自发性脑内出血后急性损伤的预测模型:一个多中心的回顾性研究
Yixin Tian1, Yu Zhang2, Jialing He1,3
1Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
European stroke journal
|June 27, 2023
概括
一个新的模型,ICH-AKIM,准确预测急性损伤 (AKI) 在脑内出血 (ICH) 的患者. 该工具改进了现有的模型,以更好地评估和管理患者的风险.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 神经学 神经学
- 医疗信息学 医疗信息学
背景情况:
- 急性损伤 (AKI) 是脑内出血 (ICH) 患者的常见并发症.
- 现有的AKI预测模型对于ICH患者群体缺乏特异性.
- 对于ICH患者的AKI,有一个专门的风险评估工具的急需.
研究的目的:
- 开发和验证急性损伤 (AKI) 在脑内出血 (ICH) 患者中的特定预测模型.
- 在ICH患者中确定AKI的关键临床和实验室预测因素.
- 创建一个工具,提高AKI风险预测在这个脆弱的患者群体的准确性.
主要方法:
- 一个多变量逻辑回归模型 (ICH-AKIM) 是使用 LASSO 回归和双向阶段方法构建的.
- 来自四个医疗中心的9649名患者的数据被用于模型开发和验证.
- 模型的性能使用接收器操作特征曲线 (AUC) 下的面积进行评估,并与现有模型进行比较.
主要成果:
- 五种临床特征 (性别,缩血压,糖尿病,格拉斯哥昏迷尺度,曼尼托尔输液) 和四种实验室测试 (血清肌素,白蛋白,尿酸,中性粒细胞与淋巴细胞的比率) 被确定为重要的预测因素.
- ICH-AKIM表现出强大的预测准确性,AUC值在导出和验证队列中从0.776到0.821不等.
- 在歧视和重新分类方面,ICH-AKIM模型显著优于单变量和先前存在的AKI预测模型.
结论:
- 开发的ICH-AKIM模型表现出强大的区分能力,用于预测ICH后患者的AKI.
- 在脑内出血的背景下,ICH-AKIM超越了目前对AKI的预测模型的性能.
- ICH-AKIM有一个可访问的在线界面,以促进其临床应用.
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