High protein intake after subarachnoid hemorrhage improves oral intake and temporal muscle volume

Changes in temporal muscle dimensions and their clinical impact in out-of-hospital cardiac arrest survivors

Objective

This study investigates temporal muscle atrophy in out-of-hospital cardiac arrest patients post-resuscitation, seeking associations with neurological outcomes and factors associated with atrophy.

Methods

Using data from six Japanese intensive care units, adult patients' post-resuscitation who underwent head computed tomography scans on admission and two to five days post-admission were assessed. Temporal muscle area, thickness, and density were quantified from a single cross-sectional image. Patients were categorized into 'atrophy' or 'no atrophy' groups based on median daily temporal muscle atrophy rates. The primary outcome was changes in temporal muscle dimensions between admission and follow-up two to five days later. Secondary outcomes included assessing the impact of temporal muscle atrophy on 30-day survival, as well as identifying any clinical factors associated with temporal muscle atrophy.

Results

A total of 185 patients were analyzed. Measurements at follow-up revealed significant decreases in temporal muscle area (214 vs. 191 mm2, p < 0.001), thickness (4.9 vs. 4.7 mm, p < 0.001), and density (46 vs. 44 HU, p < 0.001) compared to those at admission. The median daily rate for temporal muscle area atrophy was 2.0% per day. There was no significant association between temporal muscle atrophy and 30-day survival (hazard ratios, 0.71; 95% CI, 0.41-1.23, p = 0.231). Multivariable logistic regression found no clinical factors significantly associated with temporal muscle atrophy.

Conclusions

Temporal muscle atrophy in post-resuscitation patients occurs rapidly at 2.0% per day. However, there was no significant association with 30-day mortality or any identified clinical factors. Further investigation into its long-term functional implications is warranted.

Geriatric nutrition risk index predicts prolonged post-stroke dysphagia in acute ischemic stroke

BACKGROUND

Post-stroke dysphagia (PSD) is a common complication after stroke. Malnutrition inhibits stroke recovery and is associated with stroke mortality. However, no studies have investigated the effects of nutritional state at admission on prolonged PSD.

METHODS

We retrospectively analyzed ischemic stroke patients in our institute from January 2018 to December 2020. Swallowing function was assessed using the Food Oral Intake Scale; prolonged PSD was defined as levels 1-3 at 14 days after admission. The Geriatric Nutritional Risk Index (GNRI) was used to assess nutritional risks, which were classified as follows: >98, no nutritional risk; 92-98, mild nutritional risk; 82-92, moderate nutritional risk; and <82, severe nutritional risk. The association between GNRI and prolonged PSD was assessed.

RESULTS

Of 580 patients (median age, 81 years; male, 53%), prolonged PSD was detected in 117 patients. Patients with severe dysphagia had older age, higher pre-stroke modified Rankin Scale score, lower GNRI, and higher National Institutes of Health Stroke Scale score. Logistic regression analysis revealed that lower GNRI was independently associated with prolonged PSD (continuous value; adjusted odds ratio [OR] 1.03, 95% confidence interval [CI] 1.00-1.05). In addition, when "severe" and "moderate" nutritional risk was analyzed as a single class, moderate or severe nutritional risk (GNRI < 92) was independently associated with prolonged PSD (adjusted OR 2.50, 95% CI 1.29-4.87), compared with no nutritional risk patients (GNRI > 98).

CONCLUSIONS

In acute ischemic stroke, lower GNRI at admission was independently associated with prolonged PSD, suggesting that GNRI at admission might identify patients at risk of prolonged PSD.

Association of Temporalis Muscle Mass with Early Cognitive Impairment in Older Patients with Acute Ischemic Stroke

The prognostic value of temporal muscle mass has been studied in various neurological disorders. Herein, we investigated the association between temporal muscle mass and early cognitive function in patients with acute ischemic stroke. This study included 126 patients with acute cerebral infarction aged ≥65 years. Temporal muscle thickness (TMT) was measured using T2-weighted brain magnetic resonance imaging at admission for acute stroke. Within 2 weeks of stroke onset, skeletal mass index (SMI) and cognitive function were assessed using bioelectrical impedance analysis and the Korean version of the Montreal Cognitive Assessment (MoCA), respectively. Pearson's correlation analyzed the correlation between TMT and SMI, and multiple linear regression analyzed independent predictors of early post-stroke cognitive function. TMT and SMI were significantly positively correlated (R = 0.36, p < 0.001). After adjusting for covariates, TMT was an independent predictor of early post-stroke cognitive function, stratified by the MoCA score (β = 1.040, p = 0.017), age (β = -0.27, p = 0.006), stroke severity (β = -0.298, p = 0.007), and education level (β = 0.38, p = 0.008). TMT may be used as a surrogate marker for evaluating skeletal muscle mass because it is significantly associated with post-stroke cognitive function during the acute phase of ischemic stroke; therefore, TMT may help detect older patients at a high risk of early post-stroke cognitive impairment.

“Timing of percutaneous endoscopic gastrostomy tube placement in post-stroke patients does not impact mortality, complications, or outcomes”: Commentary

In this commentary, we summarize some of the key points of the original paper “Timing of percutaneous endoscopic gastrostomy tube placement in post-stroke patients does not impact mortality, complications, or outcomes” and offer support for the proposed results. Specifically, we address how early percutaneous endoscopic gastrostomy (PEG) tube placement may reduce hospital length of stay and costs. We also discuss topics related to the article including PEG weaning and post-stroke nutritional formulation. However, we note that concerns purported by previous studies that early PEG placement may worsen outcomes are not fully addressed, and further research is needed.

Predictive Values of White Blood Cell Count in Peripheral Blood at Admission on In-Hospital Complications and 90-Day Outcomes of Patients with Aneurysmal Subarachnoid Hemorrhage: Insights from the LongTEAM Registry

PURPOSE

This study aimed to explore the relationship between white blood cells (WBCs) at admission and clinical outcomes in patients with aneurysmal subarachnoid hemorrhage (aSAH).

PATIENTS AND METHODS

We analyzed data from patients with aSAH between January 2015 and September 2021 who were included in the LongTEAM (Long-term Prognosis of Emergency Aneurysmal Subarachnoid Hemorrhage) registry study. WBC is classified into four groups according to the quartile. We used the logistic model for in-hospital complications, mortality, modified Rankin scale (mRS) at discharge and 90 days to examine the relationship between WBC and clinical outcomes. We used WBC levels near odds ratio (OR) = 1 (Q1) in restricted cubic splines as the reference to evaluate whether there is a nonlinear relationship between WBC and clinical outcomes. Another Kaplan-Meier method was used to analyze the relationship between WBC levels and the risk of developing pneumonia.

RESULTS

Of the 988 patients included, the results showed that compared with patients in the Q1 group, patients in the highest quartile (Q4) had an increased incidence of 90-day unfavorable outcomes after adjusting the confounders (adjusted OR = 1.81, 95% CI = 1.02-3.20, p = 0.042), which may be caused by the increased incidence and risk of pneumonia (adjusted OR = 2.06, 95% CI = 1.30-3.29, p = 0.002; adjusted hazard ratio [HR]=1.63, 95% CI = 1.13-2.36, p < 0.001). The restricted cubic spline indicated that the incidence of developing pneumonia and 90-day unfavorable outcomes rises with increasing WBC levels (p for nonlinear = 0.135 and 0.113).

CONCLUSION

Patients with higher WBC at admission were associated with an increased incidence of 90-day unfavorable outcomes, which might be related to pneumonia.

Diagnostic Utility of Temporal Muscle Thickness as a Monitoring Tool for Muscle Wasting in Neurocritical Care

Temporalis muscle (TM) atrophy has emerged as a potential biomarker for muscle wasting. However, its diagnostic utility as a monitoring tool in intensive care remains uncertain. Hence, the objective of this study was to evaluate the diagnostic value of sequential ultrasound- and computed tomography (CT)-based measurements of TM thickness (TMT). With a prospective observational design, we included 40 patients without preexisting sarcopenia admitted to a neurointensive care unit. TMT measurements, performed upon admission and serially every 3−4 days, were correlated with rectus femoris muscle thickness (RFT) ultrasound measurements. Interrater reliability was assessed by Bland Altmann plots and intraclass correlation coefficient (ICC). Analysis of variance was performed in subgroups to evaluate differences in the standard error of measurement (SEM). RFT decline was paralleled by ultrasound- as well as CT-based TMT measurements (TMT to RFT: r = 0.746, p < 0.001; CT-based TMT to ultrasound-based RFT: r = 0.609, p < 0.001). ICC was 0.80 [95% CI 0.74, 0.84] for ultrasound-based assessment and 0.90 [95% CI 0.88, 0.92] for CT-based TMT measurements. Analysis of variance for BMI, Heckmatt score, fluid balance, and agitation showed no evidence of measurement errors in these subgroups. This study demonstrates the clinical feasibility and utility of ultrasound- and CT-based TMT measurements for the assessment of muscle wasting.

Temporal Muscle and Stroke-A Narrative Review on Current Meaning and Clinical Applications of Temporal Muscle Thickness, Area, and Volume

BACKGROUND

Evaluating muscle mass and function among stroke patients is important. However, evaluating muscle volume and function is not easy due to the disturbances of consciousness and paresis. Temporal muscle thickness (TMT) has been introduced as a novel surrogate marker for muscle mass, function, and nutritional status. We herein performed a narrative literature review on temporal muscle and stroke to understand the current meaning of TMT in clinical stroke practice.

METHODS

The search was performed in PubMed, last updated in October 2021. Reports on temporal muscle morphomics and stroke-related diseases or clinical entities were collected.

RESULTS

Four studies reported on TMT and subarachnoid hemorrhage, two studies on intracerebral hemorrhage, two studies on ischemic stroke, two studies on standard TMT values, and two studies on nutritional status. TMT was reported as a prognostic factor for several diseases, a surrogate marker for skeletal muscle mass, and an indicator of nutritional status. Computed tomography, magnetic resonance imaging, and ultrasonography were used to measure TMT.

CONCLUSIONS

TMT is gradually being used as a prognostic factor for stroke or a surrogate marker for skeletal muscle mass and nutritional status. The establishment of standard methods to measure TMT and large prospective studies to further investigate the relationship between TMT and diseases are needed.

Easily Created Prediction Model Using Automated Artificial Intelligence Framework (Prediction One, Sony Network Communications Inc., Tokyo, Japan) for Subarachnoid Hemorrhage Outcomes Treated by Coiling and Delayed Cerebral Ischemia

Introduction Reliable prediction models of subarachnoid hemorrhage (SAH) outcomes and delayed cerebral ischemia (DCI) are needed to decide the treatment strategy. Automated artificial intelligence (AutoAI) is attractive, but there are few reports on AutoAI-based models for SAH functional outcomes and DCI. We herein made models using an AutoAI framework, Prediction One (Sony Network Communications Inc., Tokyo, Japan), and compared it to other previous statistical prediction scores. Methods We used an open dataset of 298 SAH patients, who were with non-severe neurological grade and treated by coiling. Modified Rankin Scale 0-3 at six months was defined as a favorable functional outcome and DCI occurrence as another outcome. We randomly divided them into a 248-patient training dataset and a 50-patient test dataset. Prediction One made the model using training dataset with 5-fold cross-validation. We evaluated the model using the test dataset and compared the area under the curves (AUCs) of the created models. Those of the modified SAFIRE score and the Fisher computed tomography (CT) scale to predict the outcomes. Results The AUCs of the AutoAI-based models for functional outcome in the training and test dataset were 0.994 and 0.801, and those for the DCI occurrence were 0.969 and 0.650. AUCs for functional outcome calculated using modified SAFIRE score were 0.844 and 0.892. Those for the DCI occurrence calculated using the Fisher CT scale were 0.577 and 0.544. Conclusions We easily and quickly made AutoAI-based prediction models. The models' AUCs were not inferior to the previous prediction models despite the easiness.

Chronic subdural hematoma in patients over 65 years old: Results of using a postoperative cognitive evaluation to determine whether to permit return to driving

Background:

Chronic subdural hematoma (CSDH) is usually associated with good recovery with burr hole irrigation and postoperative drainage under local anesthesia. In Japan, traffic accidents by the elderly drivers over 65 years old are severely increasing, and there is no consensus on whether or not to return to driving after CSDH treatment. We perform a postoperative cognitive assessment. We retrospectively investigated the return-to-driving rate and associated factors.

Methods:

Of the 45 patients over 65 y.o. and who had usually driven, 30 patients wished to drive again. We performed tests composed of Mini-Mental State Examination (MMSE), line cancellation and line bisection task, Kohs block design test, trail making test (TMT)-A and B, Kana-hiroi test, Rey-Osterrieth complex figure test, and behavioral assessment of the dysexecutive syndrome, in order. When all tests’ scores were better than the cutoff values, we let patients drive again. When some of the scores were worse than the cutoff values, we reevaluated the patients at the outpatient every month. If the patients’ scores could not improve at the outpatient, we recommended them to stop driving.

Results:

Nineteen of 30 patients could return to driving. Worse MMSE, Kohs block design test, TMT-A, TMT-B scores, higher age, dementia, or consciousness disturbance as chief complaints were associated with driving disability.

Conclusion:

CSDH is known as treatable dementia. However, we should perform an objective cognitive assessment before discharge because only 63% of the patients over 65 y.o. who wished to drive could return to driving.

Postsurgical functional outcome prediction model using deep learning framework (Prediction One, Sony Network Communications Inc.) for hypertensive intracerebral hemorrhage

BACKGROUND

Reliable prediction models of intracerebral hemorrhage (ICH) outcomes are needed for decision-making of the treatment. Statistically 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 DL-based functional outcome prediction models for ICH outcomes after surgery. We herein made a functional outcome prediction model using DLframework, Prediction One (Sony Network Communications Inc., Tokyo, Japan), and compared it to original ICH score, ICH Grading Scale, and FUNC score.

METHODS

We used 140 consecutive hypertensive ICH patients' data in our hospital between 2012 and 2019. All patients were surgically treated. Modified Rankin Scale 0-3 at 6 months was defined as a favorable outcome. We randomly divided them into 100 patients training dataset and 40 patients validation dataset. Prediction One made the prediction model using the training dataset with 5-fold cross-validation. We calculated area under the curves (AUCs) regarding the outcome using the DL-based model, ICH score, ICH Grading Scale, and FUNC score. The AUCs were compared.

RESULTS

The model made by Prediction One using 64 variables had AUC of 0.997 in the training dataset and that of 0.884 in the validation dataset. These AUCs were superior to those derived from ICH score, ICH Grading Scale, and FUNC score.

CONCLUSION

We easily and quickly made prediction models using Prediction One, even with a small single-center dataset. The accuracy of the DL-based model was superior to those of previous statistically calculated models.