The benefits of multicentre collaboration and data-sharing in patient's with traumatic brain injury is evident from the work of BrainIT in the adult brain injury domain. In paediatric intensive care, there has not been a similar multicentre data-sharing initiative and through EU funding obtained by Milly Lo (Paediatric Intensivist - Edinburgh) and Ian Piper (BrainIT Coordinator), we have an opportunity to develop a similar initiative in the Paediatric domain.
There are two main aims of the KidsBrainIT project:
Defining Age Dependant CPPopt Thresholds in Children: In Phase I of the kidsBrainIT project, we are testing two specific hypotheses: after sustaining traumatic brain injury (TBI), paediatric patients with a longer period of measured cerebral perfusion pressure (CPP) maintained within the calculated optimal CPP (CPPopt) range have (1) an improved global clinical outcome, and (2) better tolerance against raised intracranial pressure (ICP) and that there are three different age dependant threshold from 2-6 years, 7-10 years and 11-16 years. We will retrospectively calculate CPPopt using the technology developed by the Leuven group (the LAx–CPP plots)
Identifying the Best Use of Routine Bedside Monitoring Data in Paediatric ICU: Similar to the Adult TBI work conducted by BrainIT, the kidsBrainIT project aims to demonstrate that by implementing methods for collecting and sharing standardised intensive care high resolution data (at least minute by minute), and that through generation of simple audit/practice adherence reports, best practice can be more readily defined and adopted across the paediatric intensive care domain.
The KidsBrainIT project (approved by the Steering Group) and launched at our Barcelona meeting in 2015, is a long term, multi-centre research project.
Avert-IT was an EU-funded project to develop a mechanism, for use within intensive and high-dependency care units, which will have the ability to monitor and predict the likelihood of arterial hypotension (low blood pressure) adverse events. The full project title is “Advanced Arterial Hypotension Adverse Event prediction through a Novel Bayesian Neural Network” and was completed in 2012.
Intensive Care patients can experience adverse events associated with sudden episodes of low blood pressure. These adverse events may impact all of the main organs resulting in longer lengths of stay, increased care costs and reducing quality of outcomes. Existing technologies enable clinicians to know when these events have occurred and treat the effects. Medical therapies and management for treating adverse events such as low blood pressure exist but clinicians don’t have a reliable way to predict the occurrence, so there’s no opportunity for early intervention.
Research indicates average lengths of stay in intensive care could be significantly reduced if these adverse events can be avoided through prediction and earlier intervention. By reducing the cost of intensive care intensive care services (typically > 1000 Euro's per day) could lead potentially to savings across the EU in the billions of euros, annually.
As arterial hypotension is a common form of adverse event, a model for predicting these offers potential for improving outcomes across a wide range of conditions and or illnesses.
The main scientific objective of the project is the determination of the weighted association between multiple patient parameters and subsequent arterial hypotension. The association will then be used to define the novel Bayesian neural network, which will be trained against the existing BrainIT dataset (collected from 20 centres across Europe), before undertaking a clinical trial to demonstrate the Avert-IT project concept.
The main technological objective will be the development of an IT-based decision support system (“HypoPredict&rdquot;) appropriate for deployment within intensive and high dependency care units. The system will be capable of:
Having successfully trained the BANN on existing cleaned data acquired from the BrainIT group (www.brainit.org), the final stage of the project was focused on collecting data from 60 patients in an observational study to test that the AVERT-IT technology meets the clinician’s minimum requirements for sensitivity (>30%) and false positive rate (FPR) (< 10%) for prediction of arterial hypotension (low blood pressure). This work was successfully completed and the calculated sensitivity and specificity from using the BANN system in a live clinical environment were found to be 40.09% and 92.57% respectively.
In the figure above - the ROC curve from the research phase is shown by the black line. The point estimates for mean sensitivity and false positive rate at the threshold values 0.1 to 0.9 are shown on the solid red curve. The prospective clinical study results (red line) are very close to the research results particularly in the range of interest with a threshold setting of between 0.3 and 0.4 giving a specificity range of 46.03% and 40.09% and a specificity range of 87.13% and 92.57% respectively.
The BANN hypopredict technology is showing promise. We are optimistic that this research will provide the basis for future research assessing the clinical utility of this type of medical decision support upon reducing the length of stay for patients managed in the intensive care environment.
We are currently seeking funding to further develop the AVERT-IT model towards improving it's prediction sensitivity and exploiting it's technology for use within the NHS and health care sector.
One of the findings from the AvertIT project was that we estimate that approximately 30% of potential arterial hypotension events are not quantifiable due to either missing data or artifact from blood sampling, patient handling or other clinical interventions. It is felt that before implementing models such as the AvertIT BANN, we should first investigate approaches to automatically detecting and cleaning blood pressure artifact from the raw data. Towards that end, Glasgow has a project funded from the Scottish Chief Scientists Office (CSO) collaborating with (Chris Williams) Machine Learning group at Edinburgh University. They are developing two models for detection of artifact in blood pressure time-series data: a) a Factorial Switching Linear Dynamical Systems (FSLDS) model and b) a Discrimitive Switching Linear Dynamical Systems (DSLDS) model. The figure below shows examples of the two models performance for detecting blood sample artifact and arterial pressure damped trace artifact. If this project is successful in showing proof of concept for detecting and cleaning major artifact from the Blood Pressure time-series signal in real time in a live clinical environment, this will make if much more feasible to run predictive models that use the Blood Pressure channel in the intensive care setting. This project will be completed May 2015. If successful, we plan to seek further funding to assess the effectiveness of these models on a multi-centre basis.
Figure: Example of DSLDS and FSLDS inferences for a damped trace event (top) and a blood sample event (bottom). Note the Ground Zero Truth (expert manual validation) is shown in a gold colour at the bottom bar).
Following directly on from the First EU Project, This 4 year EU funded project achieved the following aims:
We have developed new software methods including several new tools for collection of the BrainIT core dataset.
We have recruited new centres into the BrainIT network acquiring patient data from 22 Neuro intensive care centres from 11 EU countries.
We have also successfully completed a prospective data collection using these new tools and the data is validated and accessible from our SQL database.
Publications arising from this project include:
Neuman J, Chambers I, Citerio G, Enblad P, Gregson B, Howells T, Mattern J, Nilsson P, Piper I, Ragauskas A, Sahuquillo J, Yau H, Kiening K on Behalf of the BrainIT Group. The use of hyperventilation therapy after Brain Injury in Europe: An analysis of the BrainIT database. Intensive Care Medicine 2008; S00134-008-1123-7
Chambers I, Gregson B, Citerio G, Enblad E, Howells T, Kiening K, Mattern J, Nilsson P & Piper I, Ragauskas A, Sahuquillo J, Yau YH on behalf of the BrainIT Group. BrainIT collaborative network: analyses from a high time-resolution dataset of head injured patients. Acta Neurochir Suppl (2008) 102: 223–227
Shaw M, Piper I Chambers I, Citerio G, Enblad P, Gregson B, Howells T, Kiening K, Mattern J, Nilsson P, Ragauskas A, Sauquillo J, Yau YH on behalf of the BrainIT Group (www.brainit.org). The brain monitoring with Information Technology (BrainIT) collaborative network: data validation results. Acta Neurochir Suppl (2008) 102: 217–221
Chambers IR, Barnes J, Piper I, Citerio G, Enblad P, Howells T et al. BrainIT - a transnational head injury monitoring research network. In: Hoff J, Keep R, Xi G, Hua Y editors. Brain Edema XIII; 2006; Ann Arbor, Michigan: Acta Neurochirugica Sup 96:, Springer Verlag; 2006
Nilsson P, Piper I, Citerio G, Chambers I, Contant C, Enblad P, Fiddes H, Howells T, Kiening K and Yau YH for the BrainIT Group. The BrainIT Group: concept and current status 2004 Acta Neurochir (2005) [Suppl] 95: 33–37
Kiening K, Schoening W, Unterberg A, Stover J, Citerio G, Enblad P, Nilsson P and the Brain-IT Group. Assessment of the relationship between age and continuous intracranial compliance Acta Neurochir (2005) [Suppl] 95: 293–297
Barnes J, Chambers I, Piper I, Citerio G, Contant C, Enblad P, Fiddes H, Howells T, Kiening K, Nilsson P, and Yau for the BrainIT Group. Accurate data collection for head injury monitoring studies: a data validation methodology. Acta Neurochir (2005) [Suppl] 95: 39–41
Photo 1: PDA Device for Entering BrainIT Coredata.
Photo 2: Web Client Software for Entering BrainIT Coredata.
Photo 3: Odin Software (Tim Howells-Uppsala) for Browsing BrainIT Coredata.
Photo 4: Odin Software (Tim Howells-Uppsala) for Showing Burden of ICP Insults.
Photo 5: BrainIT Data Disc (Latest Version on FTP Site).
This one year EC funded project allowed us to expand the group from the 5 members interested in compliance measurement to 22 centres capable of collectng research data. We defined a core-dataset standard for the collection of high resolution intensive care patient data. We conducted a paper based pilot data collection exercise to determine the feasibility of collecting the core dataset in all centres. Four group meetings enabled us to discuss group projects, a number of which have now completed. A technical sub-committee was formed which designed the interface protocols required in each centre to collect the BrainIT data. The technical sub-committee also discussed and designed a flexible database format to hold the BrainIT group data. Building upon this base, the group was successful in obtaining further EC research and infrastructure support (BrainIT-2: QLG3-CT-2002-01160) to build IT tools in order to quantify the feasibility for collection of the core-dataset in a live clinical environment from across the 22 participatng BrainIT centres.
A BrainIT core dataset definition was published: Piper I, Citerio C, Chambers I et al. The BrainIT Group: Concept and Core Dataset Definition. Acta Neurochir 145:615-629 2003.
Photo 1: Some of the BrainIT Group During one of the First EC Funded Project Meetings
In addition to a PDF based BrainIT Core Dataset Definition Document, an XML Schema definition for the core dataset has also been defined.
Figure 1: A "Snippet" from the BrainIT XML Data Schema
BrainIT now has a "Generic Research Programme Framework"" - See the "PERFUSE-IT Project Tab". Group Members can upload their own project ideas fitting in with this theme, or join existing projects run by other members. Stand-alone database analyses on existing data are also managed through this Group Project Section.
|Chris Hawthorne, Martin Shaw||Use of Cluster Analysis on the BrainIT Database.||(Database Analysis) We plan to use cluster analysis, a form of unsupervised learning, to identify similar groups of patients within the BrainIT database using: A) demographic and admission physiological variables and B) physiological variables recorded every minute during their NICU admission.|
|Ian Piper, Anthony Stell, Laura Moss, Ian Piper||Evaluating clinical variation in the management data of ICP and CPP in brain-injured patients||Best practice dictates that if clinical guidelines exist, patients should be treated according to published guidelines. Survey’s indicate that when asked, clinicians believe they treat according to guidelines  and yet when data gathered on treatments are analysed, guideline adherence is less than expected . The Brain Trauma foundation clinical guidelines for the acute management of traumatic brain injury (TBI) suggest the following approach to management of ICP and CPP. This research project aims to test the following hypotheses: a) Treatment processes for ICP and CPP management in TBI can be expressed by a work-flow data structure, comprised of “primitive” objects (a simple point value and time stamp) and “complex” objects (many values with interacting sub-structures). b) The treatment processes that are extracted are clinically meaningful and accurately reflect clinical treatment in a neurological ICU environment and a tool can be developed that can extract and compare treatment processes against clinical guidelines. This project will use the BrainIT database as a test-set for assessing this methodology for automatic extraction of treatment processes and comparison to clinical guidelines. The figures below summarise this approach.|
|Ian Piper, Evangelos Kafantaris. Supervisor: Javier Escudero BrainIT Supervisor: Ian Piper||Variability Analysis||Project Rationale In the intensive-care patient management, the routinely collected multi-parameter bedside physiological monitoring data that is available for clinical interpretation is under-used. Vital information is discarded rather than being optimally utilized for clinical decision making and outcome prediction, research, and quality improvement . Over the last few decades, bedside monitoring technology has been substantially improved resulting in increased data recording. However, there has been a lack of development of new signal processing algorithms capable of extracting clinically relevant information from multimodal monitoring. As a result, the alert systems of currently deployed monitoring equipment are utilizing simple threshold checking methodologies that result in an extensive amount of false positive alarms. This results in the phenomenon of “alarm fatigue” during which clinical staff may choose to ignore alarms that are perceived as false even when they are accurate alarms alerting them a critical change in their patients’ condition thereby putting the patients at risk of clinically adverse events  . This project aims to develop signal processing algorithms that would allow the extraction of clinically relevant information from the physiological recordings routinely collected at the bedside in intensive-care units. This will include methods to extract the respective signal features, analyze their complexity, their dependencies and finally predict outcomes. Aims and Objectives The focus of this project is to develop signal processing and analysis algorithms that will allow the extraction of vital clinically relevant information embedded within routinely collected multi-modal physiological monitoring to provide clinicians with valuable insights when treating patients in ICU. This includes the following partial technical objectives: • The extraction of descriptive feature sets through the application of univariate analysis to each of the available signal streams. • The investigation of cross-signal dependencies that could provide insights concerning the physiological state of the monitored individual through the application of multivariate analysis. • The creation of an adaptive modelling system capable of predicting physiological outcomes in a clinical setting through the integration of the developed signal processing algorithms with machine learning architectures. Plan During its early stages the project will focus on the univariate analysis of electrocardiogram, blood pressure and respiratory signals for the extraction of descriptive feature sets which represent key signals routinely monitored that interact at the physiological mechanism level. The extracted feature sets will provide an initial framework for characterizing the respective signal and the physiological state it represents. An additional layer of analysis will be added by moving into multivariate signal analysis utilising the extracted feature sets for the investigation of cross-signal dependencies through the application of techniques such as Transfer Entropy which can provide a measure of information transfer between signals and as a result allow the detection of a cross-signal dependency . At a later stage of the project, the extracted feature sets will be fed into machine learning algorithms to develop a dynamic modelling system capable of detecting pathological patterns and provide an accurate indication of physiological events of clinical significance in an ICU setting. For this purpose the utilisation of clustering algorithms is being investigated to identify recurring patterns between sets of values of the identified features and the occurrence of physiological events . References  D. M. Sow, "Big Data Analytical Technologies and Decision Support in Critical Care," in Healthcare Information Management Systems, Springer, Cham, 2016, pp. 515-527.  J. Kendall, J. Hagadorn and D. Sink, "Alarm Safety and Alarm Fatigue," Clinics in Perinatology, vol. 44, no. 3, pp. 713-728, 2017.  C. W. Paine, V. V. Goel, E. Ely, C. D. Stave, S. Stemler, M. Zander and C. P. Bonafide, "Systematic Review of Physiologic Monitor Alarm Characteristics and Pragmatic Interventions to Reduce Alarm Frequency," Journal of Hospital Medicine, vol. 11, no. 2, pp. 136-144, 2016.  G. Valenza, L. Faes, L. Citi, M. Orini and R. Barbieri, "Instantaneous Transfer Entropy for the Study of Cardiovascular and Cardiorespiratory Nonstationary Dynamics," IEEE Transactions on Biomedical Engineering, vol. 65, no. 5, pp. 1077-1085, 2018.  V. Chudáček, M. Petrík, G. Georgoulas, M. Čepek, L. Lhotská and C. Stylios, "Comparison of seven approaches for holter ECG clustering and," in Proceedings of the 29th Annual International Conference of the IEEE EMBS, Lyon, 2007.|