Diagnosis of Epstein-Barr and cytomegalovirus infections using decision trees: an effective way to avoid antibiotic overuse in paediatric tonsillopharyngitis
Abstract Background The incidence of tonsillopharyngitis is especially prevalent in children. Despite the fact that viruses cause the majority of infections, antibiotics are frequently used as a treatment, contrary to international guidelines. This is not only an inappropriate method of treatment fo...
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| Format: | Article |
| Language: | English |
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BMC
2023-06-01
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| Series: | BMC Pediatrics |
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| Online Access: | https://doi.org/10.1186/s12887-023-04103-0 |
| _version_ | 1797801258257481728 |
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| author | Andrea Tímea Takács Mátyás Bukva Csaba Bereczki Katalin Burián Gabriella Terhes |
| author_facet | Andrea Tímea Takács Mátyás Bukva Csaba Bereczki Katalin Burián Gabriella Terhes |
| author_sort | Andrea Tímea Takács |
| collection | DOAJ |
| description | Abstract Background The incidence of tonsillopharyngitis is especially prevalent in children. Despite the fact that viruses cause the majority of infections, antibiotics are frequently used as a treatment, contrary to international guidelines. This is not only an inappropriate method of treatment for viral infections, but it also significantly contributes to the emergence of antibiotic-resistant strains. In this study, EBV and CMV-related tonsillopharyngitis were distinguished from other pathogens by using machine learning techniques to construct a classification tree based on clinical characteristics. Materials and methods In 2016 and 2017, we assessed information regarding 242 children with tonsillopharyngitis. Patients were categorized according to whether acute cytomegalovirus or Epstein-Barr virus infections were confirmed (n = 91) or not (n = 151). Based on symptoms and blood test parameters, we constructed decision trees to discriminate the two groups. The classification efficiency of the model was characterized by its sensitivity, specificity, positive predictive value, and negative predictive value. Fisher’s exact and Welch’s tests were used to perform univariable statistical analyses. Results The best decision tree distinguished EBV/CMV infection from non-EBV/CMV group with 83.33% positive predictive value, 88.90% sensitivity and 90.30% specificity. GPT (U/l) was found to be the most discriminatory variable (p < 0.0001). Using the model, unnecessary antibiotic treatment could be reduced by 66.66% (p = 0.0002). Discussion Our classification model can be used as a diagnostic decision support tool to distinguish EBC/CMV infection from non EBV/CMV tonsillopharyngitis, thereby significantly reducing the overuse of antibiotics. It is hoped that the model may become a tool worth considering in routine clinical practice and may be developed to differentiate between viral and bacterial infections. |
| first_indexed | 2024-03-13T04:47:43Z |
| format | Article |
| id | doaj.art-aa2751c55fed4daaa12733ffd289cd03 |
| institution | Directory Open Access Journal |
| issn | 1471-2431 |
| language | English |
| last_indexed | 2024-03-13T04:47:43Z |
| publishDate | 2023-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pediatrics |
| spelling | doaj.art-aa2751c55fed4daaa12733ffd289cd032023-06-18T11:25:02ZengBMCBMC Pediatrics1471-24312023-06-012311910.1186/s12887-023-04103-0Diagnosis of Epstein-Barr and cytomegalovirus infections using decision trees: an effective way to avoid antibiotic overuse in paediatric tonsillopharyngitisAndrea Tímea Takács0Mátyás Bukva1Csaba Bereczki2Katalin Burián3Gabriella Terhes4Department of Pediatrics and Pediatric Health Center, University of SzegedData Science and Me LtdDepartment of Pediatrics and Pediatric Health Center, University of SzegedInstitute of Clinical Microbiology, University of SzegedInstitute of Clinical Microbiology, University of SzegedAbstract Background The incidence of tonsillopharyngitis is especially prevalent in children. Despite the fact that viruses cause the majority of infections, antibiotics are frequently used as a treatment, contrary to international guidelines. This is not only an inappropriate method of treatment for viral infections, but it also significantly contributes to the emergence of antibiotic-resistant strains. In this study, EBV and CMV-related tonsillopharyngitis were distinguished from other pathogens by using machine learning techniques to construct a classification tree based on clinical characteristics. Materials and methods In 2016 and 2017, we assessed information regarding 242 children with tonsillopharyngitis. Patients were categorized according to whether acute cytomegalovirus or Epstein-Barr virus infections were confirmed (n = 91) or not (n = 151). Based on symptoms and blood test parameters, we constructed decision trees to discriminate the two groups. The classification efficiency of the model was characterized by its sensitivity, specificity, positive predictive value, and negative predictive value. Fisher’s exact and Welch’s tests were used to perform univariable statistical analyses. Results The best decision tree distinguished EBV/CMV infection from non-EBV/CMV group with 83.33% positive predictive value, 88.90% sensitivity and 90.30% specificity. GPT (U/l) was found to be the most discriminatory variable (p < 0.0001). Using the model, unnecessary antibiotic treatment could be reduced by 66.66% (p = 0.0002). Discussion Our classification model can be used as a diagnostic decision support tool to distinguish EBC/CMV infection from non EBV/CMV tonsillopharyngitis, thereby significantly reducing the overuse of antibiotics. It is hoped that the model may become a tool worth considering in routine clinical practice and may be developed to differentiate between viral and bacterial infections.https://doi.org/10.1186/s12887-023-04103-0TonsillopharyngitisAntibiotic treatmentInfectious mononucleosisEpstein–Barr virus (EBV)Cytomegalovirus (CMV)Elevated transaminases |
| spellingShingle | Andrea Tímea Takács Mátyás Bukva Csaba Bereczki Katalin Burián Gabriella Terhes Diagnosis of Epstein-Barr and cytomegalovirus infections using decision trees: an effective way to avoid antibiotic overuse in paediatric tonsillopharyngitis BMC Pediatrics Tonsillopharyngitis Antibiotic treatment Infectious mononucleosis Epstein–Barr virus (EBV) Cytomegalovirus (CMV) Elevated transaminases |
| title | Diagnosis of Epstein-Barr and cytomegalovirus infections using decision trees: an effective way to avoid antibiotic overuse in paediatric tonsillopharyngitis |
| title_full | Diagnosis of Epstein-Barr and cytomegalovirus infections using decision trees: an effective way to avoid antibiotic overuse in paediatric tonsillopharyngitis |
| title_fullStr | Diagnosis of Epstein-Barr and cytomegalovirus infections using decision trees: an effective way to avoid antibiotic overuse in paediatric tonsillopharyngitis |
| title_full_unstemmed | Diagnosis of Epstein-Barr and cytomegalovirus infections using decision trees: an effective way to avoid antibiotic overuse in paediatric tonsillopharyngitis |
| title_short | Diagnosis of Epstein-Barr and cytomegalovirus infections using decision trees: an effective way to avoid antibiotic overuse in paediatric tonsillopharyngitis |
| title_sort | diagnosis of epstein barr and cytomegalovirus infections using decision trees an effective way to avoid antibiotic overuse in paediatric tonsillopharyngitis |
| topic | Tonsillopharyngitis Antibiotic treatment Infectious mononucleosis Epstein–Barr virus (EBV) Cytomegalovirus (CMV) Elevated transaminases |
| url | https://doi.org/10.1186/s12887-023-04103-0 |
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