The brain monitoring with Information Technology (BrainIT) collaborative network: EC feasibility study results and future direction.

BACKGROUND: The BrainIT group works collaboratively on developing standards for collection and analyses of data from brain-injured patients and to facilitate a more efficient infrastructure for assessing new health care technology with the primary objective of improving patient care. European Community (EC) funding supported meetings over a year to discuss and define a core dataset to be collected from patients with traumatic brain injury using IT-based methods. We now present the results of a subsequent EC-funded study with the aim of testing the feasibility of collecting this core dataset across a number of European sites and discuss the future direction of this research network. METHODS: Over a 3-year period, data collection client- and web-server-based tools were developed and core data (grouped into nine categories) were collected from 200 head-injured patients by local nursing staff in 22 European neuro-intensive care centres. Data were uploaded through the BrainIT website and random samples of received data were selected automatically by computer for validation by data validation staff against primary sources held in each local centre. Validated data were compared with originally transmitted data and percentage error rates calculated by data category. Feasibility was assessed in terms of the proportion of missing data, accuracy of data collected and limitations reported by users of the IT methods. FINDINGS: Thirteen percent of data files required cleaning. Thirty "one-off" demographic and clinical data elements had significant amounts of missing data (>15%). Validation staff conducted 19,461 comparisons between uploaded database data with local data sources and error rates were commonly less than or equal to 6%, the exception being the surgery data class where an unacceptably high error rate of 34% was found. Nearly 10,000 therapies were successfully recorded with start-times but approximately a third had inaccurate or missing "end-times" which limits the analysis of duration of therapy. Over 40,000 events and procedures were recorded but events with long durations (such as transfers) were more likely to have end-times missed. CONCLUSIONS: The BrainIT core dataset is a rich dataset for hypothesis generation and post hoc analyses, provided that studies avoid known limitations in the dataset. Limitations in the current IT-based data collection tools have been identified and have been addressed. In order for multi-centre data collection projects to be viable, the resource intensive validation procedures will require a more automated process and this may include direct electronic access to hospital-based clinical data sources for both validation purposes and for minimising the duplication of data entry. This type of infrastructure may foster and facilitate the remote monitoring of patient management and protocol adherence in future trials of patient management and monitoring.

The brain monitoring with Information Technology (BrainIT) collaborative network: data validation results.

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.

BrainIT collaborative network: analyses from a high time-resolution dataset of head injured patients.

BACKGROUND: The BrainIT project was conceived in 1997 and has grown into an international collaboration with the purpose of gathering high time resolution data from head injured patients utilising standardised methodologies. MATERIALS AND METHODS: From 1998, 22 participating neuroscience centres collected three main types of information: demographic, physiological data and clinical treatment information. A data collection solution was provided for each centre dependent on their existing facilities and data were collected for the duration of monitoring as defined by the routine care in each centre. On completion of ICP monitoring all personal information was removed and then transferred to Glasgow via the internet where it was converted into a standard format and entered into a central database. Outcome was measured using the extended Glasgow Outcome Score using an interview questionnaire. FINDINGS: Data has been obtained from a total of 349 patients (277 male and 72 female) The age of these patients ranged from 1 to 87 years (median 31); 145 had been involved in a traffic accident and 32 were pedestrians; 78 had suffered a fall; 24 were assaulted and the remaining 70 of other causes. A large amount of physiological data was collected (e.g. BP 2,531 days, ICP 2,212 days in total). This dataset has provided the opportunity to perform unique analysis and these include the statistical features of blood pressure, diurnal variations in ICP, optimal sampling rate determination and a comparison of summary measures of secondary insults. CONCLUSIONS: This challenging collaboration has brought together a large number of centres and developed a successful clinical research network focussed on improving the treatment of head injured patients. It has successfully collected a vast quantity of high quality data that provides a rich source for analysis and hypothesis testing.