53 research outputs found

    The Brain Monitoring with Information Technology (BrainIT) collaborative network:: Past, Present and Future Direction

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    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 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. In this paper, we give an overview of the aims and future directions of the group as well as present the results of an 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. Over a three year period, data collection client and web-server based tools were developed and core data (grouped into 9 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 web site and random samples of received data were selected automatically by computer for validation by data validation (DV) 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 andlimitations reported by users of the IT methods. 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 10,000therapies 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. The BrainIT core dataset is a rich dataset for hypothesis generation and post-hoc analyses provided 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.&nbsp

    Quality indicators for patients with traumatic brain injury in European intensive care units

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    Background: The aim of this study is to validate a previously published consensus-based quality indicator set for the management of patients with traumatic brain injury (TBI) at intensive care units (ICUs) in Europe and to study its potential for quality measur

    Changing care pathways and between-center practice variations in intensive care for traumatic brain injury across Europe

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    Purpose: To describe ICU stay, selected management aspects, and outcome of Intensive Care Unit (ICU) patients with traumatic brain injury (TBI) in Europe, and to quantify variation across centers. Methods: This is a prospective observational multicenter study conducted across 18 countries in Europe and Israel. Admission characteristics, clinical data, and outcome were described at patient- and center levels. Between-center variation in the total ICU population was quantified with the median odds ratio (MOR), with correction for case-mix and random variation between centers. Results: A total of 2138 patients were admitted to the ICU, with median age of 49 years; 36% of which were mild TBI (Glasgow Coma Scale; GCS 13–15). Within, 72 h 636 (30%) were discharged and 128 (6%) died. Early deaths and long-stay patients (> 72 h) had more severe injuries based on the GCS and neuroimaging characteristics, compared with short-stay patients. Long-stay patients received more monitoring and were treated at higher intensity, and experienced worse 6-month outcome compared to short-stay patients. Between-center variations were prominent in the proportion of short-stay patients (MOR = 2.3, p < 0.001), use of intracranial pressure (ICP) monitoring (MOR = 2.5, p < 0.001) and aggressive treatme

    Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

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    Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations

    Variation in Structure and Process of Care in Traumatic Brain Injury: Provider Profiles of European Neurotrauma Centers Participating in the CENTER-TBI Study.

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    INTRODUCTION: The strength of evidence underpinning care and treatment recommendations in traumatic brain injury (TBI) is low. Comparative effectiveness research (CER) has been proposed as a framework to provide evidence for optimal care for TBI patients. The first step in CER is to map the existing variation. The aim of current study is to quantify variation in general structural and process characteristics among centers participating in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. METHODS: We designed a set of 11 provider profiling questionnaires with 321 questions about various aspects of TBI care, chosen based on literature and expert opinion. After pilot testing, questionnaires were disseminated to 71 centers from 20 countries participating in the CENTER-TBI study. Reliability of questionnaires was estimated by calculating a concordance rate among 5% duplicate questions. RESULTS: All 71 centers completed the questionnaires. Median concordance rate among duplicate questions was 0.85. The majority of centers were academic hospitals (n = 65, 92%), designated as a level I trauma center (n = 48, 68%) and situated in an urban location (n = 70, 99%). The availability of facilities for neuro-trauma care varied across centers; e.g. 40 (57%) had a dedicated neuro-intensive care unit (ICU), 36 (51%) had an in-hospital rehabilitation unit and the organization of the ICU was closed in 64% (n = 45) of the centers. In addition, we found wide variation in processes of care, such as the ICU admission policy and intracranial pressure monitoring policy among centers. CONCLUSION: Even among high-volume, specialized neurotrauma centers there is substantial variation in structures and processes of TBI care. This variation provides an opportunity to study effectiveness of specific aspects of TBI care and to identify best practices with CER approaches
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