儿童长期COVID发展的预测模型
Vita Perestiuk1, Andriy Sverstyuk2, Tetyana Kosovska1
1Department of Children's Diseases and Pediatric Surgery, I. Horbachevsky Ternopil National Medical University, 46001 Ternopil, Ukraine.
International journal of environmental research and public health
|November 27, 2025
概括
这项研究开发了一种机器学习模型,用于预测儿童长期COVID风险. 关键预测因素包括肥胖,过敏,发烧和维生素D水平,有助于早期发现和管理儿科长期COVID.
科学领域:
- 儿童传染病 儿童传染病
- 计算医学是一种计算医学.
- 流行病学 流行病学
背景情况:
- 长期COVID对儿童构成重大风险,需要早期干预的预测工具.
- 机器学习提供了一种有希望的方法来识别SARS-CoV-2感染后患长期并发症的高风险儿童.
研究的目的:
- 在SARS-CoV-2感染后的儿科患者中开发和验证长期COVID的预测算法.
- 确定与儿童长期COVID发育相关的人口,临床和实验室因素.
主要方法:
- 一项对305名从急性SARS-CoV-2感染中康复的儿科患者 (1个月至18岁) 的横截面研究.
- 开发两种多变量回归模型,包括各种临床和实验室参数.
- 分析包括人口统计数据,并发症,急性感染症状,维生素D,水平,全血细胞计数和凝血概况.
主要成果:
- 在分析的265名儿童中,52.0%患有长期COVID.
- 模型1 (包括非住院患者) 确定肥胖,过敏性疾病和维生素D缺乏是显著的预测因素.
- 模型2 (住院患者) 确定了发烧,≥3个急性症状,过敏状况,血栓细胞瘤,中性恋和改变的前列血时间作为显著预测因素.
结论:
- 肥胖,过敏病理,发烧,多种急性症状,血栓细胞瘤,中性恋,改变的前列血时间和低维生素D水平是儿童长期COVID的重要预测因素.
- 开发的预测模型具有高度可接受性,并且比简单的平均预测性能更好.
- 这些发现可以指导临床实践,及时检测和管理儿科长期COVID.
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