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Published on: December 16, 2021
Construction and verification of a risk prediction model for patients with kinesophobia after cerebral hemorrhage
Yan Huang1, Ya-Ting Huang2, Jie Yuan3
1Department of Neurosurgery, The First Affiliated Hospital of Anhui University of Science and Technology(First People's Hospital of Huainan), Huainan, Anhui, 232007, China.
Objective:
To establish a risk prediction model of kinesophobia in patients after cerebral hemorrhage surgery and verify its effect.
Methods:
A total of 218 patients after cerebral hemorrhage surgery were selected, and the differences in clinical data between kinesophobia patients and non-kinesophobia patients were analyzed. Using 20 indexes as independent variables, the characteristic variables were screened by LASSO regression, and then multivariate Logistic regression analysis was carried out. Based on the results, the nomogram prediction model was constructed, and the model was verified from the aspects of clinical applicability, discrimination, and calibration.
Results:
Significant differences were found in age, electronic health literacy score, depression score, NIHSS score, VAS pain score, intraoperative blood loss, and anxiety score between patients with phobia and non-phobia (P < 0.05). 12 characteristic variables were selected by LASSO regression. Multivariate Logistic regression analysis showed that age, NIHSS score, VAS pain score and depression score were independent risk factors for the occurrence of kinesophobia after cerebral hemorrhage surgery (OR > 1 and P < 0.05), and electronic health literacy score was an independent protective factor (OR < 1 and P < 0.05). Based on age, NIHSS score, VAS pain score, e-health literacy score, and depression score, a nomogram prediction model was constructed. The DCA curve shows that the model has the highest clinical net benefit when the threshold probability is between 0.14 and 0.99, indicating good clinical applicability. The area under the ROC curve (AUC) is 0.836(95% CI: 0.782-0.890), which indicates good discrimination. Spiegelhalter's z test and the calibration curve show that the calibration degree is good, and the C statistic after Bootstrap self-sampling internal verification is 0.820 (95% CI: 0.772-0.877), indicating that the prediction is robust.
Conclusion:
The nomogram prediction model of the risk of kinesophobia after cerebral hemorrhage based on multivariate regression analysis has a good prediction effect, which can provide reference for the clinical prevention of kinesophobia after cerebral hemorrhage.
