Rae Woong Park

Expression of peroxiredoxin and thioredoxin in human lung cancer and paired normal lung

Background:  Peroxiredoxins (Prxs) have been implicated in regulating many cellular processes including cell proliferation, differentiation and apoptosis. However, the pathophysiological significance of Prx proteins, especially in lung disease, has not been defined. Therefore, the authors investigated the distribution and expression of various Prx isoforms in lung cancer and compared this with normal lung from human and mouse.

Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers.

The vision of creating accessible, reliable clinical evidence by accessing the clincial experience of hundreds of millions of patients across the globe is a reality. Observational Health Data Sciences and Informatics (OHDSI) has built on learnings from the Observational Medical Outcomes Partnership to turn methods research and insights into a suite of applications and exploration tools that move the field closer to the ultimate goal of generating evidence about all aspects of healthcare to serve the needs of patients, clinicians and all other decision-makers around the world.

Detection of adverse drug reaction signals using an electronic health records database: Comparison of the Laboratory Extreme Abnormality Ratio (CLEAR) algorithm.

Electronic health records (EHRs) are expected to be a good source of data for pharmacovigilance. However, current quantitative methods are not applicable to EHR data. We propose a novel quantitative postmarketing surveillance algorithm, the Comparison of Laboratory Extreme Abnormality Ratio (CLEAR), for detecting adverse drug reaction (ADR) signals from EHR data. The methodology involves calculating the odds ratio of laboratory abnormalities between a specific drug‐exposed group and a matched unexposed group. Using a 10‐year EHR data set, we applied the algorithm to test 470 randomly selected drug–event pairs. It was found possible to analyze a single drug–event pair in just 109 ± 159 seconds. In total, 120 of the 150 detected signals corresponded with previously reported ADRs (positive predictive value (PPV) = 0.837 ± 0.113, negative predictive value (NPV) = 0.659 ± 0.180). By quickly and efficiently identifying ADR signals from EHR data, the CLEAR algorithm can significantly contribute to the utilization of EHR data for pharmacovigilance.

Differential diagnosis in idiopathic granulomatous mastitis and tuberculous mastitis.

Purpose Idiopathic granulomatous mastitis (IGM) is a rare chronic inflammatory disease of unknown etiology. The diagnosis of IGM requires that other granulomatous lesions in the breast be excluded. Tuberculous mastitis (TM) is also an uncommon disease that is often difficult to differentiate from IGM. The purpose of this study is to develop a new algorithm for the differential diagnosis and treatment of IGM and TM. Methods Medical records of 68 patients (58 with IGM and 10 with TM) between July 1999 and February 2009 were retrospectively reviewed. Results The mean age of the patients was 33.5 (IGM) and 40 (TM) years (p=0.018). The median follow-up was 84 months. Of the total 10 patients with TM, 5 patients had a history of pulmonary tuberculosis. The most common symptoms of the diseases were breast lump and pain. However, axillary lymphadenopathy was more seen in TM (50%) compared to IGM (20.6%) (p=0.048). TM showed more cancer-mimicking findings on radiologic study (p=0.028). In IGM, 48 patients (82.7%) underwent surgical wide excision and 21 patients (36.2%) were managed with corticosteroid therapy and antibiotics. All of the TM patients received anti-tuberculosis medications and 9 patients (90%) underwent wide excision. The mean treatment duration was 2.8 months in IGM and 8.4 months in TM. Recurrence developed in 5 patients (8.6%) in IGM and 1 patient (10%) in TM. Conclusion This study shows different characteristics between IGM and TM. The IGM patients were younger and had more mastalgia symptoms than the TM patients. Axillary lymphadenopathy was seen more often in TM patients. Half of the TM patients had pulmonary tuberculosis or tuberculosis lymphadenitis. Surgical wide excision might be both therapeutic and useful for providing an exact diagnosis.

Characterizing treatment pathways at scale using the OHDSI network

Observational research promises to complement experimental research by providing large, diverse populations that would be infeasible for an experiment. Observational research can test its own clinical hypotheses, and observational studies also can contribute to the design of experiments and inform the generalizability of experimental research. Understanding the diversity of populations and the variance in care is one component. In this study, the Observational Health Data Sciences and Informatics (OHDSI) collaboration created an international data network with 11 data sources from four countries, including electronic health records and administrative claims data on 250 million patients. All data were mapped to common data standards, patient privacy was maintained by using a distributed model, and results were aggregated centrally. Treatment pathways were elucidated for type 2 diabetes mellitus, hypertension, and depression. The pathways revealed that the world is moving toward more consistent therapy over time across diseases and across locations, but significant heterogeneity remains among sources, pointing to challenges in generalizing clinical trial results. Diabetes favored a single first-line medication, metformin, to a much greater extent than hypertension or depression. About 10% of diabetes and depression patients and almost 25% of hypertension patients followed a treatment pathway that was unique within the cohort. Aside from factors such as sample size and underlying population (academic medical center versus general population), electronic health records data and administrative claims data revealed similar results. Large-scale international observational research is feasible.

A novel algorithm for detection of adverse drug reaction signals using a hospital electronic medical record database

Quantitative analytic methods are being increasingly used in postmarketing surveillance. However, currently existing methods are limited to spontaneous reporting data and are inapplicable to hospital electronic medical record (EMR) data. The principal objectives of this study were to propose a novel algorithm for detecting the signals of adverse drug reactions using EMR data focused on laboratory abnormalities after treatment with medication, and to evaluate the potential use of this method as a signal detection tool.

A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques

Objectives The intensive care environment generates a wealth of critical care data suited to developing a well-calibrated prediction tool. This study was done to develop an intensive care unit (ICU) mortality prediction model built on University of Kentucky Hospital (UKH)s data and to assess whether the performance of various data mining techniques, such as the artificial neural network (ANN), support vector machine (SVM) and decision trees (DT), outperform the conventional logistic regression (LR) statistical model. Methods The models were built on ICU data collected regarding 38,474 admissions to the UKH between January 1998 and September 2007. The first 24 hours of the ICU admission data were used, including patient demographics, admission information, physiology data, chronic health items, and outcome information. Results Only 15 study variables were identified as significant for inclusion in the model development. The DT algorithm slightly outperformed (AUC, 0.892) the other data mining techniques, followed by the ANN (AUC, 0.874), and SVM (AUC, 0.876), compared to that of the APACHE III performance (AUC, 0.871). Conclusions With fewer variables needed, the machine learning algorithms that we developed were proven to be as good as the conventional APACHE III prediction.

Prediction of Daily Patient Numbers for a Regional Emergency Medical Center using Time Series Analysis

OBJECTIVES To develop and evaluate time series models to predict the daily number of patients visiting the Emergency Department (ED) of a Korean hospital. METHODS Data were collected from the hospital information system database. In order to develop a forecasting model, we used, 2 years of data from January 2007 to December 2008 data for the following 3 consecutive months were processed for validation. To establish a Forecasting Model, calendar and weather variables were utilized. Three forecasting models were established: 1) average; 2) univariate seasonal auto-regressive integrated moving average (SARIMA); and 3) multivariate SARIMA. To evaluate goodness-of-fit, residual analysis, Akaike information criterion and Bayesian information criterion were compared. The forecast accuracy for each model was evaluated via mean absolute percentage error (MAPE). RESULTS The multivariate SARIMA model was the most appropriate for forecasting the daily number of patients visiting the ED. Because its MAPE was 7.4%, this was the smallest among the models, and for this reason was selected as the final model. CONCLUSIONS This study applied explanatory variables to a multivariate SARIMA model. The multivariate SARIMA model exhibits relativelyhigh reliability and forecasting accuracy. The weather variables play a part in predicting daily ED patient volume.

Overdose Rate of Drugs Requiring Renal Dose Adjustment: Data Analysis of 4 Years Prescriptions at a Tertiary Teaching Hospital

OBJECTIVETo determine the overdose rate of drugs that require renal dose adjustment and factors related with overdose.SUBJECTSTotal of 23,635,210 records of prescriptions and laboratory data of inpatients at a tertiary teaching hospital for the period from January 2002 to December 2005.METHODSA clinical data mart was constructed. A knowledge base containing dose adjusting information about 56 drugs was built. One day dose was compared to the reference dose adjusted to the patient’s renal function.RESULTSConsidering the patient’s renal function, 5.3% of drug doses were excessive. The overdose rate in the patients with moderate to severe renal insufficiency was 28.2%. Only 25% of physicians were responsible for 50.6% of the overdoses. Of 56 drugs studied, 10 drugs, including ranitidine, amoxicillin, and piperacillin/tazobactam, were involved in 85.4% of the overdoses. The physicians with high overdose rate had patients with more impaired renal function (correlation coefficient = 0.192, P < .001). There were negative correlation between clinical experiences of physician and overdose rate (correlation coefficient = −0.221, P < .001) and workload of prescription (correlation coefficient = −0.446, P < .001), when excluding interns from the analyses. There was positive correlation between workload of prescription and overdose rate (correlation coefficient = 0.361, P < .001).CONCLUSIONA clinical data mart was useful to analyze the vast amount of electronic hospital data. Drug overdose is quite common among inpatients with renal insufficiency. Only a few drugs are responsible for most of drug overdoses. The physicians’ clinical experience, workload of prescriptions, and patients’ renal function are correlated with drug overdose.

Computerized Physician Order Entry and Electronic Medical Record Systems in Korean Teaching and General Hospitals: Results of a 2004 Survey

OBJECTIVE To determine the availability of computerized physician order entry (CPOE) and electronic medical record (EMR) systems in teaching and general hospitals in the Republic of Korea. DESIGN A combined mail and telephone survey of 283 hospitals. MEASUREMENTS The surveys assessed the availability of CPOE and EMRs in the hospitals, as well as inducement, participation, and saturation regarding CPOE use by physicians. RESULTS A total of 122 (43.1%) hospitals responded to the survey. The complete form of CPOE was available in 98 (80.3%) hospitals. The use of CPOE was mandatory in 92 (86.0%) of the 107 hospitals that responded to the questions regarding the requirement of CPOE use. In 85 (79.4%) of the hospitals in which CPOE was in use, more than 90% of physicians used the system. In addition, physicians entered more than 90% of their total orders through CPOE in 87 (81.3%) hospitals. In contrast, a complete EMR system was available in only 11 (9.0%) of the hospitals. CONCLUSION Of the teaching and general hospitals in the Republic of Korea that responded to the survey, the majority (80.3%) have CPOE systems, and a complete EMR system is available in only 9%.