构建和验证新生儿重症肺炎风险因素预测模型
Weihua Gong1, Kaijie Gao1, Jiajia Ni2
1Department of Clinical Laboratory, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou Key Laboratory of Children's Infection and Immunity, Zhengzhou, Henan, China.
Frontiers in medicine
|June 18, 2025
概括
一个新的模型准确地预测新生儿的严重肺炎,使用呼吸速率,体重,CRP,中性粒细胞,血红蛋白,尿酸和BUN. 这种工具有助于区分新生儿的严重病例和轻度至中度的肺炎.
科学领域:
- 新生儿医学 新生儿医学
- 儿科肺病学 儿科肺病学
- 医疗信息学 医疗信息学
背景情况:
- 新生儿肺炎对健康构成重大风险,需要准确预测严重程度,以便及时干预.
- 早期发现重症新生儿肺炎对于改善患者的治疗结果和资源配置至关重要.
研究的目的:
- 开发和验证用于识别新生儿严重肺炎相关风险因素的预测模型.
- 建立一个可靠的工具,在新生儿群体中区分轻度至中度和严重的肺炎.
主要方法:
- 利用652名被诊断患有肺炎的新生儿的数据.
- 雇员最小绝对缩小和选择运营商 (LASSO) 回归和后勤回归以确定风险因素.
- 构建了一个基于名ogram的预测模型,并使用校准曲线,ROC曲线和决策曲线分析 (DCA) 验证了其有效性.
主要成果:
- 确定了重症新生儿肺炎的独立风险因素:呼吸速率,体重,C反应蛋白 (CRP),中性粒细胞计数 (NEU),血红蛋白 (HGB),尿酸 (UA) 和血液尿素 (BUN).
- 预测模型表现出强的性能,ROC曲线下的面积 (AUC) 在训练组中为0.884,在测试组中为0.835.
- 校准曲线显示出显著的一致性,DCA证实了良好的预测性质.
结论:
- 开发的预测模型结合了关键的临床和实验室指标,显示了预测新生儿严重肺炎的巨大潜力.
- 这种模型可以帮助临床医生区分严重的肺炎和较轻的形式,促进适当的管理策略.
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