| 摘要: |
| 目的:探讨新生儿重症监护病房(NICU)早产儿先天性巨细胞病毒(CMV)感染的围产期高危因素,构建并验证风险预测
模型,并提出基于风险分层的早期药物干预策略。方法:回顾性纳入我院NICU 2022-2024 年收治的早产儿368 例。收集母亲
CMV-IgM/ IgG 状态、CMV-DNA 病毒载量,新生儿胎龄、出生体质量、Apgar 评分及机械通气史等临床资料。通过单因素和多因
素logistic 回归筛选独立预测因子,构建列线图模型。利用受试者工作特征曲线(ROC)、校准曲线及决策曲线分析(DCA) 评价
模型性能。结果:368 例早产儿中,CMV 阳性28 例。单因素分析显示,母亲CMV-IgM 阳性、病毒载量、胎龄、出生体质量、Apgar
评分、机械通气史与感染显著相关。多因素logistic 回归显示,母亲CMV-IgM 阳性与母亲病毒载量升高为早产儿先天性CMV 感
染的独立危险因素。胎龄与出生体质量的增加与感染风险降低相关。模型曲线下面积( AUC) 为0. 899(95% CI 0. 853 ~
0. 945),最佳截点0. 15 的灵敏度85. 7%、特异度79. 1%。结论:母亲CMV-IgM 阳性、高病毒载量、低胎龄和低出生体质量是早
产儿先天性CMV 感染的围产期高危因素。基于该模型的风险分层药物干预策略,可实现早产儿先天性CMV 感染的精准防治,
在提高治疗效果的同时减少过度医疗,具有重要的临床应用价值和卫生经济学意义。 |
| 关键词: 先天性 巨细胞病毒 早产儿 风险预测模型 |
| DOI：doi:10.13407/j.cnki.jpp.1672-108X.2026.08.002 |
|
| 基金项目:基金项目:北京市科技计划项目,编号2221100007422100。 |
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| Analysis of Perinatal Risk Factors and Nomogram Prediction Model for Congenital Cytomegalovirus Infection inPreterm Infants |
| Zhang Qian, Yu Jinqian, Shen Yingjie, Zhao Tianjiao |
| (Beijing Shunyi Women and Children’s Hospital, Shunyi Women and
Children’s Hospital of Beijing Children’s Hospital, Beijing 101300, China) |
| Abstract: |
| Objective: To explore the perinatal high-risk factors for congenital cytomegalovirus (CMV) infection in preterm infants from
the Neonatal Intensive Care Unit (NICU), construct and validate a risk prediction nomogram model, and propose an early drug
intervention strategy based on risk stratification. Methods: A total of 368 preterm infants hospitalized in NICU of our hospital from 2022
to 2024 were retrospectively enrolled. Maternal CMV-IgM/ IgG status, CMV-DNA viral load, and neonatal gestational age, birth weight,
Apgar score, history of mechanical ventilation and other clinical data were collected. Univariate and multivariate logistic regression
analysis were performed to screen independent predictors, and a nomogram model was constructed. Results: Among the 368 preterm
infants, 28 cases were CMV-positive. Univariate analysis showed that positive maternal CMV-IgM, maternal viral load, gestational age,
birth weight, Apgar score, and history of mechanical ventilation were significantly associated with infection. Multivariate logistic
regression revealed that positive maternal CMV-IgM and maternal viral load were independent risk factors for congenital CMV infection in
preterm infants. Increased gestational age and birth weight were correlated with a reduced risk of infection. The area under the curve
(AUC) of the model was 0. 899 (95% CI 0. 853 to 0. 945). At the optimal cut-off value of 0. 15, the sensitivity was 85. 7% and the
specificity was 79. 1%. Conclusion: Positive maternal CMV-IgM, elevated maternal viral load, low gestational age and low birth weight
are perinatal risk factors for congenital CMV infection in premature infants. The risk-stratified drug intervention strategy based on this
model enables precise prevention and treatment of congenital CMV infection in preterm infants, improving therapeutic efficacy while
reducing overtreatment, thereby demonstrating significant clinical application value and health economics implications. |
| Key words: congenital cytomegalovirus preterm infants risk prediction model |
