Predicting SSRI-Resistance: Clinical Features and tagSNPs Prediction Models Based on Support Vector Machine - Zhang, Huijie; Li, Xianglu; Pang, Jianyue; Zhao, Xiaofeng; Cao, Suxia; Wang, Xinyou; Wang, Xingbang; Li, Hengfen (Frontiers in Psychiatry, 2020)
Background: A large proportion of major depressive patients will experience recurring episodes. Many patients still do not response to available antidepressants. In order to meaningfully predict who will not respond to which antidepressant, it may be necessary to combine multiple biomarkers and clinical variables. Methods: 857 patients with recurrent major depressive disorder who were followed up 3-10 years involved 32 variables including socio-demographic, clinical features, and SSRIs treatment features when they received the first treatment. Also, 34 tagSNPs related to 5-HT signaling pathway, were detected by using mass spectrometry analysis. The training samples which had 12 clinical variables and four tagSNPs with statistical differences were learned repeatedly to establish prediction models based on SVM. Results: 12 clinical features (psychomotor retardation, psychotic symptoms, suicidality, weight loss, SSRIs average dose ,first-course treatment response, sleep disturbance, residual symptoms, personality, onset age, frequency of episode ,and duration ) were found significantly difference (P< 0.05) between 302 SSRI-resistance and 304 SSRI non-resistance group. 10 SSRI-resistance predicting models were finally selected by using support vector machine and our study found that mutations in tagSNPs increased the accuracy of these models to a certain degree. Conclusion: Using a data-driven machine learning method, we found 10 predictive models by mining existing clinical data, which might enable prospective identification of patients who are likely to resistance to SSRIs antidepressant.