Development and validation of a risk prediction model for venous thromboembolism in stroke patients

WANG Rongrong; ZHOU Qianyu; GUO Yuanli; WANG Panpan; HE Wenqian; ZHAO Mingyang; ZHANG Peijia; HU Bo; WU Tiantian; YAO Zihui; WANG Yu; SUN Changqing

doi:10.16462/j.cnki.zhjbkz.2023.10.008

Volume 27 Issue 10

Oct. 2023

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2023 > 27(10): 1161-1166

WANG Rongrong, ZHOU Qianyu, GUO Yuanli, WANG Panpan, HE Wenqian, ZHAO Mingyang, ZHANG Peijia, HU Bo, WU Tiantian, YAO Zihui, WANG Yu, SUN Changqing. Development and validation of a risk prediction model for venous thromboembolism in stroke patients[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(10): 1161-1166. doi: 10.16462/j.cnki.zhjbkz.2023.10.008

Citation:

WANG Rongrong, ZHOU Qianyu, GUO Yuanli, WANG Panpan, HE Wenqian, ZHAO Mingyang, ZHANG Peijia, HU Bo, WU Tiantian, YAO Zihui, WANG Yu, SUN Changqing. Development and validation of a risk prediction model for venous thromboembolism in stroke patients[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(10): 1161-1166. doi: 10.16462/j.cnki.zhjbkz.2023.10.008

Citation:

WANG Rongrong, ZHOU Qianyu, GUO Yuanli, WANG Panpan, HE Wenqian, ZHAO Mingyang, ZHANG Peijia, HU Bo, WU Tiantian, YAO Zihui, WANG Yu, SUN Changqing. Development and validation of a risk prediction model for venous thromboembolism in stroke patients[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(10): 1161-1166. doi: 10.16462/j.cnki.zhjbkz.2023.10.008

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Development and validation of a risk prediction model for venous thromboembolism in stroke patients

doi: 10.16462/j.cnki.zhjbkz.2023.10.008

1.
Department of Community Care, School of Nursing and Health, Zhengzhou University, Zhengzhou 450001, China
2.
Department of Social Medicine and Health Management, School of Public Health, Zhengzhou University, Zhengzhou 450001, China
3.
Department of Neurology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China

Funds:

National Social Science Foundation of China 20BRK041

The Key Science and Technology Program of Henan Province 212102310767

More Information

Corresponding author: SUN Changqing, E-mail: suncq@zzu.edu.cn
Received Date: 2022-10-21
Rev Recd Date: 2023-01-13

Available Online: 2023-10-23

Publish Date: 2023-10-10

Abstract

Abstract

Objective To develop and validate the risk prediction model of venous thromboembolism (VTE) in stroke patients, so as to provide a scientific basis for the prevention and control of VTE. Methods A total of 675 stroke patients were enrolled from our stroke cohort of Henan Province. The data were randomly divided into a training (473 patients) and a testing dataset (202 patients) by a ratio of 7∶3. Then, we used a random forest algorithm for variable selection and logistic regression analysis to construct the model, and a nomogram was drawn. The prediction efficiency of the model was evaluated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow test. Decision curve analysis (DCA) was used to evaluate the clinical application value of the model and the five-fold cross-validation was utilized to verify the model internally. Results The predictors that ultimately entered the prediction model were age, hospital stays, ADL, myodynamia, uric acid, D-dimer, fibrinogen, and total cholesterol. In the training dataset, the Hosmer-Lemeshow test yielded P=0.872 and the AUC was 0.924 (95% CI: 0.898-0.950). The testing dataset showed that the Hosmer-Lemeshow test yielded P=0.597 and the AUC was 0.902 (95% CI: 0.852-0.951). DCA curves indicated that the model had high clinical net benefits in both datasets. Internal verification presented that the average AUCs of the model in the training and testing datasets were 0.913 and 0.929, respectively. Conclusions The risk prediction model developed in this study can effectively predict VTE occurrence in stroke patients, offering a valuable tool for identifying high-risk individuals and implementing early preventive measures.
- Stroke,
- Venous thromboembolism,
- Prediction model,
- Random forest,
- Nomogram

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References(17)

References

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