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We describe a survey of general practitioners in the Northern Region which was carried out during the summer of 1977 as the result of a commission from the Education Committee of the North of England Faculty of the Royal College of General Practitioners. Seventy-five per cent of a one in two random sample of general practitioner principals returned a postal questionnaire in which their perceptions of postgraduate education were sought and their behaviour measured by the number of sessions they attended during the previous year at their 'usual' and other postgraduate centres. Almost half the respondents had more than a basic qualification and all but three per cent had held full-time hospital appointments, two thirds of them at the level of senior house officer or above. Their experience as general practitioner principals averaged 15 years and 57 per cent held part-time appointments outside their practices. Only four per cent had not attended any postgraduate events during the previous year but the remaining respondents had attended eight sessions on average, six of which were at their usual centres. Those attending more than the average number of sessions tended to have registered between 1950 and 1969, to work in larger practices, to hold additional appointments, or to be trainers or College tutors. Most of the respondents were conservative in their perceptions of teaching methods, the topics discussed at meetings, and the contributors to postgraduate education but the younger general practitioners and a group of established general practitioners affiliated to the Royal College of General Practitioners held more radical views. They agreed about the primacy of traditional clinical topics but were sceptical of the value of ward rounds and formal lectures and favoured the seminar and clinical attachments. They saw a need for more material about practice management and wanted experienced general practitioners and community paramedical staff as teachers in addition to hospital consultants. Most of the respondents believed that their usual centres were well organized and managed but failed to cater for the special requirements of general practitioners in non-clinical aspects of practice. Lunchtime and evenings were seen as the most convenient for weekday meetings and Sunday as the most convenient day of the week. A majority of respondents believed that post-graduate education had altered their practice of clinical medicine but only 15 per cent believed it had caused changes in their practice organization.

The BrainIT group works collaboratively on developing standards for collection and analyses of data from brain injured patients towards providing a more efficient infrastructure for assessing new health technology. Materials and methods Over a 2 year period, core dataset data (grouped by nine categories) were collected from 200 head-injured patients by local nursing staff. Data were uploaded by the BrainIT web and random samples of received data were selected automatically by computer for validation by data validation (DV) research nurse staff against gold standard sources held in the local centre. Validated data was compared with original data sent and percentage error rates calculated by data category. Findings Comparisons, 19,461, were made in proportion to the size of the data received with the largest number checked in laboratory data (5,667) and the least in the surgery data (567). Error rates were generally less than or equal to 6%, the exception being the surgery data class where an unacceptably high error rate of 34% was found.\ud Conclusions The BrainIT core dataset (with the exception of the surgery classification) is feasible and accurate to collect. The surgery classification needs to be revised.

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 39.6vs. 32.3; p&nbsp

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.

There are many serious and acute physiological conditions about which we have incomplete medical knowledge. To address this and develop effective treatments it is often the case that a wealth of clinical data is required for collection, analysis and feedback. Whilst such data often exists it is typically held in a variety of different formats and locations. This paper describes the EU FP7-funded Avert-IT project (www.avert-it.org), which has developed an integrated, real-time physiological data infrastructure (ICUnet) to address the specific issue of prediction of hypotensive events in the brain trauma domain. This system has been used to support a major multi-centre clinical trial. In this paper, the implementation and application of the ICUnet system is described, followed by the design and results of the clinical trial. © 2012 IEEE.

Hypotension is a recognized -secondary insult after traumatic brain injury (TBI). There are many definitions of hypotension, an often cited example being the Brain Trauma Foundation's current (2007) "Guidelines for the Management of Severe Traumatic Brain Injury," which defines hypotension as systolic pressure

Background: Hypotension is recognized as a potentially damaging secondary insult after traumatic brain injury. Systems to give clinical teams some early warning of likely hypotensive instability could be added to the range of existing techniques used in the management of this group of patients. By using the Edinburgh University Secondary Insult Grades (EUSIG) definitions for-hypotension (systolic arterial pressure

There are many serious and acute physiological conditions about which we have incomplete medical knowledge that can support optimal healthcare intervention. To develop effective treatments a wealth of clinical data is required for collection, analysis and feedback. Such data often does exist but is typically held in a variety of different formats and locations. This paper describes the EU FP7-funded Avert-IT project (www.avert-it.org), which has developed an integrated, real-time physiological data grid infrastructure (HypoNet) to address the specific issue of prediction of hypotensive events in the brain trauma domain and is currently being used as part of a large multi-centre clinical trial. The implementation and application of the HypoNet system is described here. © 2010 IEEE.

The ability to predict adverse hypotensive events, where a patient's arterial blood pressure drops to abnormally low (and dangerous) levels, would be of major benefit to the fields of primary and secondary health care, and especially to the traumatic brain injury domain. A wealth of data exist in health care systems providing information on the major health indicators of patients in hospitals (blood pressure, temperature, heart rate, etc.). It is believed that if enough of these data could be drawn together and analysed in a systematic way, then a system could be built that will trigger an alarm predicting the onset of a hypotensive event over a useful time scale, e.g. half an hour in advance. In such circumstances, avoidance measures can be taken to prevent such events arising. This is the basis for the Avert-IT project (http://www.avert-it.org), a collaborative EU-funded project involving the construction of a hypotension alarm system exploiting Bayesian neural networks using techniques of data federation to bring together the relevant information for study and system development.

Objective: To assess the use of hyperventilation and the adherence to Brain Trauma Foundation-Guidelines (BTF-G) after traumatic brain injury (TBI). Setting: Twenty-two European centers are participating in the BrainIT initiative. Design: Retrospective analysis of monitoring data. Patients and participants: One hundred and fifty-one patients with a known time of trauma and at least one recorded arterial blood-gas (ABG) analysis. Measurements and results: A total number of 7,703 ABGs, representing 2,269 ventilation episodes (VE) were included in the analysis. Related minute-by-minute ICP data were taken from a 30 min time window around each ABG collection. Data are given as mean with standard deviation. (1) Patients without elevated intracranial pressure (ICP) (

OBJECTIVE: The aim of this study was to evaluate the robustness and zero-drift of an intracranial pressure sensor, Neurovent-P (Raumedic AG, Munchberg, Germany), when used in the clinical environment. METHODS: A prospective multicenter trial, conforming to the International Organization for Standardization 14155 Standard, was conducted in 6 European BrainIT centers between July 2005 and December 2006. Ninety-nine catheters were used. The study was observational, followed by a centralized sensor bench test after catheter removal. RESULTS: The mean recorded value before probe insertion was 0.17 ± 1.1 mm Hg. Readings outside the range ± 1 mm Hg were recorded in only 3 centers on a total of 15 catheters. Complications were minimal and mainly related to the insertion bolt. The mean recorded pressure value at removal was 0.8 ± 2.2 mm Hg. No relationship was identified between postremoval reading and length of monitoring. The postremoval : bench test indicated the probability of a system failure, defined as a drift of more than 3 mm Hg, at a range between 12 and 17%. : CONCLUSION: The Neurovent-P catheter performed well in clinical use in terms of robustness. The majority of technical complications were associated with the bolt fixation technology. Adverse events were rare and clinically nonsignificant. Despite the earlier reported excellent bench test zero-drift rates, under the more demanding clinical conditions, zero-drift rate remains a concern with catheter tip strain gauge technology. This performance is similar, and not superior, to other intracranial pressure devices.

Background The BrainIT group works collaboratively on developing standards for collection and analyses of data from brain injured patients towards providing a more efficient infrastructure for assessing new health technology. Materials and methods Over a 2 year period, core dataset data (grouped by nine categories) were collected from 200 head-injured patients by local nursing staff. Data were uploaded by the BrainIT web and random samples of received data were selected automatically by computer for validation by data validation (DV) research nurse staff against gold standard sources held in the local centre. Validated data was compared with original data sent and percentage error rates calculated by data category. Findings Comparisons, 19,461, were made in proportion to the size of the data received with the largest number checked in laboratory data (5,667) and the least in the surgery data (567). Error rates were generally less than or equal to 6%, the exception being the surgery data class where an unacceptably high error rate of 34% was found. Conclusions The BrainIT core dataset (with the exception of the surgery classification) is feasible and accurate to collect. The surgery classification needs to be revised. © 2008 Springer-Verlag/Wien.

Objective: To determine the statistical characteristics of blood pressure (BP) readings from a large number of head-injured patients. Methods: The BrainIT group has collected high time-resolution physiological and clinical data from head-injured patients who require intracranial pressure (ICP) monitoring. The statistical features of this dataset of BP measurements with time resolution of 1 min from 200 patients is examined. The distributions of BP measurements and their relationship with simultaneous ICP measurements are described. Results: The distributions of mean, systolic and diastolic readings are close to normal with modest skewing towards higher values. There is a trend towards an increase in blood pressure with advancing age, but this is not significant. Simultaneous blood pressure and ICP values suggest a triphasic relationship with a BP rising at 0.28 mm Hg/mm Hg of ICP, for ICP up to 32 mm Hg, and 0.9 mm Hg/mm Hg of ICP for ICP from 33 to 55 mm Hg, and falling sharply with rising ICP for ICP >55 mm Hg. Conclusions: Patients with head injury appear to have a near normal distribution of blood pressure readings that are skewed towards higher values. The relationship between BP and ICP may be triphasic.