儿童百日咳发病的危险因素分析及列线图预测模型构建

Risk factor and construction of nomogram prediction model for pertussis in children

  • 摘要:
    目的 探讨儿童百日咳发病的危险因素,构建儿童百日咳发病风险的列线图预测模型。
    方法 选取2024年2—6月收治的175例迁延性咳嗽患儿作为建模组,根据是否确诊百日咳,将其分成百日咳组和对照组。比较2组患儿的一般情况、临床特征及实验室检查指标水平,采用Logistic回归模型筛选儿童百日咳的危险因素,构建列线图预测模型,并进行内部验证。另选取2024年7—8月收治的53例迁延性咳嗽患儿作为验证组,对列线图模型进行外部验证。
    结果 建模组175例患儿中, 52例(29.71%)确诊百日咳。百日咳组室内通风差者占比、发病前3周与咳嗽患者接触者占比、发病前3周被动吸烟者占比、未接种百日咳疫苗者占比及血小板水平均高于对照组,差异有统计学意义(P < 0.05); 多因素Logistic回归分析结果显示,室内通风差、发病前3周与咳嗽患者接触、发病前3周被动吸烟、未接种百日咳疫苗为儿童百日咳发病的独立危险因素(OR=2.983、4.943、3.998、5.943, P < 0.05), 据此构建预测儿童百日咳发病的列线图模型。内部验证结果显示,该列线图模型预测儿童百日咳发病的曲线下面积(AUC)为0.824, 灵敏度为82.70%, 特异度为74.80%, 拟合优度Hosmer-Lemeshow检验示χ2=7.591, P=0.425; 外部验证结果显示, AUC为0.799, 灵敏度为80.80%, 特异度为63.40%, 拟合优度Hosmer-Lemeshow检验示χ2=10.369, P=0.169。
    结论 室内通风差、发病前3周与咳嗽患者接触、发病前3周被动吸烟、未接种百日咳疫苗为儿童百日咳发病的独立危险因素,据此构建的列线图预测模型能够有效预测儿童百日咳发病风险。

     

    Abstract:
    Objective To explore the risk factors for pertussis in children and construct a nomogram prediction model for the risk of pertussis in children.
    Methods A total of 175 children with prolonged cough admitted from February to June 2024 were selected as modeling group and divided into pertussis group and control group based on whether pertussis was confirmed. The general conditions, clinical characteristics, and laboratory examination indicators of the two groups were compared. Logistic regression analysis was used to screen risk factors for pertussis in children, and a nomogram prediction model was constructed and internally validated. Additionally, 53 children with prolonged cough admitted from July to August 2024 were selected as validation group to externally validate the nomogram model.
    Results Among 175 children in the modeling group, 52 (29.71%) were diagnosed with pertussis. The proportions of children with poor indoor ventilation, contacting with coughing patients in 3 weeks before onset, passive smoking in 3 weeks before onset, and those who had not received the pertussis vaccine, as well as platelet levels, were higher in the pertussis group than in the control group (P < 0.05). Multivariate Logistic regression analysis showed that poor indoor ventilation, contacting with coughing patients in 3 weeks before onset, passive smoking in 3 weeks before onset, and not receiving the pertussis vaccine were independent risk factors for pertussis in children (OR=2.983, 4.943, 3.998, 5.943; P < 0.05). Based on these results, a nomogram model for predicting pertussis in children was constructed. The internal validation results showed that the area under the curve (AUC) of this nomogram model for predicting pertussis in children was 0.824, with a sensitivity of 82.70%, a specificity of 74.80%, and a goodness-of-fit Hosmer-Lemeshow test result of χ2=7.591, P=0.425. The external validation results showed an AUC of 0.799, with sensitivity of 80.80%, with specificity of 63.40%, and a goodness-of-fit Hosmer-Lemeshow test result of χ2=10.369, P=0.169.
    Conclusion Poor indoor ventilation, contacting with coughing patients in 3 weeks before onset, passive smoking in 3 weeks before onset, and not receiving the pertussis vaccine are independent risk factors for pertussis in children. The nomogram prediction model constructed based on these factors can effectively predict the risk of pertussis in children.

     

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