Model to predict risk for hospital admission and indicate the safety of reverse triage in a hospital emergency department: a prospective validation study. / Validación prospectiva de un modelo predictivo de ingreso y orientar la seguridad de la derivación inversa desde el triaje de los servicios de urgencias hospitalarios.

OBJECTIVES: To prospectively validate a model to predict hospital admission of patients given a low-priority classification on emergency department triage and to indicate the safety of reverse triage. MATERIAL AND METHODS: Single-center observational study of a prospective cohort to validate a risk model incorporating demographic and emergency care process variables as well as vital signs. The cohort included emergency visits from patients over the age of 15 years with priority level classifications of IV and V according to the Andorran-Spanish triage system (Spanish acronym, MAT-SET) between October 2018 and June 2019. The area under the receiver operating characteristic curve (AUC) of the model was calculated to evaluate discrimination. Based on the model, we identified cut-off points to distinguish patients with low, intermediate, or high risk for hospital admission. RESULTS: A total of 2110 emergencies were included in the validation cohort; 109 patients (5.2%) were hospitalized. The median age was 43.5 years (interquartile range, 31-60.3 years); 55.5% were female. The AUC was 0.71 (95% CI, 0.64-0.75). The model identified 357 patients (16.9%) at low risk of hospitalization and 240 (11.4%) at high risk. A total of 15.8% of the high-risk patients and 2.8% of the low-risk patients were hospitalized. CONCLUSION: The validated model is able to identify risk for hospitalization among patients classified as low priority on triage. Patients identified as having high risk of hospitalization could be offered preferential treatment within the same level of priority at triage, while those at low risk of admission could be referred to a more appropriate care level on reverse triage.

OBJETIVO: Validar prospectivamente un modelo predictivo de ingreso hospitalario para los pacientes atendidos en el servicio de urgencias hospitalario (SUH) con baja prioridad de visita y determinar la capacidad predictiva del modelo para realizar con seguridad la derivación inversa. METODO: Estudio observacional unicéntrico de una cohorte prospectiva de validación de un modelo predictivo basado en variables demográficas, de proceso y las constantes vitales (modelo 3). Se incluyeron los episodios de pacientes >15 años con prioridades IV y V MAT-SET atendidos entre octubre 2018 y junio 2019. Se evaluó la discriminación mediante el área bajo la curva de la característica operativa del receptor (ABC). Para determinar la capacidad de discriminación se crearon 3 categorías de riesgo: bajo, intermedio y alto. RESULTADOS: Se incluyeron 2.110 episodios, de los cuales 109 (5,2%) ingresaron. La mediana de edad fue de 43,5 años (RIC 31-60,3) con un 55,5% de mujeres. El ABC fue de 0,71 (IC 95%: 0,64-0,75). Según el modelo predictivo, 357 episodios (16,9%) puntuaron de bajo riesgo de ingreso y 240 (11,4%) de alto riesgo. El porcentaje de ingreso observado de los pacientes clasificados de alto riesgo fue de 15,8% mientras que el de los pacientes de bajo riego fue de 2,8%. CONCLUSIONES: El modelo predictivo validado permite estratificar el riesgo de ingreso de los pacientes con baja prioridad de visita. Los pacientes con alto riesgo de ingreso se les podría ofrecer una atención preferente dentro del mismo nivel de prioridad, mientras que los de bajo riesgo podrían ser redirigidos al recurso asistencial más adecuado (derivación inversa).

Validación prospectiva de un modelo predictivo de ingreso y orientar la seguridad de la derivación inversa desde el triaje de los servicios de urgencias hospitalarios / Model to predict risk for hospital admission and indicate the safety of reverse triage in a hospital emergency department: a prospective validation study

Objetivo. Validar prospectivamente un modelo predictivo de ingreso hospitalario para los pacientes atendidos en el servicio de urgencias hospitalario (SUH) con baja prioridad de visita y determinar la capacidad predictiva del modelo para realizar con seguridad la derivación inversa. Método. Estudio observacional unicéntrico de una cohorte prospectiva de validación de un modelo predictivo basado en variables demográficas, de proceso y las constantes vitales (modelo 3). Se incluyeron los episodios de pacientes >15 años con prioridades IV y V MAT-SET atendidos entre octubre 2018 y junio 2019. Se evaluó la discriminación mediante el área bajo la curva de la característica operativa del receptor (ABC). Para determinar la capacidad de discriminación se crearon 3 categorías de riesgo: bajo, intermedio y alto. Resultados. Se incluyeron 2.110 episodios, de los cuales 109 (5,2%) ingresaron. La mediana de edad fue de 43,5 años (RIC 31-60,3) con un 55,5% de mujeres. El ABC fue de 0,71 (IC 95%: 0,64-0,75). Según el modelo predictivo, 357 episodios (16,9%) puntuaron de bajo riesgo de ingreso y 240 (11,4%) de alto riesgo. El porcentaje de ingreso observado de los pacientes clasificados de alto riesgo fue de 15,8% mientras que el de los pacientes de bajo riego fue de 2,8%. Conclusiones. El modelo predictivo validado permite estratificar el riesgo de ingreso de los pacientes con baja priori- dad de visita. Los pacientes con alto riesgo de ingreso se les podría ofrecer una atención preferente dentro del mismo nivel de prioridad, mientras que los de bajo riesgo podrían ser redirigidos al recurso asistencial más adecuado (derivación inversa).

Objectives. To prospectively validate a model to predict hospital admission of patients given a low-priority classification on emergency department triage and to indicate the safety of reverse triage. Methods. Single-center observational study of a prospective cohort to validate a risk model incorporating demographic and emergency care process variables as well as vital signs. The cohort included emergency visits from patients over the age of 15 years with priority level classifications of IV and V according to the Andorran–Spanish triage system (Spanish acronym, MAT-SET) between October 2018 and June 2019. The area under the receiver operating characteristic curve (AUC) of the model was calculated to evaluate discrimination. Based on the model, we identified cut-off points to distinguish patients with low, intermediate, or high risk for hospital admission. Results. A total of 2110 emergencies were included in the validation cohort; 109 patients (5.2%) were hospitalized. The median age was 43.5 years (interquartile range, 31-60.3 years); 55.5% were female. The AUC was 0.71 (95% CI, 0.64-0.75). The model identified 357 patients (16.9%) at low risk of hospitalization and 240 (11.4%) at high risk. A total of 15.8% of the high-risk patients and 2.8% of the low-risk patients were hospitalized. Conclusions. The validated model is able to identify risk for hospitalization among patients classified as low priority on triage. Patients identified as having high risk of hospitalization could be offered preferential treatment within the same level of priority at triage, while those at low risk of admission could be referred to a more appropriate care level on reverse triage.

Predicting hospital admission of patients with emergencies considered low priority according to assigned triage level. / Predicción de ingreso hospitalario en los pacientes con bajo nivel de prioridad de triaje atendidos en un servicio de urgencias.

OBJECTIVES: To develop a model to predict hospital admission of patients in cases assessed as nonurgent or semiurgent on emergency department triage. MATERIAL AND METHODS: Single-center observational study of a retrospective cohort. We included cases of patients older than 15 years whose emergency was classified as level IV-V according to the Andorran-Spanish triage model (MAT-SET, the Spanish acronym). Fourteen independent variables included demographic and care process items as well as vital signs. The dependent variable was hospital admission. The regression models were based on generalized estimating equations. RESULTS: A total of 53 860 episodes were included; 3430 patients (6.4%) were admitted. The median (interquartile range) age was 44.5 (31.1-63.9) years, and 54.1% were female. Vital signs were recorded in 19.3% of the episodes. The model that best predicted admission included the following variables: age > 84 years (adjusted odds ratio [aOR], 6.72; 95% CI, 5.26-8.60); male sex (aOR, 1.46; 95% CI, 1.28-1.66); referral from a primary care center (aOR, 1.94; 95% CI, 1.64-2.29); referral from another acute-care hospital (aOR, 11.22; 95% CI, 4.42-28.51); arrival by ambulance (aOR, 3.72; 95% CI, 3.16-4.40); revisit 72 hours (aOR, 2.15; 95% CI, 1.60-2.87); systolic blood pressure $ 150 mmHg (aOR, 0.83; 95% CI, 0.71-0.97); diastolic blood pressure 60 mmHg (aOR, 1.57; 95% CI, 1.25-1.98); axillary temperature > 37°C (aOR, 2.29; 95% CI, 1.91-2.74); heart rate > 100 beats/min (aOR, 1.65; 95% CI, 1.40-1.96); baseline oxygen saturation in arterial blood (SaO2) 93% (aOR, 2.66; 95% CI, 1.86-3.81); and SaO2 93%-95% (aOR, 1.70; 95% CI, 1.42-2.05). The area under the receiver operating characteristic curve for the model was 0.82 (95% CI; 95% CI, 0.80-0.83). CONCLUSION: The model predicts which patients are more likely to be admitted after their cases were initially considered nonurgent or semi-urgent on triage. Patients found to be at risk can then be given greater attention than others in the same triage level.

OBJETIVO: Desarrollar un modelo predictivo de ingreso hospitalario desde triaje de los pacientes atendidos en el servicio de urgencias hospitalario (SUH) con el nivel poco urgente-no urgente de prioridad de visita. METODO: Estudio observacional de cohortes retrospectivo unicéntrico. Se incluyeron los episodios de pacientes > 15 años con niveles IV-V MAT-SET atendidos en un SUH durante 2015. Se evaluaron 14 variables demográficas, datos de proceso y constantes vitales. La variable dependiente fue el ingreso hospitalario. Se utilizaron modelos de regresión basados en ecuaciones de estimación generalizadas. RESULTADOS: Se incluyeron 53.860 episodios, 3.430 (6,4%) ingresaron. La mediana de edad fue de 44,5 años (RIC 31,1-63,9), 54,1% mujeres. Un 19,3% de los episodios tenían registrados las constantes vitales (CV). El modelo con mayor capacidad predictiva incluía las siguientes variables: edad $ 85 años (ORa = 6,72; IC 95%: 5,26-8,60), sexo masculino (ORa = 1,46; IC 95% 1,28-1,66), procedencia de atención primaria (ORa = 1,94; IC 95% 1,64-2,29), de otro hospital de agudos (ORa = 11,22; IC 95% 4,42-28,51), llegada en ambulancia (ORa = 3,72; IC 95%:3,16-4,40), consulta previa a urgencias las 72 horas previas (ORa = 2,15; IC 95% 1,60-2,87), presión arterial sistólica $ 150 mmHg (ORa = 0,83; IC 95%:0,71-0,97), presión arterial diastólica 60 mmHg (ORa = 1,57; IC 95% 1,25-1,98), temperatura axilar > 37ºC (ORa = 2,29; IC 95% 1,91-2,74), frecuencia cardiaca > 100 latidos/minuto (ORa 1,65; IC 95% 1,40-1,96) y saturación basal de oxígeno 93% (ORa = 2,66; IC 95% 1,86-3,81) y 93-95% (ORa = 1,70; IC 95% 1,42-2,05). El área bajo la curva COR fue de 0,82 (IC 95% 0,80-0,83). CONCLUSIONES: Este modelo predictivo permitiría identificar desde el triaje a aquellos pacientes que, siendo poco urgentes o no urgentes, tienen mayor probabilidad de ingreso y darles una atención diferencial dentro del mismo nivel de prioridad.

Predicción de ingreso hospitalario en los pacientes con bajo nivel de prioridad de triaje atendidos en un servicio de urgencias / Predicting hospital admission of patients with emergencies considered low priority according to assigned triage level

OBJETIVOS: Desarrollar un modelo predictivo de ingreso hospitalario desde triaje de los pacientes atendidos en el servicio de urgencias hospitalario (SUH) con el nivel poco urgente-no urgente de prioridad de visita. MÉTODO: Estudio observacional de cohortes retrospectivo unicéntrico. Se incluyeron los episodios de pacientes > 15años con niveles IV-V MAT-SET atendidos en un SUH durante 2015. Se evaluaron 14 variables demográficas, datos de proceso y constantes vitales. La variable dependiente fue el ingreso hospitalario. Se utilizaron modelos de regresión basados en ecuaciones de estimación generalizadas. RESULTADOS: Se incluyeron 53.860 episodios, 3.430 (6,4%) ingresaron. La mediana de edad fue de 44,5 años (RIC31,1-63,9), 54,1% mujeres. Un 19,3% de los episodios tenían registrados las constantes vitales (CV). El modelo con mayor capacidad predictiva incluía las siguientes variables: edad ≥ 85 años (ORa = 6,72; IC 95%: 5,26-8,60), sexo masculino (ORa = 1,46; IC 95% 1,28-1,66), procedencia de atención primaria (ORa = 1,94; IC 95% 1,64-2,29), de otro hospital de agudos (ORa = 11,22; IC 95% 4,42-28,51), llegada en ambulancia (ORa = 3,72; IC 95%:3,16-4,40),consulta previa a urgencias las 72 horas previas (ORa = 2,15; IC 95% 1,60-2,87), presión arterial sistólica ≥ 150mmHg (ORa = 0,83; IC 95%:0,71-0,97), presión arterial diastólica < 60 mmHg (ORa = 1,57; IC 95% 1,25-1,98), temperatura axilar > 37ºC (ORa = 2,29; IC 95% 1,91-2,74), frecuencia cardiaca > 100 latidos/minuto (ORa 1,65; IC 95%1,40-1,96) y saturación basal de oxígeno < 93% (ORa = 2,66; IC 95% 1,86-3,81) y 93-95% (ORa = 1,70; IC 95%1,42-2,05). El área bajo la curva COR fue de 0,82 (IC 95% 0,80-0,83). CONCLUSIONES: Este modelo predictivo permitiría identificar desde el triaje a aquellos pacientes que, siendo poco urgentes o no urgentes, tienen mayor probabilidad de ingreso y darles una atención diferencial dentro del mismo nivel de prioridad

OBJECTIVE: To develop a model to predict hospital admission of patients in cases assessed as nonurgent or semi-urgent on emergency department triage. METHODS: Single-center observational study of a retrospective cohort. We included cases of patients older than 15 years whose emergency was classified as level IV-V according to the Andorran-Spanish triage model (MAT-SET, the Spanish acronym). Fourteen independent variables included demographic and care process items as well as vital signs. The dependent variable was hospital admission. The regression models were based on generalized estimating equations. RESULTS: A total of 53 860 episodes were included; 3430 patients (6.4%) were admitted. The median (interquartile range) age was 44.5 (31.1-63.9) years, and 54.1% were female. Vital signs were recorded in 19.3% of the episodes. The model that best predicted admission included the following variables: age > 84 years (adjusted odds ratio [aOR], 6.72; 95% CI,5.26-8.60); male sex (aOR, 1.46; 95% CI, 1.28-1.66); referral from a primary care center (aOR, 1.94; 95% CI, 1.64-2.29); referral from another acute-care hospital (aOR, 11.22; 95% CI, 4.42-28.51); arrival by ambulance (aOR, 3.72; 95% CI,3.16-4.40); revisit < 72 hours (aOR, 2.15; 95% CI, 1.60-2.87); systolic blood pressure ≥ 150 mmHg (aOR, 0.83; 95% CI,0.71-0.97); diastolic blood pressure < 60 mmHg (aOR, 1.57; 95% CI, 1.25-1.98); axillary temperature > 37°C (aOR, 2.29;95% CI, 1.91-2.74); heart rate > 100 beats/min (aOR, 1.65; 95% CI, 1.40-1.96); baseline oxygen saturation in arterialblood (SaO2) < 93% (aOR, 2.66; 95% CI, 1.86-3.81); and SaO293%-95% (aOR, 1.70; 95% CI, 1.42-2.05). The area under the receiver operating characteristic curve for the model was 0.82 (95% CI; 95% CI, 0.80-0.83). CONCLUSION: The model predicts which patients are more likely to be admitted after their cases were initially considered non urgent or semi-urgent on triage. Patients found to be at risk can then be given greater attention than others in the same triage level

Análisis de la demanda de atención urgente en un municipio de la Cataluña Central / Analysis of the urgent attention demand in a municipality of the Central Catalonia

No disponible

Importancia del anión GAP ante la falta de información clínica: descripción de un caso clínico de intoxicación oral grave por metanol / Importance of GAP anion in the absence of clinical information: Description of one clinical case of severe oral methanol poisoning

No disponible

Nasal flaring as a clinical sign of respiratory acidosis in patients with dyspnea.

OBJECTIVE: To determine whether the presence of nasal flaring is a clinical sign of respiratory acidosis in patients attending emergency departments for acute dyspnea. METHODS: Single-center, prospective, observational study of patients aged over 15 requiring urgent attention for dyspnea, classified as level II or III according to the Andorran Triage Program and who underwent arterial blood gas test on arrival at the emergency department. The presence of nasal flaring was evaluated by two observers. Demographic and clinical variables, signs of respiratory difficulty, vital signs, arterial blood gases and clinical outcome (hospitalization and mortality) were recorded. Bivariate and multivariate analyses were performed using logistic regression models. RESULTS: The sample comprised 212 patients, mean age 78years (SD=12.8), of whom 49.5% were women. Acidosis was recorded in 21.2%. Factors significantly associated with the presence of acidosis in the bivariate analysis were the need for pre-hospital medical care, triage level II, signs of respiratory distress, presence of nasal flaring, poor oxygenation, hypercapnia, low bicarbonates and greater need for noninvasive ventilation. Nasal flaring had a positive likelihood ratio for acidosis of 4.6 (95% CI 2.9-7.4). In the multivariate analysis, triage level II (aOR 5.16; 95% CI: 1.91 to 13.98), the need for oxygen therapy (aOR 2.60; 95% CI: 1.13-5.96) and presence of nasal flaring (aOR 6.32; 95% CI: 2.78-14.41) were maintained as factors independently associated with acidosis. CONCLUSIONS: Nasal flaring is a clinical sign of severity in patients requiring urgent care for acute dyspnea, which has a strong association with acidosis and hypercapnia.

Aleteo nasal como factor pronóstico de mortalidad en el paciente con disnea grave / Nasal flaring as a predictor of mortality in patients with severe dyspnea

Objetivos: Determinar si la presencia de aleteo nasal es un factor de gravedad clínica y pronóstico de mortalidad hospitalaria en el paciente que consulta en urgencias por disnea. Método: Estudio prospectivo observacional un céntrico. Se incluyeron pacientes mayores de 15 años, que demandaron atención urgente por disnea, catalogados como niveles II y III por el Modelo Andorrano de Triaje (MAT). Se evaluó la presencia de aleteo nasal por dos observadores. Se recogieron variables demográficas, clínicas, signos de dificultad respiratoria, signos vitales, gasometría arterial y evolución clínica (ingreso hospitalario y mortalidad). Se realizaron análisis bivariantes y multivariantes con modelos de regresión logística. Resultados: Se incluyeron 246 pacientes, de edad media ± DE 77 (13) años (DE: 13,2) y un 52% de mujeres. Un19,5% presentaron aleteo nasal. Los pacientes con aleteo nasal tuvieron mayor gravedad en el triaje, más taquipnea, peor oxigenación, más acidosis y más hipercapnia. En el análisis bivariante los factores pronósticos de mortalidad hospitalaria fueron la edad (OR 1,05; IC95%: 1,01-1,10), la atención prehospitalaria por el servicio emergencias médicas (OR 3,97; IC95%: 1,39-11,39), el nivel de triaje II (OR 4,19; IC95%: 1,63-10,78), la presencia de signos de dificultad respiratoria como el aleteo nasal (OR 3,79; IC 95%: 1,65-8,69), la presencia de acidosis (OR 7,09; IC95%: 2,97-16,94) y la hipercapnia (OR 2,67; IC95%: 1,11-6,45). En el análisis multivariante, la edad, el nivel de triaje y el aleteonasal se mantuvieron como factores pronósticos independientes de mortalidad (AU)

Objective: To determine whether the presence of nasal flaring is a clinical sign of severity and a predictor of hospital mortality in emergency patients with dyspnea. Methods: Prospective, observational, single-center study. We enrolled patients older than 15 years of age who required attention for dyspnea categorized as level II or III emergencies according to the Andorran Medical Triage system. Two observers evaluated the presence of nasal flaring. We recorded demographic and clinical variables, including respiratory effort, vital signs, arterial blood gases, and clinical course (hospital admission and mortality). Bivariable analysis was performed and multivariable logistic regression models were constructed. Results: We enrolled 246 patients with a mean (SD) age of 77 (13) years; 52% were female. Nasal flaring was present in 19.5%. Patients with nasal flaring had triage levels indicating greater severity and they had more severe tachypnea, worse oxygenation, and greater acidosis and hypercapnia. Bivariable analysis detected that the following variables were associated with mortality: age (odds ratio [OR], 1.05; 95% CI, 1.01–1.10), prehospital care from the emergency medical service (OR, 3.97; 95% CI, 1.39–11.39), triage level II (OR, 4.19; 95% CI, 1.63–10.78), signs of respiratory effort such as nasal flaring (OR, 3.79; 95% CI, 1.65–8.69), presence of acidosis (OR, 7.09; 95% CI, 2.97–16.94), and hypercapnia (OR, 2.67; 95% CI, 1,11–6,45). The factors that remained independent predictors of mortality in the multivariable analysis were age, severity (triage level), and nasal flaring. Conclusions: In patients requiring emergency care for dyspnea, nasal flaring is a clinical sign of severity and a predictor of mortality (AU)

[Nasal flaring as a predictor of mortality in patients with severe dyspnea]. / Aleteo nasal como factor pronóstico de mortalidad en el paciente con disnea grave.

OBJECTIVES: To determine whether the presence of nasal flaring is a clinical sign of severity and a predictor of hospital mortality in emergency patients with dyspnea. MATERIAL AND METHODS: Prospective, observational, single-center study. We enrolled patients older than 15 years of age who required attention for dyspnea categorized as level II or III emergencies according to the Andorran Medical Triage system. Two observers evaluated the presence of nasal flaring. We recorded demographic and clinical variables, including respiratory effort, vital signs, arterial blood gases, and clinical course (hospital admission and mortality). Bivariable analysis was performed and multivariable logistic regression models were constructed. RESULTS: We enrolled 246 patients with a mean (SD) age of 77 (13) years; 52% were female. Nasal flaring was present in 19.5%. Patients with nasal flaring had triage levels indicating greater severity and they had more severe tachypnea, worse oxygenation, and greater acidosis and hypercapnia. Bivariable analysis detected that the following variables were associated with mortality: age (odds ratio [OR], 1.05; 95% CI, 1.01-1.10), prehospital care from the emergency medical service (OR, 3.97; 95% CI, 1.39-11.39), triage level II (OR, 4.19; 95% CI, 1.63-10.78), signs of respiratory effort such as nasal flaring (OR, 3.79; 95% CI, 1.65-8.69), presence of acidosis (OR, 7.09; 95% CI, 2.97-16.94), and hypercapnia (OR, 2.67; 95% CI, 1,11-6,45). The factors that remained independent predictors of mortality in the multivariable analysis were age, severity (triage level), and nasal flaring. CONCLUSION: In patients requiring emergency care for dyspnea, nasal flaring is a clinical sign of severity and a predictor of mortality.

OBJETIVO: Determinar si la presencia de aleteo nasal es un factor de gravedad clínica y pronóstico de mortalidad hospitalaria en el paciente que consulta en urgencias por disnea. METODO: Estudio prospectivo observacional unicéntrico. Se incluyeron pacientes mayores de 15 años, que demandaron atención urgente por disnea, catalogados como niveles II y III por el Modelo Andorrano de Triaje (MAT). Se evaluó la presencia de aleteo nasal por dos observadores. Se recogieron variables demográficas, clínicas, signos de dificultad respiratoria, signos vitales, gasometría arterial y evolución clínica (ingreso hospitalario y mortalidad). Se realizaron análisis bivariantes y multivariantes con modelos de regresión logística. RESULTADOS: Se incluyeron 246 pacientes, de edad media ± DE 77 (13) años (DE: 13,2) y un 52% de mujeres. Un 19,5% presentaron aleteo nasal. Los pacientes con aleteo nasal tuvieron mayor gravedad en el triaje, más taquipnea, peor oxigenación, más acidosis y más hipercapnia. En el análisis bivariante los factores pronósticos de mortalidad hospitalaria fueron la edad (OR 1,05; IC95%: 1,01-1,10), la atención prehospitalaria por el servicio emergencias médicas (OR 3,97; IC95%: 1,39-11,39), el nivel de triaje II (OR 4,19; IC95%: 1,63-10,78), la presencia de signos de dificultad respiratoria como el aleteo nasal (OR 3,79; IC 95%: 1,65-8,69), la presencia de acidosis (OR 7,09; IC95%: 2,97- 16,94) y la hipercapnia (OR 2,67; IC95%: 1,11-6,45). En el análisis multivariante, la edad, el nivel de triaje y el aleteo nasal se mantuvieron como factores pronósticos independientes de mortalidad. CONCLUSIONES: El aleteo nasal es un signo clínico de gravedad y predictor de mortalidad en los pacientes que demandan atención urgente por disnea.