Automated Measurement of Adherence to Traumatic Brain Injury (TBI) Guidelines using Neurological ICU Data

Using a combination of physiological and treatment information from neurological ICU data-sets, adherence to traumatic brain injury (TBI) guidelines on hypotension, intracranial pressure (ICP) and cerebral perfusion pressure (CPP) is calculated automatically. The ICU output is evaluated to capture pressure events and actions taken by clinical staff for patient management, and are then re-expressed as simplified process models. The official TBI guidelines from the Brain Trauma Foundation are similarly evaluated, so the two structures can be compared and a quantifiable distance between the two calculated (the measure of adherence). The methods used include: the compilation of physiological and treatment information into event logs and subsequently process models; the expression of the BTF guidelines in process models within the real-time context of the ICU; a calculation of distance between the two processes using two algorithms (“Direct” and “Weighted”) building on work conducted in th e business process domain. Results are presented across two categories each with clinical utility (minute-by-minute and single patient stays) using a real ICU data-set. Results of two sample patients using a weighted algorithm show a non-adherence level of 6.25% for 42 mins and 56.25% for 708 mins and non-adherence of 18.75% for 17 minutes and 56.25% for 483 minutes. Expressed as two combinatorial metrics (duration/non-adherence (A) and duration * non-adherence (B)), which together indicate the clinical importance of the non-adherence, one has a mean of A=4.63 and B=10014.16 and the other a mean of A=0.43 and B=500.0

Monitoring of Intracranial Pressure in Patients with Traumatic Brain Injury

Since Monro published his observations on the nature of the contents of the intracranial space in 1783 there has been investigation of the unique relationship between the contents of the skull and the intracranial pressure (ICP). This is particularly true following traumatic brain injury (TBI), where it is clear that elevated ICP due to the underlying pathological processes is associated with a poorer clinical outcome. Consequently, there is considerable interest in monitoring and manipulating ICP In patients with TBI.The two techniques most commonly used in clinical practice to monitor ICP are via an intraventricular or intraparenchymal catheter with a microtransducer system. Both of these techniques are invasive and are thus associated with complications such as haemorrhage and infection. For this reason, significant research effort has been directed towards development of a non-invasive method to measure ICP. These include imaging based studies using computed tomography (CT) and magnetic resonance imaging (MRI), transcranial Doppler sonography (TCD), near-infrared spectroscopy (NIRS), tympanic membrane displacement (TMD), visual-evoked potentials (VEPs), measurements of optic nerve sheath diameter (ONSD) and other measurements of the optic nerve, retina, pupil and ophthalmic artery.The principle aims of ICP monitoring in TBI are to allow early detection of secondary haemorrhage or ischaemic processes and to guide therapies that limit intracranial hypertension and optimise cerebral perfusion. However, information from the ICP value and the ICP waveform can also be used to estimate intracranial compliance, assess cerebrovascular pressure reactivity and attempt to forecast future episodes of intracranial hypertension

Transition to higher education : prospective and retrospective student experiences

Pre-university (foundation or Level 3) study attracts significant student numbers annually, but approximately 10% of successful Level 3 students do not progress into their university degrees. This project aimed to identify the experiences of current and previous Level 3 students, using questionnaires and focus groups to explore differences by gender, ethnicity and intention to study. One hundred and two current and 56 previous level 3 students participated. Those who felt part of the university were significantly more likely to agree that the foundation course met their expectations. Personal support from academic staff, was highly ranked by students in all year groups, peaking in the final year. Despite considerable student diversity, the foundation year met expectations. However this was significantly lower for Black students compared with other ethnicities, which needs further exploration. Fostering ‘belonging’ to university is important for foundation year students to improve retention rates into their degree courses

Public perception of the collection and use of critical care patient data beyond treatment: a pilot study

No abstract available

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

Eliciting and specifying requirements for highly interactive systems using activity theory

The processes of eliciting user requirements and formalising these into specifications are critical for the success of highly interactive systems. These processes are still poorly understood, partly because current methods are usually ad hoc and lack any theoretical basis. A number of researchers have used Activity Theory (AT) to refine these processes and have met with some success. To date, this approach has been more useful explaining the processes post hoc. This positional paper proposes an AT method for requirement elicitation and specification definition. The method is sufficiently prescriptive and well formed that it does not require any detailed understanding of AT