• C M Elvira Martínez, C Fernández, J González Del Castillo, J J González Armengol, P Villarroel, and F J Martín Sánchez.
• Servicio Admisión y Documentación Clínica, Hospital Clínico San Carlos, Madrid, Spain.
• An Sist Sanit Navar. 2012 May 1; 35 (2): 207-17.

BackgroundTo 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 MethodsA 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.ResultsWe 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]).ConclusionsThe 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.

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