Conversion and Data Quality Assessment of Electronic Health Record Data at a Korean Tertiary Teaching Hospital to a Common Data Model for Distributed Network Research

Yoon, Dukyong,Ahn, Eun Kyoung,Park, Man Young,Cho, Soo Yeon,Ryan, Patrick,Schuemie, Martijn J.,Shin, Dahye,Park, Hojun,Park, Rae Woong Korean Society of Medical Informatics 2016 Healthcare Informatics Research Vol.22 No.1

<P><B>Objectives</B></P><P>A distributed research network (DRN) has the advantages of improved statistical power, and it can reveal more significant relationships by increasing sample size. However, differences in data structure constitute a major barrier to integrating data among DRN partners. We describe our experience converting Electronic Health Records (EHR) to the Observational Health Data Sciences and Informatics (OHDSI) Common Data Model (CDM).</P><P><B>Methods</B></P><P>We transformed the EHR of a hospital into Observational Medical Outcomes Partnership (OMOP) CDM ver. 4.0 used in OHDSI. All EHR codes were mapped and converted into the standard vocabulary of the CDM. All data required by the CDM were extracted, transformed, and loaded (ETL) into the CDM structure. To validate and improve the quality of the transformed dataset, the open-source data characterization program ACHILLES was run on the converted data.</P><P><B>Results</B></P><P>Patient, drug, condition, procedure, and visit data from 2.07 million patients who visited the subject hospital from July 1994 to November 2014 were transformed into the CDM. The transformed dataset was named the AUSOM. ACHILLES revealed 36 errors and 13 warnings in the AUSOM. We reviewed and corrected 28 errors. The summarized results of the AUSOM processed with ACHILLES are available at http://ami.ajou.ac.kr:8080/.</P><P><B>Conclusions</B></P><P>We successfully converted our EHRs to a CDM and were able to participate as a data partner in an international DRN. Converting local records in this manner will provide various opportunities for researchers and data holders.</P>

Quantitative Evaluation of the Relationship between T-Wave-Based Features and Serum Potassium Level in Real-World Clinical Practice

Yoon, Dukyong,Lim, Hong Seok,Jeong, Jong Cheol,Kim, Tae Young,Choi, Jung-gu,Jang, Jong-Hwan,Jeong, Eugene,Park, Chan Min Hindawi 2018 BioMed research international Vol.2018 No.-

<P><B>Background</B></P><P> Proper management of hyperkalemia that leads to fatal cardiac arrhythmia has become more important because of the increased prevalence of hyperkalemia-prone diseases. Although T-wave changes in hyperkalemia are well known, their usefulness is debatable. We evaluated how well T-wave-based features of electrocardiograms (ECGs) are correlated with estimated serum potassium levels using ECG data from real-world clinical practice.</P><P><B> Methods</B></P><P> We collected ECGs from a local ECG repository (MUSE™) from 1994 to 2017 and extracted the ECG waveforms. Of about 1 million reports, 124,238 were conducted within 5 minutes before or after blood collection for serum potassium estimation. We randomly selected 500 ECGs and two evaluators measured the amplitude (T-amp) and right slope of the T-wave (T-right slope) on five lead waveforms (V3, V4, V5, V6, and II). Linear correlations of T-amp, T-right slope, and their normalized feature (T-norm) with serum potassium levels were evaluated using Pearson correlation coefficient analysis.</P><P><B> Results</B></P><P> Pearson correlation coefficients for T-wave-based features with serum potassium between the two evaluators were 0.99 for T-amp and 0.97 for T-right slope. The coefficient for the association between T-amp, T-right slope, and T-norm, and serum potassium ranged from -0.22 to 0.02. In the normal ECG subgroup (normal ECG or otherwise normal ECG), there was no correlation between T-wave-based features and serum potassium level.</P><P><B> Conclusions</B></P><P> T-wave-based features were not correlated with serum potassium level, and their use in real clinical practice is currently limited.</P>

Statins and risk for new-onset diabetes mellitus : A real-world cohort study using a clinical research database

Yoon, Dukyong,Sheen, Seung Soo,Lee, Sukhyang,Choi, Yong Jun,Park, Rae Woong,Lim, Hong-Seok Wolters Kluwer Health 2016 Medicine Vol.95 No.46

<▼1><P>Supplemental Digital Content is available in the text</P></▼1><▼2><P><B>Abstract</B></P><P>Although concern regarding the increased risk for new-onset diabetes mellitus (NODM) after statin treatment has been raised, there has been a lack of evidence in real-world clinical practice, particularly in East Asians. We investigated whether statin use is associated with risk for NODM in Koreans. We conducted a retrospective cohort study using the clinical research database from electronic health records. The study cohort consisted of 8265 statin-exposed and 33,060 matched nonexposed patients between January 1996 and August 2013. Matching at a 1:4 ratio was performed using a propensity score based on age, gender, baseline glucose levels (mg/dL), and hypertension. The comparative risks for NODM with various statins (atorvastatin, fluvastatin, pitavastatin, pravastatin, rosuvastatin, and simvastatin) were estimated by both statin exposure versus matched nonexposed and within-class comparisons. The incidence of NODM among the statin-exposed group (6.000 per 1000 patient-years [PY]) was higher than that of the nonexposed group (3.244 per 1000 PY). The hazard ratio (HR) of NODM after statin exposure was 1.872 (95% confidence interval [CI], 1.432–2.445). Male gender (HR, 1.944; 95% CI, 1.497–2.523), baseline glucose per mg/dL (HR, 1.014; 95% CI, 1.013–1.016), hypertension (HR, 2.232; 95% CI, 1.515–3.288), and thiazide use (HR, 1.337; 95% CI, 1.081–1.655) showed an increased risk for NODM, while angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker showed a decreased risk (HR, 0.774; 95% CI, 0.668–0.897). Atorvastatin-exposed patients showed a higher risk for NODM than their matched nonexposed counterparts (HR, 1.939; 95% CI, 1.278–2.943). However, the risk for NODM was not significantly different among statins in within-class comparisons. In conclusion, an increased risk for NODM was observed among statin users in a practical healthcare setting in Korea.</P></▼2>

Medical Data Analysis using Artificial Intelligence

Dukyong YOON 한국생물공학회 2020 한국생물공학회 학술대회 Vol.2020 No.10

Discovering hidden information in biosignals from patients by artificial intelligence

Yoon Dukyong,Jang Jong-Hwan,Choi Byung Jin,Kim Tae Young,Han Chang Ho 대한마취통증의학회 2020 Korean Journal of Anesthesiology Vol.73 No.4

Biosignals such as electrocardiogram or photoplethysmogram are widely used for determining and monitoring the medical condition of patients. It was recently discovered that more information could be gathered from biosignals by applying artificial intelligence (AI). At present, one of the most impactful advancements in AI is deep learning. Deep learning-based models can extract important features from raw data without feature engineering by humans, provided the amount of data is sufficient. This AI-enabled feature presents opportunities to obtain latent information that may be used as a digital biomarker for detecting or predicting a clinical outcome or event without further invasive evaluation. However, the black box model of deep learning is difficult to understand for clinicians familiar with a conventional method of analysis of biosignals. A basic knowledge of AI and machine learning is required for the clinicians to properly interpret the extracted information and to adopt it in clinical practice. This review covers the basics of AI and machine learning, and the feasibility of their application to real-life situations by clinicians in the near future.

Baseline Clinical and Biomarker Characteristics of Biobank Innovations for Chronic Cerebrovascular Disease With Alzheimer’s Disease Study: BICWALZS

Roh Hyun Woong,Kim Na-Rae,이동기,Cheong Jae-Youn,Seo Sang Won,Choi Seong Hye,Kim Eun-Joo,Cho Soo Hyun,Kim Byeong C.,Kim Seong Yoon,Kim Eun Young,Chang Jaerak,Lee Sang Yoon,Yoon Dukyong,Choi Jin Wook,An Y 대한신경정신의학회 2022 PSYCHIATRY INVESTIGATION Vol.19 No.2

Objective We aimed to present the study design and baseline cross-sectional participant characteristics of biobank innovations for chronic cerebrovascular disease with Alzheimer’s disease study (BICWALZS) participants.Methods A total of 1,013 participants were enrolled in BICWALZS from October 2016 to December 2020. All participants underwent clinical assessments, basic blood tests, and standardized neuropsychological tests (n=1,013). We performed brain magnetic resonance imaging (MRI, n=817), brain amyloid positron emission tomography (PET, n=713), single nucleotide polymorphism microarray chip (K-Chip, n=949), locomotor activity assessment (actigraphy, n=200), and patient-derived dermal fibroblast sampling (n=175) on a subset of participants.Results The mean age was 72.8 years, and 658 (65.0%) were females. Based on clinical assessments, total of 168, 534, 211, 80, and 20 had subjective cognitive decline, mild cognitive impairment (MCI), Alzheimer’s dementia, vascular dementia, and other types of dementia or not otherwise specified, respectively. Based on neuroimaging biomarkers and cognition, 199, 159, 78, and 204 were cognitively normal (CN), Alzheimer’s disease (AD)-related cognitive impairment, vascular cognitive impairment, and not otherwise specified due to mixed pathology (NOS). Each group exhibited many differences in various clinical, neuropsychological, and neuroimaging results at baseline. Baseline characteristics of BICWALZS participants in the MCI, AD, and vascular dementia groups were generally acceptable and consistent with 26 worldwide dementia cohorts and another independent AD cohort in Korea.Conclusion The BICWALZS is a prospective and longitudinal study assessing various clinical and biomarker characteristics in older adults with cognitive complaints. Details of the recruitment process, methodology, and baseline assessment results are described in this paper.

Impact of Depressive Symptoms during the Second Trimester of Pregnancy on Maternal and Fetal Heart Rate Variability

( Innsun Park ),( Heeyeon Kim ),( Kyungun Jhung ),( Dukyong Yoon ),( Jihoon Seo ),( Jin Young Park ),( Hee Young Cho ) 대한산부인과학회 2023 대한산부인과학회 학술대회 Vol.109 No.-

Association between Full Electronic Medical Record System Adoption and Drug Use: Antibiotics and Polypharmacy

Young-Taek Park,김동환,Rae Woong Park,Koray Atalag,In Ho Kwon,Dukyong Yoon,최모나 대한의료정보학회 2020 Healthcare Informatics Research Vol.26 No.1

Objectives: We investigated associations between full Electronic Medical Record (EMR) system adoption and drug use in healthcare organizations (HCOs) to explore whether EMR system features such as electronic prescribing, medicines reconciliation, and decision support, might be related to drug use by using the relevant nation-wide data. Methods: The study design was cross-sectional. Survey data of the level of adoption of EMR systems were collected for the Organization for Economic Co-operation and Development benchmarking information and communication technologies (ICT) study between November 2013 and January 2014, in Korea. Survey respondents were hospital chief information officers and medical practitioners in primary care clinics. From the national health insurance administrative dataset, two outcomes, the rate of antibiotic prescription and polypharmacy with ≥6 drugs, were extracted. Results: We found that full EMR adoption showed a 16.1% lower antibiotic drug prescription than partial adoption including paper-based medical charts in the hospital only (p = 0.041). Between EMR adoption status and polypharmacy prescription, only those clinics which fully adopted EMR showed significant associations with higher polypharmacy prescriptions (36.9%, p = 0.001). Conclusions: The findings suggested that there might be some confounding effects present and sophisticated ICT may provide some benefits to the quality of care even with some mixed results. Although a negative relationship between full EMR system adoption and antibiotic drug use was only significant in hospitals, EMR system functions searching drugs or listing specific patients might facilitate antibiotic drug use reduction. Positive relationships between full EMR system adoption and polypharmacy rate in general hospitals and clinics, but not hospitals, require further research.

The Best Prediction Model for Trauma Outcomes of the Current Korean Population: a Comparative Study of Three Injury Severity Scoring Systems

Kyoungwon Jung,John Cook-Jong Lee,Rae Woong Park,Dukyong Yoon,Sungjae Jung,Younghwan Kim,Jonghwan Moon,Yo Huh,Junsik Kwon 대한중환자의학회 2016 Acute and Critical Care Vol.31 No.3

Background: Injury severity scoring systems that quantify and predict trauma outcomes have not been established in Korea. This study was designed to determine the best system for use in the Korean trauma population. Methods: We collected and analyzed the data from trauma patients admitted to our institution from January 2010 to December 2014. Injury Severity Score (ISS), Revised Trauma Score (RTS), and Trauma and Injury Severity Score (TRISS) were calculated based on the data from the enrolled patients. Area under the receiver operating characteristic (ROC) curve (AUC) for the prediction ability of each scoring system was obtained, and a pairwise comparison of ROC curves was performed. Additionally, the cut-off values were estimated to predict mortality, and the corresponding accuracy, positive predictive value, and negative predictive value were obtained. Results: A total of 7,120 trauma patients (6,668 blunt and 452 penetrating injuries) were enrolled in this study. The AUCs of ISS, RTS, and TRISS were 0.866, 0.894, and 0.942, respectively, and the prediction ability of the TRISS was significantly better than the others (p < 0.001, respectively). The cut-off value of the TRISS was 0.9082, with a sensitivity of 81.9% and specificity of 92.0%; mortality was predicted with an accuracy of 91.2%; its positive predictive value was the highest at 46.8%. Conclusions: The results of our study were based on the data from one institution and suggest that the TRISS is the best prediction model of trauma outcomes in the current Korean population. Further study is needed with more data from multiple centers in Korea.

Risk Evaluation of Azithromycin-Induced QT Prolongation in Real-World Practice

Choi, Young,Lim, Hong-Seok,Chung, Dahee,Choi, Jung-gu,Yoon, Dukyong Hindawi 2018 BioMed research international Vol.2018 No.-

<P><B>Background</B></P><P> Azithromycin exposure has been reported to increase the risk of QT prolongation and cardiovascular death. However, findings on the association between azithromycin and cardiovascular death are controversial, and azithromycin is still used in actual practice. Additionally, quantitative assessments of risk have not been performed, including the risk of QT prolongation when patients are exposed to azithromycin in a real-world clinical setting. Therefore, in this study, we aimed to evaluate the risk of exposure to azithromycin on QT prolongation in a real-world clinical setting using a 21-year medical history database of a tertiary medical institution.</P><P><B> Methods</B></P><P> We analyzed the electrocardiogram results and relevant electronic health records of 402,607 subjects in a tertiary teaching hospital in Korea from 1996 to 2015. To evaluate the risk of QT prolongation of azithromycin, we conducted a case-control analysis using amoxicillin for comparison. Multiple logistic regression analysis was performed to correct for age, sex, accompanying drugs, and disease.</P><P><B> Results</B></P><P> The odds ratio (OR) for QT prolongation (QTc>450 ms in male and >460 ms in female) on azithromycin exposure was 1.40 (95% confidence interval [CI], 1.23-1.59), and the OR for severe QT prolongation (QTc>500 ms) was 1.43 (95% CI, 1.13-1.82). On the other hand, the ORs on exposure to amoxicillin were 1.06 (95% CI, 0.97-1.15) and 0.88 (95% CI, 0.70-1.09). In a subgroup analysis, the risk of QT prolongation in patients aged between 60 and 80 years was significantly higher when they are exposed to azithromycin.</P><P><B> Conclusions</B></P><P> The risk of QT prolongation was increased when patients, particularly the elderly aged 60-79 years, were exposed to azithromycin. Therefore, clinicians should pay exercise caution using azithromycin or consider using other antibiotics, such as amoxicillin, instead of azithromycin.</P>