Volume 23 Issue 9
Sep.  2025
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Article Navigation >  Chinese Journal of General Practice  > 2025  >  23(9): 1475-1479
ZHANG Xi, YANG Hongge, FENG Yuyang, JIANG Wang, ZHANG Xiangtong, LIANG Hongsheng. Development and validation of a nomogram for predicting thalamic pain occurrence after spontaneous thalamic hemorrhage[J]. Chinese Journal of General Practice, 2025, 23(9): 1475-1479. doi: 10.16766/j.cnki.issn.1674-4152.004158
Citation: ZHANG Xi, YANG Hongge, FENG Yuyang, JIANG Wang, ZHANG Xiangtong, LIANG Hongsheng. Development and validation of a nomogram for predicting thalamic pain occurrence after spontaneous thalamic hemorrhage[J]. Chinese Journal of General Practice, 2025, 23(9): 1475-1479. doi: 10.16766/j.cnki.issn.1674-4152.004158
shu

Development and validation of a nomogram for predicting thalamic pain occurrence after spontaneous thalamic hemorrhage

doi: 10.16766/j.cnki.issn.1674-4152.004158

  • Department of Neurosurgery, the First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, China

Funds:

 82271340

 HYD2020JQ0016

  • Received Date: 2024-12-24
    Available Online: 2025-11-17
    Abstract
  •   Objective  The occurrence of thalamic pain significantly impacts patients' quality of life. Early identification of patients at risk for developing thalamic pain can facilitate timely clinical interventions and decision-making to alleviate patient suffering. This study aims to develop and validate a nomogram model to predict the occurrence of thalamic pain following spontaneous thalamic hemorrhage.  Methods  A total of 294 patients with spontaneous thalamic hemorrhage were retrospectively included from the First Affiliated Hospital of Harbin Medical University between June 2021 and December 2022. Patients were randomly assigned to the training cohort (n=206) and internal validation cohort (n=88) in a 7∶3 ratio using computer-generated random numbers, with the training cohort used for model development and the internal validation cohort for validation. Feature variables most suitable for model construction were selected using the least absolute shrinkage and selection operator (LASSO) algorithm. Subsequently, the selected features were incorporated into a multivariable logistic regression model to develop the nomogram. The performance of the model was assessed in the training and internal validation cohorts using the area under the receiver operating characteristic curve, and decision curve analysis.  Results  A total of 294 patients were included in this study, of whom 94 cases (31.97%) developed thalamic pain, while 200 cases (68.03%) did not. The LASSO-logistic regression analysis in the training cohort identified two independent predictive factors: hematoma location (P < 0.001) and modified Graeb score (mGS) score (P < 0.001). These predictors were incorporated into the nomogram prediction model. ROC curve analysis demonstrated high predictive performance of the nomogram model (training cohort AUC: 0.890; internal validation cohort AUC: 0.820). Decision curve analyses further indicated that the model has strong predictive ability for the occurrence of thalamic pain following spontaneous thalamic hemorrhage.  Conclusion  This study develops a nomogram model to assess the risk of thalamic pain in individual patients following spontaneous thalamic hemorrhage, demonstrating excellent predictive performance. The model provides clinicians with a practical tool to guide treatment decisions for high-risk patients.

     

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