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• W3204694822 abstract "Due to ambiguities in terminology, acute lower respiratory infections (ALRI) in childhood are frequently not properly recorded, especially during outpatient visits. A tool that accurately identifies them, would assess the impact on respiratory health of massive harms, and design policies to prevent or mitigate their effects. We aimed to design an algorithm that allows identifying children with ALRI based on data from the electronic clinical record (ECR) of the Government of the City of Buenos Aires (GCBA).From the ECR-GCBA database, we randomly selected 1000 outpatient visits of patients aged under 2 years. Terms showing that the visit was due to LARI were searched using an algorithm based on hard rules. Another dataset including 800 visits was used to adjust the algorithm and, finally, its performance was tested in a third dataset of 800 queries corresponding to the entire year 2018.In the validation set, our tool identified LARI with sensitivity 88.24%, specificity 97.5%, PPV 86.07% and NPV 97.93%.Our search algorithm allows us to identify with acceptable precision the outpatient visits related to LARI in children under 2 years of age from electronic clinical records.Debido a ambigüedades en la nomenclatura, las infecciones respiratorias agudas bajas (IRAB) en la infancia frecuentemente no son debidamente registradas, especialmente durante las consultas ambulatorias. Contar con una herramienta que las identifique con precisión, permitirá evaluar el impacto en la salud respiratoria de noxas de alcance masivo y diseñar las políticas para prevenirlas o mitigar sus efectos. Nuestro objetivo fue construir un algoritmo que permita identificar niños con IRAB a partir de los datos de la historia clínica electrónica (HCE) del Gobierno de la Ciudad de Buenos Aires (GCBA).Utilizando la HCE-GCBA, se seleccionaron aleatoriamente 1000 consultas ambulatorias de pacientes menores de 2 años. Se buscaron términos que hicieran referencia a que la consulta era motivada por IRAB, con los que se desarrolló un algoritmo basado en reglas duras. Se utilizó otro set de datos de 800 consultas para ajustar el algoritmo y, finalmente, se validó su desempeño en un tercer set de 800 consultas correspondientes a todo el año 2018.En el set de validación, la herramienta desarrollada identificó IRAB con sensibilidad 88,24%, especificidad 97,5%, VPP 86,07% y VPN 97,93%.El algoritmo de búsqueda desarrollado permite identificar con aceptable precisión las consultas ambulatorias relacionadas con IRAB en niños menores de 2 años." @default.
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• W3204694822 date "2021-09-06" @default.
• W3204694822 modified "2023-09-27" @default.
• W3204694822 title "Algoritmo para identificación de consultas por infección respiratoria aguda baja en pediatría en registros clínicos electrónicos" @default.
• W3204694822 doi "https://doi.org/10.31053/1853.0605.v78.n3.30162" @default.
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