J. Clin. Med. 2014, 3(1), 218-232; doi:10.3390/jcm3010218

Improving Clinical Prediction of Bipolar Spectrum Disorders in Youth

1,* email, 2email, 3email, 4email, 5email, 6email, 3email, 5email, 5email, 7email and 8email
Received: 13 December 2013; in revised form: 8 February 2014 / Accepted: 12 February 2014 / Published: 10 March 2014
(This article belongs to the Special Issue Bipolar Disorder in Children and Adolescents)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: 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 vs. 0.18) at the expense of slightly decreased specificity and positive predictive power. The advantage of CTA algorithms for clinical decision making is demonstrated by the combinations of predictors most useful for altering the probability of BPSD. The 24% sample probability of BPSD was substantially decreased in youth with low screening and baseline parent ratings of mania, negative parental history of mania, and low levels of stressful life events (2%). High screening plus high baseline parent-rated mania nearly doubled the BPSD probability (46%). Future work will benefit from examining additional, powerful predictors, such as alternative data sources (e.g., clinician ratings, neurocognitive test data); these may increase the clinical utility of CTA models further.
Keywords: bipolar disorder; children; risk factors; clinical decision making; classification tree analysis
PDF Full-text Download PDF Full-Text [417 KB, uploaded 10 March 2014 08:37 CET]

Export to BibTeX |

MDPI and ACS Style

Frazier, T.W.; Youngstrom, E.A.; Fristad, M.A.; Demeter, C.; Birmaher, B.; Kowatch, R.A.; Arnold, L.E.; Axelson, D.; Gill, M.K.; Horwitz, S.M.; Findling, R.L. Improving Clinical Prediction of Bipolar Spectrum Disorders in Youth. J. Clin. Med. 2014, 3, 218-232.

AMA Style

Frazier TW, Youngstrom EA, Fristad MA, Demeter C, Birmaher B, Kowatch RA, Arnold LE, Axelson D, Gill MK, Horwitz SM, Findling RL. Improving Clinical Prediction of Bipolar Spectrum Disorders in Youth. Journal of Clinical Medicine. 2014; 3(1):218-232.

Chicago/Turabian Style

Frazier, Thomas W.; Youngstrom, Eric A.; Fristad, Mary A.; Demeter, Christine; Birmaher, Boris; Kowatch, Robert A.; Arnold, L. E.; Axelson, David; Gill, Mary K.; Horwitz, Sarah M.; Findling, Robert L. 2014. "Improving Clinical Prediction of Bipolar Spectrum Disorders in Youth." J. Clin. Med. 3, no. 1: 218-232.

J. Clin. Med. EISSN 2077-0383 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert