■甲型H1N1流感病毒活性显著反弹,特别是在儿童中,随着住院病例数量的快速增长,在后COVID-19时代是一个主要问题。本研究旨在建立后COVID-19时代H1N1感染住院患儿重症病例预测模型。
■这是一项跨云南9家公立三甲医院的多中心回顾性研究,中国,将2023年2月1日至7月1日在其中5个中心住院的儿科H1N1住院患者纳入开发数据集.筛选40个变量,包括人口统计信息,临床特征,和实验室参数使用最小绝对收缩和选择算子(LASSO)回归和逻辑回归来确定严重H1N1感染的独立危险因素,从而构造一个预测列线图。接收机工作特性(ROC)曲线,校正曲线,以及决策曲线分析(DCA)用于评估模型的性能。在2023年7月25日至10月31日期间,来自另外四家医院的儿科H1N1住院患者的四个独立队列的数据被用于外部验证这个列线图。
■开发数据集包括527个受试者,122人(23.1%)发生严重H1N1感染。外部验证数据集包括352名受试者,其中72人(20.5%)最终被确认为严重H1N1感染。LASSO回归确定了19个候选预测因子,Logistic回归进一步缩小到11个独立的危险因素,包括潜在条件,早产,发烧持续时间,喘息,食欲不振,白细胞计数,中性粒细胞-淋巴细胞比率(NLR),红细胞沉降率(ESR),乳酸脱氢酶(LDH),白细胞介素-10(IL-10),和肿瘤坏死因子-α(TNF-α)。通过整合这11个因素,建立了预测列线图。在预测严重H1N1感染方面,优秀的辨别能力,良好的准确性,通过ROC曲线在内部和外部验证了该模型的令人满意的临床有效性,校正曲线,和DCA,分别。
■我们的研究成功地建立并验证了一种新颖的列线图模型,该模型整合了基础条件,早产,发烧持续时间,喘息,食欲不振,白细胞计数,NLR,ESR,LDH,IL-10和TNF-α。此列线图可以有效预测后COVID-19时代儿科H1N1住院患者严重病例的发生,促进对此类患者的早期识别和更有效的临床管理。
UNASSIGNED: The significant rebound of influenza A (H1N1) virus activity, particularly among children, with rapidly growing number of hospitalized cases is of major concern in the post-COVID-19 era. The present study was performed to establish a prediction model of severe case in pediatric patients hospitalized with H1N1 infection during the post-COVID-19 era.
UNASSIGNED: This is a multicenter retrospective study across nine public tertiary hospitals in Yunnan, China, recruiting pediatric H1N1 inpatients hospitalized at five of these centers between February 1 and July 1, 2023, into the development dataset. Screening of 40 variables including demographic information, clinical features, and laboratory parameters were performed utilizing Least Absolute Shrinkage and Selection Operator (LASSO) regression and logistic regression to determine independent risk factors of severe H1N1 infection, thus constructing a prediction nomogram. Receiver operating characteristic (ROC) curve, calibration curve, as well as decision curve analysis (DCA) were employed to evaluate the model\'s performance. Data from four independent cohorts comprised of pediatric H1N1 inpatients from another four hospitals between July 25 and October 31, 2023, were utilized to externally validate this nomogram.
UNASSIGNED: The development dataset included 527 subjects, 122 (23.1 %) of whom developed severe H1N1 infection. The external validation dataset included 352 subjects, 72 (20.5 %) of whom were eventually confirmed as severe H1N1 infection. The LASSO regression identified 19 candidate predictors, with logistic regression further narrowing down to 11 independent risk factors, including underlying conditions, prematurity, fever duration, wheezing, poor appetite, leukocyte count, neutrophil-lymphocyte ratio (NLR), erythrocyte sedimentation rate (ESR), lactate dehydrogenase (LDH), interleukin-10 (IL-10), and tumor necrosis factor-α (TNF-α). By integrating these 11 factors, a predictive nomogram was established. In terms of prediction of severe H1N1 infection, excellent discriminative capacity, favorable accuracy, and satisfactory clinical usefulness of this model were internally and externally validated via ROC curve, calibration curve, and DCA, respectively.
UNASSIGNED: Our study successfully established and validated a novel nomogram model integrating underlying conditions, prematurity, fever duration, wheezing, poor appetite, leukocyte count, NLR, ESR, LDH, IL-10, and TNF-α. This nomogram can effectively predict the occurrence of serious case in pediatric H1N1 inpatients during the post-COVID-19 era, facilitating the early recognition and more efficient clinical management of such patients.