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AimHearing loss, affecting a significant portion of the global population, is prevented with peroxisome proliferator-activated receptor γ agonism. Understanding potential protective treatments is crucial for public health. We examine the effect of telmisartan, an antihypertensive drug and partial peroxisome proliferator-activated receptor γ agonist, on hearing loss in patients with hypertension.Method and resultsThis retrospective cohort analysis used data from the OMOP Common Data Model database, encompassing information from three tertiary institutions in South Korea. The study included a substantial sample size of 860,103 people diagnosed with hypertension. The study included individuals who had been medically diagnosed with hypertension and had been prescribed antihypertensive drugs, including telmisartan. The study design was established to evaluate the comparative effects of telmisartan and other hypertension medications on hearing loss. We used propensity score matching (PSM) to create a balanced cohort, reducing potential biases between the telmisartan and non-telmisartan groups. From the initial 860,103 patients with hypertension, a propensity score matched cohort was derived from 20,010 patients, with 2,193 in the telmisartan group. After PSM, lower incidence of total hearing loss was observed in the telmisartan group compared to the non-telmisartan group during the 3-year follow-up (0.5% vs. 1.5%, log-rank p = 0.005). In subgroup analysis, this study showed consistent results that lower incidence of total hearing loss was higher in the telmisartan group than in the non-telmisartan group.ConclusionTelmisartan was associated with reducing certain types of hearing loss in patients with hypertension. Further research is needed to confirm these findings and understand the mechanisms.

Objectives Electrocardiography (ECG)-based diagnosis by experts cannot maintain uniform quality because individual differences may occur. Previous public databases can be used for clinical studies, but there is no common standard that would allow databases to be combined. For this reason, it is difficult to conduct research that derives results by combining databases. Recent commercial ECG machines offer diagnoses similar to those of a physician. Therefore, the purpose of this study was to construct a standardized ECG database using computerized diagnoses. Methods The constructed database was standardized using Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Observational Medical Outcomes Partnership– common data model (OMOP-CDM), and data were then categorized into 10 groups based on the Minnesota classification. In addition, to extract high-quality waveforms, poor-quality ECGs were removed, and database bias was minimized by extracting at least 2,000 cases for each group. To check database quality, the difference in baseline displacement according to whether poor ECGs were removed was analyzed, and the usefulness of the database was verified with seven classification models using waveforms. Results The standardized KURIAS-ECG database consists of high-quality ECGs from 13,862 patients, with about 20,000 data points, making it possible to obtain more than 2,000 for each Minnesota classification. An artificial intelligence classification model using the data extracted through SNOMED-CT showed an average accuracy of 88.03%. Conclusions The KURIAS-ECG database contains standardized ECG data extracted from various machines. The proposed protocol should promote cardiovascular disease research using big data and artificial intelligence.

Abstract 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.

Abstract 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 Improved prediction of atrial fibrillation (AF) may allow for earlier interventions for stroke prevention, as well as mortality and morbidity from other AF‐related complications. We developed a clinically feasible and accurate AF prediction model using electronic health records and computerized ECG interpretation. Methods and Results A total of 671 318 patients were screened from 3 tertiary hospitals. After careful exclusion of cases with missing values and a prior AF diagnosis, AF prediction models were developed from the derivation cohort of 25 584 patients without AF at baseline. In the internal/external validation cohort of 117 523 patients, the model using 6 clinical features and 5 ECG diagnoses showed the highest performance for 3‐year new‐onset AF prediction (C‐statistic, 0.796 [95% CI, 0.785–0.806]). A more simplified model using age, sex, and 5 ECG diagnoses (atrioventricular block, fusion beats, marked sinus arrhythmia, supraventricular premature complex, and wide QRS complex) had comparable predictive power (C‐statistic, 0.777 [95% CI, 0.766–0.788]). The simplified model showed a similar or better predictive performance than the previous models. In the subgroup analysis, the models performed relatively better in patients without risk factors. Specifically, the predictive power was lower in patients with heart failure or decreased renal function. Conclusions Although the 3‐year AF prediction model using both clinical and ECG variables showed the highest performance, the simplified model using age, sex, and 5 ECG diagnoses also had a comparable prediction power with broad applicability for incident AF.

BackgroundThe fact that medical terms require special expertise and are becoming increasingly complex makes it difficult to employ natural language processing techniques in medical informatics. Several human-validated reference standards for medical terms have been developed to evaluate word embedding models using the semantic similarity and relatedness of medical word pairs. However, there are very few reference standards in non-English languages. In addition, because the existing reference standards were developed a long time ago, there is a need to develop an updated standard to represent recent findings in medical sciences. ObjectiveWe propose a new Korean word pair reference set to verify embedding models. MethodsFrom January 2010 to December 2020, 518 medical textbooks, 72,844 health information news, and 15,698 medical research articles were collected, and the top 10,000 medical terms were selected to develop medical word pairs. Attending physicians (n=16) participated in the verification of the developed set with 607 word pairs. ResultsThe proportion of word pairs answered by all participants was 90.8% (551/607) for the similarity task and 86.5% (525/605) for the relatedness task. The similarity and relatedness of the word pair showed a high correlation (ρ=0.70, P

This study aims to evaluate the accuracy and effectiveness of real-time personal monitoring of exposure to PM concentrations using low-cost sensors, in comparison to conventional data collection method based on fixed stations. PM2.5 data were measured every 5 min using a low-cost sensor attached to a bag carried by 47 asthmatic children living in the Seoul Metropolitan area between November 2019 and March 2020, along with the real-time GPS location, temperature, and humidity. The mobile sensor data were then matched with station-based hourly PM2.5 data using the time and location. Despite some uncertainty and inaccuracy of the sensor data, similar temporal patterns were found between the two sources of PM2.5 data on an aggregate level. However, average PM2.5 concentrations via personal monitoring tended to be lower than those from the fixed stations, particularly when the subjects were indoors, during nighttime, and located farther from the fixed station. On an individual level, a substantial discrepancy is observed between the two PM2.5 data sources while staying indoors. This study provides guidance to policymakers and researchers on improving the feasibility of personal monitoring via low-cost mobile sensors as an alternative or supplement to the conventional station-based monitoring.

Background/Aims Clostridioides difficile infection (CDI) poses a considerable threat to global public health. However, there has been insufficient propensity score-matched data on its demographic characteristics and economic burden. Using nationwide claims data, we assessed longitudinal changes in the demographic characteristics and economic burden of CDI between 2011 and 2019 after propensity score matching. Methods It is a retrospective nationwide population-based study using the HIRA database, which contains all inpatient and outpatient nationwide data in South Korea. We obtained nationwide claims data for 10 years (2010–2019) from the HIRA database with permission (M20210916515). We performed a regression analysis to compare the differences in the length of hospital stay and medical costs between patients with CDI and controls (gastroenteritis and colitis). Results The annual incidence rate of CDI/10,000 persons significantly increased in both sexes and all age groups. The length of hospital stay and medical costs were 3.3 folds and 5.0 folds greater, respectively, in the CDI than in the control group (both p<0.001; Figure 2). Although the length of hospital stay decreased, total medical costs increased in all age groups and both sexes between 2011 and 2019 (all p<0.001; Figure 3). When compared with the control group, the CDI-attributable length of hospital stay and medical cost were greater by 15.3 days and 3,413 (x103) won, respectively, after matching. Conclusion In conclusion, CDI incidence, particularly among the elderly population with comorbidities, has been increasing. In addition, the length of hospital stay and total medical costs of the CDI group were greater than those of the control group. [ABSTRACT FROM AUTHOR]

Background/Aims A stool DNA-based SDC2 methylation (meSDC2) test is an emerging tool of an early detection of colorectal neoplasia (CRN). The aim of this study was to evaluate the clinical performance of a meSDC2 stool test in a multicenter, real-world setting. Methods: Consecutive subjects with an asymptomatic, average-risk of CRN completing a meSDC2 stool test from 18 hospitals were identified during 54 months. Performance of meSDC2 stool test, including positive rate, adherence of confirmatory colonoscopy, and the positive predictive value (PPV) of CRN, were measured. Results Among 4,910 subjects included in this analysis, 249 (5.1%) of them were positive for a meSDC2 stool test. Subjects who had a positive meSDC2 stool test were characterized by elderly, smoker, and positive history of colorectal cancer (CRC). Only 152 subjects (61.0%) underwent diagnostic colonoscopy at a median of 61.5 days after a positive result. Any CRN were discovered in 39.7% of them, and the PPV of advanced neoplasia and CRC were 12.4% and 2.5%, respectively. Among the subjects who had a positive result, a family history of CRC was a predictor of any CRN (p = 0.029) as well as advanced neoplasia (p = 0.003). Conclusion Real-world study of meSDC2 stool test in average-risk subjects for CRC showed a high PPV for any CRN at diagnostic colonoscopy. However, it is necessary to identify an optimal target population for meSDC2 stool test for CRC screening, as the overall performance of this test was lower than those of high-risk population. [ABSTRACT FROM AUTHOR]