预测两个学术医疗系统中喘儿童的急诊室访问
Tyler J Gorham1, Dmitry Tumin2, Judith Groner3,4
1Information Technology Research & Innovation, Nationwide Children's Hospital, Columbus, OH, USA.
概括
一个新的喘紧急风险 (AER) 评分有效预测儿科喘紧急情况. 在外部验证中,AER评分的本地重新训练提高了其准确性,突出了需要进行特定地点的调整的需要.
科学领域:
- 儿科急诊医学 儿科急诊医学
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 喘是儿童常见的慢性呼吸道疾病.
- 预测喘相关的紧急情况对于及时干预和资源分配至关重要.
- 现有的风险预测模型可能需要适应不同的医疗保健环境.
研究的目的:
- 开发和验证一个预测算法,喘紧急风险 (AER) 评分,用于儿科喘紧急情况.
- 评估AER评分在外部临床场所的表现.
- 评估本地再培训对算法的外部有效性的影响.
主要方法:
- 通过使用26,008名儿科喘患者 (年龄在2-18岁) 的回顾性数据开发了一种拉索调节后勤回归模型.
- 内部验证对8,634例患者接触进行,外部验证对1,313例第二站点接触进行.
- 通过使用外部站点数据进行物流回归来重新加权AER评分的组件,以提高本地性能.
主要成果:
- AER评分显示出强大的内部有效性,AUROC为0.769.
- 第二个地点的初始外部验证结果为AUROC为0.684.
- 当地再培训显著提高了交叉验证的AUROC到0.737 (p=0.037),表明增强了外部有效性.
结论:
- AER评分显示了儿童喘紧急情况的强大的内部预测能力.
- AER评分的外部有效性受到当地数据特征的重大影响.
- 预测模型的本地再培训对于优化不同临床环境中的性能至关重要.
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