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Top quality and also protection inside health care, portion V: breakdown of spanning the product quality chasm.
Intracranial pressure (ICP) is a commonly collected neurocritical parameter, but accurate signal modelling remains challenging. The goal of this project was to mimic clinical ICP waveforms using a physical model.

A physical head model was developed. The skull was segmented from a head computed tomography (CT) scan, remodelled, 3D-printed, and filled with a brain tissue mimicking material and a pressure generator. Pressure measurements and tissue displacement around an attached pressure sensor were explored.

Analysis of the measured pressure demonstrated that the waveform did not perfectly resemble that of the clinical ICP. Through iterative improvements and using a revised second pressure generator, subpeaks could be seen in the waveform. A speckle image recorded using ultrasound during pressure application enabled visualization of tissue displacement around the pressure sensor. Comparison with measured ICP signals revealed that minuscule patterns were not distinct in the displacement images.

We present the first steps towards mimicking clinical ICP using a physical head phantom model. The physical model enabled pressure tests and visualization of tissue displacement and will be foundational for further improvements.
We present the first steps towards mimicking clinical ICP using a physical head phantom model. The physical model enabled pressure tests and visualization of tissue displacement and will be foundational for further improvements.With the appearance of publicly available, high-resolution, physiological datasets in neurocritical care, like Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI), there is a growing need for tools that could be used by clinical researchers to interrogate this information-rich data. The ICM+ software is widely used for processing data acquired from bedside monitors. Considering the growing popularity of scripting simple-syntax programming languages like Python, particularly among clinical researchers, we have developed an interface in ICM+ that provides a streamlined way of adding Python scripting functionality to the ICM+ calculation engine. The new interface imposes certain requirements on the scripts and needs an accompanying descriptor file that tells ICM+ about the functions implemented, so that they become available to the end user in the same way as native ICM+ functions. ICM+ also now includes a tool that eases the creation of Python functions to be imported. The Python extension works very efficiently, and any user with some degree of experience in scripting can use it to enrich capabilities of ICM+. Depending on the data analysed and calculations performed, Python functions are 15-60% slower than built-in ICM+ functions, which is a more-than-acceptable trade-off for empowering ICM+ with the unlimited analytical freedom offered by extensive Python libraries.Plateau waves are recurrent phenomena observed in traumatic brain injury (TBI) patients, characterised by an increase in intracranial pressure (ICP) above 40 mmHg combined with an almost zero arterial blood pressure (ABP) variation and, hence, a decrease in cerebral perfusion pressure (CPP). A raised ICP for a long period of time, namely plateau waves, can lead to a secondary brain injury. Due to the impaired cerebral autoregulation mechanism these TBI patients present, they are admitted to neurocritical care units (NCCUs) to be under continuous multimodal monitoring, which allows a correct diagnosis for each patient. Plateau waves can end naturally by activating a vasoconstriction mechanism which decreases the amount of blood available in the brain. Alternatively, the phenomenon can end with therapeutic treatment.In this sense, the present study consists in the development of an algorithm capable of automatically detecting plateau waves using offline data, i.e. data already collected from patients. This creates an extra tool which allows for faster detection of events to assist their identification and final diagnosis. Despite the additional steps that can be included to improve the algorithm, the results show good performance, and thus it may be applied in NCCUs.
For further insight into the possibly predictive quality of the intracranial pressure (ICP) waveform morphology a definite and reliable identification of its components is a prerequisite but presents the problem of artefacts in physiological signals.

ICP and electrocardiogram (ECG) data were recorded to depict not only their numerical value but also their respective waveforms and were analysed by two algorithms, which were then compared for their artefact resistance.The algorithms in question identify the start point of every ICP wave, one (AR[SA]) by scale analysis, the other (AR[ECG]) by analysing the ICP wave linked to the ECG.

Start-point identification accuracy in rhythmic patients showed sensitivity of 95.14% for AR[SA] and 99.99% for AR[ECG], with a positive predictive value (ppv) of 98.30% for AR[SA] and 99.76% for AR[ECG].In arrhythmic patients sensitivity was 98.05% for AR[SA] and 99.73% for AR[ECG], with a ppv of 100% for AR[SA] and 99.78% for AR[ECG].

AR[ECG] has proven to be more resistant to artefacts than AR[SA], even in cases such as cardiac arrhythmia. It facilitates reliable, three-dimensional visualisation of long-term changes in ICP-wave morphology and is thus suited for analysis in cases of more complex or irregular vital parameters.
AR[ECG] has proven to be more resistant to artefacts than AR[SA], even in cases such as cardiac arrhythmia. It facilitates reliable, three-dimensional visualisation of long-term changes in ICP-wave morphology and is thus suited for analysis in cases of more complex or irregular vital parameters.Waveform physiological data are important in the treatment of critically ill patients in the intensive care unit. Such recordings are susceptible to artefacts, which must be removed before the data can be reused for alerting or reprocessed for other clinical or research purposes. Accurate removal of artefacts reduces bias and uncertainty in clinical assessment, as well as the false positive rate of ICU alarms, and is therefore a key component in providing optimal clinical care. In this work, we present DeepClean, a prototype self-supervised artefact detection system using a convolutional variational autoencoder deep neural network that avoids costly and painstaking manual annotation, requiring only easily obtained 'good' data for training. For a test case with invasive arterial blood pressure, we demonstrate that our algorithm can detect the presence of an artefact within a 10s sample of data with sensitivity and specificity around 90%. click here Furthermore, DeepClean was able to identify regions of artefacts within such samples with high accuracy, and we show that it significantly outperforms a baseline principal component analysis approach in both signal reconstruction and artefact detection. DeepClean learns a generative model and therefore may also be used for imputation of missing data.High-resolution, waveform-level data from bedside monitors carry important information about a patient's physiology but is also polluted with artefactual data. Manual mark-up is the standard practice for detecting and eliminating artefacts, but it is time-consuming, prone to errors, biased and not suitable for real-time processing.In this paper we present a novel automatic artefact detection technique based on a Symbolic Aggregate approXimation (SAX) technique which makes it possible to represent individual pulses as 'words'. It does that by coding each pulse with a specified number of letters (here six) from a predefined alphabet of characters (here six). The word is then fed to a support vector machine (SVM) and classified as artefactual or physiological.To define the universe of acceptable pulses, the arterial blood pressure from 50 patients was analysed, and acceptable pulses were manually chosen by looking at the average pulse that each 'word' generated. This was then used to train a SVM classifier. To test this algorithm, a dataset with a balanced ratio of clean and artefactual pulses was built, classified and independently evaluated by two observers achieving a sensitivity of 0.972 and 0.954 and a specificity of 0.837 and 0.837 respectively.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.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.Refractory intracranial hypertension (RIH) refers to a dramatic increase in intracranial pressure (ICP) that cannot be controlled by treatment and leads to patient death. Detrimental sequelae of raised ICP in acute brain injury (ABI) are unclear because the underlying physiopathological mechanisms of raised ICP have not been sufficiently investigated. Recent reports have shown that autonomic activity is altered during changes in ICP. The aim of our study was to evaluate the feasibility of assessing autonomic activity during RIH with our adopted methodology. We selected 24 ABI patients for retrospective review who developed RIH. They were monitored based on ICP, arterial blood pressure, and electrocardiogram using ICM+ software. Secondary parameters reflecting autonomic activity were computed in time and frequency domains through the continuous measurement of heart rate variability and baroreflex sensitivity. The results of the analysis will be presented later in a full paper. This preliminary analysis shows the feasibility of the adopted methodology.
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