Research Papers:
A risk prediction model for post-stroke depression in Chinese stroke survivors based on clinical and socio-psychological features
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Abstract
Rui Liu1,*, Yingying Yue2,*, Haitang Jiang2, Jian Lu1, Aiqin Wu3, Deqin Geng4, Jun Wang5, Jianxin Lu6, Shenghua Li7, Hua Tang8, Xuesong Lu9, Kezhong Zhang10, Tian Liu11, Yonggui Yuan2 and Qiao Wang1
1School of Information Science and Engineering, Southeast University, Nanjing, China
2Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
3Department of Psychosomatics, The Affiliated First Hospital of Suzhou University, Suzhou, China
4Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Xuzhou, China
5Department of Neurology, Nanjing First Hospital, Nanjing, China
6Department of Neurology, Gaochun People’s Hospital, Nanjing, China
7Department of Neurology, Jiangning Nanjing Hospital, Nanjing, China
8Department of Psychiatry, Huai’an No.3 People’s Hospital, Huai’an, China
9Department of Rehabilitation, Affiliated Zhongda Hospital of Southeast University, Nanjing, China
10Department of Neurology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
11The 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: [email protected]
Qiao Wang, email: [email protected]
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.
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