Risk factors and prediction model of severe pertussis in infants < 12 months of age in Tianjin, China
Background To identify risk factors associated with the prognosis of pertussis in infants (< 12 months). Methods 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. Results 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%. Conclusions The findings indicated risk factor prediction models may be useful for the early identification of severe pertussis in infants..
Medienart: |
E-Artikel |
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Erscheinungsjahr: |
2022 |
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Erschienen: |
2022 |
Enthalten in: |
Zur Gesamtaufnahme - volume:22 |
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Enthalten in: |
BMC infectious diseases - 22(2022), 1 vom: 04. Jan. |
Sprache: |
Englisch |
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Beteiligte Personen: |
Zhang, Cui [VerfasserIn] |
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Links: |
Volltext [kostenfrei] |
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Themen: |
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Anmerkungen: |
© The Author(s) 2021 |
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doi: |
10.1186/s12879-021-07001-x |
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funding: |
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PPN (Katalog-ID): |
SPR050336002 |
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520 | |a Background To identify risk factors associated with the prognosis of pertussis in infants (< 12 months). Methods 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. Results 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%. Conclusions The findings indicated risk factor prediction models may be useful for the early identification of severe pertussis in infants. | ||
650 | 4 | |a Pertussis |7 (dpeaa)DE-He213 | |
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700 | 1 | |a Zong, Yanmei |4 aut | |
700 | 1 | |a Wang, Zhe |4 aut | |
700 | 1 | |a Wang, Li |4 aut | |
700 | 1 | |a Li, Ying |4 aut | |
700 | 1 | |a Yang, Yuejie |4 aut | |
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