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The Rabbit Blood-shunt Model for the Study of Acute and Late Sequelae of Subarachnoid Hemorrhage: Technical Aspects
Published on: October 2, 2014
Prediction model for poor short-term prognosis in patients with chronic subdural hematoma after burr hole drainage: a
Jie Zhang1,2, Aili Gao3, Xiangyi Meng1,2
1Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Youzheng Street 23, Nangang District, Harbin, Heilongjiang Province, 150001, P. R. China.
Insights
This study developed a prediction model for chronic subdural hematoma (CSDH) patients to forecast short-term outcomes after surgery. The model uses age, GCS score, hemiparesis, and hemoglobin to predict prognosis, aiding clinical decisions.
Area of Science:
- Neurosurgery
- Clinical Prediction Models
- Geriatric Medicine
Background:
- Chronic subdural hematoma (CSDH) is a prevalent neurosurgical condition.
- Prognosis following surgical intervention for CSDH is crucial, especially in an aging population.
- Effective perioperative decision-making requires accurate short-term prognostic tools.
Purpose of the Study:
- To develop and validate a predictive model for short-term postoperative prognosis in CSDH patients.
- To identify independent predictors of poor prognosis one month after surgical drainage.
- To create a nomogram to assist clinicians in perioperative management.
Main Methods:
- A cohort of 505 CSDH patients undergoing burr hole drainage was analyzed.
- Least Absolute Shrinkage and Selection Operator (LASSO) regression identified risk factors in the training cohort.
- A nomogram was constructed using independent predictors: age, admission Glasgow Coma Scale (GCS) score, hemiparesis, and hemoglobin.
- Model performance was assessed using ROC curves, calibration curves, and Decision Curve Analysis (DCA).
Main Results:
- 18.8% of patients experienced a poor prognosis (mRS > 2) one month post-discharge.
- Four independent predictors for poor prognosis were identified: age, admission GCS score, hemiparesis, and hemoglobin.
- The developed nomogram demonstrated strong internal validity in predicting short-term outcomes.
Conclusions:
- A novel nomogram effectively predicts short-term postoperative prognosis in CSDH patients.
- The model, incorporating age, GCS, hemiparesis, and hemoglobin, aids clinical decision-making.
- Further external validation is recommended to confirm the model's generalizability.
Abstract:
Chronic subdural hematoma (CSDH) is a common condition in neurosurgery. With an aging population, there is increasing attention on the prognosis of patients following surgical intervention. We developed a postoperative short-term prognostic prediction model using preoperative clinical indicators, aiming to assist in perioperative medical decision-making and management. The dataset was randomly divided into training and validation cohorts. An mRS score greater than 2 one month after discharge was considered indicative of a poor prognosis. In the training cohort, the least absolute shrinkage and selection operator (LASSO) regression analysis was used for multivariate analysis to identify independent risk factors and construct a prediction nomogram for poor prognosis one month after discharge. The performance of the nomogram was assessed using the Receiver Operating Characteristic (ROC) curve and calibration curve. A Decision Curve Analysis (DCA) was also conducted to determine the net benefit threshold of the prediction model. Among the 505 participants, 18.8% (95/505) had a poor prognosis one month after discharge. The baseline characteristics did not significantly differ between the training cohort and the validation cohort. LASSO regression analysis in the training cohort reduced the predictors to four potential factors. Further multivariate logistic analyses in the training cohort identified four independent predictors: age, admission Glasgow Coma Scale (GCS) score, hemiparesis, and hemoglobin count. These predictors were incorporated into the nomogram prediction model. Internal validation using ROC analysis, calibration curves, and other methods demonstrated a strong correlation between the observed and predicted likelihood of poor prognosis one month after discharge. The visualized nomogram prediction model we developed for short-term postoperative prognosis of chronic subdural hematoma after burr hole drainage aids in predicting short-term outcomes and guiding clinical treatment decisions. Further external validation is needed in the future to confirm its effectiveness.
