| Journal of Clinical Medicine | |
| Improving Clinical Prediction of Bipolar Spectrum Disorders in Youth | |
| Thomas W. Frazier1  Eric A. Youngstrom4  Mary A. Fristad7  Christine Demeter3  Boris Birmaher5  Robert A. Kowatch6  L. Eugene Arnold7  David Axelson5  Mary K. Gill5  Sarah M. Horwitz2  | |
| [1] Center for Autism, Cleveland Clinic, Cleveland, OH 44104, USA;Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY 10016, USA; E-Mail:;Department of Psychiatry, Division of Child and Adolescent Psychiatry, University Hospitals Case Medical Center, Cleveland, OH 44106, USA; E-Mail:;Department of Psychology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; E-Mail:;Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA 15213, USA; E-Mails:;Department of Psychiatry, Nationwide Children’s Hospital, Columbus, OH 4320, USA; E-Mail:;Department of Psychiatry, Division of Child and Adolescent Psychiatry, Ohio State University, Columbus, OH 43210, USA; E-Mails: | |
| 关键词: bipolar disorder; children; risk factors; clinical decision making; classification tree analysis; | |
| DOI : 10.3390/jcm3010218 | |
| 来源: mdpi | |
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【 摘 要 】
This report evaluates whether classification tree algorithms (CTA) may improve the identification of individuals at risk for bipolar spectrum disorders (BPSD). Analyses used the Longitudinal Assessment of Manic Symptoms (LAMS) cohort (629 youth, 148 with BPSD and 481 without BPSD). Parent ratings of mania symptoms, stressful life events, parenting stress, and parental history of mania were included as risk factors. Comparable overall accuracy was observed for CTA (75.4%) relative to logistic regression (77.6%). However, CTA showed increased sensitivity (0.28
【 授权许可】
CC BY
© 2014 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
| Files | Size | Format | View |
|---|---|---|---|
| RO202003190028235ZK.pdf | 416KB |
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