Background and PurposeIn Alzheimer's continuum (a comprehensive of preclinical Alzheimer's disease [AD], mild cognitive impairment [MCI] due to AD, and AD dementia), cognitive dysfunctions are often related to cortical atrophy in specific brain regions. The purpose of this study was to investigate the association between anatomical pattern of cortical atrophy and specific neuropsychological deficits.MethodsA total of 249 participants with Alzheimer's continuum (125 AD dementia, 103 MCI due to AD, and 21 preclinical AD) who were confirmed to be positive for amyloid deposits were collected from the memory disorder clinic in the department of neurology at Samsung Medical Center in Korea between September 2013 and March 2018. To analyze neuropsychological test-specific neural correlates representing the relationship between cortical atrophy measured by cortical thickness and performance in specific neuropsychological tests, a linear regression analysis was performed. Two neural correlates acquired by 2 different standardized scores in neuropsychological tests were also compared.ResultsCortical atrophy in several specific brain regions was associated with most neuropsychological deficits, including digit span backward, naming, drawing-copying, verbal and visual recall, semantic fluency, phonemic fluency, and response inhibition. There were a few differences between 2 neural correlates obtained by different z-scores.ConclusionsThe poor performance of most neuropsychological tests is closely related to cortical thinning in specific brain areas in Alzheimer's continuum. Therefore, the brain atrophy pattern in patients with Alzheimer's continuum can be predict by an accurate analysis of neuropsychological tests in clinical practice.

Background In a clinical setting, an individual subject classification model rather than a group analysis would be more informative. Specifically, the subtlety of cortical atrophy in some frontotemporal dementia (FTD) patients and overlapping patterns of atrophy among three FTD clinical syndromes including behavioral variant FTD (bvFTD), non-fluent/agrammatic variant primary progressive aphasia (nfvPPA), and semantic variant PPA (svPPA) give rise to the need for classification models at the individual level. In this study, we aimed to classify each individual subject into one of the diagnostic categories in a hierarchical manner by employing a machine learning-based classification method. Methods We recruited 143 patients with FTD, 50 patients with Alzheimer's disease (AD) dementia, and 146 cognitively normal subjects. All subjects underwent a three-dimensional volumetric brain magnetic resonance imaging (MRI) scan, and cortical thickness was measured using FreeSurfer. We applied the Laplace Beltrami operator to reduce noise in the cortical thickness data and to reduce the dimension of the feature vector. Classifiers were constructed by applying both principal component analysis and linear discriminant analysis to the cortical thickness data. For the hierarchical classification, we trained four classifiers using different pairs of groups: Step 1 - CN vs. FTD + AD, Step 2 - FTD vs. AD, Step 3 - bvFTD vs. PPA, Step 4 - svPPA vs. nfvPPA. To evaluate the classification performance for each step, we used a10-fold cross-validation approach, performed 1000 times for reliability. Results The classification accuracy of the entire hierarchical classification tree was 75.8%, which was higher than that of the non-hierarchical classifier (73.0%). The classification accuracies of steps 1–4 were 86.1%, 90.8%, 86.9%, and 92.1%, respectively. Changes in the right frontotemporal area were critical for discriminating behavioral variant FTD from PPA. The left frontal lobe discriminated nfvPPA from svPPA, while the bilateral anterior temporal regions were critical for identifying svPPA. Conclusions In the present study, our automated classifier successfully classified FTD clinical subtypes with good to excellent accuracy. Our classifier may help clinicians diagnose FTD subtypes with subtle cortical atrophy and facilitate appropriate specific interventions.

Purpose Severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19), has spread worldwide. Global health systems, including emergency medical systems, are suffering from a lack of medical resources. Using a method for classifying patients visiting the emergency department (ED), we aimed to investigate trends in emergency medical system usage during the COVID-19 epidemic in Korea. Materials and Methods This retrospective observational study included patients who visited emergency medical institutions registered with the National Emergency Department Information System database from January 1, 2017 to May 31, 2020. The primary outcome was identification of changes in the distribution of patients visiting the ED according to the type of emergency medical institution. The secondary outcome was a detailed comparison of Korean Triage and Acuity Scale (KTAS) levels and patient distributions before and during the infectious disaster crisis period. Results Severe patients visited regional emergency centers (RECs) and local emergency centers (LECs) more frequently during the COVID-19 period, and disposition status warranting admission to the intensive care unit or resulting in death was more common in RECs and LECs during the COVID-19 period [RECs, before COVID-19: 300686 (6.3%), during COVID-19: 33548 (8.0%) ( p <0.001); LECs, before COVID-19: 373593 (3.7%), during COVID-19: 38873 (4.5%) ( p <0.001)]. Conclusion During the COVID-19 period, severe patients were shifted to advanced emergency medical institutions, and the KTAS better reflected severe patients. Patient distribution according to the stage of emergency medical institution improved, and validation of the KTAS triage increased more in RECs.

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In this study, we developed machine learning-based prediction models for early childhood caries and compared their performances with the traditional regression model. We analyzed the data of 4195 children aged 1–5 years from the Korea National Health and Nutrition Examination Survey data (2007–2018). Moreover, we developed prediction models using the XGBoost (version 1.3.1), random forest, and LightGBM (version 3.1.1) algorithms in addition to logistic regression. Two different methods were applied for variable selection, including a regression-based backward elimination and a random forest-based permutation importance classifier. We compared the area under the receiver operating characteristic (AUROC) values and misclassification rates of the different models and observed that all four prediction models had AUROC values ranging between 0.774 and 0.785. Furthermore, no significant difference was observed between the AUROC values of the four models. Based on the results, we can confirm that both traditional logistic regression and ML-based models can show favorable performance and can be used to predict early childhood caries, identify ECC high-risk groups, and implement active preventive treatments. However, further research is essential to improving the performance of the prediction model using recent methods, such as deep learning.

Background Studying structural brain aging is important to understand age-related pathologies, as well as to identify the early manifestations of the Alzheimer’s disease (AD) continuum. In this study, we investigated the long-term trajectory of physiological and pathological brain aging in a large number of participants ranging from the 50s to over 80 years of age. Objective To explore the distinct brain regions that distinguish pathological brain aging from physiological brain aging using sophisticated measurements of cortical thickness. Methods A total of 2,823 cognitively normal (CN) individuals and 2,675 patients with AD continuum [874 with subjective memory impairment (SMI), 954 with amnestic mild cognitive impairment (aMCI), and 847 with AD dementia] who underwent a high-resolution 3.0-tesla MRI were included in this study. To investigate pathological brain aging, we further classified patients with aMCI and AD according to the severity of cognitive impairment. Cortical thickness was measured using a surface-based method. Multiple linear regression analyses were performed to evaluate age, diagnostic groups, and cortical thickness. Results Aging extensively affected cortical thickness not only in CN individuals but also in AD continuum patients; however, the precuneus and inferior temporal regions were relatively preserved against age-related cortical thinning. Compared to CN individuals, AD continuum patients including those with SMI showed a decreased cortical thickness in the perisylvian region. However, widespread cortical thinning including the precuneus and inferior temporal regions were found from the late-stage aMCI to the moderate to severe AD. Unlike the other age groups, AD continuum patients aged over 80 years showed prominent cortical thinning in the medial temporal region with relative sparing of the precuneus. Conclusion Our findings suggested that the precuneus and inferior temporal regions are the key regions in distinguishing between physiological and pathological brain aging. Attempts to differentiate age-related pathology from physiological brain aging at a very early stage would be important in terms of establishing new strategies for preventing accelerated pathological brain aging.

Introduction: Inhaled corticosteroids (ICSs) are recommended for patients with frequent exacerbation of chronic obstructive pulmonary disease (COPD). However, accumulating evidence has indicated the risk of pneumonia from the use of ICS. This study aimed to investigate the association between ICS and pneumonia in the real-world clinical setting. Methods: A retrospective cohort study was performed using nationwide population data from the Korea National Health Insurance Service. Subjects who had a new diagnosis of COPD and who received inhaled bronchodilators without a diagnosis of pneumonia before the initiation of bronchodilators were identified. Subjects were followed up until their first diagnosis of pneumonia. The risk of pneumonia in ICS users was compared to that in non-ICS users. Results: A total of 87,594 subjects were identified and 1:1 matched to 22,161 ICS users and non-ICS users. More ICS users were diagnosed with pneumonia compared to non-ICS users (33.73% versus 24.51%, P<0.0001). The incidence rate per 100,000 person-years was 8904.98 for ICS users and 6206.79 for non-ICS users. The hazard ratio (HR) of pneumonia for ICS users was 1.62 (95% CI 1.54-1.70). The HR of subjects prescribed with the lowest ICS cumulative dose was 1. 35 (1.27-1.43). The HR increased to 1.51 (1.42-1.60), 1.96 (1.85-2.09), and 2.03 (1.89-2.18) as the cumulative dose increased. Pneumonia was strongly associated with fluticasone propionate (1.79 (1.70-1.89)) and fluticasone furoate (1.80 (1.61-2.01)) use, compared to the use of other types of ICS. Conclusion: ICS increases the risk of pneumonia in patients with COPD. Hence, ICS should be carefully prescribed in patients with risk factors for pneumonia while considering the cumulative doses and subtypes of ICS.