Introduction to the database “efficacy of different treatment modalities for lower respiratory tract infections in children”
This study aimed to establish a registry database of different treatment modalities for lower respiratory tract infections (LRTIs) in Chinese children, thereby filling gaps in knowledge on clinical characteristics, treatment modalities, and rational drug use in relation to LRTIs in Chinese children,...
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Veröffentlicht in: | Pediatric Discovery (Online) 2024-07 |
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Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext |
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Zusammenfassung: | This study aimed to establish a registry database of different treatment modalities for lower respiratory tract infections (LRTIs) in Chinese children, thereby filling gaps in knowledge on clinical characteristics, treatment modalities, and rational drug use in relation to LRTIs in Chinese children, and providing large amounts of continuous, complete, scientific, and objective clinical data, and an information exchange platform. Multicenter data from these children's clinical visits were collected, pooled, and analyzed using medical informatics and statistical techniques to explore their potential value. The database was preliminarily established and a real‐world study cohort was constructed based on a total of 4805 patients registered in this database. Pneumonia was identified as the most common type of LRTIs (72.44%), followed by acute bronchitis (20.71%). The mean age of the enrolled children with LRTIs was 3.26 ± 2.84 years, and boys accounted for 59.21% of the samples. Among the enrolled children, pneumonia and acute bronchitis had the highest incidence in children aged 1–3 years (27.44%) and those aged 3–6 years (34.16%), respectively. In this national, multicenter, observational database of LRTIs in children, the real‐world characteristics and treatment modalities for LRTIs in Chinese children are elucidated. This database will help improve the research efficiency of clinicians and facilitate the exploration of underlying clinical patterns in real‐world medical big data. |
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ISSN: | 2835-558X 2835-5598 |
DOI: | 10.1002/pdi3.79 |