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中国循证儿科杂志

中国循证儿科杂志 ›› 2024, Vol. 19 ›› Issue (3): 200-204.DOI: 10.3969/j.issn.1673-5501.2024.03.007

• 论著 • 上一篇    下一篇

儿童危重症甲型流感病毒感染危险因素的病例对照研究

谢利娜,冯特,张万存,李远哲,郭燕军   

  1. 郑州大学附属儿童医院呼吸科郑州，450000
  • 收稿日期:2024-01-29 修回日期:2024-07-26 出版日期:2024-06-25 发布日期:2024-06-25
  • 通讯作者: 郭燕军

Risk factors for severe influenza A in children：A case-control study

XIE Lina,FENG Te, ZHANG Wancun, LI Yuanzhe, GUO Yanjun   

  1. Department of Respiratory Medicine, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou  450000,China
  • Received:2024-01-29 Revised:2024-07-26 Online:2024-06-25 Published:2024-06-25
  • Contact: GUO Yanjun, email：15838106076@163.com

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摘要： 背景：部分危重症甲型流感病毒（IAV）感染患儿可发生严重的后遗症甚至死亡，但其早期的临床表现无特异性，目前国内外缺乏相关预测模型的研究。 目的：建立儿童危重症IAV感染的列线图预测模型，以帮助临床早期识别危重症IAV感染。 设计：病例对照研究。 方法：纳入2018年1月至2023年11月在郑州大学附属儿童医院住院的IAV感染连续病例，根据出院诊断结合临床资料分为危重症和非危重症患儿，截取患儿的人口学资料、入院时症状、入院时实验室检查项目、合并其他病原体感染情况。根据IAV在河南省的发生率计算样本量需＞320例，将纳入患儿按7∶3的比例随机分为建模组和验证组，在建模组病例中筛选危重症IAV感染的影响因素，并采用R 4.3.2软件包构建危重症IAV感染的列线图预测模型。 主要结局指标：儿童危重症IAV感染的影响因素。 结果：391例IAV感染住院患儿中，危重症134例，其中20例（14.9%）出现后遗症，均为神经系统受损，12例（9.0%）死亡；非危重症患儿均治愈出院；建模组274例，验证组117例，两组临床资料差异均无统计学意义。多因素Logistic回归分析显示，合并其他病原菌感染（OR=3.092，95%CI：1.379~6.934）、出现神经系统症状（OR=6.923，95%CI：2.569~18.656）、中性粒细胞与淋巴细胞比值（NLR）升高（OR=1.404，95%CI：1.029~1.914）和IL-6升高（OR=1.009，95%CI：1.000~1.018）是发生危重症IAV感染的危险因素，白蛋白升高（OR=0.925，95%CI：0.862~0.992）是发生危重症IAV感染的保护因素。以危重症IAV感染为预测结局，以有神经系统症状、合并其他病原体感染以及实验室指标NLR、IL-6、白蛋白，构建列线图预测模型。建模组和验证组AUC分别为0.949（95%CI：0.915~0.982）和0.912（95%CI：0.871~0.952）；该列线图模型拟合较好（χ2=5.077，P=0.749），具有较高的临床净获益率。 结论：基于合并其他病原体感染、神经系统症状以及实验室指标NLR、IL-6、白蛋白构建的列线图预测模型，其有效性和鉴别能力良好。

关键词: 甲型流感, 危重症, 儿童, 列线图模型, 影响因素

Abstract: Background: Some critically ill children with influenza A virus (IAV) infection may have severe sequelae or even die, but its early clinical manifestations are non-specific. At present, there is a lack of relevant prediction models at home and abroad. Objective: To establish a nomogram prediction model for critical IAV infection in children to help early clinical identification of critical IAV infection. Design: Case-control study. Methods: Consecutive patients with IAV infection who were hospitalized in Children's Hospital Affiliated to Zhengzhou University from January 2018 to November 2023 were enrolled. According to the discharge diagnosis and clinical data, they were divided into critically ill children and non-critically ill children. The demographic data, symptoms on admission, laboratory tests on admission, and co-infection with other pathogens were collected. According to the incidence of IAV in Henan Province, the samle size should be greater than 320 cases. The enrolled children were randomly divided into a modeling group and a validation group at a ratio of 7∶3. The influencing factors of critical IAV infection were screened in the modeling group, and the R 4.3.2 software package was used to construct a nomogram prediction model for critical IAV infection. Main outcome measures: Risk factors for critically ill children with influenza A virus (IAV) infection. Results: Among 391 hospitalized children with IAV infection, 134 cases were critically ill, of whom 20 cases (14.9%) had sequelae, all of them were nervous system damage, and 12 cases (9.0%) died. All the non-critically ill children were cured and discharged. There were 274 cases in the modeling group and 117 cases in the validation group. There was no significant difference in clinical data between the two groups. Multivariate Logistic regression analysis showed that neurological symptoms (OR=6.923, 95%CI: 2.569-18.656), co-infection with other pathogens (OR=3.092, 95%CI: 1.379-6.934), and elevated NLR (OR=1.404, 95%CI: 1.029-1.914) and increased IL-6 (OR=1.009, 95%CI: 1.000-1.018) are risk factors, and propagated rise (OR=0.925, 95%CI: 0.862-0.992) is a protection factor. Taking critical IAV infection as the prediction outcome, a nomogram prediction model was constructed based on neurological symptoms, combined with other pathogen infections, and laboratory indicators such as NLR, IL-6, and ALB. The AUC of the model was 0.949 (95%CI: 0.915-0.982) in the modeling group and 0.912 (95%CI: 0.871-0.952) in the validation group. The nomogram model fitted well (χ2=5.077，P=0.749), the predicted probability was in good agreement with the actual probability, and had a high net clinical benefit rate. Conclusion: The nomogram prediction model based on neurological symptoms, infection with other pathogens and laboratory indexes of NLR, IL-6 and ALB is effective and has good discriminative ability.

Key words: Influenza A, Critically ill, Children, Nomogram model, Influencing factors