Cargando…

Anticipating manic and depressive transitions in patients with bipolar disorder using early warning signals

BACKGROUND: In bipolar disorder treatment, accurate episode prediction is paramount but remains difficult. A novel idiographic approach to prediction is to monitor generic early warning signals (EWS), which may manifest in symptom dynamics. EWS could thus form personalized alerts in clinical care. T...

Descripción completa

Detalles Bibliográficos
Autores principales: Bos, Fionneke M., Schreuder, Marieke J., George, Sandip V., Doornbos, Bennard, Bruggeman, Richard, van der Krieke, Lian, Haarman, Bartholomeus C. M., Wichers, Marieke, Snippe, Evelien
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994809/
https://www.ncbi.nlm.nih.gov/pubmed/35397076
http://dx.doi.org/10.1186/s40345-022-00258-4
_version_ 1784684183066509312
author Bos, Fionneke M.
Schreuder, Marieke J.
George, Sandip V.
Doornbos, Bennard
Bruggeman, Richard
van der Krieke, Lian
Haarman, Bartholomeus C. M.
Wichers, Marieke
Snippe, Evelien
author_facet Bos, Fionneke M.
Schreuder, Marieke J.
George, Sandip V.
Doornbos, Bennard
Bruggeman, Richard
van der Krieke, Lian
Haarman, Bartholomeus C. M.
Wichers, Marieke
Snippe, Evelien
author_sort Bos, Fionneke M.
collection PubMed
description BACKGROUND: In bipolar disorder treatment, accurate episode prediction is paramount but remains difficult. A novel idiographic approach to prediction is to monitor generic early warning signals (EWS), which may manifest in symptom dynamics. EWS could thus form personalized alerts in clinical care. The present study investigated whether EWS can anticipate manic and depressive transitions in individual patients with bipolar disorder. METHODS: Twenty bipolar type I/II patients (with ≥ 2 episodes in the previous year) participated in ecological momentary assessment (EMA), completing five questionnaires a day for four months (Mean = 491 observations per person). Transitions were determined by weekly completed questionnaires on depressive (Quick Inventory for Depressive Symptomatology Self-Report) and manic (Altman Self-Rating Mania Scale) symptoms. EWS (rises in autocorrelation at lag-1 and standard deviation) were calculated in moving windows over 17 affective and symptomatic EMA states. Positive and negative predictive values were calculated to determine clinical utility. RESULTS: Eleven patients reported 1–2 transitions. The presence of EWS increased the probability of impending depressive and manic transitions from 32-36% to 46–48% (autocorrelation) and 29–41% (standard deviation). However, the absence of EWS could not be taken as a sign that no transition would occur in the near future. The momentary states that indicated nearby transitions most accurately (predictive values: 65–100%) were full of ideas, worry, and agitation. Large individual differences in the utility of EWS were found. CONCLUSIONS: EWS show theoretical promise in anticipating manic and depressive transitions in bipolar disorder, but the level of false positives and negatives, as well as the heterogeneity within and between individuals and preprocessing methods currently limit clinical utility. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40345-022-00258-4.
format Online
Article
Text
id pubmed-8994809
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Springer Berlin Heidelberg
record_format MEDLINE/PubMed
spelling pubmed-89948092022-04-22 Anticipating manic and depressive transitions in patients with bipolar disorder using early warning signals Bos, Fionneke M. Schreuder, Marieke J. George, Sandip V. Doornbos, Bennard Bruggeman, Richard van der Krieke, Lian Haarman, Bartholomeus C. M. Wichers, Marieke Snippe, Evelien Int J Bipolar Disord Research BACKGROUND: In bipolar disorder treatment, accurate episode prediction is paramount but remains difficult. A novel idiographic approach to prediction is to monitor generic early warning signals (EWS), which may manifest in symptom dynamics. EWS could thus form personalized alerts in clinical care. The present study investigated whether EWS can anticipate manic and depressive transitions in individual patients with bipolar disorder. METHODS: Twenty bipolar type I/II patients (with ≥ 2 episodes in the previous year) participated in ecological momentary assessment (EMA), completing five questionnaires a day for four months (Mean = 491 observations per person). Transitions were determined by weekly completed questionnaires on depressive (Quick Inventory for Depressive Symptomatology Self-Report) and manic (Altman Self-Rating Mania Scale) symptoms. EWS (rises in autocorrelation at lag-1 and standard deviation) were calculated in moving windows over 17 affective and symptomatic EMA states. Positive and negative predictive values were calculated to determine clinical utility. RESULTS: Eleven patients reported 1–2 transitions. The presence of EWS increased the probability of impending depressive and manic transitions from 32-36% to 46–48% (autocorrelation) and 29–41% (standard deviation). However, the absence of EWS could not be taken as a sign that no transition would occur in the near future. The momentary states that indicated nearby transitions most accurately (predictive values: 65–100%) were full of ideas, worry, and agitation. Large individual differences in the utility of EWS were found. CONCLUSIONS: EWS show theoretical promise in anticipating manic and depressive transitions in bipolar disorder, but the level of false positives and negatives, as well as the heterogeneity within and between individuals and preprocessing methods currently limit clinical utility. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40345-022-00258-4. Springer Berlin Heidelberg 2022-04-09 /pmc/articles/PMC8994809/ /pubmed/35397076 http://dx.doi.org/10.1186/s40345-022-00258-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research
Bos, Fionneke M.
Schreuder, Marieke J.
George, Sandip V.
Doornbos, Bennard
Bruggeman, Richard
van der Krieke, Lian
Haarman, Bartholomeus C. M.
Wichers, Marieke
Snippe, Evelien
Anticipating manic and depressive transitions in patients with bipolar disorder using early warning signals
title Anticipating manic and depressive transitions in patients with bipolar disorder using early warning signals
title_full Anticipating manic and depressive transitions in patients with bipolar disorder using early warning signals
title_fullStr Anticipating manic and depressive transitions in patients with bipolar disorder using early warning signals
title_full_unstemmed Anticipating manic and depressive transitions in patients with bipolar disorder using early warning signals
title_short Anticipating manic and depressive transitions in patients with bipolar disorder using early warning signals
title_sort anticipating manic and depressive transitions in patients with bipolar disorder using early warning signals
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994809/
https://www.ncbi.nlm.nih.gov/pubmed/35397076
http://dx.doi.org/10.1186/s40345-022-00258-4
work_keys_str_mv AT bosfionnekem anticipatingmanicanddepressivetransitionsinpatientswithbipolardisorderusingearlywarningsignals
AT schreudermariekej anticipatingmanicanddepressivetransitionsinpatientswithbipolardisorderusingearlywarningsignals
AT georgesandipv anticipatingmanicanddepressivetransitionsinpatientswithbipolardisorderusingearlywarningsignals
AT doornbosbennard anticipatingmanicanddepressivetransitionsinpatientswithbipolardisorderusingearlywarningsignals
AT bruggemanrichard anticipatingmanicanddepressivetransitionsinpatientswithbipolardisorderusingearlywarningsignals
AT vanderkriekelian anticipatingmanicanddepressivetransitionsinpatientswithbipolardisorderusingearlywarningsignals
AT haarmanbartholomeuscm anticipatingmanicanddepressivetransitionsinpatientswithbipolardisorderusingearlywarningsignals
AT wichersmarieke anticipatingmanicanddepressivetransitionsinpatientswithbipolardisorderusingearlywarningsignals
AT snippeevelien anticipatingmanicanddepressivetransitionsinpatientswithbipolardisorderusingearlywarningsignals