用诊断码对风险分析指数进行脆弱性评估的调整
Alis J Dicpinigaitis1,2, Yekaterina Khamzina3, Daniel E Hall1,4,5,6
1Department of Neurology, New York Presbyterian-Weill Cornell Medical Center, New York, New York.
JAMA network open
|May 24, 2024
概括
风险分析指数 (RAI) 现在可以使用行政数据 (RAI-ICD) 来量化患者的脆弱性. 这种新方法准确地预测了住院成人的不良结果,改善了护理.
科学领域:
- 老年学是一门学科.
- 医疗保健服务研究 医疗服务研究
- 医疗信息学 医疗信息学
背景情况:
- 虚弱是生理压力后不良结果的重要预测因素.
- 现有的风险分析指数 (RAI) 方法仅限于面试或特定质量的数据集.
- 需要将脆弱性量化扩展到广泛可用的行政数据.
研究的目的:
- 调整和验证风险分析指数 (RAI) 以用于国际疾病分类第十版临床修改 (ICD-10-CM) 的行政数据.
- 创建一个新的脆弱性评估工具,RAI-ICD,适用于大规模的住院患者数据集.
- 评估RAI-ICD与住院死亡率和其他不良结果的关联.
主要方法:
- 系统地将RAI参数调整为ICD-10-CM代码 (RAI-ICD).
- 使用国家住院患者样本 (NIS) 数据集 (2019-2020) 推导和验证RAI-ICD.
- 在多医院医疗保健系统 (UPMC) 和在手术/非手术住院期间进行外部验证.
- 后勤回归建模以将RAI-ICD与住院死亡率联系起来; 歧视的C统计.
主要成果:
- 在超过950万住院患者中推导并验证了RAI-ICD,证明了在住院死亡率的优秀歧视 (C统计,0.810).
- 适应的RAI-ICD参数使用了323个ICD-10-CM代码.
- 在大型数据集 (NIS,UPMC) 中的外部验证证实了良好的到优秀的歧视 (C统计数据范围从0.778到0.860).
- 定义的脆弱性层 (坚固,正常,脆弱,非常脆弱) 显示,随着脆弱性水平的提高,不良结果越来越多.
结论:
- RAI-ICD是一个严格适应,衍生和验证的工具,用于量化脆弱性.
- 这种新的方法将脆弱性评估扩展到ICD-10-CM编码的大型住院患者数据集.
- 在临床实践和研究中,RAI-ICD促进了脆弱性评估的更广泛应用.
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