LC风险评分 - - 开发和评估一个评估患长期COVID风险的尺度
Mateusz Babicki1, Mateusz Lejawa2, Tadeusz Osadnik2
1Department of Family Medicine, Wroclaw Medical University, Wroclaw, Poland.
Archives of medical science : AMS
|April 7, 2025
概括
一个新的风险评分工具可以使用基本的患者数据来预测长期COVID (LC) 风险. 这种实用工具有助于识别患有长期COVID后感染风险较高的个体.
科学领域:
- 传染性疾病 传染性疾病
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 长期COVID (LC) 构成了重大的公共卫生挑战,需要风险评估工具.
- 早期识别患有LC高风险的个体对于有针对性的干预和资源分配至关重要.
研究的目的:
- 开发和验证一个实际的风险评分来预测患长期COVID的可能性.
- 创建一个用户友好的工具,利用现有的患者信息.
主要方法:
- 使用机器学习框架 (AutoScore) 来开发LC风险评分.
- 使用了来自波兰STOP-COVID注册表 (PoLoCOV研究) 的1150名患者的数据,随机将数据分配给培训 (70%) 和测试 (30%) 队列.
- 由于研究小组的大小,使用交叉验证,并使用ROC曲线和AUC值评估模型性能.
主要成果:
- 确定了八个关键变量用于LC风险评分:女性性别,年龄40-49岁,BMI<18.5公斤/米2,住院治疗,关节痛,肌痛,味觉/嗅觉丧失,症状持续时间≥14天,以及未接种疫苗的状态.
- 开发的LC风险得分在58点的切断值下达到0.630 (95%CI:0.571-0.688) 的AUC.
- 该得分的特异性为85.1%,预测值为80.8%,用于识别长期COVID风险的个体.
结论:
- 开发的LC风险评分是评估长期COVID风险的实用和可访问的工具.
- 该得分有效地整合了社会人口统计数据,疫苗接种情况和急性COVID-19症状.
- 这个实用程序可以帮助临床医生识别那些可能从更密切的监测或长期COVID的预防策略中受益的患者.
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