title: Building a Decision Support System for Inpatient Admission Prediction With the Manchester Triage System and Administrative Check-in Variables.
abstract: The usage of decision support tools in emergency departments, based on predictive models, capable of estimating the probability of admission for patients in the emergency department may give nursing staff the possibility of allocating resources in advance. We present a methodology for developing and building one such system for a large specialized care hospital using a logistic regression and an artificial neural network model using nine routinely collected variables available right at the end of the triage process.A database of 255.668 triaged nonobstetric emergency department presentations from the Ramon y Cajal University Hospital of Madrid, from January 2011 to December 2012, was used to develop and test the models, with 66% of the data used for derivation and 34% for validation, with an ordered nonrandom partition. On the validation dataset areas under the receiver operating characteristic curve were 0.8568 (95% confidence interval, 0.8508-0.8583) for the logistic regression model and 0.8575 (95% confidence interval, 0.8540-0. 8610) for the artificial neural network model. χ Values for Hosmer-Lemeshow fixed "deciles of risk" were 65.32 for the logistic regression model and 17.28 for the artificial neural network model. A nomogram was generated upon the logistic regression model and an automated software decision support system with a Web interface was built based on the artificial neural network model.
authors.affiliation: Author Affiliations: GTH, Department of Electronic Engineering, Technical University of Madrid, ETSI Telecomunicación, Ciudad Universitaria, Madrid (Mr Zlotnik and Dr Martínez); Ramón y Cajal University Hospital C/ de Colmenar Viejo, Madrid (Mr Zlotnik, Dr Alfaro, and Ms Pérez); and Department of Signal Theory and Communications, Carlos III University C/ Madrid, Getafe, Spain (Dr Gallardo-Antolín).
authors.forename: Alexander
authors.initials: A
authors.lastname: Zlotnik
authors.forename: Miguel Cuchí
authors.initials: MC
authors.lastname: Alfaro
authors.forename: María Carmen Pérez
authors.initials: MC
authors.lastname: Pérez
authors.forename: Ascensión
authors.initials: A
authors.lastname: Gallardo-Antolín
authors.forename: Juan Manuel Montero
authors.initials: JM
authors.lastname: Martínez
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D003625:Data Collection; D020000:Decision Support Systems, Clinical; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009874:Operations Research; D018570:Risk Assessment; D013030:Spain; D014218:Triage / Q000706:statistics & numerical data*