title: Predictive modeling using a nationally representative database to identify patients at risk of developing microalbuminuria.
abstract: OBJECTIVE
  Predictive models allow clinicians to identify higher- and lower-risk patients and make targeted treatment decisions. Microalbuminuria (MA) is a condition whose presence is understood to be an early marker for cardiovascular disease. The aims of this study were to develop a patient data-driven predictive model and a risk-score assessment to improve the identification of MA.
  
  
  METHODS
  The 2007-2008 National Health and Nutrition Examination Survey (NHANES) was utilized to create a predictive model. The dataset was split into thirds; one-third was used to develop the model, while the other two-thirds were utilized for internal validation. The 2012-2013 NHANES was used as an external validation database. Multivariate logistic regression was performed to create the model. Performance was evaluated using three criteria: (1) receiver operating characteristic curves; (2) pseudo-R (2) values; and (3) goodness of fit (Hosmer-Lemeshow). The model was then used to develop a risk-score chart.
  
  
  RESULTS
  A model was developed using variables for which there was a significant relationship. Variables included were systolic blood pressure, fasting glucose, C-reactive protein, blood urea nitrogen, and alcohol consumption. The model performed well, and no significant differences were observed when utilized in the validation datasets. A risk score was developed, and the probability of developing MA for each score was calculated.
  
  
  CONCLUSIONS
  The predictive model provides new evidence about variables related with MA and may be used by clinicians to identify at-risk patients and to tailor treatment. The risk score developed may allow clinicians to measure a patient's MA risk.
authors.affiliation: Facultad de Farmacia, Universidad de Concepción, Barrio Universitario s/n, Concepción, Chile. lorenzovilla@udec.cl.
authors.forename: Lorenzo
authors.identifier: http://orcid.org/0000-0002-3821-4595
authors.initials: L
authors.lastname: Villa-Zapata
authors.affiliation: Center for Health Outcomes and PharmacoEconomic Research, University of Arizona, College of Pharmacy, 1295 N. Martin, Tucson, AZ, 85721, USA. warholak@pharmacy.arizonaedu.
authors.forename: Terri
authors.initials: T
authors.lastname: Warholak
authors.affiliation: Pharmacy Practice and Science, University of Arizona, College of Pharmacy, 1295 N. Martin, Tucson, AZ, 85721, USA. slack@pharmacy.arizona.edu.
authors.forename: Marion
authors.initials: M
authors.lastname: Slack
authors.affiliation: Pharmacy Practice and Science, University of Arizona, College of Pharmacy, 1295 N. Martin, Tucson, AZ, 85721, USA. malone@pharmacy.arizona.edu.
authors.forename: Daniel
authors.initials: D
authors.lastname: Malone
authors.affiliation: Biomedical Informatics, Arizona State University, 550 N. 3rd Street, Phoenix, AZ, 85004-0698, USA. Anita.Murcko@asu.edu.
authors.forename: Anita
authors.initials: A
authors.lastname: Murcko
authors.affiliation: Biomedical Informatics, Arizona State University, Mayo Clinic, Samuel C. Johnson Research Bldg. 13212 East Shea Boulevard, Phoenix, AZ, USA. George.Runger@asu.edu.
authors.forename: George
authors.initials: G
authors.lastname: Runger
authors.affiliation: College of Pharmacy, The University of Arizona, 1295 N. Martin, Tucson, AZ, 85721, USA. mjlevengood@gmail.com.
authors.forename: Michael
authors.initials: M
authors.lastname: Levengood
mesh_terms: D000328:Adult; D000419:Albuminuria / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology; D015415:Biomarkers / Q000032:analysis*; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009749:Nutrition Surveys / Q000379:methods*; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D014481:United States / Q000453:epidemiology
chemical_list: D015415:Biomarkers
keywords: Albuminuria; Microalbuminuria; Predictive model; Proteinuria