ABSTRACT
Introducción La infección periamigdalina (IPA) supone un motivo de consulta urgente entre las molestias de garganta. Un diagnóstico diferido o incorrecto puede comprometer la vía aerodigestiva alta y resultar mortal en su evolución. Nuestro objetivo fue desarrollar un modelo predictivo de presencia de IPA que ayude en su rápida detección. Pacientes y métodos Un estudio observacional retrospectivo de 66 meses desde 2017 fue desarrollado en un hospital comarcal y su centro terciario de referencia, recogiendo datos de todos los pacientes diagnosticados de IPA y un volumen proporcional de sujetos con sintomatología faríngea sin IPA. Recopilación de datos clínicos, exploratorios y demográficos entre participantes. Su mayor riesgo relativo de presencia de IPA los etiquetó como variables a testar. Elaboración de una escala de puntuación de probabilidad de padecerla y análisis de regresión logística, con obtención de la curva ROC que ofreciera mejor correlación diagnóstica. Validación interna y cálculo de los valores predictivos de este modelo. Resultados Sobre 348 casos de IPA, la escala de valoración puntuó la presencia de 6 variables: trismus (3), disfagia-odinofagia unilateral (2), abombamiento velar (2), otalgia refleja (1), faringolalia (1) y edad de 16-46 años (1). Con un rango de 0 a 10, un cut-off≥6 ofreció una sensibilidad del 96,1%, una especificidad del 93,9% y una eficienca del 94,9%. El área bajo la curva ROC fue de 0,979. Conclusiones La validación interna de este modelo basado en signos y síntomas la faculta como herramienta muy útil para detectar precozmente IPA en otorrinolaringología y atención primaria (AU)
Background Peritonsillar infection (PTI) is a reason for urgent consultation due to intense throat discomfort. A delayed or inaccurate diagnosis can jeopardize the upper aerodigestive tract and be fatal in its evolution. Our objective was to develop a predictive model for the presence of IPA helping in its rapid detection. Patients and methods A 66-month retrospective observational study from 2017 was carried out in a county and tertiary referral hospitals, registering data from all patients diagnosed with PTI and a proportional volume of subjects with pharyngeal symptoms without PTI. Collection of clinical, exploratory and demographic data among participants. Their higher relative risk of PTI presence allowed them to be considered as variables to be tested. Development of a scoring scale for the probability of suffering from it and logistic regression analysis, obtaining the ROC curve with the best diagnostic correlation. Internal validation and estimation of the predictive values of the model. Results On 348 cases of PTI, the assessment scale scored the presence of six variables: trismus (3), unilateral dysphagia-odynophagia (2), velar bulging (2), reflex otalgia (1), pharyngolalia (1) and age between 16 and 46 years (1). With a range of 0-10, a cut-off ≥6 offered a sensitivity of 96.1%, a specificity of 93.9%, and an efficiency of 94.9%. The area under the ROC curve was 0.979. Conclusions The internal validation of this model based on signs and symptoms makes it a very useful tool for early detection of PTI in otorhinolaryngology and primary care (AU)
Subject(s)
Humans , Male , Female , Adolescent , Young Adult , Adenoids , Tonsillitis/complications , Tonsillitis/diagnosis , Predictive Value of Tests , Retrospective Studies , ROC CurveABSTRACT
BACKGROUND: Peritonsillar infection (PTI) is a reason for urgent consultation due to intense throat discomfort. A delayed or inaccurate diagnosis can jeopardize the upper aerodigestive tract and be fatal in its evolution. Our objective was to develop a predictive model for the presence of IPA helping in its rapid detection. PATIENTS AND METHODS: A 66-month retrospective observational study from 2017 was carried out in a county and tertiary referral hospitals, registering data from all patients diagnosed with PTI and a proportional volume of subjects with pharyngeal symptoms without PTI. Collection of clinical, exploratory and demographic data among participants. Their higher relative risk of PTI presence allowed them to be considered as variables to be tested. Development of a scoring scale for the probability of suffering from it and logistic regression analysis, obtaining the ROC curve with the best diagnostic correlation. Internal validation and estimation of the predictive values of the model. RESULTS: On 348 cases of PTI, the assessment scale scored the presence of six variables: trismus (3), unilateral dysphagia-odynophagia (2), velar bulging (2), reflex otalgia (1), pharyngolalia (1) and age between 16 and 46 years (1). With a range of 0-10, a cut-off ≥6 offered a sensitivity of 96.1%, a specificity of 93.9%, and an efficiency of 94.9%. The area under the ROC curve was 0.979. CONCLUSIONS: The internal validation of this model based on signs and symptoms makes it a very useful tool for early detection of PTI in otorhinolaryngology and primary care.