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Establishment and validation of a prediction model for self-absorption probability of chronic subdural hematoma
Ye Tian1,2,3,4, Dong Wang1,2,3,4, Xinjie Zhang1,2,3,4
1Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
Insights
A new prediction model helps identify chronic subdural hematoma (CSDH) patients likely to benefit from non-surgical treatment, improving therapeutic decisions and patient outcomes.
Area of Science:
- Neurosurgery
- Medical Prediction Modeling
- Clinical Decision Support
Background:
- Chronic subdural hematoma (CSDH) is prevalent in the elderly, often linked to traumatic brain injury.
- Surgical intervention for CSHD carries risks of recurrence and suboptimal outcomes.
- Non-surgical management is a viable alternative for select CSDH patients, necessitating predictive tools.
Purpose of the Study:
- To develop and validate a novel prediction model for estimating the probability of spontaneous resolution (self-absorption) in chronic subdural hematoma patients.
- To aid clinicians in selecting the most appropriate treatment strategy (surgical vs. non-surgical) for individual CSDH cases.
Main Methods:
- A prediction model was developed using data from a randomized clinical trial (n=196) of CSDH patients.
- Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed for feature selection and dimensionality reduction.
- Multivariable logistic regression and nomogram visualization were used, with performance evaluated by discrimination, calibration, and clinical usefulness. External validation was performed on 60 patients.
Main Results:
- Key predictors for CSDH self-absorption included diabetes mellitus history, hematoma volume, basal ganglia suppression, septate hematoma, and atorvastatin use.
- The prediction model demonstrated good discrimination (AUC=0.713) and calibration in the initial cohort.
- The nomogram maintained good discrimination (AUC=0.709) and calibration in the external validation cohort, proving clinically useful.
Conclusions:
- The developed prediction model accurately estimates self-absorption probability in CSDH patients.
- This tool can guide therapeutic choices between non-surgical management and surgical intervention.
- The model supports personalized treatment strategies for chronic subdural hematoma.
Background:
Chronic subdural hematoma (CSDH) is common in elderly people with a clear or occult traumatic brain injury history. Surgery is a traditional method to remove the hematomas, but it carries a significant risk of recurrence and poor outcomes. Non-surgical treatment has been recently considered effective and safe for some patients with CSDH. However, it is a challenge to speculate which part of patients could obtain benefits from non-surgical treatment.
Objective:
To establish and validate a new prediction model of self-absorption probability with chronic subdural hematoma.
Method:
The prediction model was established based on the data from a randomized clinical trial, which enrolled 196 patients with CSDH from February 2014 to November 2015. The following subjects were extracted: demographic characteristics, medical history, hematoma characters in imaging at admission, and clinical assessments. The outcome was self-absorption at the 8th week after admission. A least absolute shrinkage and selection operator (LASSO) regression model was implemented for data dimensionality reduction and feature selection. Multivariable logistic regression was adopted to establish the model, while the experimental results were presented by nomogram. Discrimination, calibration, and clinical usefulness were used to evaluate the performance of the nomogram. A total of 60 consecutive patients were involved in the external validation, which enrolled in a proof-of-concept clinical trial from July 2014 to December 2018.
Results:
Diabetes mellitus history, hematoma volume at admission, presence of basal ganglia suppression, presence of septate hematoma, and usage of atorvastatin were the strongest predictors of self-absorption. The model had good discrimination [area under the curve (AUC), 0.713 (95% CI, 0.637-0.788)] and good calibration (p = 0.986). The nomogram in the validation cohort still had good discrimination [AUC, 0.709 (95% CI, 0.574-0.844)] and good calibration (p = 0.441). A decision curve analysis proved that the nomogram was clinically effective.
Conclusions:
This prediction model can be used to obtain self-absorption probability in patients with CSDH, assisting in guiding the choice of therapy, whether they undergo non-surgical treatment or surgery.
