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Identifying Patients at Risk for Psychotherapy Dropout in Community Settings.

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Τίτλος: Identifying Patients at Risk for Psychotherapy Dropout in Community Settings.
Συγγραφείς: Giber B; Department of Psychiatry, Weill Cornell Medicine, New York, New York., Sharma A; Department of Psychiatry, Harvard University, Boston, Massachusetts., Broadie D; Department of Psychiatry, Weill Cornell Medicine, New York, New York., McCoy TH Jr; Department of Psychiatry, Harvard University, Boston, Massachusetts., Gunning FM; Department of Psychiatry, Weill Cornell Medicine, New York, New York., Perlis RH; Department of Psychiatry, Harvard University, Boston, Massachusetts.; Center for Quantitative Health, Massachusetts General Hospital, Boston, Massachusetts., Doshi-Velez F; School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts., Solomonov N; Department of Psychiatry, Weill Cornell Medicine, New York, New York.
Πηγή: JAMA network open [JAMA Netw Open] 2026 Aug 03; Vol. 9 (8), pp. e2631565. Date of Electronic Publication: 2026 Aug 03.
Τύπος έκδοσης: Journal Article; Research Support, N.I.H., Extramural
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: American Medical Association Country of Publication: United States NLM ID: 101729235 Publication Model: Electronic Cited Medium: Internet ISSN: 2574-3805 (Electronic) Linking ISSN: 25743805 NLM ISO Abbreviation: JAMA Netw Open Subsets: MEDLINE
Imprint Name(s): Original Publication: Chicago, IL : American Medical Association, [2018]-
Ιατρικοί όροι (MeSH): Patient Dropouts*/statistics & numerical data , Patient Dropouts*/psychology , Psychotherapy*/statistics & numerical data , Depression*/therapy, Risk Assessment/methods ; Humans ; Female ; Middle Aged ; Male ; Adult ; Massachusetts ; Risk Factors ; Aged ; Logistic Models ; Random Forest
Περίληψη: Importance: Psychotherapy dropout poses a significant public health problem and is associated with depression relapse and illness persistence. Identifying who is at risk of psychotherapy dropout can guide personalized and scalable strategies to mitigate risk and improve outcomes.
Objective: To estimate the prevalence and predictors of psychotherapy dropout in a large community setting.
Design, Setting, and Participants: In this prognostic study, electronic health record data collected between 2008 and 2022 were analyzed from 2 large academic medical centers, 6 community hospitals, and their affiliated outpatient networks in Massachusetts. Logistic regression (LR) and random forests (RF) machine learning were applied to identify predictors of dropout. Participants were individuals aged 18 to 80 years with depression who received at least 1 individual psychotherapy session. Data analysis was performed between January 2023 and March 2026.
Main Outcomes and Measures: Predictors included age, self-identified race and ethnicity, medical conditions, and health care encounters. Dropout rate was assessed after 3 or fewer psychotherapy sessions. LR and RF models were trained to maximize accuracy (area under the curve [AUC]) and extracted top predictors of dropout.
Results: The final cohort included 40 732 individuals (mean [SD] age, 44.2 [14.2] years; 69.3% female; 2% Asian, 8% Black, 71% White, and 18% other races; 9% Hispanic). The psychotherapy dropout rate was 28.4% (n = 11 571), with 60.9% dropping out after the first session. AUC values were 0.64 (95% CI, 0.63-0.65) for LR and 0.66 (95% CI, 0.65-0.67) for RF. Prior mental health encounters (eg, group psychotherapy [odds ratio (OR), 0.91; 95% CI, 0.90-0.91]), psychiatric evaluation (OR, 0.86; 95% CI, 0.85-0.87), and White race (OR, 0.90; 95% CI, 0.89-0.90) and/or non-Hispanic ethnicity (OR, 0.95; 95% CI, 0.95-0.95) predicted decreased dropout likelihood. A mental disorder due to a physiological condition (OR, 1.05; 95% CI, 1.04-1.05), and prior medical admission (OR, 1.03; 95% CI, 1.03-1.04) predicted increased likelihood of dropout.
Conclusions and Relevance: In this prognostic study, more than one-quarter of patients dropped out of psychotherapy, most after a single session, highlighting the need for early risk detection. Brief mental health encounters early in the health care pathway were associated with lower dropout risk, suggesting that exposure to mental health within medical settings may facilitate engagement. Electronic health record data can inform risk detection and targeted interventions in the community.
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Entry Date(s): Date Created: 20260827 Date Completed: 20260827 Latest Revision: 20260926
Update Code: 20260926
PubMed Central ID: PMC13522969
DOI: 10.1001/jamanetworkopen.2026.31565
PMID: 42658492
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:2574-3805
DOI:10.1001/jamanetworkopen.2026.31565