Temporal muscle thickness and area with various characteristics data of the patients with aneurysmal subarachnoid hemorrhage who underwent endovascular coiling

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.

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

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.

The dataset on the characteristics of the intracerebral hemorrhage patients treated by endoscopic hematoma removal or craniotomy

These data present the characteristics of 148 intracerebral hemorrhage (ICH) patients surgically treated. We retrospectively collected data from the medical records of Suwa Red Cross Hospital, including neurological and physiological symptoms, laboratory data, radiological data on admission, complication rate, Glasgow Coma Scale scores on admission or postoperative day 7, and modified Rankin Scale scores at 6 months. Our two articles on the endoscopic hematoma removal and craniotomy for ICH were based on these data [1,2]. This dataset includes detailed laboratory data and radiological features, and it would be useful for reference value for other neurosurgeons or further analysis.