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Background: The progression of Alzheimer's dementia (AD) can be classified into three stages: cognitive unimpairment (CU), mild cognitive impairment (MCI), and AD. The purpose of this study was to implement a machine learning (ML) framework for AD stage classification using the standard uptake value ratio (SUVR) extracted from 18F-flortaucipir positron emission tomography (PET) images. We demonstrate the utility of tau SUVR for AD stage classification. We used clinical variables (age, sex, education, mini-mental state examination scores) and SUVR extracted from PET images scanned at baseline. Four types of ML frameworks, such as logistic regression, support vector machine (SVM), extreme gradient boosting, and multilayer perceptron (MLP), were used and explained by Shapley Additive Explanations (SHAP) to classify the AD stage. Results: Of a total of 199 participants, 74, 69, and 56 patients were in the CU, MCI, and AD groups, respectively; their mean age was 71.5 years, and 106 (53.3%) were men. In the classification between CU and AD, the effect of clinical and tau SUVR was high in all classification tasks and all models had a mean area under the receiver operating characteristic curve (AUC) > 0.96. In the classification between MCI and AD, the independent effect of tau SUVR in SVM had an AUC of 0.88 (p < 0.05), which was the highest compared to other models. In the classification between MCI and CU, the AUC of each classification model was higher with tau SUVR variables than with clinical variables independently, which yielded an AUC of 0.75(p < 0.05) in MLP, which was the highest. As an explanation by SHAP for the classification between MCI and CU, and AD and CU, the amygdala and entorhinal cortex greatly affected the classification results. In the classification between MCI and AD, the para-hippocampal and temporal cortex affected model performance. Especially entorhinal cortex and amygdala showed a higher effect on model performance than all clinical variables in the classification between MCI and CU. Conclusions: The independent effect of tau deposition indicates that it is an effective biomarker in classifying CU and MCI into clinical stages using MLP. It is also very effective in classifying AD stages using SVM with clinical information that can be easily obtained at clinical screening. [ABSTRACT FROM AUTHOR]

Pregnancy is a risk factor for asthma exacerbation and may trigger new-onset asthma in nonasthmatics. This study evaluated the epidemiology of newly diagnosed asthma during pregnancy and the associated risk factors among previously nonasthmatic women. Twelve-year medical data from the Korean National Health Insurance claims database (from January 2007 to December 2018) of Korean women who gave birth between January 2012 and December 2015 were collected. Previously nonasthmatic women were defined as those who had not been diagnosed with asthma for at least 4 years before pregnancy. Asthma flare-up was defined as asthma diagnosed three times or more and treated at least once with an oral corticosteroid. A nested case-control study was performed, and then the derived risk factors were applied to whole study population. Among the nonasthmatic women, 7.5% experienced asthma during pregnancy including episodes requiring hospitalization and 18.6% of them visited emergency room. Older age, primiparity, multi-fetal pregnancy, and rhinitis were identified as the risk factors. Among the entire study population, moderate to severe rhinitis was a significant risk factor across all age groups, while primiparity with multi-fetal pregnancy was one for older pregnant women; 22.7% in those ≥ 34 years old experienced asthma flare-ups compared to only 3.5% in the < 34 age group. A substantial portion of pregnant women with no history of asthma experienced an asthma flare-up during pregnancy. Multi-fetal pregnancy as primiparity at a later age and moderate to severe rhinitis are risk factors for the new development of asthma., Competing Interests: There are no financial or other issues that might lead to conflict of interest., (Copyright © 2024 The Korean Academy of Asthma, Allergy and Clinical Immunology • The Korean Academy of Pediatric Allergy and Respiratory Disease.)