Background: Data standardization is essential in electronic health records (EHRs) for both clinical practice and retrospective research. However, it is still not easy to standardize EHR data because of nonidentical duplicates, typographical errors, or inconsistencies. To overcome this drawback, standardization efforts have been undertaken for collecting data in a standardized format as well as for curating the stored data in EHRs. To perform clinical big data research, the stored data in EHR should be standardized, starting from laboratory results, given their importance. However, most of the previous efforts have been based on labor-intensive manual methods. Objective: We aimed to develop an automatic standardization method for eliminating the noises of categorical laboratory data, grouping, and mapping of cleaned data using standard terminology. Methods: We developed a method called standardization algorithm for laboratory test–categorical result (SALT-C) that can process categorical laboratory data, such as pos +, 250 4+ (urinalysis results), and reddish (urinalysis color results). SALT-C consists of five steps. First, it applies data cleaning rules to categorical laboratory data. Second, it categorizes the cleaned data into 5 predefined groups (urine color, urine dipstick, blood type, presence-finding, and pathogenesis tests). Third, all data in each group are vectorized. Fourth, similarity is calculated between the vectors of data and those of each value in the predefined value sets. Finally, the value closest to the data is assigned. Results: The performance of SALT-C was validated using 59,213,696 data points (167,938 unique values) generated over 23 years from a tertiary hospital. Apart from the data whose original meaning could not be interpreted correctly (eg, ** and _^), SALT-C mapped unique raw data to the correct reference value for each group with accuracy of 97.6% (123/126; urine color tests), 97.5% (198/203; (urine dipstick tests), 95% (53/56; blood type tests), 99.68% (162,291/162,805; presence-finding tests), and 99.61% (4643/4661; pathogenesis tests). Conclusions: The proposed SALT-C successfully standardized the categorical laboratory test results with high reliability. SALT-C can be beneficial for clinical big data research by reducing laborious manual standardization efforts.

Few studies have investigated the effects of calcium supplementation on cardiovascular outcomes in individuals with low calcium intake in real-world settings. This study examined the association between calcium supplementation and cardiovascular outcomes in the Korean population in a real-world setting. This large retrospective cohort study included patients aged ≥45 years first prescribed calcium supplements in 2010. Age- and sex-matched controls were recruited among those who had no prescription for calcium supplements. Longitudinal data were collected on 31 December 2018. Kaplan–Meier estimation and Cox proportional hazard regression analysis were performed. The cumulative incidence of acute myocardial infarction, ischemic stroke, and death was significantly higher in the calcium supplementation group than in the control group (p < 0.05 by log-rank test). The calcium supplementation group had a significantly higher risk of myocardial infarction, ischemic stroke, and death than the control group. Compared to the control group, the hazard ratios (95% confidence intervals) of the incidence of myocardial infarction, stroke, and death in the supplementation group were 1.14 (1.03–1.27), 1.12 (1.05–1.20), and 1.40 (1.32–1.50), respectively, after adjusting for confounding variables. Considering the associated cardiovascular risk, calcium supplementation for osteoporosis treatment should be administered cautiously.

Background/Objectives: Type 2 diabetes mellitus (T2DM) is a major contributor to cognitive decline and dementia in older adults; however, the role of the age of onset of T2DM in younger patients remains uncertain. We explored the association between the risk of dementia and its subtypes in relation to the age at T2DM diagnosis. Methods: This population cohort study included a total of 612,201 newly diagnosed T2DM patients. The controls were randomly selected from the general population and matched at a 1:2 ratio based on the propensity score. The outcomes of interest were all-cause dementia, Alzheimer’s disease (AD), and vascular dementia (VD). The association of T2DM with dementia was stratified by the age at diagnosis of T2DM. Results: The mean ages of the subjects in the T2DM and control groups were 55.7 ± 13.0 and 55.7 ± 13.0. The patients with T2DM diagnosed at <50 years had the highest excess risk for most outcomes relative to the controls, with a hazard ratio (HR) (95% CI) of 3.29 (3.11–3.49) for all-cause dementia, 4.08 (3.18–5.24) for AD, and 5.82 (3.84–8.81) for VD. All risks were attenuated progressively with each increasing decade at the diagnostic age, but remained significant; for T2DM diagnosed at ≥80 years, the HR (95% CI) was 1.38 (1.34–1.41) for all-cause dementia, 1.35 (1.31–1.40) for AD, and 1.98 (1.70–2.30) for VD. Conclusions: We need to stratify T2DM management according to the age of diagnosis. Physicians should closely monitor cognitive function in patients with T2DM, especially in younger individuals.

Background and Aims: Antiarrhythmic drugs (AADs) are the primary treatment for maintaining sinus rhythm in patients with atrial fibrillation (AF). While amiodarone is mainly used in patients with structural heart disease, its effects on all-cause mortality compared to other AADs remain unclear. Methods: This study utilized nationwide healthcare insurance data involving patients with new-onset AF from 2013 to 2019. We identified patients who were prescribed with AADs ≥ six months within the first year of diagnosis (medication possession ratio ≥ 0.5). All-cause mortality was assessed between amiodarone and other AAD users up to three years post-AAD-prescription. Results: Among 770,977 new-onset AF patients, 12,243 were amiodarone users and 33,036 were prescribed with other AADs. Significant differences in mean age and prevalence of medical conditions such as heart failure, myocardial infarction, chronic kidney disease, diabetes, and dyslipidemia were noted. After propensity score matching, 12,017 amiodarone users were compared to an equal number of other AAD users with similar baseline characteristics. Multivariate analysis indicated a 2.9-fold increase in all-cause mortality for amiodarone users (hazard ratio = 2.88; 95% confidence interval = 2.64–3.15; p < 0.001). This increased risk was more pronounced among women compared with men (hazard ratio = 3.38 vs. 2.56; p for interaction = 0.004). Amiodarone was associated with increased mortality in AF patients with heart failure and myocardial infarction. Conclusions: Amiodarone, compared with non-amiodarone AADs, was associated with increased risk of all-cause mortality in AAD-naive new-onset AF patients. Increased all-cause mortality associated with amiodarone was consistent throughout various subgroups. Significant interaction was observed with the sex category, with women being more vulnerable to amiodarone.