External validation of the international risk prediction algorithm for major depressive episode in the US general population: the PredictD-US study

Abstract

Background: 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. Methods: 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. Results: 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. Conclusions: 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.

Prediction of major depressive disorder onset in college students

“…(WHO) World Mental Health Surveys(Auerbach et al, 2016). Prediction accuracy (AUC = 0.73) was comparable to the few prediction algorithms that have been evaluated for depression within a general population (AUC = 0.71)(Nigatu, Liu, & Wang, 2016) and primary care samples (AUC = 0.82)(Bellon et al, 2011) and are also comparable to other fields of medicine(Karnes et al, 2017;ten Haaf et al, 2017). Our data suggest that the first year in college constitutes a risk period for the onset of MDD.…”

“…Second, our study further adds to the cumulating evidence that the development of risk-prediction for psychiatric disorders is feasible (Bernardini et al, 2017) and provides evidence that a multivariate prediction model can be a useful tool to accurately predict the onset of MDD during college. Prediction accuracy (AUC = 0.73) was comparable to the few prediction algorithms that have been evaluated for depression within a general population (AUC = 0.71) (Nigatu, Liu, & Wang, 2016) and primary care samples (AUC = 0.82) (Bellon et al, 2011) and are also comparable to other fields of medicine (Karnes et al, 2017;ten Haaf et al, 2017). However, to achieve optimal performance, recalibration of models is needed prior to applying the models to a new population.…”

Clinical and Cost-Effectiveness of Personalized Tele-Based Coaching for Farmers, Foresters and Gardeners to Prevent Depression: Study Protocol of an 18-Month Follow-Up Pragmatic Randomized Controlled Trial (TEC-A)

“…Furthermore, we will explore variables associated with depression (72)(73)(74) or that have shown to predict treatment outcome for depression and anxiety (75). Clinical characteristics that shall be investigated include depressive symptom severity (76), lifetime history of MDD or any mental disorder (77)(78)(79)(80), past or present suicidal thoughts or plans (35,(79)(80)(81), experience with psychotherapy (82), treatment motivation (83), treatment preference (84)(85)(86), family history of mental illness (77, 87-89) (chronic) illness, self-perceived health and energy (61,78,(90)(91)(92)(93)(94)(95), traumatic or adverse childhood experience (abuse, parental death, or divorce) (80,87,88,(96)(97)(98)(99)(100)(101), body satisfaction and eating disorder (94,(102)(103)(104)(105)(106)(107), sleep quality (44,90,94,108,…”

Predicting the risk of depression among adolescents in Nepal using a model developed in Brazil: the IDEA Project

“…Models that predict MDD have conventionally been derived in adult not adolescent populations [20][21][22][23], and limited to patients who have experienced chronic or lifethreatening medical conditions [24][25][26][27] or to predict recurrence of depression [28,29]. The Chicago Adolescent Risk Assessment is one known model developed in a US adolescent population to predict 1-year risk of depression in adolescents [30].…”