Prediction of Major Depressive Disorder Following Beta-Blocker Therapy in Patients with Cardiovascular Diseases.
Suho JinKristin KostkaJose D PosadaYeesuk KimSeung In SeoDong Yun LeeNigam H ShahSungwon RohYoung-Hyo LimSun Geu ChaeUram JinSang Joon SonChristian ReichPeter R RijnbeekRae Woong ParkSeng Chan YouPublished in: Journal of personalized medicine (2020)
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.
Keyphrases
- major depressive disorder
- cardiovascular disease
- end stage renal disease
- ejection fraction
- machine learning
- poor prognosis
- bipolar disorder
- chronic kidney disease
- randomized controlled trial
- electronic health record
- type diabetes
- peritoneal dialysis
- mental health
- coronary artery disease
- dna methylation
- depressive symptoms
- stem cells
- emergency department
- mesenchymal stem cells
- metabolic syndrome
- genome wide
- physical activity
- health insurance
- deep learning
- smoking cessation
- body composition
- cross sectional