从行政数据中识别住院住院病人的情况,并通过持续的脑电图监测来确定住院病人的情况
Marta Fernandes1,2, M Brandon Westover3, Sahar F Zafar4,5
1Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA. mbentofernandes@mgh.harvard.edu.
BMC health services research
|November 11, 2023
概括
我们开发了一个模型,使用行政数据准确地识别在急性医院环境中接受连续脑电图 (cEEG) 的患者与监测单元 (EMU).
科学领域:
- 神经学 神经学
- 医疗信息学 医疗信息学
- 医学数据科学 医学数据科学
背景情况:
- 连续电脑电图 (cEEG) 对于住院患者的发作检测和治疗至关重要.
- 行政数据集为CEEG的比较性和成本效益提供了洞察力.
- 由于急性脑电图和监测单元 (EMU) 住院的行政代码不分化,准确的患者队列鉴定具有挑战性.
研究的目的:
- 开发和验证基于医院行政数据的模型.
- 通过cEEG监测准确识别急性住院患者的入院情况.
- 为了区分这些与专门的EMU入学.
主要方法:
- 从2016年1月到2022年4月,对2016年1月至2022年4月使用cEEG的成年住院患者的回顾性队列研究.
- 使用本地EEG平台数据作为分类的黄金标准.
- 使用行政数据 (人口统计,诊断/程序代码,药物) 训练了一个极端梯度增强模型.
主要成果:
- 该队列包括9523名患者和10783名入院者 (91.5%的急性住院CEEEG,8.5%的EMU).
- 该模型实现了接收器操作曲线 (AUC) 下的面积为0.92 (95%CI [0.91-0.94]).
- 在精度回忆曲线 (AUPRC) 下的面积为0.99 (95% CI [0.98-0.99]) 获得了急性住院cEEG分类.
结论:
- 开发的模型有效地识别了行政数据集中的cEEG监控录取.
- 这种工具使得基于行政数据的大规模关于EEG利用的研究成为可能.
- 有助于更好地理解CEEG在各种医院环境中的作用.
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