Risk factors for identifying pulmonary aspergillosis in pediatric patients
ObjectivesThis study aimed to identify the independent risk factors and develop a predictive model for pulmonary aspergillosis (PA) in pediatric populations.MethodsThis retrospective study compromised 97 pediatric patients with pulmonary infections (38 PA cases and 59 non-PA cases) at Children’s Hos...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Cellular and Infection Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcimb.2025.1616773/full |
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| author | Shangmin Yang Shangmin Yang Yanmeng Sun Yanmeng Sun Mengyuan Wang Mengyuan Wang Huan Xu Shifu Wang Shifu Wang |
| author_facet | Shangmin Yang Shangmin Yang Yanmeng Sun Yanmeng Sun Mengyuan Wang Mengyuan Wang Huan Xu Shifu Wang Shifu Wang |
| author_sort | Shangmin Yang |
| collection | DOAJ |
| description | ObjectivesThis study aimed to identify the independent risk factors and develop a predictive model for pulmonary aspergillosis (PA) in pediatric populations.MethodsThis retrospective study compromised 97 pediatric patients with pulmonary infections (38 PA cases and 59 non-PA cases) at Children’s Hospital Affiliated to Shandong University between January 2020 and October 2024. Multivariate binary logistic regression was used to identify PA-associated risk factors. Receiver operating characteristic (ROC) curves, calibration plots, and Brier scoring were used to evaluate the diagnostic model.Results8 clinical variables significantly differed between the PA and non-PA groups. Multivariate binary logistic regression analysis identified six significant independent risk factors: a history of surgery (OR: 9.52; 95% CI: 1.96–46.23; P = 0.005), hematologic diseases (OR: 11.68; 95% CI: 0.89–153.62; P = 0.062), absence of fever (OR: 8.244; 95% CI: 1.84–36.932; P = 0.006), viral coinfection (OR: 15.99; 95% CI: 3.55–72.00; P < 0.001), elevated (1, 3) -β -D-glucan levels (BDG, > 61.28 pg/mL; OR: 7.38; 95% CI: 1.26–43.31; P = 0.027), and shorter symptom-to-admission interval (< 4.5 days; OR: 38.68; 95% CI: 5.38–277.94; P < 0.001) were risk factors for PA. The predictive model demonstrated excellent discrimination (AUC 0.93, 95% CI 0.88-0.98) and calibration (Hosmer-Lemeshow p=0.606, R²=0.96, Brier score 0.097). metagenomic next - generation sequencing (mNGS) revealed significantly higher rates of polymicrobial infections in PA cases (86.84% vs 18.64%, p<0.001).ConclusionsThis study established and validated a high-performance predictive model incorporating six clinically accessible parameters for the diagnosis of pediatric PA. |
| format | Article |
| id | doaj-art-badf0585bf8941ba80aade1db33abf44 |
| institution | OA Journals |
| issn | 2235-2988 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Cellular and Infection Microbiology |
| spelling | doaj-art-badf0585bf8941ba80aade1db33abf442025-08-20T02:35:29ZengFrontiers Media S.A.Frontiers in Cellular and Infection Microbiology2235-29882025-06-011510.3389/fcimb.2025.16167731616773Risk factors for identifying pulmonary aspergillosis in pediatric patientsShangmin Yang0Shangmin Yang1Yanmeng Sun2Yanmeng Sun3Mengyuan Wang4Mengyuan Wang5Huan Xu6Shifu Wang7Shifu Wang8Department of Microbiology Laboratory, Children’s Hospital Affiliated to Shandong University (Jinan Children’s Hospital), Jinan, ChinaDepartment of Clinical Microbiology, Shandong Provincial Clinical Research Center for Children’s Health and Disease, Jinan, ChinaDepartment of Microbiology Laboratory, Children’s Hospital Affiliated to Shandong University (Jinan Children’s Hospital), Jinan, ChinaDepartment of Clinical Microbiology, Shandong Provincial Clinical Research Center for Children’s Health and Disease, Jinan, ChinaDepartment of Microbiology Laboratory, Children’s Hospital Affiliated to Shandong University (Jinan Children’s Hospital), Jinan, ChinaDepartment of Clinical Microbiology, Shandong Provincial Clinical Research Center for Children’s Health and Disease, Jinan, ChinaDepartment of Scientific Affairs, Vision Medicals Center for Infectious Diseases, Guangzhou, ChinaDepartment of Microbiology Laboratory, Children’s Hospital Affiliated to Shandong University (Jinan Children’s Hospital), Jinan, ChinaDepartment of Clinical Microbiology, Shandong Provincial Clinical Research Center for Children’s Health and Disease, Jinan, ChinaObjectivesThis study aimed to identify the independent risk factors and develop a predictive model for pulmonary aspergillosis (PA) in pediatric populations.MethodsThis retrospective study compromised 97 pediatric patients with pulmonary infections (38 PA cases and 59 non-PA cases) at Children’s Hospital Affiliated to Shandong University between January 2020 and October 2024. Multivariate binary logistic regression was used to identify PA-associated risk factors. Receiver operating characteristic (ROC) curves, calibration plots, and Brier scoring were used to evaluate the diagnostic model.Results8 clinical variables significantly differed between the PA and non-PA groups. Multivariate binary logistic regression analysis identified six significant independent risk factors: a history of surgery (OR: 9.52; 95% CI: 1.96–46.23; P = 0.005), hematologic diseases (OR: 11.68; 95% CI: 0.89–153.62; P = 0.062), absence of fever (OR: 8.244; 95% CI: 1.84–36.932; P = 0.006), viral coinfection (OR: 15.99; 95% CI: 3.55–72.00; P < 0.001), elevated (1, 3) -β -D-glucan levels (BDG, > 61.28 pg/mL; OR: 7.38; 95% CI: 1.26–43.31; P = 0.027), and shorter symptom-to-admission interval (< 4.5 days; OR: 38.68; 95% CI: 5.38–277.94; P < 0.001) were risk factors for PA. The predictive model demonstrated excellent discrimination (AUC 0.93, 95% CI 0.88-0.98) and calibration (Hosmer-Lemeshow p=0.606, R²=0.96, Brier score 0.097). metagenomic next - generation sequencing (mNGS) revealed significantly higher rates of polymicrobial infections in PA cases (86.84% vs 18.64%, p<0.001).ConclusionsThis study established and validated a high-performance predictive model incorporating six clinically accessible parameters for the diagnosis of pediatric PA.https://www.frontiersin.org/articles/10.3389/fcimb.2025.1616773/fullpulmonary aspergillosismetagenomic next-generation sequencingROC curverisk factorspediatrics |
| spellingShingle | Shangmin Yang Shangmin Yang Yanmeng Sun Yanmeng Sun Mengyuan Wang Mengyuan Wang Huan Xu Shifu Wang Shifu Wang Risk factors for identifying pulmonary aspergillosis in pediatric patients Frontiers in Cellular and Infection Microbiology pulmonary aspergillosis metagenomic next-generation sequencing ROC curve risk factors pediatrics |
| title | Risk factors for identifying pulmonary aspergillosis in pediatric patients |
| title_full | Risk factors for identifying pulmonary aspergillosis in pediatric patients |
| title_fullStr | Risk factors for identifying pulmonary aspergillosis in pediatric patients |
| title_full_unstemmed | Risk factors for identifying pulmonary aspergillosis in pediatric patients |
| title_short | Risk factors for identifying pulmonary aspergillosis in pediatric patients |
| title_sort | risk factors for identifying pulmonary aspergillosis in pediatric patients |
| topic | pulmonary aspergillosis metagenomic next-generation sequencing ROC curve risk factors pediatrics |
| url | https://www.frontiersin.org/articles/10.3389/fcimb.2025.1616773/full |
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