[Prediction model for in-hospital admission in patients arriving in the emergency department]. / Modelo predictor de ingreso hospitalario a la llegada al servicio de urgencias.

BACKGROUND: To develop a prediction model for in-hospital admission to provide an almost "real time" determination of hospital beds needed, so as to predict the resources required as early as possible. MATERIAL AND METHODS: A prospective observational study in the emergency department of a university hospital. We included all consecutive patients between 8.00-22.00 hours during one month. We analyzed 7 variables taken when the patient arrived at the emergency department: age, sex, level of triage, initial disposition, first diagnosis, diagnostic test and medication, and we performed a logistic regression. RESULTS: We included 2,476 visits of which 114 (4.6%) were admitted. A significant direct correlation was seen between: age >65 years old (odds ratio[OR]=2.1, confidence interval [CI] 95%,1.3-3.2; p=0.001); male sex (OR=1.6, IC 95%,1.1-2.4; p=0.020); dyspnea (OR=5.2, IC 95%, 2.8-9.7; p<0.0001), abdominal pain (OR=4.7, IC 95%, 2.7-8.3; p<0.0001); acute care initial disposition (OR=8.9, IC 95%, 5.4-14.9; p<0.0001), diagnostic test (OR=1.1, IC 95%,0.9-1.3; p=0.064) and treatment prescription (OR=2.6, IC95%,1.6-4.2; p=<0.0001). The model had a sensitivity of 76% and a specificity of 82% (area under curve 0.85 [IC 95% 0.81-0.88; p<0.001]). CONCLUSIONS: The in-hospital admission prediction model is a good and useful tool for predicting the in-hospital beds needed when patients arrive at the emergency department.

Modelo predictor de ingreso hospitalario a la llegada al servicio de Urgencias / Prediction model for in-hospital admission in patients arriving in the Emergency Department

Fundamento. Desarrollar un modelo de predicción de ingreso hospitalario a la llegada del paciente al servicio de Urgencias, con el fin de conocer la necesidad de camas hospitalarias casi a tiempo real, y así prever los recursos asistenciales necesarios de forma precoz. Material y métodos. Estudio observacional de cohorte prospectivo. Se incluyeron todos los pacientes consecutivos filiados para el triaje entre las 8-22 horas del servicio de Urgencias de un hospital terciario durante un mes. Se analizaron 7 variables a la llegada del paciente, que pudieran influir en el ingreso: edad, sexo, nivel de gravedad según el triaje, ubicación inicial, diagnóstico de entrada, solicitud de prueba complementaria y prescripción de medicación. Serealizó un estudio multivariable según regresión logística. Resultados. Se incluyeron 2.476 episodios de los que 114 (4,6%) ingresaron. Se asociaron de forma significativa: edad>65 años (Odds ratio [OR]=2,1, intervalo de confianza [IC] 95%, 1,3-3,2; p=0,001); sexo masculino (OR=1,6, IC 95%, 1,1-2,4;p=0,020). Diagnóstico de entrada disnea: (OR=5,2, IC 95%, 2,8-9,7; p<0,0001); dolor abdominal (OR=4,7, IC 95%, 2,7-8,3; p<0,0001); ubicación inicial en sala de agudos (OR=8,9, IC95%, 5,4-14,9; p<0,0001); solicitud de pruebas complementarias (OR=1,1, IC95%, 0,9-1,3; p=0,064) y prescripción de tratamiento (OR=2,6, IC 95%,1,6-4,2; p=<0,0001). Con dichas variables se diseñó un modelo matemático que tenía una sensibilidad del 76% y una especificidad del 82% (área bajo la curva es de 0,85 [IC 95% 0,81-0,88; p<0,001]). Conclusiones. El modelo de predicción de ingreso es una herramienta que puede ser de utilidad a la hora de preverla necesidad del recurso cama hospitalaria a la llegada del paciente al servicio de Urgencias(AU)

Background. To develop a prediction model for in-hospital admission to provide an almost «real time» determination of hospital beds needed, so as to predict the resources required as early as possible. Material and methods. A prospective observational study in the emergency department of a university hospital. We included all consecutive patients between 8.00-22.00hours during one month. We analyzed 7 variables taken when the patient arrived at the emergency department: age, sex, level of triage, initial disposition, first diagnosis, diagnostic test and medication, and we performed a logistic regression. Results. We included 2,476 visits of which 114 (4.6%) were admitted. A significant direct correlation was seen between: age >65 years old (odds ratio[OR]=2.1, confidence interval[CI] 95%, 1.3-3.2; p=0.001); male sex (OR=1.6, IC 95%, 1.1-2.4;p=0.020); dyspnea (OR=5.2, IC 95%, 2.8-9.7; p<0.0001), abdominal pain (OR=4.7, IC 95%, 2.7-8.3; p<0.0001); acute care initial disposition (OR=8.9, IC 95%, 5.4-14.9; p<0.0001), diagnostic test (OR=1.1, IC 95%, 0.9-1.3; p=0.064) and treatment prescription (OR=2.6, IC95%, 1.6-4.2; p=<0.0001). The model had a sensitivity of 76% and a specificity of 82% (area under curve 0.85 [IC 95% 0.81-0.88; p<0.001]). Conclusions. The in-hospital admission prediction model is a good and useful tool for predicting the in-hospital beds needed when patients arrive at the emergency department(AU)