (Ω)] was measured in four conscious, resting horses to investigate the feasibility and changes in TCBI over time (80 minutes). Data were compared using a paired t test. TCBI was then measured throughout anaesthesia (duration 92 ± 28 minutes) in 16 horses in dorsal and lateral recumbency. Data were analysed using a general linear model; gamma regression was chosen as a model of characteristic impedance [Zc; (Ω)] against time. Data are presented as mean ± standard deviation. RESULTS: No change in R0 was seen in conscious horses (age = 15.3 ± 7.3 years, body mass = 512 ± 38 kg) over 80 minutes. The technique was well tolerated and caused no apparent adverse effects. In 16 horses (age = 7.4 ± 4.7 years; body mass = 479 ± 134 kg) anaesthetized for 92 ± 28 minutes, Zc fell during anaesthesia, decreasing more in horses in lateral recumbency than in horses in dorsal recumbency (p = 0.008). There was no relationship between Zc and body mass or age. CONCLUSIONS AND CLINICAL RELEVANCE: TCBI is readily measured in horses. TCBI did not change with time in conscious horses, but decreased with time in anaesthetized horses; this change was greater in horses in lateral recumbency, indicating that TCBI changes in anaesthetized horses may be related to the effects of recumbency, general anaesthesia, surgery or a combination of these factors.

Application of Dispersion Entropy to Healthy and Pathological Heartbeat ECG Segments.

Entropy quantification algorithms are a prominent tool for the quantification of irregularity in biological signal segments towards the characterization of the physiological state of individuals. This paper investigates the potential of Dispersion Entropy (DisEn) as a non-linear method to quantify the uncertainty of ECG signal segments for different types of heartbeats and the stratification of healthy heartbeats for the potential detection of developing pathologies in individuals. Our results indicate that the DisEn algorithm produces distributions with significant differences for the considered types of heartbeats, with higher DisEn values being more prominent in pathological heartbeats and normal heartbeats preceding them. This suggests that, with further research, DisEn algorithms can be integrated with heartbeat detection and classification algorithms for the improvement of medical prognosis through ECG signal processing.

Forewarning of hypotensive events using a Bayesian artificial neural network in neurocritical care.

Traumatically brain injured (TBI) patients are at risk from secondary insults. Arterial hypotension, critically low blood pressure, is one of the most dangerous secondary insults and is related to poor outcome in patients. The overall aim of this study was to get proof of the concept that advanced statistical techniques (machine learning) are methods that are able to provide early warning of impending hypotensive events before they occur during neuro-critical care. A Bayesian artificial neural network (BANN) model predicting episodes of hypotension was developed using data from 104 patients selected from the BrainIT multi-center database. Arterial hypotension events were recorded and defined using the Edinburgh University Secondary Insult Grades (EUSIG) physiological adverse event scoring system. The BANN was trained on a random selection of 50% of the available patients (n = 52) and validated on the remaining cohort. A multi-center prospective pilot study (Phase 1, n = 30) was then conducted with the system running live in the clinical environment, followed by a second validation pilot study (Phase 2, n = 49). From these prospectively collected data, a final evaluation study was done on 69 of these patients with 10 patients excluded from the Phase 2 study because of insufficient or invalid data. Each data collection phase was a prospective non-interventional observational study conducted in a live clinical setting to test the data collection systems and the model performance. No prediction information was available to the clinical teams during a patient's stay in the ICU. The final cohort (n = 69), using a decision threshold of 0.4, and including false positive checks, gave a sensitivity of 39.3% (95% CI 32.9-46.1) and a specificity of 91.5% (95% CI 89.0-93.7). Using a decision threshold of 0.3, and false positive correction, gave a sensitivity of 46.6% (95% CI 40.1-53.2) and specificity of 85.6% (95% CI 82.3-88.8). With a decision threshold of 0.3, > 15 min warning of patient instability can be achieved. We have shown, using advanced machine learning techniques running in a live neuro-critical care environment, that it would be possible to give neurointensive teams early warning of potential hypotensive events before they emerge, allowing closer monitoring and earlier clinical assessment in an attempt to prevent the onset of hypotension. The multi-centre clinical infrastructure developed to support the clinical studies provides a solid base for further collaborative research on data quality, false positive correction and the display of early warning data in a clinical setting.

Cerebral Perfusion Pressure Variability Between Patients and Between Centres.

INTRODUCTION: The aim of this analysis was to investigate to what extent median cerebral perfusion pressure (CPP) differs between severe traumatic brain injury (TBI) patients and between centres, and whether the 2007 change in CPP threshold in the Brain Trauma Foundation guidelines is reflected in patient data collected at several centres over different time periods. METHODS: Data were collected from the Brain-IT database, a multi-centre project between 2003 and 2005, and from a recent project in four centres between 2009 and 2013. For patients nursed with their head up at 30° and with the blood pressure transducer at atrium level, CPP was corrected by 10 mmHg. Median CPP, interquartile ranges and total CPP ranges over the monitoring time were calculated per patient and per centre. RESULTS: Per-centre medians pre-2007 were situated between 50 and 70 mmHg in 6 out of 16 centres, while 10 centres had medians above 70 mmHg and 4 above 80 mmHg. Post-2007, three out of four centres had medians between 60 and 70 mmHg and one above 80 mmHg. One out of two centres with data pre- and post-2007 shifted from a median CPP of 76 mmHg to 60 mmHg, while the other remained at 68-67 mmHg. CONCLUSIONS: CPP data are characterised by a high inter-individual variability, but the data also suggest differences in CPP policies between centres. The 2007 guideline change may have affected policies towards lower CPP in some centres. Deviations from the guidelines occur in the direction of CPP > 70 mmHg.

Transcranial Bioimpedance Measurement as a Non-invasive Estimate of Intracranial Pressure.

OBJECTIVES: We have previously demonstrated a relationship between transcranial bioimpedance (TCB) measurements and intracranial pressure (ICP) in an animal model of raised ICP. The primary objective of this study was to explore the relationship between non-invasive bioelectrical impedance measurements of the brain and skull and ICP in traumatic brain injury (TBI) patients. MATERIALS AND METHODS: Included patients were adults admitted to the Neurological Intensive Care Unit with TBI and undergoing invasive ICP monitoring as part of their routine clinical care. Multi-frequency TCB measurements were performed hourly through bi-temporal electrodes. The bioimpedance parameters of Z c (impedance at the characteristic frequency) and R 0 (resistance to a direct current) were then modelled against ICP using unadjusted and adjusted linear models. RESULTS: One hundred and sixty-eight TCB measurements were available from ten study participants. Using an unadjusted linear modelling approach, there was no significant relationship between measured ICP and Zc or R0. The most significant relationship between ICP and TCB parameters was found by adjusting for multiple patient specific variables and using Zc and R0 normalised per patient (p < 0.0001, r 2 = 0.32). CONCLUSIONS: These pilot results confirm some degree of relationship between TCB parameters and invasively measured ICP. The magnitude of this relationship is small and, on the basis of the current study, TCB is unlikely to provide a clinically useful estimate of ICP in patients admitted with TBI.

Medical Waveform Format Encoding Rules Representation of Neurointensive Care Waveform Data.

OBJECTIVE: Technology in neurointensive care units can collect and store vast amounts of complex patient data. The CHART-ADAPT project is aimed at developing technology that will allow for the collection, analysis and use of these big data at the patient's bedside in neurointensive care units. A requirement of this project is to automatically extract and transfer high-frequency waveform data (e.g. ICP) from monitoring equipment to high performance computing infrastructure for analysis. Currently, no agreed data standard exists in neurointensive care for the description of this type of data. In this pilot study, we investigated the use of Medical Waveform Format Encoding Rules (MFER- www.mfer.org-ISO 11073-92001) as a possible data standard for neurointensive care waveform data. MATERIALS AND METHODS: Several waveform formats were explored (e.g. XML, DICOM waveform) and evaluated for suitability given existing computing infrastructure constraints, e.g. NHS network capacity and the processing capabilities of existing integration software. Key requirements of the format included a compact data size and the use of a recognised standard. The MFER waveform format (ISO/TS 11073-92001) met both requirements. To evaluate the practicality of the MFER waveform format, seven waveform signals (ICP, ECG, ART, CVP, EtCO2, Pleth, Resp) collected over a period of 8 h from a patient at the Institute of Neurological Sciences in Glasgow were converted into MFER waveform format. RESULTS: The MFER waveform format has two main components: sampling information and frame information. Sampling information describes the frequency of the data sampling and the resolution of the data. Frame information describes the data itself; it consists of three elements: data block (the actual data), channel (each type of waveform data occupies a channel) and sequence (the repetition of the data). All seven waveform signals were automatically and successfully converted into the MFER waveform format. One MFER file was created for each minute of data (total of 479 files, 181 KB each). CONCLUSIONS: The MFER waveform format has potential as a lightweight standard for representing high-frequency neurointensive care waveform data. Further work will include a comparison with other waveform data formats and a live trial of using the MFER waveform format to stream patient data over a longer period.

Investigation of the Relationship Between the Burden of Raised ICP and the Length of Stay in a Neuro-Intensive Care Unit.

OBJECTIVES: Raised intracranial pressure (ICP) is well known to be indicative of a poor outcome in traumatic brain injury (TBI). This phenomenon was quantified using a pressure time index (PTI) model of raised ICP burden in a paediatric population. Using the PTI methodology, this pilot study is aimed at investigating the relationship between raised ICP and length of stay (LOS) in adults admitted to a neurological intensive care unit (neuro-ICU). MATERIALS AND METHODS: In 10 patients admitted to the neuro-ICU following TBI, ICP was measured and data from the first 24 h were analysed. The PTI is a bounded area under the curve, where the bound is the threshold limit of interest for the signal. The upper bound of 20 mmHg for ICP is commonly used in clinical practice. To fully investigate the relationship between ICP and LOS, further bounds from 1 to 40 mmHg were used during the PTI calculations. A backwards step Poisson regression model with a log link function was used to find the important thresholds for the prediction of full LOS, measured in hours, in the neuro-ICU. RESULTS: The fit was assessed using a Chi-squared deviance goodness of fit method, which showed a non-significant p value of 0.97, indicating a correctly specified model. The backwards step strategy, minimising the model's Akaike information criteria (AIC) at each change, found that levels 13-16, 18 and 20-21 combined were the most predictive. From this model it can be shown that for every 1 mmHg/h increase in burden, as measured by the PTI, the LOS has a base exponential increase of approximately 2 h, with the largest increases in the LOS given at the 20-mmHg threshold level. CONCLUSIONS: This model demonstrates that increased duration of raised ICP in the early monitoring period is associated with a prolonged LOS in the neuro-ICU. Further validation of the PTI model in a larger cohort is currently underway as part of the CHART-ADAPT project. Second, further adjustment with known predictors of outcome, such as severity of injury, would help to improve the fit and validate the current combination of predictors.

Visualizing Cerebrovascular Autoregulation Insults and Their Association with Outcome in Adult and Paediatric Traumatic Brain Injury.

OBJECTIVE: The aim of this study is to assess visually the impact of duration and intensity of cerebrovascular autoregulation insults on 6-month neurological outcome in severe traumatic brain injury. MATERIAL AND METHODS: Retrospective analysis of prospectively collected minute-by-minute intracranial pressure (ICP) and mean arterial blood pressure data of 259 adult and 99 paediatric traumatic brain injury (TBI) patients from multiple European centres. The relationship of the 6-month Glasgow Outcome Scale with cerebrovascular autoregulation insults (defined as the low-frequency autoregulation index above a certain threshold during a certain time) was visualized in a colour-coded plot. The analysis was performed separately for autoregulation insults occurring with cerebral perfusion pressure (CPP) below 50 mmHg, with ICP above 25 mmHg and for the subset of adult patients that did not undergo decompressive craniectomy. RESULTS: The colour-coded plots showed a time-intensity-dependent association with outcome for cerebrovascular autoregulation insults in adult and paediatric TBI patients. Insults with a low-frequency autoregulation index above 0.2 were associated with worse outcomes and below -0.6 with better outcomes, with and approximately exponentially decreasing transition curve between the two intensity thresholds. All insults were associated with worse outcomes when CPP was below 50 mmHg or ICP was above 25 mmHg. CONCLUSIONS: The colour-coded plots indicate that cerebrovascular autoregulation is disturbed in a dynamic manner, such that duration and intensity play a role in the determination of a zone associated with better neurological outcome.