Shaw Martin, Piper Ian, Chambers Iain, Citerio Giuseppe, Enblad Per, Gregson Barbara, Howells Tim, Kiening Karl, Mattern Julia, Nilsson Pelle, Ragauskas Arminas, Sahuquillo Juan, Yau Y H
Clinical Physics, Southern General Hospital, 1345 Govan Road, Glasgow, UK.
Acta Neurochir Suppl. 2008;102:217-21. doi: 10.1007/978-3-211-85578-2_42.
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.
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.
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.
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小组合作制定脑损伤患者数据收集和分析标准，旨在为评估新的健康技术提供更高效的基础设施。
在两年时间里，当地护理人员从200名头部受伤患者处收集了核心数据集数据（分为九类）。数据通过BrainIT网站上传，计算机自动选取所接收数据的随机样本，由数据验证（DV）研究护士人员对照本地中心保存的金标准来源进行验证。将验证后的数据与发送的原始数据进行比较，并按数据类别计算错误率百分比。
根据所接收数据的规模进行了19461次比较，其中实验室数据检查次数最多（5667次），手术数据检查次数最少（567次）。错误率一般小于或等于6%，手术数据类别除外，该类别发现了高达34%的不可接受的高错误率。
BrainIT核心数据集（手术分类除外）收集起来是可行且准确的。手术分类需要修订。
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