Association of temporalis muscle thickness with functional outcomes in patients undergoing endovascular thrombectomy

Lin, Yen‐Heng; Chung, Chi-Ting; Chen, Chih‐Hao; Cheng, Chang-Jie; Chu, Hai‐Jui; Chen, Kuo-Wei; Yeh, Shin-Joe; Tsai, Li‐Kai; Lee, Chung-Wei; Tang, Sung‐Chun; Jeng, Jiann‐Shing

doi:10.1016/j.ejrad.2023.110808

Cited by 7 publications

(1 citation statement)

References 29 publications

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“…We found that patients with low TMT (males/females: <4.475/<3.125 mm) had three‐fold higher two‐year and overall mortality rates compared to those with normal TMT. Although this is the first study to suggest a prognostic role for TMT measurements in CSDH, previous observations have associated low TMT with poor prognosis in glioblastoma multiforme, 27 central nervous system lymphoma, 18 stroke, 28 and following endovascular thrombectomy, 29 supporting our results.…”

Section: Discussionsupporting

confidence: 90%

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

Korhonen,

Arponen,

Steinruecke

et al. 2024

J cachexia sarcopenia muscle

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

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Section: Discussionsupporting

confidence: 90%

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

Korhonen,

Arponen,

Steinruecke

et al. 2024

J cachexia sarcopenia muscle

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Machine learning models for outcome prediction in thrombectomy for large anterior vessel occlusion

Shirvani,

Warnat‐Herresthal,

Savchuk

et al. 2024

Ann Clin Transl Neurol

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ObjectivePredicting long‐term functional outcomes shortly after a stroke is challenging, even for experienced neurologists. Therefore, we aimed to evaluate multiple machine learning models and the importance of clinical/radiological parameters to develop a model that balances minimal input data with reliable predictions of long‐term functional independency.MethodsOur study utilized data from the German Stroke Registry on patients with large anterior vessel occlusion who underwent endovascular treatment. We trained seven machine learning models using 30 parameters from the first day postadmission to predict a modified Ranking Scale of 0–2 at 90 days poststroke. Model performance was assessed using a 20‐fold cross‐validation and one‐sided Wilcoxon rank‐sum tests. Key features were identified through backward feature selection.ResultsWe included 7485 individuals with a median age of 75 years and a median NIHSS score at admission of 14 in our analysis. Our Deep Neural Network model demonstrated the best performance among all models including data from 24 h postadmission. Backward feature selection identified the seven most important features to be NIHSS after 24 h, age, modified Ranking Scale after 24 h, premorbid modified Ranking Scale, intracranial hemorrhage within 24 h, intravenous thrombolysis, and NIHSS at admission. Narrowing the Deep Neural Network model's input data to these features preserved the high performance with an AUC of 0.9 (CI: 0.89–0.91).InterpretationOur Deep Neural Network model, trained on over 7000 patients, predicts 90‐day functional independence using only seven clinical/radiological features from the first day postadmission, demonstrating both high accuracy and practicality for clinical implementation on stroke units.

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Temporalis muscle thickness as a predictor of functional outcome after reperfusion therapies for acute ischemic stroke: a retrospective, cohort study

Ravera,

Lombardi,

Bellavia

et al. 2024

J Neurol

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Association of temporalis muscle thickness with functional outcomes in patients undergoing endovascular thrombectomy

Cited by 7 publications

References 29 publications

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

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

Machine learning models for outcome prediction in thrombectomy for large anterior vessel occlusion

Temporalis muscle thickness as a predictor of functional outcome after reperfusion therapies for acute ischemic stroke: a retrospective, cohort study

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