Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports
Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine-learning (ML) models developed f...
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| Published in: | Molecular psychiatry Vol. 21; no. 10; pp. 1366 - 1371 |
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| Main Authors: | , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Nature Publishing Group UK
01.10.2016
Nature Publishing Group |
| Subjects: | |
| ISSN: | 1359-4184, 1476-5578, 1476-5578 |
| Online Access: | Get full text |
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| Abstract | Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine-learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10–12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71–0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62–0.70) despite the latter models including more predictors. A total of 34.6–38.1% of respondents with subsequent high persistence chronicity and 40.8–55.8% with the severity indicators were in the top 20% of the baseline ML-predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML-predicted risk distribution. These results confirm that clinically useful MDD risk-stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models. |
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| AbstractList | Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. While efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity, and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1,056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared to observed scores assessed 10–12 years after baseline. ML model prediction accuracy was also compared to that of conventional logistic regression models. Area under the receiver operating characteristic curve (AUC) based on ML (.63 for high chronicity and .71–.76 for the other prospective outcomes) was consistently higher than for the logistic models (.62–.70) despite the latter models including more predictors. 34.6–38.1% of respondents with subsequent high persistence-chronicity and 40.8–55.8% with the severity indicators were in the top 20% of the baseline ML predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML predicted risk distribution. These results confirm that clinically useful MDD risk stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models. Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine-learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10-12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71-0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62-0.70) despite the latter models including more predictors. A total of 34.6-38.1% of respondents with subsequent high persistence chronicity and 40.8-55.8% with the severity indicators were in the top 20% of the baseline ML-predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML-predicted risk distribution. These results confirm that clinically useful MDD risk-stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models. Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine-learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10-12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71-0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62-0.70) despite the latter models including more predictors. A total of 34.6-38.1% of respondents with subsequent high persistence chronicity and 40.8-55.8% with the severity indicators were in the top 20% of the baseline ML-predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML-predicted risk distribution. These results confirm that clinically useful MDD risk-stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine-learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10-12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71-0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62-0.70) despite the latter models including more predictors. A total of 34.6-38.1% of respondents with subsequent high persistence chronicity and 40.8-55.8% with the severity indicators were in the top 20% of the baseline ML-predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML-predicted risk distribution. These results confirm that clinically useful MDD risk-stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models. Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine-learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10-12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71-0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62-0.70) despite the latter models including more predictors. A total of 34.6-38.1% of respondents with subsequent high persistence chronicity and 40.8-55.8% with the severity indicators were in the top 20% of the baseline ML-predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML-predicted risk distribution. These results confirm that clinically useful MDD risk-stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models. Molecular Psychiatry (2016) 21, 1366-1371; doi: 10.1038/mp.2015.198; published online 5 January 2016 |
| Audience | Academic |
| Author | Petukhova, M V Ebert, D D Schoevers, R A de Jonge, P Li, J Wilcox, M A van Loo, H M Rosellini, A J Cai, T Nierenberg, A A Zaslavsky, A M Brenner, L A Hwang, I Kessler, R C Bossarte, R M Sampson, N A Wardenaar, K J |
| Author_xml | – sequence: 1 givenname: R C surname: Kessler fullname: Kessler, R C email: Kessler@hcp.med.harvard.edu organization: Department of Health Care Policy, Harvard Medical School – sequence: 2 givenname: H M surname: van Loo fullname: van Loo, H M organization: Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center Groningen – sequence: 3 givenname: K J surname: Wardenaar fullname: Wardenaar, K J organization: Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center Groningen – sequence: 4 givenname: R M surname: Bossarte fullname: Bossarte, R M organization: Department of Veterans Affairs, Office of Public Health – sequence: 5 givenname: L A surname: Brenner fullname: Brenner, L A organization: Departments of Physical Medicine and Rehabilitation, Psychiatry, and Neurology, University of Colorado, Anschutz Medical Campus, Aurora, Colorado; Rocky Mountain Mental Illness Research Education and Clinical Center, Rocky Mountain Mental Illness Research Education and Clinical Center – sequence: 6 givenname: T surname: Cai fullname: Cai, T organization: Department of Biostatistics, Harvard School of Public Health – sequence: 7 givenname: D D surname: Ebert fullname: Ebert, D D organization: Department of Health Care Policy, Harvard Medical School, Department of Psychology, Clinical Psychology and Psychotherapy, Friedrich-Alexander University Nuremberg-Erlangen – sequence: 8 givenname: I surname: Hwang fullname: Hwang, I organization: Department of Health Care Policy, Harvard Medical School – sequence: 9 givenname: J surname: Li fullname: Li, J organization: Department of Biostatistics, Harvard School of Public Health – sequence: 10 givenname: P surname: de Jonge fullname: de Jonge, P organization: Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center Groningen – sequence: 11 givenname: A A surname: Nierenberg fullname: Nierenberg, A A organization: Department of Psychiatry and Depression Clinical and Research Program, Harvard Medical School and Massachusetts General Hospital – sequence: 12 givenname: M V surname: Petukhova fullname: Petukhova, M V organization: Department of Health Care Policy, Harvard Medical School – sequence: 13 givenname: A J surname: Rosellini fullname: Rosellini, A J organization: Department of Health Care Policy, Harvard Medical School – sequence: 14 givenname: N A surname: Sampson fullname: Sampson, N A organization: Department of Health Care Policy, Harvard Medical School – sequence: 15 givenname: R A surname: Schoevers fullname: Schoevers, R A organization: Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center Groningen – sequence: 16 givenname: M A surname: Wilcox fullname: Wilcox, M A organization: Epidemiology, Janssen Research & Development, LLC – sequence: 17 givenname: A M surname: Zaslavsky fullname: Zaslavsky, A M organization: Department of Health Care Policy, Harvard Medical School |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26728563$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | Macmillan Publishers Limited 2016 COPYRIGHT 2016 Nature Publishing Group Copyright Nature Publishing Group Oct 2016 Macmillan Publishers Limited 2016. |
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| SubjectTerms | 692/699/476 Adolescent Adult Algorithms Behavioral Sciences Biological Psychology Biomarkers Clinical decision making Cluster analysis Comorbidity Consent Decision making Depression (Mood disorder) Depressive Disorder, Major - diagnosis Diagnosis Diagnostic and Statistical Manual of Mental Disorders Disease Progression Female Forecasting - methods Health care policy Human subjects Humans Interviews Learning algorithms Logistic Models Logistic regression Longitudinal Studies Machine Learning Male Medical schools Medicine Medicine & Public Health Mental depression Mental disorders Mental health Middle Aged Neurosciences original-article Pharmacotherapy Polls & surveys Prognosis Prospective Studies Psychiatry Public health R&D Regression analysis Research & development Risk factors Self Report Severe acute respiratory syndrome Severity of Illness Index Suicide Surveys and Questionnaires |
| Title | Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports |
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