Department of Infectious Disease, Tianjin Second People's Hospital, No. 7 Sudinan Road, Nankai District, Tianjin, 300192, China.
Department of Pharmacy, Tianjin Second People's Hospital, Tianjin, 300192, China.
BMC Infect Dis. 2022 Jan 4;22(1):24. doi: 10.1186/s12879-021-07001-x.
To identify risk factors associated with the prognosis of pertussis in infants (< 12 months).
A retrospective study on infants hospitalized with pertussis January 2017 to June 2019. The infants were divided into two groups according to the severity of disease: severe pertussis and non-severe pertussis groups. We collected all case data from medical records including socio-demographics, clinical manifestations, and auxiliary examinations. Univariate analysis and Logistic regression were used.
Finally, a total of 84 infants with severe pertussis and 586 infants with non-severe pertussis were admitted. The data of 75% of the cases (severe pertussis group, n = 63; non-severe pertussis group, n = 189) were randomly selected for univariate and multivariate logistic regression analysis. The results showed rural area [P = 0.002, OR = 6.831, 95% CI (2.013-23.175)], hospital stay (days) [P = 0.002, OR = 1.304, 95% CI (1.107-1.536)], fever [P = 0.040, OR = 2.965, 95% CI (1.050-8.375)], cyanosis [P = 0.008, OR = 3.799, 95% CI (1.419-10.174)], pulmonary rales [P = 0.021, OR = 4.022, 95% CI (1.228-13.168)], breathing heavily [P = 0.001, OR = 58.811, 95% CI (5.503-628.507)] and abnormal liver function [P < 0.001, OR = 9.164, 95% CI (2.840-29.565)] were independent risk factors, and higher birth weight [P = 0.006, OR = 0.380, 95% CI (0.191-0.755)] was protective factor for severe pertussis in infants. The sensitivity and specificity of logistic regression model for remaining 25% data of severe group and common group were 76.2% and 81.0%, respectively, and the consistency rate was 79.8%.
The findings indicated risk factor prediction models may be useful for the early identification of severe pertussis in infants.
目的 确定与婴儿（<12 个月）百日咳预后相关的危险因素。
回顾性分析 2017 年 1 月至 2019 年 6 月因百日咳住院的婴儿病例。根据疾病严重程度将婴儿分为严重百日咳组和非严重百日咳组。我们从病历中收集所有病例数据，包括社会人口统计学、临床表现和辅助检查。采用单因素分析和 Logistic 回归分析。
最终共纳入 84 例严重百日咳患儿和 586 例非严重百日咳患儿。严重百日咳组和非严重百日咳组分别随机抽取 75%（严重百日咳组，n=63；非严重百日咳组，n=189）的病例数据进行单因素和多因素 Logistic 回归分析。结果显示，农村地区（P=0.002，OR=6.831，95%CI（2.013-23.175））、住院天数（P=0.002，OR=1.304，95%CI（1.107-1.536））、发热（P=0.040，OR=2.965，95%CI（1.050-8.375））、发绀（P=0.008，OR=3.799，95%CI（1.419-10.174））、肺部啰音（P=0.021，OR=4.022，95%CI（1.228-13.168））、呼吸急促（P=0.001，OR=58.811，95%CI（5.503-628.507））和肝功能异常（P<0.001，OR=9.164，95%CI（2.840-29.565））是独立的危险因素，出生体重大（P=0.006，OR=0.380，95%CI（0.191-0.755））是婴儿严重百日咳的保护因素。严重组和普通组剩余 25%数据的逻辑回归模型的灵敏度和特异度分别为 76.2%和 81.0%，一致性率为 79.8%。
这些发现表明，危险因素预测模型可能有助于早期识别婴儿严重百日咳。
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