title: External validation of the international risk prediction algorithm for major depressive episode in the US general population: the PredictD-US study.
abstract: Multivariable risk prediction algorithms are useful for making clinical decisions and for health planning. While prediction algorithms for new onset of major depression in the primary care attendees in Europe and elsewhere have been developed, the performance of these algorithms in different populations is not known. The objective of this study was to validate the PredictD algorithm for new onset of major depressive episode (MDE) in the US general population.
  
  
  
  Longitudinal study design was conducted with approximate 3-year follow-up data from a nationally representative sample of the US general population. A total of 29,621 individuals who participated in Wave 1 and 2 of the US National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) and who did not have an MDE in the past year at Wave 1 were included. The PredictD algorithm was directly applied to the selected participants. MDE was assessed by the Alcohol Use Disorder and Associated Disabilities Interview Schedule, based on the DSM-IV criteria.
  
  
  
  Among the participants, 8 % developed an MDE over three years. The PredictD algorithm had acceptable discriminative power (C-statistics = 0.708, 95 % CI: 0.696, 0.720), but poor calibration (p < 0.001) with the NESARC data. In the European primary care attendees, the algorithm had a C-statistics of 0.790 (95 % CI: 0.767, 0.813) with a perfect calibration.
  
  
  
  The PredictD algorithm has acceptable discrimination, but the calibration capacity was poor in the US general population despite of re-calibration. Therefore, based on the results, at current stage, the use of PredictD in the US general population for predicting individual risk of MDE is not encouraged. More independent validation research is needed.
authors.affiliation: Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Canada. yeshambel.nigatu1@ucalgary.ca.|Mathison Centre for Mental Health Research and Education, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada. yeshambel.nigatu1@ucalgary.ca.
authors.forename: Yeshambel T
authors.initials: YT
authors.lastname: Nigatu
authors.affiliation: Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Canada.|Mathison Centre for Mental Health Research and Education, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.
authors.forename: Yan
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Canada.|Mathison Centre for Mental Health Research and Education, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.|Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Canada.
authors.forename: JianLi
authors.initials: J
authors.lastname: Wang
mesh_terms: D000328:Adult; D000465:Algorithms; D003865:Major Depressive Disorder / Q000175:diagnosis / Q000453:epidemiology*; D039721:Diagnostic and Statistical Manual of Mental Disorders; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D015995:Prevalence; D011320:Primary Health Care; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D014481:United States / Q000453:epidemiology
keywords: Depression; Prediction algorithm; Risk factors; Validation