Significance of Temporal Muscle Thickness in Chronic Subdural Hematoma

Reduced temporal muscle thickness predicts shorter survival in patients undergoing chronic subdural haematoma drainage

BACKGROUND

Chronic subdural haematoma (CSDH) drainage is a common neurosurgical procedure. CSDHs cause excess mortality, which is exacerbated by frailty. Sarcopenia contributes to frailty - its key component, low muscle mass, can be assessed using cross-sectional imaging. We aimed to examine the prognostic role of temporal muscle thickness (TMT) measured from preoperative computed tomography head scans among patients undergoing surgical CSDH drainage.

METHODS

We retrospectively identified all patients who underwent CSDH drainage within 1 year of February 2019. We measured their mean TMT from preoperative computed tomography scans, tested the reliability of these measurements, and evaluated their prognostic value for postoperative survival.

RESULTS

One hundred and eighty-eight (122, 65% males) patients (median age 78 years, IQR 70-85 years) were included. Thirty-four (18%) patients died within 2 years, and 51 (27%) died at a median follow-up of 39 months (IQR 34-42 months). Intra- and inter-observer reliability of TMT measurements was good-to-excellent (ICC 0.85-0.97, P < 0.05). TMT decreased with age (Pearson's r = -0.38, P < 0.001). Females had lower TMT than males (P < 0.001). The optimal TMT cut-off values for predicting two-year survival were 4.475 mm for males and 3.125 mm for females. TMT below these cut-offs was associated with shorter survival in both univariate (HR 3.24, 95% CI 1.85-5.67) and multivariate (HR 1.86, 95% CI 1.02-3.36) analyses adjusted for age, ASA grade and bleed size. The effect of TMT on mortality was not mediated by age.

CONCLUSIONS

In patients with CSDH, TMT measurements from preoperative imaging were reliable and contained prognostic information supplemental to previously known predictors of poor outcomes.

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.

Frailty in Traumatic Brain Injury-The Significance of Temporal Muscle Thickness

BACKGROUND

Temporal muscle thickness (TMT) on cranial CT scans has recently been identified as a prognostic imaging parameter for assessing a patient's baseline frailty. Here, we analyzed whether TMT correlates with Traumatic brain injury (TBI) severity and whether it can be used to predict outcome(s) after TBI.

METHODS

We analyzed the radiological and clinical data sets of 193 patients with TBI who were admitted to our institution and correlated the radiological data with clinical outcomes after stratification for TMT.

RESULTS

Our analyses showed a significant association between high TMT and increased risk for intracranial hemorrhage (p = 0.0135) but improved mRS at 6 months (p = 0.001) as compared to patients with low TMT. Congruent with such findings, a lower TMT was associated with falls and reduced outcomes at 6 months (p < 0.0001 and p < 0.0001).

CONCLUSION

High TMT was robustly associated with head trauma sequelae but was also associated with good clinical outcomes in TBI patients. These findings consolidate the significance of TMT as an objective marker of frailty in TBI patients; such measurements may ultimately be leveraged as prognostic indicators.

Advances in Musculoskeletal Imaging and Their Applications

Modern medical imaging systems provide ever-more information about the patient's health condition [...].

Frailty in cerebellar ischemic stroke-The significance of temporal muscle thickness

While comprising only 2% of all ischemic strokes, cerebellar strokes are responsible for substantial morbidity and mortality due to their subtle initial presentation and the morbidity of posterior fossa swelling. Furthermore, low temporal muscle thickness (TMT) has recently been identified as a prognostic imaging parameter to assess patient frailty and outcome. We analyzed radiological and clinical data sets of 282 patients with cerebellar ischemic stroke. Our analysis showed a significant association between low TMT, reduced NIHSS and mRS at discharge (p = 0.035, p = 0.004), and reduced mRS at 12 months (p = 0.001). TMT may be used as a prognostic imaging marker and objective tool to assess outcomes in patients with cerebellar ischemic stroke.

Initial Temporal Muscle Thickness and Area: Poor Predictors of Neurological Outcome in Aneurysmal Subarachnoid Hemorrhage in a Central European Patient Cohort

The temporalis muscle area (TMA) has been proclaimed as a surrogate parameter for estimating skeletal muscle mass. Pilot studies in Asian populations suggested temporal muscle thickness (TMT) and TMA as prognostic factors for neurological outcomes in aneurysmal subarachnoid hemorrhage (aSAH) patients. This study aimed to validate these findings in a larger European patient cohort. We retrospectively analyzed age, sex, aneurysm location, treatment, World Federation of Neurosurgical Societies (WFNS) grade, Fisher score, and modified Rankin Score (mRS) at six months in patients with aSAH. TMT and TMA measurements were obtained from initial native CT scans. Logistic regression with the dichotomized six-month mRS as the outcome incorporating TMT, weighted average of TMT, or TMA as predictors was performed. Of the included 478 patients, 66% were female, the mean age was 56, and 48% of patients had an mRS of three to six after six months. The mean TMT at the level of the Sylvian fissure was 5.9 (±1.7) mm in males and 4.8 (±1.8) mm in females. The mean TMA was 234.5 (±107.9) mm2 in females and 380 (±134.1) mm2 in males. WFNS grade (p < 0.001), Fisher score (p < 0.001), and age (p < 0.05) correlated significantly with the mRS after six months. No correlation was found between mRS after six months and the TMT at the Sylvian fissure (p = 0.3), the weighted average of TMT (p = 0.1), or the TMA (p = 0.1). In this central European patient cohort of 478 individuals, no significant associations were found between TMT/TMA and neurological outcomes following aSAH. Further prospective studies in diverse patient populations are necessary to determine the prognostic value of TMA and TMT in aSAH patients.