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Prediction of Major Depressive Disorder Following Beta-Blocker Therapy in Patients with Cardiovascular Diseases

Incident depression has been reported to be associated with poor prognosis in patients with cardiovascular disease (CVD), which might be associated with beta-blocker therapy. Because early detection and intervention can alleviate the severity of depression, we aimed to develop a machine learning (ML...

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Autores principales: Jin, Suho, Kostka, Kristin, Posada, Jose D., Kim, Yeesuk, Seo, Seung In, Lee, Dong Yun, Shah, Nigam H., Roh, Sungwon, Lim, Young-Hyo, Chae, Sun Geu, Jin, Uram, Son, Sang Joon, Reich, Christian, Rijnbeek, Peter R., Park, Rae Woong, You, Seng Chan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7766565/
https://www.ncbi.nlm.nih.gov/pubmed/33352870
http://dx.doi.org/10.3390/jpm10040288
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author Jin, Suho
Kostka, Kristin
Posada, Jose D.
Kim, Yeesuk
Seo, Seung In
Lee, Dong Yun
Shah, Nigam H.
Roh, Sungwon
Lim, Young-Hyo
Chae, Sun Geu
Jin, Uram
Son, Sang Joon
Reich, Christian
Rijnbeek, Peter R.
Park, Rae Woong
You, Seng Chan
author_facet Jin, Suho
Kostka, Kristin
Posada, Jose D.
Kim, Yeesuk
Seo, Seung In
Lee, Dong Yun
Shah, Nigam H.
Roh, Sungwon
Lim, Young-Hyo
Chae, Sun Geu
Jin, Uram
Son, Sang Joon
Reich, Christian
Rijnbeek, Peter R.
Park, Rae Woong
You, Seng Chan
author_sort Jin, Suho
collection PubMed
description Incident depression has been reported to be associated with poor prognosis in patients with cardiovascular disease (CVD), which might be associated with beta-blocker therapy. Because early detection and intervention can alleviate the severity of depression, we aimed to develop a machine learning (ML) model predicting the onset of major depressive disorder (MDD). A model based on L1 regularized logistic regression was trained against the South Korean nationwide administrative claims database to identify risk factors for the incident MDD after beta-blocker therapy in patients with CVD. We identified 50,397 patients initiating beta-blockers for CVD, with 774 patients developing MDD within 365 days after initiating beta-blocker therapy. An area under the receiver operating characteristic curve (AUC) of 0.74 was achieved. A history of non-selective beta-blockers and factors related to anxiety disorder, sleeping problems, and other chronic diseases were the most strong predictors. AUCs of 0.62–0.71 were achieved in the external validation conducted on six independent electronic health records and claims databases in the USA and South Korea. In conclusion, an ML model that identifies patients at high-risk for incident MDD was developed. Application of ML to identify susceptible patients for adverse events of treatment may serve as an important approach for personalized medicine.
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spelling pubmed-77665652020-12-28 Prediction of Major Depressive Disorder Following Beta-Blocker Therapy in Patients with Cardiovascular Diseases Jin, Suho Kostka, Kristin Posada, Jose D. Kim, Yeesuk Seo, Seung In Lee, Dong Yun Shah, Nigam H. Roh, Sungwon Lim, Young-Hyo Chae, Sun Geu Jin, Uram Son, Sang Joon Reich, Christian Rijnbeek, Peter R. Park, Rae Woong You, Seng Chan J Pers Med Article Incident depression has been reported to be associated with poor prognosis in patients with cardiovascular disease (CVD), which might be associated with beta-blocker therapy. Because early detection and intervention can alleviate the severity of depression, we aimed to develop a machine learning (ML) model predicting the onset of major depressive disorder (MDD). A model based on L1 regularized logistic regression was trained against the South Korean nationwide administrative claims database to identify risk factors for the incident MDD after beta-blocker therapy in patients with CVD. We identified 50,397 patients initiating beta-blockers for CVD, with 774 patients developing MDD within 365 days after initiating beta-blocker therapy. An area under the receiver operating characteristic curve (AUC) of 0.74 was achieved. A history of non-selective beta-blockers and factors related to anxiety disorder, sleeping problems, and other chronic diseases were the most strong predictors. AUCs of 0.62–0.71 were achieved in the external validation conducted on six independent electronic health records and claims databases in the USA and South Korea. In conclusion, an ML model that identifies patients at high-risk for incident MDD was developed. Application of ML to identify susceptible patients for adverse events of treatment may serve as an important approach for personalized medicine. MDPI 2020-12-18 /pmc/articles/PMC7766565/ /pubmed/33352870 http://dx.doi.org/10.3390/jpm10040288 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jin, Suho
Kostka, Kristin
Posada, Jose D.
Kim, Yeesuk
Seo, Seung In
Lee, Dong Yun
Shah, Nigam H.
Roh, Sungwon
Lim, Young-Hyo
Chae, Sun Geu
Jin, Uram
Son, Sang Joon
Reich, Christian
Rijnbeek, Peter R.
Park, Rae Woong
You, Seng Chan
Prediction of Major Depressive Disorder Following Beta-Blocker Therapy in Patients with Cardiovascular Diseases
title Prediction of Major Depressive Disorder Following Beta-Blocker Therapy in Patients with Cardiovascular Diseases
title_full Prediction of Major Depressive Disorder Following Beta-Blocker Therapy in Patients with Cardiovascular Diseases
title_fullStr Prediction of Major Depressive Disorder Following Beta-Blocker Therapy in Patients with Cardiovascular Diseases
title_full_unstemmed Prediction of Major Depressive Disorder Following Beta-Blocker Therapy in Patients with Cardiovascular Diseases
title_short Prediction of Major Depressive Disorder Following Beta-Blocker Therapy in Patients with Cardiovascular Diseases
title_sort prediction of major depressive disorder following beta-blocker therapy in patients with cardiovascular diseases
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7766565/
https://www.ncbi.nlm.nih.gov/pubmed/33352870
http://dx.doi.org/10.3390/jpm10040288
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