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Record W2912345299 · doi:10.4088/jcp.18m12202

Symptomatic and Functional Outcomes and Early Prediction of Response to Escitalopram Monotherapy and Sequential Adjunctive Aripiprazole Therapy in Patients With Major Depressive Disorder

2019· article· en· W2912345299 on OpenAlexafffundabout
Sidney H. Kennedy, Raymond W. Lam, Susan Rotzinger, Roumen Milev, Pierre Blier, Jonathan Downar, Kenneth Evans, Faranak Farzan, Jane A. Foster, Benício N. Frey, Peter Giacobbe, Geoffrey B. Hall, Kate L. Harkness, Stefanie Hassel, Zahinoor Ismail, Francesco Leri, Shane McInerney, Glenda MacQueen, Luciano Minuzzi, Daniel J. Müller, Sagar V. Parikh, Franca Placenza, Lena C. Quilty, Arun Ravindran, Roberto B. Sassi, Cláudio N. Soares, Stephen C. Strother, Gustavo Turecki, Anthony L. Vaccarino, Fidel Vila‐Rodriguez, Joanna Yu, Rudolf Uher

Bibliographic record

VenueThe Journal of Clinical Psychiatry · 2019
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsDouglas Mental Health University InstituteBaycrest HospitalDalhousie UniversityHotchkiss Brain InstituteUniversity of CalgarySt. Joseph’s Healthcare HamiltonIndoc ResearchMcGill UniversityUniversity of OttawaUniversity Health NetworkUniversity of TorontoQueen's UniversityUniversity of GuelphCentre for Addiction and Mental HealthVancouver Coastal HealthSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsEscitalopramAripiprazoleMajor depressive disorderInternal medicineRating scaleHamilton Rating Scale for DepressionDepression (economics)PsychologyMedicinePsychiatryAntidepressantSchizophrenia (object-oriented programming)

Abstract

fetched live from OpenAlex

OBJECTIVE: To report the symptomatic and functional outcomes in patients with major depressive disorder (MDD) during a 2-phase treatment trial and to estimate the value of early improvement after 2 weeks in predicting clinical response to escitalopram and subsequently to adjunctive treatment with aripiprazole. METHODS: Participants with MDD (N = 211) identified with the Montgomery-Asberg Depression Rating Scale (MADRS) and confirmed with the Mini-International Neuropsychiatric Interview were recruited from 6 outpatient centers across Canada (August 2013 through December 2016) and treated with open-label escitalopram (10-20 mg) for 8 weeks (Phase 1). Clinical and functional outcomes were evaluated using the MADRS, Quick Inventory of Depressive Symptomatology-Self-Rated (QIDS-SR), Sheehan Disability Scale (SDS), and Lam Employment Absence and Productivity Scale (LEAPS). Participants were evaluated at 8 and 16 weeks for clinical and functional response and remission. Phase 1 responders continued escitalopram while nonresponders received adjunctive aripiprazole (2-10 mg) for a further 8 weeks (Phase 2). RESULTS: After Phase 1, MADRS response (≥ 50% decrease from baseline) and remission (score ≤ 10) were, respectively, 47% and 31%, and SDS response (score ≤ 12) and remission (score ≤ 6) were, respectively, 53% and 24%. Response to escitalopram was maintained in 91% of participants at week 16, while 61% of the adjunctive aripiprazole group achieved MADRS response during Phase 2. Response and remission rates with the QIDS-SR were lower than with the MADRS. The LEAPS demonstrated significant occupational improvement (P < .05). Early symptomatic improvement predicted outcomes with modest accuracy. CONCLUSIONS: This study demonstrates comparable symptomatic and functional outcomes to those of other large practical-design studies. There was a high response rate with the adjunctive use of aripiprazole in escitalopram nonresponders. Given the limited value of early clinical improvement to predict outcome, integration of clinical and biological markers deserves further exploration. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01655706.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.310
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations88
Published2019
Admission routes3
Has abstractyes

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