使用监督机器学习预测急性胰腺炎住院患者早期进入重症监护病房的风险
Hassam Ali1, Faisal Inayat2, Rubaid Dhillon3
1Department of Gastroenterology, East Carolina University Brody School of Medicine, Greenville, North Carolina, USA.
Proceedings (Baylor University. Medical Center)
|April 17, 2024
概括
一个新的模型预测了急性胰腺炎 (AP) 患者的重症监护室 (ICU) 入院风险. 该工具有助于临床医生早期识别高风险个体,以便及时干预.
科学领域:
- 医学研究 医学研究
- 临床信息学是一种临床信息学.
- 机器学习在医疗保健中的应用
背景情况:
- 急性胰腺炎 (AP) 是一种严重的,危及生命的疾病.
- 预测患者的结果,及早识别需要重症监护室 (ICU) 住院的患者,对于有效的管理至关重要.
- 开发一个实用的模型来评估AP患者早期ICU入院的个体风险是必不可少的.
研究的目的:
- 开发和验证一个实用模型来预测急性胰腺炎诊断的患者早期ICU入院的风险.
- 创建个性化的风险评分,以指导临床决策.
主要方法:
- 利用2019年全国再接收数据库来识别最初未被收到ICU的AP患者.
- 使用国家住院患者样本和倾向分数匹配,开发了一个匹配的AP患者队列,在7天内进入ICU,使用国家住院患者样本和倾向分数匹配.
- 采用最小绝对收缩和选择操作员 (LASSO) 回归来选择预测因素并构建ICU急性胰腺炎风险 (IAPR) 评分,通过10倍交叉验证进行验证.
主要成果:
- 该研究包括1513名AP患者;中位数年龄为50岁.
- 确定了ICU入院的关键预测因素是缺氧 (AUC 0.78),急性损伤 (AUC 0.72),心律失常 (AUC 0.61).
- 开发的诺米图表表现出了很好的区别 (AUC 0.874) 并得到了良好的校准,具有高灵敏度 (68.94%) 和特异性 (92.66%) 识别高风险患者 (得分>6).
结论:
- 一个受监督的机器学习模型,IAPR得分,有效地识别了面临ICU入院高风险的AP住院.
- 临床医生可以利用IAPR得分来主动管理可能在住院第一周内需要重症监护的AP患者.
- 该模型有助于早期识别和干预严重急性胰腺炎病例.
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