A risk prediction model for post-stroke depression in Chinese stroke survivors based on clinical and socio-psychological features

// Rui Liu 1, * , Yingying Yue 2, * , Haitang Jiang 2 , Jian Lu 1 , Aiqin Wu 3 , Deqin Geng 4 , Jun Wang 5 , Jianxin Lu 6 , Shenghua Li 7 , Hua Tang 8 , Xuesong Lu 9 , Kezhong Zhang 10 , Tian Liu 11 , Yonggui Yuan 2 and Qiao Wang 1 1 School of Information Science and Engineering, Southeast University, Nanjing, China 2 Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China 3 Department of Psychosomatics, The Affiliated First Hospital of Suzhou University, Suzhou, China 4 Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Xuzhou, China 5 Department of Neurology, Nanjing First Hospital, Nanjing, China 6 Department of Neurology, Gaochun People’s Hospital, Nanjing, China 7 Department of Neurology, Jiangning Nanjing Hospital, Nanjing, China 8 Department of Psychiatry, Huai’an No.3 People’s Hospital, Huai’an, China 9 Department of Rehabilitation, Affiliated Zhongda Hospital of Southeast University, Nanjing, China 10 Department of Neurology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China 11 The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong Univerisity, Xi’an, China * These authors have contributed equally to this work Correspondence to: Yonggui Yuan, email: yygylh2000@sina.com Qiao Wang, email: qiaowang@seu.edu.cn Keywords: post-stroke depression, socio-psychological factor, risk prediction model, logistic regression, decision tree Received: November 15, 2016      Accepted: March 14, 2017      Published: April 07, 2017 ABSTRACT Background: Post-stroke depression (PSD) is a frequent complication that worsens rehabilitation outcomes and patient quality of life. This study developed a risk prediction model for PSD based on patient clinical and socio-psychology features for the early detection of high risk PSD patients. Results: Risk predictors included a history of brain cerebral infarction (odds ratio [OR], 3.84; 95% confidence interval [CI], 2.22-6.70; P < 0.0001) and four socio-psychological factors including Eysenck Personality Questionnaire with Neuroticism/Stability (OR, 1.18; 95% CI, 1.12-1.20; P < 0.0001), life event scale (OR, 0.99; 95% CI, 0.98-0.99; P = 0.0007), 20 items Toronto Alexithymia Scale (OR, 1.06; 95% CI, 1.02-1.10; P = 0.002) and Social Support Rating Scale (OR, 0.91; 95% CI, 0.87-0.90; P < 0.001) in the logistic model. In addition, 11 rules were generated in the tree model. The areas under the curve of the ROC and the accuracy for the tree model were 0.85 and 0.86, respectively. Methods: This study recruited 562 stroke patients in China who were assessed for demographic data, medical history, vascular risk factors, functional status post-stroke, and socio-psychological factors. Multivariate backward logistic regression was used to extract risk factors for depression in 1-month after stroke. We converted the logistic model to a visible tree model using the decision tree method. Receiver operating characteristic (ROC) was used to evaluate the performance of the model. Conclusion: This study provided an effective risk model for PSD and indicated that the socio-psychological factors were important risk factors of PSD.