RESUMEN
Introducción: La presencia de apneas en la bronquiolitis aguda (BA) varía según las series entre el 1,2 y el 28,8%, y es una de sus complicaciones más temibles. Nuestro objetivo es conocer la incidencia de apneas en pacientes ingresados con diagnóstico de BA y definir sus factores de riesgo asociados para construir un modelo de predicción. Pacientes y método: Estudio observacional retrospectivo de los últimos 5 años de pacientes ingresados en un hospital terciario con diagnóstico de BA según los criterios clásicos. Se recogieron la frecuencia de apneas y las variables clínicas relacionadas, para encontrar factores de riesgo en un modelo de regresión logística binaria para la predicción de apneas. Para evaluar el modelo se elaboró una curva ROC. Resultados: De 1.197 casos, se registró apnea durante el ingreso en 53 (4,4%). Los factores de riesgo incluidos en la ecuación fueron: sexo femenino (OR 0,6; IC del 95%: 0,27-1,37), cesárea (OR: 3,44; IC del 95%: 1,5-7,7), edad posmenstrual ≤ 43 semanas (OR: 6,62; IC del 95%: 2,38-18,7), fiebre (OR: 0,33; IC del 95%: 0,09-1,97), bajo peso al ingreso (OR: 3,06; IC del 95%: 1,23-7,67), apneas antes del ingreso observada por los cuidadores (OR: 5,93; IC del 95%: 2,64-13,3) y sobreinfección bacteriana grave (OR: 3,98; IC del 95%: 1,68-9,46). La sensibilidad y la especificidad óptima del modelo en la curva ROC fueron de 0,842 y 0,846, respectivamente (p < 0,001). Conclusiones: La incidencia de apneas durante el ingreso fue de 4,4 por cada 100 ingresos de BA y año. La ecuación del modelo de predicción estimado puede ser de ayuda al clínico para clasificar a los pacientes con mayor riesgo de apnea durante el ingreso en la BA (AU)
Introduction: The presence of apnoea in acute bronchiolitis (AB) varies between 1.2% and 28.8%, depending on the series, and is one of its most fearsome complications. The aim of this study is to determine the incidence of apnoea in hospitalised patients diagnosed with AB, and to define their associated risk factors in order to construct a prediction model. Patients and method: A retrospective observational study of patients admitted to a tertiary hospital in the last 5 years with a diagnosis of AB, according to the classic criteria. Data was collected on the frequency of apnoea and related clinical variables to find risk factors in a binary logistic regression model for the prediction of apnoea. A ROC curve was developed with the model. Results: Apnoea was recorded during the admission of 53 (4.4%) patients out of a total 1,197 cases found. The risk factors included in the equation were: Female (OR 0.6, 95% CI: 0.27-1.37), Caesarean delivery (OR: 3.44, 95% CI: 1.5-7.7), Postmenstrual age ≤43 weeks (OR: 6.62, 95% CI: 2.38-18.7), Fever (OR: 0.33, 95% CI: 0.09-1.97), Low birth weight (OR: 5.93, 95% CI: 2.23-7.67), Apnoea observed by caregivers before admission (OR: 5.93, 95% CI: 2.64-13.3), and severe bacterial infection (OR: 3.98, 95% CI: 1.68-9.46). The optimal sensitivity and specificity of the model in the ROC curve was 0.842 and 0.846, respectively (P < .001). Conclusions: The incidence of apnoea during admission was 4.4 per 100 admissions of AB and year. The estimated prediction model equation may be of help to the clinician in order to classify patients with increased risk of apnoea during admission due to AB (AU)
Asunto(s)
Humanos , Masculino , Femenino , Recién Nacido , Lactante , Bronquiolitis/epidemiología , Apnea/epidemiología , Factores de Riesgo , Bronquiolitis/etiología , Bronquiolitis/prevención & control , Apnea/prevención & control , Estudios Retrospectivos , Curva ROC , Modelos Logísticos , SobreinfecciónRESUMEN
INTRODUCTION: The presence of apnoea in acute bronchiolitis (AB) varies between 1.2% and 28.8%, depending on the series, and is one of its most fearsome complications. The aim of this study is to determine the incidence of apnoea in hospitalised patients diagnosed with AB, and to define their associated risk factors in order to construct a prediction model. PATIENTS AND METHOD: A retrospective observational study of patients admitted to a tertiary hospital in the last 5 years with a diagnosis of AB, according to the classic criteria. Data was collected on the frequency of apnoea and related clinical variables to find risk factors in a binary logistic regression model for the prediction of apnoea. A ROC curve was developed with the model. RESULTS: Apnoea was recorded during the admission of 53 (4.4%) patients out of a total 1,197 cases found. The risk factors included in the equation were: Female (OR 0.6, 95% CI: 0.27-1.37), Caesarean delivery (OR: 3.44, 95% CI: 1.5-7.7), Postmenstrual age ≤43 weeks (OR: 6.62, 95% CI: 2.38-18.7), Fever (OR: 0.33, 95% CI: 0.09-1.97), Low birth weight (OR: 5.93, 95% CI: 2.23-7.67), Apnoea observed by caregivers before admission (OR: 5.93, 95% CI: 2.64-13.3), and severe bacterial infection (OR: 3.98, 95% CI: 1.68-9.46). The optimal sensitivity and specificity of the model in the ROC curve was 0.842 and 0.846, respectively (P<.001). CONCLUSIONS: The incidence of apnoea during admission was 4.4 per 100 admissions of AB and year. The estimated prediction model equation may be of help to the clinician in order to classify patients with increased risk of apnoea during admission due to AB.