External validation of prognostic models predicting outcome after chronic subdural hematoma

Impact of surgical timing on chronic subdural hematoma outcomes: novel insights from a multicenter study

OBJECTIVE

Chronic Subdural Hematoma (CSDH) is one of the most frequently encountered conditions in the neurosurgical practice. The role of timing in CSDH surgery in mild symptomatic patients remains uncertain. The aim of this study was to analyze the prognostic role of surgical timing in patients with mild symptomatic CSDH.

METHODS

In this multicenter retrospective study, patients diagnosed with mild symptomatic CSDH who underwent surgical evacuation were enrolled. Marwalder Grading System (MGS) and GCS scores were used for neurological evaluation. Patients presenting with preoperative GCS score ≥ 13 and MGS score ≤ 2 scores were defined as "mild symptomatic". A ROC curve analysis was used to identify the optimal surgical timing associated with favorable postoperative outcome. Univariate and multivariate analysis were used to verify the association between surgical timing and postoperative neurological outcome, length of hospitalization, and postoperative complication.

RESULTS

A total of 160 patients were enrolled in the study. The mean latency from hospital admission to surgical intervention was 2.5 ± 3.2 days. All patients treated with surgical evacuation demonstrated postoperative clinical improvement in terms of GCS and/or MGS scores. The univariate and multivariate analyses demonstrated significantly better neurological outcomes and shorter length of hospitalization in patients treated within 3 days from hospital admission. No statistically significant associations were demonstrated between surgical timing and postoperative complication.

CONCLUSIONS

This is the first study to identify a specific surgical timing cut-off in the treatment of mildly symptomatic CSDH associated with improved clinical outcomes and recovery, offering a potential reference point for clinical decision-making. Patients who underwent surgery within three days from hospital admission exhibited significantly better postoperative neurological outcomes and shorter hospital stays. Surgical timing did not influence postoperative complications, including hematoma recurrence or the need for early reintervention.

Prediction model for poor short-term prognosis in patients with chronic subdural hematoma after burr hole drainage: a retrospective cohort study

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.

Synthetic minority over-sampling technique-enhanced machine learning models for predicting recurrence of postoperative chronic subdural hematoma

Objective

Chronic subdural hematoma (CSDH) is a neurological condition with high recurrence rates, primarily observed in the elderly population. Although several risk factors have been identified, predicting CSDH recurrence remains a challenge. Given the potential of machine learning (ML) to extract meaningful insights from complex data sets, our study aims to develop and validate ML models capable of accurately predicting postoperative CSDH recurrence.

Methods

Data from 447 CSDH patients treated with consecutive burr-hole irrigations at Wenzhou Medical University's First Affiliated Hospital (December 2014-April 2019) were studied. 312 patients formed the development cohort, while 135 comprised the test cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) method was employed to select crucial features associated with recurrence. Eight machine learning algorithms were used to construct prediction models for hematoma recurrence, using demographic, laboratory, and radiological features. The Border-line Synthetic Minority Over-sampling Technique (SMOTE) was applied to address data imbalance, and Shapley Additive Explanation (SHAP) analysis was utilized to improve model visualization and interpretability. Model performance was assessed using metrics such as AUROC, sensitivity, specificity, F1 score, calibration plots, and decision curve analysis (DCA).

Results

Our optimized ML models exhibited prediction accuracies ranging from 61.0% to 86.2% for hematoma recurrence in the validation set. Notably, the Random Forest (RF) model surpassed other algorithms, achieving an accuracy of 86.2%. SHAP analysis confirmed these results, highlighting key clinical predictors for CSDH recurrence risk, including age, alanine aminotransferase level, fibrinogen level, thrombin time, and maximum hematoma diameter. The RF model yielded an accuracy of 92.6% with an AUC value of 0.834 in the test dataset.

Conclusion

Our findings underscore the efficacy of machine learning algorithms, notably the integration of the RF model with SMOTE, in forecasting the recurrence of postoperative chronic subdural hematoma. Leveraging the RF model, we devised an online calculator that may serve as a pivotal instrument in tailoring therapeutic strategies and implementing timely preventive interventions for high-risk patients.

A Linear Regression Equation for Predicting the Residual Volume of Chronic Subdural Hematoma 1 Week After Surgery

Objective

The outcome of chronic subdural hematoma (CSDH) is influenced not only by the choice of treatment but also by various baseline characteristics. The main objective of this study is to identify the risk factors that can affect the prognosis of CSDH and develop a regression equation based on these risk factors.

Methods

A total of 212 patients with CSDH were included in the study. We collected clinical data including age, gender, and so on, and radiological data including preoperative hematoma volume (V1), effusion volume 1 day after surgery (V2), gas volume 1 day after surgery (V3), and so on. These were considered independent variables, while residual volume 1 week after surgery (V4) was the dependent variable. Univariate linear regression analysis was performed to identify factors that were significantly related. Subsequently, multivariate linear regression analysis was conducted to determine the relationship between each independent variable and the dependent variable. Multiple linear regression analysis was used to obtain a regression equation predicting V4.

Results

We have found that age (t = 3.109, P = 0.002), aspirin (t = 2.762, P = 0.006), hemostatic agents (haemocoagulase, t = 3.731, P < 0.001; vitamin K, t = 2.824, P = 0.005 < 0.05), V2 (t = 8.73, P < 0.001), and V3 (t = 5.968, P < 0.001) are significantly associated with V4. Furthermore, we have developed a regression equation that can predict this volume with CSDH. The fit of the model is robust with an R-squared value of 65.2% > 50%.

Conclusion

Age, aspirin, hemostatic agent, V2, and V3 are significantly associated with V4. We developed a regression equation to predict this volume with CSDH.