In Reply to the Letter to the Editor Regarding “Temporal Muscle as an Indicator of Sarcopenia Is Independently Associated with Hunt and Kosnik Grade on Admission and the Modified Rankin Scale at 6 Months of Patients with Subarachnoid Hemorrhage Treated by Endovascular Coiling”

Kakizawa

Nishikawa

et al. 2021

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Background: Skeletal muscle mass is an important factor for various diseases’ outcomes. As for its indicators, temporal muscle thickness (TMT) and temporal muscle area (TMA) on the head computed tomography are useful, and TMT and TMA were reported as potential prognostic factors for aneurysmal subarachnoid hemorrhage (SAH). We examined the clinical characteristics, including TMT and TMA, of SAH patients aged 75 or younger. Methods: We retrospectively investigated 127 SAH patients with all World Federation of Neurosurgical Societies (WFNS) grades and treated by clipping between 2009 and 2019. Clinical outcome was measured with the modified Rankin Scale (mRS) at 6 months, with favorable outcome defined as mRS 0–2. The associations between the clinical variables and the outcomes were analyzed. Results: The mean age was 60.6 (32–74) years, and 65% were women. The mean ± standard deviation of WFNS grade was 2.8 ± 1.4. TMT and TMA were larger in the favorable outcome group than the poor one. Multivariate analysis revealed that age, smoking, WFNS grade, and TMT or TMA were associated with favorable outcome. Receiver operating characteristic analysis found that the threshold of TMT was 4.9 mm in female and 6.7 mm in male, and that of TMA was 193 mm2 in female and 333 mm2 in male. Conclusion: The odds ratios for TMT and TMA related to clinical outcome were lower than for smoking and WFNS grade; however, on multivariate analysis they remained independent prognostic factors in SAH patients aged 75 or younger treated by clipping. Further studies are needed to confirm these findings.

Section: Discussionmentioning

confidence: 99%

Temporal muscle thickness and area are an independent prognostic factors in patients aged 75 or younger with aneurysmal subarachnoid hemorrhage treated by clipping

Kakizawa

Nishikawa

et al. 2021

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“…[ 6 ] Mainly, the systemic muscle mass and its function are important for diagnosis and indicators of sarcopenia,[ 4 ] and muscle strength was at the forefront as it was recognized that strength was better than mass in predicting adverse outcomes. [ 3 , 5 , 23 , 35 ] However, we sometimes cannot perform muscle function tests for neurosurgical and neurological patients due to the patients’ impaired consciousness, gait disturbance, paresis, aneurysm rerupture, and cerebral hemorrhage rebleeding concerns. Therefore, we hypothesized that TMT on MRI would be a useful surrogate marker of skeletal muscle mass, and it would be helpful to clinical application for various neurosurgical and neurological diseases patients.…”

Section: Discussionmentioning

confidence: 99%

Standard values for temporal muscle thickness in the Japanese population who undergo brain check-up by magnetic resonance imaging

Narita

Sasaki³

et al. 2021

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Background: Skeletal muscle mass is an important factor for various diseases’ outcomes. The psoas muscle cross-sectional area on the abdominal computed tomography (CT), gait speed, and handgrip strength is used to measure it. However, it is difficult to measure the neurological patients’ muscle mass or function because (1) we do not perform abdominal CT. (2) Such patients have impaired consciousness, gait disturbance, paresis, and need of rest. Temporal muscle thickness (TMT) on magnetic resonance imaging (MRI) is now attractive for skeletal muscle volume indicator, but the reference values are not established. We herein investigated the standard value of the Japanese TMT using the brain check-up database by MRI. Methods: We retrospectively investigated 360 Japanese individuals from two institutions between 2017 and 2019. We measured TMT on the T1-weighted images in the previously reported way. The associations between TMT and other variables were analyzed. Results: TMT of 214 women and 146 men, ranging from 35 to 84 years old, was investigated. TMT ranged from 3.69 to 16.90 mm. Mean TMT values were significantly higher in men compared to women except for the over 70-year-old cohort. TMT was correlated to weight and body mass index in both sexes. Conclusion: This is the first retrospective study on the standard TMT values from the Japanese brain check-up database. Our results were just reference values, but these would be useful for further investigation in other neurosurgical and neurological diseases regarding muscle volume or sarcopenia.

Easily created prediction model using deep learning software (Prediction One, Sony Network Communications Inc.) for subarachnoid hemorrhage outcomes from small dataset at admission

Kakizawa

Nishikawa

et al. 2020

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Background: Reliable prediction models of subarachnoid hemorrhage (SAH) outcomes are needed for decision-making of the treatment. SAFIRE score using only four variables is a good prediction scoring system. However, making such prediction models needs a large number of samples and time-consuming statistical analysis. Deep learning (DL), one of the artificial intelligence, is attractive, but there were no reports on prediction models for SAH outcomes using DL. We herein made a prediction model using DL software, Prediction One (Sony Network Communications Inc., Tokyo, Japan) and compared it to SAFIRE score. Methods: We used 153 consecutive aneurysmal SAH patients data in our hospital between 2012 and 2019. Modified Rankin Scale (mRS) 0–3 at 6 months was defined as a favorable outcome. We randomly divided them into 102 patients training dataset and 51 patients external validation dataset. Prediction one made the prediction model using the training dataset with internal cross-validation. We used both the created model and SAFIRE score to predict the outcomes using the external validation set. The areas under the curve (AUCs) were compared. Results: The model made by Prediction One using 28 variables had AUC of 0.848, and its AUC for the validation dataset was 0.953 (95%CI 0.900–1.000). AUCs calculated using SAFIRE score were 0.875 for the training dataset and 0.960 for the validation dataset, respectively. Conclusion: We easily and quickly made prediction models using Prediction One, even with a small single-center dataset. The accuracy of the model was not so inferior to those of previous statistically calculated prediction models.