科研之友
微信
新浪微博
Facebook
分享链接
摘要
Bipolar disorder is frequently clinically diagnosed in youths who do not actually satisfy Diagnostic and Statistical Manual of Mental Disorders (4th ed., text revision; DSM-IV-TR; American Psychiatric Association, 1994) criteria, yet cases that would satisfy full DSM-IV-TR criteria are often undetected clinically. Evidence-based assessment methods that incorporate Bayesian reasoning have demonstrated improved diagnostic accuracy and consistency; however, their clinical utility is largely unexplored. The present study examines the effectiveness of promising evidence-based decision-making strategies compared with the clinical gold standard. Participants were 562 youths, ages 5 to 17 and predominantly African American, drawn from a community mental health clinic. Research diagnoses combined a semistructured interview with youths' psychiatric, developmental, and family mental health histories. Independent Bayesian estimates that relied on published risk estimates from other samples discriminated bipolar diagnoses (area under curve = .75, p <. 00005). The Bayes and confidence ratings correlated at r(s) = .30. Agreement about an evidence-based assessment intervention threshold model (wait/assess/treat) was k = .24, p < .05. No potential moderators of agreement between the Bayesian estimates and confidence ratings, including type of bipolar illness, were significant. Bayesian risk estimates were highly correlated with logistic regression estimates using optimal sample weights (r = .81, p < .0005). Clinical and Bayesian approaches agree in terms of overall concordance and deciding next clinical action, even when Bayesian predictions are based on published estimates from clinically and demographically different samples. Evidence-based assessment methods may be useful in settings in which gold standard assessments cannot be routinely used, and they may help decrease rates of overdiagnosis while promoting earlier identification of true cases.
- 出版日期2012-6
