Predicting prognosis for adults with depression using individual symptom data: a comparison of modelling approaches.

Humans Adult Depression / psychology Prognosis Treatment Outcome Psychiatric Status Rating Scales Anxiety
Depressive symptoms major depression network analysis prediction modelling prognosis

Journal

Psychological medicine
ISSN: 1469-8978
Titre abrégé: Psychol Med
Pays: England
ID NLM: 1254142

Informations de publication

Date de publication:
01 2023
Historique:
medline: 4 5 2023
pubmed: 7 5 2021
entrez: 6 5 2021
Statut: ppublish

Résumé

This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data. Individual patient data from all six eligible randomised controlled trials were used to develop ( Models 1-7 all outperformed the null model and model 8. Model performance was very similar across models 1-6, meaning that differential weights applied to the baseline sum scores had little impact. Any of the modelling techniques (models 1-7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression.

Sections du résumé

BACKGROUND
This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data.
METHODS
Individual patient data from all six eligible randomised controlled trials were used to develop (
RESULTS
Models 1-7 all outperformed the null model and model 8. Model performance was very similar across models 1-6, meaning that differential weights applied to the baseline sum scores had little impact.
CONCLUSIONS
Any of the modelling techniques (models 1-7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression.

Identifiants

DOI: 10.1017/S0033291721001616 PMID: 33952358 PMC: PMC9899563
pubmed: 33952358
doi: 10.1017/S0033291721001616
pii: S0033291721001616
pmc: PMC9899563
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

408-418

Subventions

Organisme : Medical Research Council
ID : G0200243
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_00004/06
Pays : United Kingdom
Organisme : Department of Health
Pays : United Kingdom

Auteurs

J E J Buckman (JEJ)

Research Department of Clinical, Educational & Health Psychology, Centre for Outcomes Research and Effectiveness (CORE), University College London, 1-19 Torrington Place, London, UK.
iCope - Camden & Islington Psychological Therapies Services - Camden & Islington NHS Foundation Trust, St Pancras Hospital, London, UK.
ORCID: 0000-0002-2281-0907

Z D Cohen (ZD)

Department of Psychiatry, University of California, Los Angeles, Los Angeles, CA, USA.

C O'Driscoll (C)

Research Department of Clinical, Educational & Health Psychology, Centre for Outcomes Research and Effectiveness (CORE), University College London, 1-19 Torrington Place, London, UK.

E I Fried (EI)

Department of Clinical Psychology, Leiden University, Leiden, The Netherlands.

R Saunders (R)

Research Department of Clinical, Educational & Health Psychology, Centre for Outcomes Research and Effectiveness (CORE), University College London, 1-19 Torrington Place, London, UK.

G Ambler (G)

Statistical Science, University College London, 1-19 Torrington Place, London, UK.

R J DeRubeis (RJ)

Department of Psychology, School of Arts and Sciences, 425 S. University Avenue, Philadelphia PA, USA.

S Gilbody (S)

Department of Health Sciences, University of York, Seebohm Rowntree Building, Heslington, York, UK.

S D Hollon (SD)

Department of Psychology, Vanderbilt University, Nashville, TN, USA.

T Kendrick (T)

Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Aldermoor Health Centre, Southampton, UK.

E Watkins (E)

Department of Psychology, University of Exeter, Sir Henry Wellcome Building for Mood Disorders Research, Perry Road, Exeter, UK.

T C Eley (TC)

Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.

A J Peel (AJ)

Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.

C Rayner (C)

Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.

D Kessler (D)

Centre for Academic Primary Care, Population Health Sciences, Bristol Medical School, University of Bristol, Canynge Hall, Bristol, UK.

N Wiles (N)

Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Bristol, UK.

G Lewis (G)

Division of Psychiatry, University College London, Maple House, London, UK.

S Pilling (S)

Research Department of Clinical, Educational & Health Psychology, Centre for Outcomes Research and Effectiveness (CORE), University College London, 1-19 Torrington Place, London, UK.
Camden & Islington NHS Foundation Trust, St Pancras Hospital, London, UK.

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Classifications MeSH

questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Predicting prognosis for adults with...
questionsmedicales.fr › Personnes › Tranches d'âge › Adulte › Predicting prognosis for adults with...
questionsmedicales.fr › Comportement et mécanismes comportementaux › Émotions › Dépression › Predicting prognosis for adults with...
questionsmedicales.fr › Diagnostic › Pronostic › Predicting prognosis for adults with...
questionsmedicales.fr › Qualité, accès, évaluation des soins de santé › Qualité des soins de santé › Mécanismes d'évaluation des soins de santé › Évaluation des résultats et des processus en soins de santé › Évaluation de résultat (soins) › Résultat thérapeutique › Predicting prognosis for adults with...
questionsmedicales.fr › Disciplines et activités comportementales › Tests psychologiques › Tests neuropsychologiques › Échelles d'évaluation en psychiatrie › Predicting prognosis for adults with...
questionsmedicales.fr › Comportement et mécanismes comportementaux › Émotions › Anxiété › Predicting prognosis for adults with...