开发一种喘恶化风险预测模型,用于英格兰成年人的对话使用
Constantinos Kallis1, Rafael A Calvo2, Bjorn Schuller3
1National Heart and Lung Institute, and School of Public Health, Imperial College London, London, UK.
Pragmatic and observational research
|October 11, 2023
概括
使用常规医疗保健数据,可以准确预测喘恶化风险. 一个简化模型,适合聊天机器人,显示类似的预测性能复杂的一个,帮助个性化喘管理.
科学领域:
- 医疗信息学 医疗信息学
- 临床流行病学 临床流行病学
- 公共卫生 公共卫生
背景情况:
- 喘恶化对患者构成重大风险,并增加医疗保健成本.
- 准确的风险评估和行为干预对于喘管理至关重要.
- 个性化风险预测模型可以增强患者护理并减少医疗保健负担.
研究的目的:
- 开发一个针对喘恶化个性化的风险预测模型.
- 用例行医疗保健数据进行风险评估.
- 将风险建模集成到患者支持的自动对话系统中.
主要方法:
- 使用了来自临床实践研究数据链接 (CPRD) Aurum数据库的匿名初级保健电子健康记录.
- 使用后勤回归,结合年龄,性别,种族,社会经济地位和临床喘病史等变量.
- 在三个时间队列中验证了该模型,总共超过120万名患者.
主要成果:
- 完整模型的ROC曲线下的面积 (AUC) 为0.72,限制模型为0.71.
- 使用易于自我报告变量的简化模型显示了可比的预测性能.
- 确定了恶化的关键预测因素,包括年龄较大,女性性别,药物史,并发症和社会经济贫困.
结论:
- 使用例行电子医疗记录数据开发了一种适度准确的模型,用于在3个月内预测喘恶化.
- 一个简化的模型,适合通过WhatsApp等平台进行自我报告,在预测性能上没有显著差异.
- 这种方法促进了个性化风险评估和喘恶化潜在干预措施.
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