Development a prediction model for identifying bacterial meningitis in young infants aged 29–90 days: a retrospective analysis
Abstract Background The early diagnosis and treatment of bacterial meningitis (BM) in young infants was very critical. But, it was difficult to make a definite diagnosis in the early stage due to nonspecific clinical symptoms. Our objectives were to find the risk factors associated with BM and devel...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-02-01
|
| Series: | BMC Pediatrics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12887-022-03813-1 |
| _version_ | 1811165732223844352 |
|---|---|
| author | Jiahui Wu Ting Shi Yongfei Yue Xiaoxing Kong Fangfang Cheng Yanqun Jiang Yuanxi Bian Jianmei Tian |
| author_facet | Jiahui Wu Ting Shi Yongfei Yue Xiaoxing Kong Fangfang Cheng Yanqun Jiang Yuanxi Bian Jianmei Tian |
| author_sort | Jiahui Wu |
| collection | DOAJ |
| description | Abstract Background The early diagnosis and treatment of bacterial meningitis (BM) in young infants was very critical. But, it was difficult to make a definite diagnosis in the early stage due to nonspecific clinical symptoms. Our objectives were to find the risk factors associated with BM and develop a prediction model of BM especially for young infants. Methods We retrospectively reviewed the clinical data of young infants with meningitis between January 2011 and December 2020 in Children’s Hospital of Soochow University. The independent risk factors of young infants with BM were screened using univariate and multivariate logistic regression analyses. The independent risk factors were used to construct a new scoring model and compared with Bacterial Meningitis Score (BMS) and Meningitis Score for Emergencies (MSE) models. Results Among the 102 young infants included, there were 44 cases of BM and 58 of aseptic meningitis. Group B Streptococcus (22, 50.0%) and Escherichia coli (14, 31.8%) were the main pathogens of BM in the young infants. Multivariate logistic regression analysis identified procalcitonin (PCT), cerebrospinal fluid (CSF) glucose, CSF protein as independent risk factors for young infants with BM. We assigned one point for CSF glucose ≤ 1.86 mmol/L, two points were assigned for PCT ≥ 3.80 ng/ml and CSF protein ≥ 1269 mg/L. Using the not low risk criterion (score ≥ 1) with our new prediction model, we identified the young infantile BM with 100% (95% CI 91.9%-100%) sensitivity and 60.3% (95% CI 46.4%-72.9%) specificity. Compared with BMS and MSE model, our prediction model had larger area under receiver operating characteristic curve and higher specificity, the differences were statistically significant. Conclusion Our new scoring model for young infants can facilitate early identification of BM and has a better performance than BMS and MSE models. |
| first_indexed | 2024-04-10T15:41:23Z |
| format | Article |
| id | doaj.art-cc16f564dc0543809a3295f00e9bf811 |
| institution | Directory Open Access Journal |
| issn | 1471-2431 |
| language | English |
| last_indexed | 2024-04-10T15:41:23Z |
| publishDate | 2023-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pediatrics |
| spelling | doaj.art-cc16f564dc0543809a3295f00e9bf8112023-02-12T12:22:56ZengBMCBMC Pediatrics1471-24312023-02-012311810.1186/s12887-022-03813-1Development a prediction model for identifying bacterial meningitis in young infants aged 29–90 days: a retrospective analysisJiahui Wu0Ting Shi1Yongfei Yue2Xiaoxing Kong3Fangfang Cheng4Yanqun Jiang5Yuanxi Bian6Jianmei Tian7Department of Infectious Diseases, Children’s Hospital of Soochow UniversityDepartment of Infectious Diseases, Children’s Hospital of Soochow UniversityDepartment of Obstetrics and Gynecology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal HospitalDepartment of Infectious Diseases, Children’s Hospital of Soochow UniversityDepartment of Infectious Diseases, Children’s Hospital of Soochow UniversityDepartment of Infectious Diseases, Children’s Hospital of Soochow UniversityDepartment of Infectious Diseases, Children’s Hospital of Soochow UniversityDepartment of Infectious Diseases, Children’s Hospital of Soochow UniversityAbstract Background The early diagnosis and treatment of bacterial meningitis (BM) in young infants was very critical. But, it was difficult to make a definite diagnosis in the early stage due to nonspecific clinical symptoms. Our objectives were to find the risk factors associated with BM and develop a prediction model of BM especially for young infants. Methods We retrospectively reviewed the clinical data of young infants with meningitis between January 2011 and December 2020 in Children’s Hospital of Soochow University. The independent risk factors of young infants with BM were screened using univariate and multivariate logistic regression analyses. The independent risk factors were used to construct a new scoring model and compared with Bacterial Meningitis Score (BMS) and Meningitis Score for Emergencies (MSE) models. Results Among the 102 young infants included, there were 44 cases of BM and 58 of aseptic meningitis. Group B Streptococcus (22, 50.0%) and Escherichia coli (14, 31.8%) were the main pathogens of BM in the young infants. Multivariate logistic regression analysis identified procalcitonin (PCT), cerebrospinal fluid (CSF) glucose, CSF protein as independent risk factors for young infants with BM. We assigned one point for CSF glucose ≤ 1.86 mmol/L, two points were assigned for PCT ≥ 3.80 ng/ml and CSF protein ≥ 1269 mg/L. Using the not low risk criterion (score ≥ 1) with our new prediction model, we identified the young infantile BM with 100% (95% CI 91.9%-100%) sensitivity and 60.3% (95% CI 46.4%-72.9%) specificity. Compared with BMS and MSE model, our prediction model had larger area under receiver operating characteristic curve and higher specificity, the differences were statistically significant. Conclusion Our new scoring model for young infants can facilitate early identification of BM and has a better performance than BMS and MSE models.https://doi.org/10.1186/s12887-022-03813-1PredictionYoung infantsBacterial meningitisCerebrospinal fluid pleocytosis |
| spellingShingle | Jiahui Wu Ting Shi Yongfei Yue Xiaoxing Kong Fangfang Cheng Yanqun Jiang Yuanxi Bian Jianmei Tian Development a prediction model for identifying bacterial meningitis in young infants aged 29–90 days: a retrospective analysis BMC Pediatrics Prediction Young infants Bacterial meningitis Cerebrospinal fluid pleocytosis |
| title | Development a prediction model for identifying bacterial meningitis in young infants aged 29–90 days: a retrospective analysis |
| title_full | Development a prediction model for identifying bacterial meningitis in young infants aged 29–90 days: a retrospective analysis |
| title_fullStr | Development a prediction model for identifying bacterial meningitis in young infants aged 29–90 days: a retrospective analysis |
| title_full_unstemmed | Development a prediction model for identifying bacterial meningitis in young infants aged 29–90 days: a retrospective analysis |
| title_short | Development a prediction model for identifying bacterial meningitis in young infants aged 29–90 days: a retrospective analysis |
| title_sort | development a prediction model for identifying bacterial meningitis in young infants aged 29 90 days a retrospective analysis |
| topic | Prediction Young infants Bacterial meningitis Cerebrospinal fluid pleocytosis |
| url | https://doi.org/10.1186/s12887-022-03813-1 |
| work_keys_str_mv | AT jiahuiwu developmentapredictionmodelforidentifyingbacterialmeningitisinyounginfantsaged2990daysaretrospectiveanalysis AT tingshi developmentapredictionmodelforidentifyingbacterialmeningitisinyounginfantsaged2990daysaretrospectiveanalysis AT yongfeiyue developmentapredictionmodelforidentifyingbacterialmeningitisinyounginfantsaged2990daysaretrospectiveanalysis AT xiaoxingkong developmentapredictionmodelforidentifyingbacterialmeningitisinyounginfantsaged2990daysaretrospectiveanalysis AT fangfangcheng developmentapredictionmodelforidentifyingbacterialmeningitisinyounginfantsaged2990daysaretrospectiveanalysis AT yanqunjiang developmentapredictionmodelforidentifyingbacterialmeningitisinyounginfantsaged2990daysaretrospectiveanalysis AT yuanxibian developmentapredictionmodelforidentifyingbacterialmeningitisinyounginfantsaged2990daysaretrospectiveanalysis AT jianmeitian developmentapredictionmodelforidentifyingbacterialmeningitisinyounginfantsaged2990daysaretrospectiveanalysis |