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Various single‐pill combinations (SPCs) have been introduced to improve drug compliance and clinical efficacy. However, there is a lack of real‐world evidence regarding the effectiveness of these SPCs for hypertension. This study evaluated the real‐world clinical efficacy and safety of amlodipine/losartan‐based SPC therapies in patients with hypertension in a real‐world setting. A total of 15 538 patients treated with amlodipine/losartan‐based SPCs [amlodipine + losartan (AL), amlodipine + losartan + rosuvastatin (ALR), and amlodipine + losartan + chlorthalidone (ALC)] were selected from the database of three tertiary hospitals in Korea. The efficacy endpoints were target blood pressure (BP) and low‐density lipoprotein cholesterol (LDL‐C) achievement rates. Safety was evaluated based on laboratory parameters. Drug adherence was defined as the proportion of medication days covered (PDC). The target BP attainment rate was above 90% and was similar among the three groups. Although many patients in the AL and ALC groups took statins, the target LDL‐C attainment rate was significantly higher in the ALR group than in the AL and ALC groups. Safety endpoints were not significantly different among the groups, except serum uric acid level and incidence rate of new‐onset hyperuricemia, which were significantly lower in the AL and ALR groups than in the ALC group. The PDC was > 90% in all groups. In the real‐world hypertensive patients, amlodipine/losartan‐based SPC therapy demonstrated good target BP achievement rates. Especially, rosuvastatin‐combination SPC showed better target LDL‐C goal achievement rate compared to the other SPCs. All three amlodipine/losartan‐based SPC had excellent drug adherence.

With advances in deep learning and natural language processing (NLP), the analysis of medical texts is becoming increasingly important. Nonetheless, despite the importance of processing medical texts, no research on Korean medical-specific language models has been conducted. The Korean medical text is highly difficult to analyze because of the agglutinative characteristics of the language, as well as the complex terminologies in the medical domain. To solve this problem, we collected a Korean medical corpus and used it to train the language models. In this paper, we present a Korean medical language model based on deep learning NLP. The model was trained using the pre-training framework of BERT for the medical context based on a state-of-the-art Korean language model. The pre-trained model showed increased accuracies of 0.147 and 0.148 for the masked language model with next sentence prediction. In the intrinsic evaluation, the next sentence prediction accuracy improved by 0.258, which is a remarkable enhancement. In addition, the extrinsic evaluation of Korean medical semantic textual similarity data showed a 0.046 increase in the Pearson correlation, and the evaluation for the Korean medical named entity recognition showed a 0.053 increase in the F1-score.

An increase in antibiotic usage is considered to contribute to the emergence of antimicrobial resistance. Although experts are counting on the antimicrobial stewardship programs to reduce antibiotic usage, their effect remains uncertain. In this study, we aimed to evaluate the impact of antibiotic usage and forecast the prevalence of hospital-acquired extended spectrum β-lactamase (ESBL)—producing Escherichia coli (E. coli) using time-series analysis. Antimicrobial culture information of E. coli was obtained using a text processing technique that helped extract free-text electronic health records from standardized data. The antimicrobial use density (AUD) of antibiotics of interest was used to estimate the quarterly antibiotic usage. Transfer function model was applied to forecast relationship between antibiotic usage and ESBL-producing E. coli. Of the 1938 hospital-acquired isolates, 831 isolates (42.9%) were ESBL-producing E. coli. Both the proportion of ESBL-producing E. coli and AUD increased over time. The transfer model predicted that ciprofloxacin AUD is related to the proportion of ESBL-producing E. coli two quarters later. In conclusion, excessive use of antibiotics was shown to affect the prevalence of resistant organisms in the future. Therefore, the control of antibiotics with antimicrobial stewardship programs should be considered to restrict antimicrobial resistance.

Background There are several differences in the clinical course of hypertension due to the biological and social differences between men and women. Resistant hypertension is an advanced disease state, and significant gender difference could be expected, but much has not been revealed yet. The purpose of this study was to compare gender differences on the current status of blood pressure (BP) control and clinical prognosis in patients with resistant hypertension. Methods This is a multicenter, retrospective cohort study using common data model databases of 3 tertiary hospitals in Korea. Total 4,926 patients with resistant hypertension were selected from January 2017 to December 2018. Occurrence of dialysis, heart failure (HF) hospitalization, myocardial infarction, stroke, dementia or all-cause mortality was followed up for 3 years. Results Male patients with resistant hypertension were younger but had a higher cardiovascular risk than female patients. Prevalence of left ventricular hypertrophy and proteinuria was higher in men than in women. On-treatment diastolic BP was lower in women than in men and target BP achievement rate was higher in women than in men. During 3 years, the incidence of dialysis and myocardial infarction was higher in men, and the incidence of stroke and dementia was higher in women. After adjustment, male sex was an independent risk factor for HF hospitalization, myocardial infarction, and all-cause death. Conclusion In resistant hypertension, men were younger than women, but end-organ damage was more common and the risk of cardiovascular event was higher. More intensive cardiovascular prevention strategies may be required in male patients with resistant hypertension.