title: Neural networks as a tool to predict syncope risk in the Emergency Department.
abstract: OBJECTIVE
  There is no universally accepted tool for the risk stratification of syncope patients in the Emergency Department. The aim of this study was to investigate the short-term predictive accuracy of an artificial neural network (ANN) in stratifying the risk in this patient group.
  
  
  RESULTS
  We analysed individual level data from three prospective studies, with a cumulative sample size of 1844 subjects. Each dataset was reanalysed to reduce the heterogeneity among studies defining abnormal electrocardiogram (ECG) and serious outcomes according to a previous consensus. Ten variables from patient history, ECG, and the circumstances of syncope were used to train and test the neural network. Given the exploratory nature of this work, we adopted two approaches to train and validate the tool. One approach used 4/5 of the data for the training set and 1/5 for the validation set, and the other approach used 9/10 for the training set and 1/10 for the validation set. The sensitivity, specificity, and area under the receiver operating characteristic curve of ANNs in identifying short-term adverse events after syncope were 95% [95% confidence interval (CI) 80-98%], 67% (95% CI 62-72%), 0.69 with the 1/5 approach and 100% (95% CI 84-100%), 79% (95% CI 72-85%), 0.78 with the 1/10 approach.
  
  
  CONCLUSIONS
  The results of our study suggest that ANNs are effective in predicting the short-term risk of patients with syncope. Prospective studies are needed in order to compare ANNs' predictive capability with existing rules and clinical judgment.
authors.affiliation: Dipartimento di Medicina Interna e Specializzazioni Mediche, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milano, Italy.
authors.forename: Giorgio
authors.initials: G
authors.lastname: Costantino
authors.affiliation: CNR-IRCrES, Research Institute on Sustainable Economic Growth, Moncalieri, Italy.
authors.forename: Greta
authors.initials: G
authors.lastname: Falavigna
authors.affiliation: Dipartimento di Medicina Interna e Specializzazioni Mediche, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milano, Italy.|Dipartimento di Scienze Cliniche e di Comunità, Università degli Studi di Milano, Milano, Italy.
authors.forename: Monica
authors.initials: M
authors.lastname: Solbiati
authors.affiliation: Department of Emergency Medicine, Ospedale di Alessandria, Alessandria, Italy.
authors.forename: Ivo
authors.initials: I
authors.lastname: Casagranda
authors.affiliation: Department of Emergency Medicine, Center for Policy Research-Emergency Medicine, Oregon Health and Science University, Portland, OR, USA.
authors.forename: Benjamin C
authors.initials: BC
authors.lastname: Sun
authors.affiliation: Department of Emergency Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA.
authors.forename: Shamai A
authors.initials: SA
authors.lastname: Grossman
authors.affiliation: Division of Emergency Medicine, Stanford University, Stanford, CA, USA.
authors.forename: James V
authors.initials: JV
authors.lastname: Quinn
authors.affiliation: Emergency Medicine Research Group Edinburgh, Royal Infirmary of Edinburgh, Edinburgh, UK.
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Reed
authors.affiliation: Syncope Unit, Geriatric Medicine and Cardiology, Careggi University Hospital, Firenze, Italy.
authors.forename: Andrea
authors.initials: A
authors.lastname: Ungar
authors.affiliation: Dipartimento di Medicina Interna e Specializzazioni Mediche, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milano, Italy.|Dipartimento di Scienze Cliniche e di Comunità, Università degli Studi di Milano, Milano, Italy.
authors.forename: Nicola
authors.initials: N
authors.lastname: Montano
authors.affiliation: Department of Biomedical Sciences, Humanitas University-Humanitas Research Hospital, Rozzano, Italy.
authors.forename: Raffaello
authors.initials: R
authors.lastname: Furlan
authors.affiliation: Ospedale di Alessandria, Alessandria, Italy.
authors.forename: Roberto
authors.initials: R
authors.lastname: Ippoliti
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D016507:Cardiology Service, Hospital; D000066491:Clinical Decision-Making; D016208:Databases, Factual; D003661:Decision Support Techniques; D004562:Electrocardiography; D004636:Emergency Service, Hospital; D005260:Female; D006801:Humans; D007558:Italy; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D013575:Syncope / Q000175:diagnosis* / Q000503:physiopathology / Q000628:therapy; D013997:Time Factors; D014218:Triage
keywords: Artificial neural networks; Calibration; Discrimination; Emergency Department; Risk stratification; Syncope