title: Development and validation of risk assessment models for diabetes-related complications based on the DCCT/EDIC data.
abstract: OBJECTIVE
  To derive and validate a set of computational models able to assess the risk of developing complications and experiencing adverse events for patients with diabetes. The models are developed on data from the Diabetes Control and Complications Trial (DCCT) and the Epidemiology of Diabetes Interventions and Complications (EDIC) studies, and are validated on an external, retrospectively collected cohort.
  
  
  METHODS
  We selected fifty-one clinical parameters measured at baseline during the DCCT as potential risk factors for the following adverse outcomes: Cardiovascular Diseases (CVD), Hypoglycemia, Ketoacidosis, Microalbuminuria, Proteinuria, Neuropathy and Retinopathy. For each outcome we applied a data-mining analysis protocol in order to identify the best-performing signature, i.e., the smallest set of clinical parameters that, considered jointly, are maximally predictive for the selected outcome. The predictive models built on the selected signatures underwent both an interval validation on the DCCT/EDIC data and an external validation on a retrospective cohort of 393 diabetes patients (49 Type I and 344 Type II) from the Chorleywood Medical Center, UK.
  
  
  RESULTS
  The selected predictive signatures contain five to fifteen risk factors, depending on the specific outcome. Internal validation performances, as measured by the Concordance Index (CI), range from 0.62 to 0.83, indicating good predictive power. The models achieved comparable performances for the Type I and, quite surprisingly, Type II external cohort.
  
  
  CONCLUSIONS
  Data-mining analyses of the DCCT/EDIC data allow the identification of accurate predictive models for diabetes-related complications. We also present initial evidences that these models can be applied on a more recent, European population.
authors.affiliation: Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece. Electronic address: vlagani@ics.forth.gr.
authors.forename: Vincenzo
authors.initials: V
authors.lastname: Lagani
authors.affiliation: Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece.
authors.forename: Franco
authors.initials: F
authors.lastname: Chiarugi
authors.affiliation: Herts Valley Clinical Commission Group, Hertfordshire, United Kingdom.
authors.forename: Shona
authors.initials: S
authors.lastname: Thomson
authors.affiliation: Chorleywood Health Center, Chorleywood, United Kingdom.
authors.forename: Jo
authors.initials: J
authors.lastname: Fursse
authors.affiliation: Chorleywood Health Center, Chorleywood, United Kingdom.
authors.forename: Edin
authors.initials: E
authors.lastname: Lakasing
authors.affiliation: Chorleywood Health Center, Chorleywood, United Kingdom.
authors.forename: Russell W
authors.initials: RW
authors.lastname: Jones
authors.affiliation: Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece; Computer Science Department, University of Crete, Heraklion, Greece.
authors.forename: Ioannis
authors.initials: I
authors.lastname: Tsamardinos
mesh_terms: D000293:Adolescent; D000328:Adult; D003198:Computer Simulation; D048909:Diabetes Complications / Q000175:diagnosis* / Q000453:epidemiology*; D003922:Diabetes Mellitus, Type 1 / Q000150:complications / Q000453:epidemiology; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D011379:Prognosis; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D055815:Young Adult
keywords: Diabetes complications; Risk assessment models; Risk factors; Risk model external validation; Risk stratification