Background: and Purpose Endovascular recanalization therapy (ERT) is becoming increasingly important in the management of acute ischemic stroke (AIS). However, the hospital volume threshold for optimal ERT remains unknown. We investigated the relationship between hospital volume of ERT and risk-adjusted patient outcomes. Methods: From the National Health Insurance claims data in Korea, 11,745 patients with AIS who underwent ERT from July 2011 to June 2016 in 111 hospitals were selected. We measured the hospital’s ERT volume and patient outcomes, including the 30-day mortality, readmission, and postprocedural intracranial hemorrhage (ICH) rates. For each outcome measure, we constructed risk-adjusted prediction models incorporating demographic variables, the modified Charlson comorbidity index, and the stroke severity index (SSI), and validated them. Risk-adjusted outcomes of AIS cases were compared across hospital quartiles to confirm the volume-outcome relationship (VOR) in ERT. Spline regression was performed to determine the volume threshold. Results: The mean AIS volume was 14.8 cases per hospital/year and the unadjusted means of mortality, readmission, and ICH rates were 11.6%, 4.6%, and 8.6%, respectively. The VOR was observed in the risk-adjusted 30-day mortality rate across all quartile groups, and in the ICH rate between the first and fourth quartiles (P<0.05). The volume threshold was 24 cases per year. Conclusions There was an association between hospital volume and outcomes, and the volume threshold in ERT was identified. Policies should be developed to ensure the implementation of the AIS volume threshold for hospitals performing ERT.

No abstract available.
Humans ; Reperfusion ; Stroke

Facial grimacing can be a manifestation of complex partial seizures from the temporal lobe. We observed a case of seizure with facial grimacing and partial loss of consciousness during an electroencephalography recording. The recording revealed interictal spikes on the left-sided inferior temporal electrodes and ictal discharges starting on the same electrodes. The current source appeared to be in the inferior and lateral temporal areas of the left cerebral hemisphere. These results show that it is possible to localize the current sources responsible for interictal spikes.
Cerebrum ; Electrodes ; Electroencephalography ; Humans ; Seizures ; Temporal Lobe ; Unconsciousness

PURPOSE: To compare the accuracy of intraocular lens (IOL) power calculation formulas in eyes with primary angle closure glaucoma (ACG). METHODS: This retrospective study compared the refractive outcomes of 63 eyes with primary ACG with the results of 93 eyes with normal open angles undergoing uneventful cataract surgery. Anterior segment biometry including anterior chamber depth, axial length, and anterior chamber depth to axial length ratio were compared by the IOL Master. Third generation formulas (Hoffer Q and SRK/T) and a fourth generation formula (Haigis) were used to predict IOL powers in both groups. The predictive accuracy of the formulas was analyzed by comparison of the mean error and the mean absolute error (MAE). RESULTS: In ACG patients, anterior chamber depth and the anterior chamber depth to axial length ratio were smaller than normal controls (all p < 0.05). The MAEs from the ACG group were larger than that from the control group in the Haigis formula. The mean absolute error from the Haigis formula was the largest and the mean absolute error from the Hoffer Q formula was the smallest. CONCLUSIONS: IOL power prediction may be inaccurate in ACG patients. The Haigis formula produced more inaccurate results in ACG patients, and it is more appropriate to use the Hoffer Q formula to predict IOL powers in eyes with primary ACG.
Aged ; Biometry ; Cataract Extraction ; Glaucoma, Angle-Closure/*complications ; Glaucoma, Open-Angle/complications ; Humans ; *Lens Implantation, Intraocular ; *Lenses, Intraocular ; Middle Aged ; *Optics and Photonics ; Reproducibility of Results ; Retrospective Studies

OBJECTIVE: Uric acid is a non-enzymatic antioxidant associated with depression. Despite its known protective role in other brain disorders, little is known about its influence on the structural characteristics of brains of patients with major depressive disorder (MDD). This study explored the association between uric acid and characteristics of white matter (WM) in patients with MDD. METHODS: A total of 32 patients with MDD and 23 healthy controls (HCs) were examined. All participants were scored based on the Beck Depression Inventory and Beck Anxiety Inventory at baseline. All patients were also rated with the Hamilton Depression Rating Scale. We collected blood samples from all participants immediately after their enrollment and before the initiation of antidepressants in case of patients. Tract-based spatial statistics were used for all imaging analyses. RESULTS: Lower fractional anisotropy (FA) and higher radial diffusivity (RD) values were found in the MDD group than in the HC group. Voxelwise correlation analysis revealed that the serum uric acid levels positively correlated with the FA and negatively with the RD in WM regions that previously showed significant group differences in the MDD group. The correlated areas were located in the left anterior corona radiata, left frontal lobe WM, and left anterior cingulate cortex WM. CONCLUSION: The present study suggests a significant association between altered WM connectivity and serum uric acid levels in patients with MDD, possibly through demyelination.
Anisotropy ; Antidepressive Agents ; Antioxidants ; Anxiety ; Brain ; Brain Diseases ; Demyelinating Diseases ; Depression* ; Depressive Disorder ; Depressive Disorder, Major ; Frontal Lobe ; Gyrus Cinguli ; Humans ; Neuroimaging ; Oxidative Stress ; Uric Acid* ; White Matter*

The authors discovered that the p-value for group difference in sex (male/female) in Table 1 was incorrect. And the authors described unclearly whether the p-value for the sex distribution was obtained by chi-square test or Fisher's exact test.

OBJECTIVES: The objective of this study is to investigate differences in clinical characteristics between female panic disorder (PD) patients with abortion history (PD+A) and without abortion history (PD−A).METHODS: We examined data from 341 female patients diagnosed with PD. We divided the patients with PD into PD+A (82 patients) and PD−A (259 patients) to compare demographic and clinical characteristics. The following instruments were applied : stress coping strategies, NEO-neuroticism, the Anxiety Sensitivity Index-Revised (ASI-R), the Albany Panic and Phobia Questionnaire (APPQ), the Beck Depression Inventory, the Beck Anxiety Inventory (BAI) and the Sheehan Disability Scale.RESULTS: Compared to the PD−A, the PD+A group showed no significant difference in coping strategies. However, significantly higher scores in neuroticism, the ASI-R, the APPQ and the BAI were observed. In terms of health-related disability, the PD+A group did not show significant difference.CONCLUSIONS: Our results suggest that the PD+A group may differ from the PD−A group in trait markers such as neuroticism and anxiety sensitivity, and abortion history may be associated with panic-related symptom severity. Our study suggests that further consideration is needed on such clinical characteristics in PD patients with abortion history.
Abortion, Induced ; Anxiety ; Depression ; Female ; Humans ; Panic Disorder ; Panic ; Phobic Disorders

Background: It is difficult to distinguish subtle differences shown in computed tomography (CT) images of coronavirus disease 2019 (COVID-19) and bacterial pneumonia patients, which often leads to an inaccurate diagnosis. It is desirable to design and evaluate interpretable feature extraction techniques to describe the patient's condition. Methods: This is a retrospective cohort study of 170 confirmed patients with COVID-19 or bacterial pneumonia acquired at Yeungnam University Hospital in Daegu, Korea. The lung and lesion regions were segmented to crop the lesion into 2D patches to train a classifier model that could differentiate between COVID-19 and bacterial pneumonia. The K-means algorithm was used to cluster deep features extracted by the trained model into 20 groups.Each lesion patch cluster was described by a characteristic imaging term for comparison.For each CT image containing multiple lesions, a histogram of lesion types was constructed using the cluster information. Finally, a Support Vector Machine classifier was trained with the histogram and radiomics features to distinguish diseases and severity. Results: The 20 clusters constructed from 170 patients were reviewed based on common radiographic appearance types. Two clusters showed typical findings of COVID-19, with two other clusters showing typical findings related to bacterial pneumonia. Notably, there is one cluster that showed bilateral diffuse ground-glass opacities (GGOs) in the central and peripheral lungs and was considered to be a key factor for severity classification. The proposed method achieved an accuracy of 91.2% for classifying COVID-19 and bacterial pneumonia patients with 95% reported for severity classification. The CT quantitative parameters represented by the values of cluster 8 were correlated with existing laboratory data and clinical parameters. Conclusion Deep chest CT analysis with constructed lesion clusters revealed well-known COVID-19 CT manifestations comparable to manual CT analysis. The constructed histogram features improved accuracy for both diseases and severity classification, and showedcorrelations with laboratory data and clinical parameters. The constructed histogram features can provide guidance for improved analysis and treatment of COVID-19.

Background: It is difficult to distinguish subtle differences shown in computed tomography (CT) images of coronavirus disease 2019 (COVID-19) and bacterial pneumonia patients, which often leads to an inaccurate diagnosis. It is desirable to design and evaluate interpretable feature extraction techniques to describe the patient's condition. Methods: This is a retrospective cohort study of 170 confirmed patients with COVID-19 or bacterial pneumonia acquired at Yeungnam University Hospital in Daegu, Korea. The lung and lesion regions were segmented to crop the lesion into 2D patches to train a classifier model that could differentiate between COVID-19 and bacterial pneumonia. The K-means algorithm was used to cluster deep features extracted by the trained model into 20 groups.Each lesion patch cluster was described by a characteristic imaging term for comparison.For each CT image containing multiple lesions, a histogram of lesion types was constructed using the cluster information. Finally, a Support Vector Machine classifier was trained with the histogram and radiomics features to distinguish diseases and severity. Results: The 20 clusters constructed from 170 patients were reviewed based on common radiographic appearance types. Two clusters showed typical findings of COVID-19, with two other clusters showing typical findings related to bacterial pneumonia. Notably, there is one cluster that showed bilateral diffuse ground-glass opacities (GGOs) in the central and peripheral lungs and was considered to be a key factor for severity classification. The proposed method achieved an accuracy of 91.2% for classifying COVID-19 and bacterial pneumonia patients with 95% reported for severity classification. The CT quantitative parameters represented by the values of cluster 8 were correlated with existing laboratory data and clinical parameters. Conclusion Deep chest CT analysis with constructed lesion clusters revealed well-known COVID-19 CT manifestations comparable to manual CT analysis. The constructed histogram features improved accuracy for both diseases and severity classification, and showedcorrelations with laboratory data and clinical parameters. The constructed histogram features can provide guidance for improved analysis and treatment of COVID-19.