Significance of Temporal Muscle Thickness in Chronic Subdural Hematoma

Dubinski, Daniel; Won, Sae‐Yeon; Behmanesh, Bedjan; Cantré, Daniel; Mattes, Isabell; Trnovec, Svorad; Baumgarten, Peter; Schuss, Patrick; Freiman, Thomas M.; Geßler, Florian

doi:10.3390/jcm11216456

Cited by 6 publications

(10 citation statements)

References 28 publications

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“…In patients with brain metastases, thicker TMTs (>median) were associated with higher OS in studies of melanoma (146 patients) [ 34 ], and a median-based sex-related TMT cutoff in non-small cell lung cancer (221 patients) was identified as a potential prognostic factor [ 16 ]. TMT has also been investigated as a predictor of clinical outcomes in other pathologies, including primary central nervous system lymphoma (128 patients), head and neck squamous cell carcinoma (106 patients), chronic subdural hematoma (171 patients), and acute ischemic stroke (265 patients) [ 17 , 20 , 21 , 35 ]. However, the results are often contradictory, as, for example, in stroke patients, variations of TMT in terms of OS or mRS after three months show different results in different patient cohorts [ 17 , 30 ].…”

Section: Discussionmentioning

confidence: 99%

“…Recent studies have suggested that skeletal muscle mass may be a useful prognostic marker in critically ill patients. The loss of muscle mass, known as sarcopenia, is associated with poor outcomes in various medical and surgical conditions, including brain metastasis, head and neck carcinoma, chronic subdural hemorrhage, intracranial aneurysm, stroke, and glioblastoma [ 16 , 17 , 18 , 19 , 20 , 21 ]. However, clinical diagnosis of sarcopenia, including measuring walking speed or hand strength, can be challenging to implement in aSAH patients with neurological deficits or impaired vigilance [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

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

Karadağ

Kamp

Fischer

et al. 2023

JCM

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Karadağ

Kamp

Fischer

et al. 2023

JCM

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“…The diminished thickness of the temporal muscle (TMT) has been identified as an adverse prognostic factor in patients with brain tumors [26]. Separately, chronic subdural hematoma (CSDH) is a neurosurgical condition notorious for its high recurrence and elusive outcome prediction models.…”

Section: Applications Of Image Analysis In Musculoskeletal Imagingmentioning

confidence: 99%

Advances in Musculoskeletal Imaging and Their Applications

Piórkowski,

Obuchowicz,

Urbanik

et al. 2023

JCM

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“…Several factors, such as age, previous bleeding episodes, cerebral atrophy, alcohol consumption, the presence of subdural air, radiological signs, and surgical techniques, have been identified as potential contributors to recurrence (9)(10)(11). Nevertheless, existing predictive measures have shown inconsistent results and have not seen widespread clinical adoption (12). In recent years, several models predicting the recurrence of CSDH after surgery have been published.…”

Section: Introductionmentioning

confidence: 99%

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

Ni,

Zhu,

Qian

et al. 2024

Front. Neurol.

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ObjectiveChronic 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.MethodsData 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).ResultsOur 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.ConclusionOur 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.

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Significance of Temporal Muscle Thickness in Chronic Subdural Hematoma

Cited by 6 publications

References 28 publications

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

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

Advances in Musculoskeletal Imaging and Their Applications

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

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