Identification of factors associated with vancomycin-induced acute kidney injury: A retrospective analysis using the Common Data Model.

OBJECTIVE: Previous findings on predictors of vancomycin-induced acute kidney injury (AKI) are inconsistent. We aimed to identify the predictors of vancomycin-induced AKI using the Observational Medical Outcome Partnership Common Data Model. MATERIALS AND METHODS: We analyzed data from patients treated with vancomycin between January 1, 2012, and May 31, 2022, who were positive for Staphylococcus aureus and had undergone oxacillin susceptibility tests. After excluding patients without data for vancomycin or baseline serum creatinine levels, 116 patients were included in the final dataset. Data up to the third measured vancomycin concentration were collected for each patient. Logistic regression models were used to estimate the odds ratio and 95% confidence interval for each variable associated with vancomycin-induced AKI. RESULTS: High baseline serum creatinine levels, intensive care unit admission, and concurrent renal disorders were significantly associated with vancomycin-induced AKI. Although high trough levels or area under the curve values were not significantly associated with vancomycin-induced AKI, both were significantly higher in patients with AKI than in those without AKI at the second vancomycin concentration measurement. The proportion with trough levels > 20 mg/L was higher in patients with AKI than in those without AKI at the third measurement. CONCLUSION: Our findings revealed that underlying renal disease and intensive care unit admission are more significantly associated with vancomycin-induced AKI than vancomycin pharmacokinetic parameters or dosage, likely due to vancomycin concentration-based dosage adjustment in clinical settings. Our findings may help develop strategies for reducing the incidence of vancomycin-induced AKI; however, further prospective studies are essential.

A Comprehensive Study on a Deep-Learning-Based Electrocardiography Analysis for Estimating the Apnea-Hypopnea Index.

This study introduces a deep-learning-based automatic sleep scoring system to detect sleep apnea using a single-lead electrocardiography (ECG) signal, focusing on accurately estimating the apnea-hypopnea index (AHI). Unlike other research, this work emphasizes AHI estimation, crucial for the diagnosis and severity evaluation of sleep apnea. The suggested model, trained on 1465 ECG recordings, combines the deep-shallow fusion network for sleep apnea detection network (DSF-SANet) and gated recurrent units (GRUs) to analyze ECG signals at 1-min intervals, capturing sleep-related respiratory disturbances. Achieving a 0.87 correlation coefficient with actual AHI values, an accuracy of 0.82, an F1 score of 0.71, and an area under the receiver operating characteristic curve of 0.88 for per-segment classification, our model was effective in identifying sleep-breathing events and estimating the AHI, offering a promising tool for medical professionals.

Lower-Body Fractures and the Risk of Dementia: A Nationwide Population-Based Study.

BACKGROUND AND PURPOSE: The association between physical activity and dementia has been shown in various observational studies. We aimed to determine the risk of dementia in the elderly with lower-body fractures. METHODS: We reconstructed a population-based matched cohort from the National Health Insurance Service-Senior Cohort data set that covers 511,953 recipients of medical insurance in South Korea. RESULTS: Overall 53,776 subjects with lower-body fractures were identified during 2006-2012, and triplicate control groups were matched randomly by sex, age, and years from the index date for each subject with a fracture. There were 3,573 subjects (6.6%) with and 7,987 subjects (4.9%) without lower-body fractures who developed dementia from 2008 up to 2015. Lower-body fractures were independently associated with a subsequent dementia diagnosis with a higher adjusted hazard ratio (aHR) (1.55, 95% confidence interval [CI]=1.49-1.62) compared with upper-body fractures (aHR=1.19, 95% CI=1.14-1.23). CONCLUSIONS: These results support the protective role of physical activity against dementia and highlight the importance of promoting fracture prevention in the elderly.

Machine learning application for classification of Alzheimer's disease stages using 18F-flortaucipir positron emission tomography.

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.

Trends of Epilepsy-Related Mortality in South Korea.

BACKGROUND AND PURPOSE: Epilepsy increases the risk of death in affected individuals of any age. We aimed to determine the mortality caused by epilepsy and its time trends in Korea. METHODS: We obtained population and cause of death data between 1993 and 2019 from Statistics Korea. We identified death caused by epilepsy or status epilepticus. We calculated the crude mortality rate (CMR), age-specific mortality rate, age-standardized mortality rate (ASMR, corresponding to epilepsy-related deaths per 100,000 persons in the general population), and the proportional mortality (PM, corresponding to the proportion of epilepsy-related deaths among all-cause deaths). RESULTS: In 2019, 471 deaths were caused by epilepsy (CMR=0.92), accounting for 0.16% of all deaths in that year. The age-specific mortality rate increased with age, up to 7.01% among individuals aged 80 years and older, while the PM was the highest (3.80%) among individuals aged 5-14 years, which decreased with age. Between 1993 and 2019, the CMR, ASMR, and PM peaked in 2002, and the CMR then rebounded after the trough in this trend in 2011 while the ASMR continued to decrease, and the PM became relatively stable from 2011. Starting in 2005, the age-specific mortality rate for epilepsy had an increasing tendency over time among those aged 75 years or older, and a decreasing tendency in the younger age groups. CONCLUSIONS: A declining tendency of mortality from epilepsy was found in the overall population of Korea over recent decades. However, epilepsy is a notable cause of death in children, and epilepsy-related mortality is increasing in the elderly population.

Analysis of treatment pattern of anti-dementia medications in newly diagnosed Alzheimer's dementia using OMOP CDM.

Anti-dementia medications are widely prescribed to patients with Alzheimer's dementia (AD) in South Korea. This study investigated the pattern of medical management in newly diagnosed patients with AD using a standardized data format-the Observational Medical Outcome Partnership Common Data Model from five hospitals. We examined the anti-dementia treatment patterns from datasets that comprise > 5 million patients during 2009-2019. The medication utility information was analyzed with respect to treatment trends and persistence across 11 years. Among the 8653 patients with newly diagnosed AD, donepezil was the most commonly prescribed anti-dementia medication (4218; 48.75%), followed by memantine (1565; 18.09%), rivastigmine (1777; 8.98%), and galantamine (494; 5.71%). The rising prescription trend during observation period was found only with donepezil. The treatment pathways for the three cholinesterase inhibitors combined with N-methyl-D-aspartate receptor antagonist were different according to the drugs (19.6%; donepezil; 28.1%; rivastigmine, and 17.2%; galantamine). A 12-month persistence analysis showed values of approximately 50% for donepezil and memantine and approximately 40% for rivastigmine and galantamine. There were differences in the prescribing pattern and persistence among anti-dementia medications from database using the Observational Medical Outcome Partnership Common Data Model on the Federated E-health Big Data for Evidence Renovation Network platform in Korea.