Machine Learning for Detecting Blood Transfusion Needs Using Biosignals

Adequate oxygen in red blood cells carrying through the body to the heart and brain is important to maintain life. For those patients requiring blood, blood transfusion is a common procedure in which donated blood or blood components are given through an intravenous line. However, detecting the need for blood transfusion is time-consuming and sometimes not easily diagnosed, such as internal bleeding. This study considered physiological signals such as electrocardiogram (ECG), photoplethysmogram (PPG), blood pressure, oxygen saturation (SpO2), and respiration, and proposed the machine learning model to detect the need for blood transfusion accurately. For the model, this study extracted 14 features from the physiological signals and used an ensemble approach combining extreme gradient boosting and random forest. The model was evaluated by a stratified five-fold cross-validation: the detection accuracy and area under the receiver operating characteristics were 92.7% and 0.977, respectively. © 2023 CRL Publishing. All rights reserved.ope

Evaluation and prediction of drug-drug interaction of tegoprazan using physiologically based pharmacokinetic modeling

학위논문(박사) -- 서울대학교대학원 : 의과대학 협동과정 임상약리학전공, 2022.2. 장인진.Introduction: Tegoprazan, a potassium-competitive acid blocker, is a potential substrate of cytochrome P450 (CYP) 3A4. The clinical drug-drug interaction (DDI) studies of tegoprazan conducted so far have been limited to the DDI between tegoprazan and clarithromycin or clarithromycin and amoxicillin. Therefore, further studies may be required to assess the DDI between tegoprazan and other CYP3A4 perpetrators, which can affect both pharmacokinetics (PKs) and pharmacodynamics of tegoprazan by inducing or inhibiting the activity of CYP3A4. Physiologically based pharmacokinetic (PBPK) modeling is an in silico mechanistic approach combining the concept of the anatomical and physiological properties of the human body and the physicochemical and biological properties of a drug to simulate and predict the PK profile of the drug. This study aimed to develop a PBPK model of tegoprazan and to predict the potential of DDI between tegoprazan and CYP3A4 perpetrators. Methods: A minimal PBPK model with a single adjusted compartment was constructed, reflecting enzyme kinetic elimination, using the SimCYP simulator. The model was refined and verified by comparing the model-predicted PKs of tegoprazan with the observed data from various phase 1 clinical studies including DDI study between tegoprazan and clarithromycin. DDIs between tegoprazan and five CYP3A4 perpetrators (i.e., clarithromycin, ketoconazole, carbamazepine, rifampicin and phenobarbital) were predicted using a validated PBPK model by simulating the change of tegoprazan exposure after multiple doses with or without the perpetrators over a clinically used dose range. Results: The final PBPK model adequately predicted the biphasic distribution profiles of tegoprazan and DDI between tegoprazan and clarithromycin. All ratios of the predicted-to-observed pharmacokinetic parameters were within 0.5 and 2.0, which met the conventionally accepted criteria. In the DDI simulation, systemic exposure to tegoprazan was expected to increase by about threefold when co-administered with the maximum recommended dose of clarithromycin or ketoconazole. Meanwhile, tegoprazan exposure was expected to decrease to ~30% when carbamazepine, rifampicin or phenobarbital was co-administered. Conclusion: The PBPK model of tegoprazan was successfully established and it adequately predicted the DDI between tegoprazan and clarithromycin. Based on the simulation by the PBPK model, the DDI potential should be considered when tegoprazan is used with CYP3A4 perpetrator, because the acid suppression effect of tegoprazan is known to be associated with systemic exposure.서론: 칼륨 경쟁적 위산 분비 차단제인 테고프라잔은 CYP3A4의 잠재적 기질이다. 테고프라잔의 약물-약물 상호작용 임상시험은 제한적이다. 지금까지 수행된 테고프라잔의 약물-약물 상호작용 임상시험은 테고프라잔과 클라리스로마이신 또는 클라리스로마이신 및 아목시실린 간 약물-약물 상호작용 연구로 제한되었다. 따라서, 테고프라잔과 CYP3A의 활성을 유도하거나 억제하여 테고프라산의 약동학 및 약력학 모두에 영향을 미칠 수 있는 CYP3A4 가해약물 사이의 약물-약물 상호작용을 평가하기 위한 추가 연구가 필요할 수 있다. 생리학적 기반 약물동태(PBPK) 모델링은 인체의 해부학적 및 생리학적 특성과 약물의 물리화학적 및 생물학적 특성의 개념을 통합하여 약물의 약동학 양상을 시뮬레이션하고 예측하기 위한 in silico 기계론적 접근법이다. 본 연구의 목적은 테코프라잔의 생리학 기반 약물동태 모델을 개발하고 테코프라잔과 CYP3A4 가해약물 사이의 약물-약물 상호작용 가능성을 평가하는 것이다. 방법: SimCYP 시뮬레이터를 사용하여 효소 역학 제거를 반영하는 단일 조정 구획을 가진 최소 PBPK 모델을 구축하였다. 본 모델은 모델을 통해 예측된 테고프라잔의 약동학과 테고프라잔과 클라리스로마이신 사이의 약물-약물 상호작용연구를 포함한 다양한 1상 임상시험을 통해 얻어진 관찰값을 비교하여 개선되고 검증되었다. 검증된 PBPK 모델을 이용한 테고프라잔의 노출 변화를 시뮬레이션하여 테고프라잔과 5개의 CYP3A4 가해약물(클라리스로마이신, 케토코나졸, 카바마제핀, 리팜피신 및 페노바비탈) 사이의 약물-약물 상호작용을 예측하였다. 결과: 최종 PBPK 모델은 테고프라잔의 이중 분포 양상 및 테고프라잔과 클라리스로마이신 사이의 약물-약물 상호작용을 적절하게 예측하였다. 예측 대비 관측된 약동학 파라미터의 모든 비는 0.5와 2.0 사이였으며, 이는 일반적인 허용 기준을 충족하였다. 약물-약물 상호작용 시뮬레이션에서, 테고프라잔의 전신 노출은 최대 권장 용량의 클라리스로마이신 또는 케토코나졸 병용 투여 시 약 3배 증가할 것으로 예상되었다. 한편, 카바마제핀, 리팜피신 또는 페노바비탈 병용 투여 시 테고프라잔의 노출은 최대 30%까지 감소할 것으로 예상되었다. 결론: 테고프라잔의 PBPK 모델은 성공적으로 구축되었고 본 모델은 테고프라잔과 클라리스로마이신 사이의 약물-약물 상호작용을 적절하게 예측하였다. 테고프라잔의 위산 억제 효과가 전신 노출과 관련이 있는 것으로 알려져 있기 때문에, PBPK 모델을 통한 시뮬레이션 결과를 바탕으로 테고프라잔을 CYP3A4 가해약물과 병용 투여 시 약물-약물 상호작용 가능성을 고려해야 한다.ABSTRACT i TABLE OF CONTENTS iv LIST OF FIGURES vi LIST OF TABLES viii LIST OF ABBREVIATION x INTRODUCTION １ METHODS ５ Development of the PBPK model ５ Refinement and verification of the PBPK model １０ Prediction of a DDI Potential １８ Establishing PK-PD Relationship ２０ RESULTS ２１ Pharmacokinetic predictions of tegoprazan ２１ Performance of the PBPK Model in Predicting DDI ２４ DDI potential of tegoprazan ２８ PK-PD relationship of tegoprazan ３１ DISCUSSION ３４ CONCLUSION ４４ REFERENCE ４５ APPENDIX ４８ 국문 초록 ５０박

Risk of QT prolongation through drug interactions between hydroxychloroquine and concomitant drugs prescribed in real world practice

Hydroxychloroquine has recently received attention as a treatment for COVID-19. However, it may prolong the QTc interval. Furthermore, when hydroxychloroquine is administered concomitantly with other drugs, it can exacerbate the risk of QT prolongation. Nevertheless, the risk of QT prolongation due to drug-drug interactions (DDIs) between hydroxychloroquine and concomitant medications has not yet been identified. To evaluate the risk of QT prolongation due to DDIs between hydroxychloroquine and 118 concurrent drugs frequently used in real-world practice, we analyzed the electrocardiogram results obtained for 447,632 patients and their relevant electronic health records in a tertiary teaching hospital in Korea from 1996 to 2018. We repeated the case-control analysis for each drug. In each analysis, we performed multiple logistic regression and calculated the odds ratio (OR) for each target drug, hydroxychloroquine, and the interaction terms between those two drugs. The DDIs were observed in 12 drugs (trimebutine, tacrolimus, tramadol, rosuvastatin, cyclosporin, sulfasalazine, rofecoxib, diltiazem, piperacillin/tazobactam, isoniazid, clarithromycin, and furosemide), all with a p value of < 0.05 (OR 1.70-17.85). In conclusion, we found 12 drugs that showed DDIs with hydroxychloroquine in the direction of increasing QT prolongation.ope

Approach for Electronic Medical Record Data Analysis

As the healthcare environment is being digitalized and changed rapidly, research using medical big data is increasing. One of the most applicable data is electronic medical records which can provide a large amount of clinically practical meaning. Electronic medical data include patient's demographic information, laboratory test results, imaging and biosignal data. In this article, we provide support for a wide variety of researchers in their efforts to use electronic medical record data accurately and usefully in their work. From the basic concept of the research using electronic medical records to challenging aspects like data integration between multiple institutions are described. Also, examples of each type of data are covered; structured such as numeric data and unstructured such as images, biosignals and narrative text. Using these kinds of electronic medical records, analyses are processed by data cleansing, transforming, and reducing in order. Many kinds of variables such as the exposure and outcome of interest, covariate and the research design can be chosen during the preprocessing. As many machine-learning-based studies as well as epidemiologic-based studies have been conducted using electronic medical records, various research frameworks have been proposed. However, data quality management and data standardization for multi-center data analysis are still remaining as challenging tasks.ope

Preparing for a New World: Making Friends with Digital Health

While digital health solutions have shown good outcomes in numerous studies, the adoption of digital health solutions in clinical practice faces numerous challenges. To prepare for widespread adoption of digital health, stakeholders in digital health will need to establish an objective evaluation process, consider uncertainty through critical evaluation, be aware of inequity, and consider patient engagement. By "making friends" with digital health, health care can be improved for patients.ope

Digital Health Profile of South Korea: A Cross Sectional Study

(1) Backgroud: For future national digital healthcare policy development, it is vital to collect baseline data on the infrastructure and services of medical institutions' information and communication technology (ICT). To assess the state of medical ICT across the nation, we devised and administered a comprehensive digital healthcare survey to medical institutions across the nation. (2) Methods: From 16 November through 11 December 2020, this study targeted 42 tertiary hospitals, 311 general hospitals, and 1431 hospital locations countrywide. (3) Results: Since 2015, most hospitals have implemented electronic medical record (EMR) systems (90.5 percent of hospitals, which is the smallest unit, and 100 percent of tertiary hospitals). The rate of implementation of personal health records (PHRs) varied significantly between 61.9 percent and 2.4 percent, depending on the size of the hospital. Hospitals have implemented around three to seven government-sponsored information/data transmission and receiving systems for statistical or investigative objectives. For secondary usage of medical data, more than half of tertiary hospitals have implemented a clinical data warehouse or shared data model. However, new service establishments utilizing modern medical technologies such as artificial intelligence or lifelogging were scarce and in the planning stages. (4) Conclusion: This study shows that the level of digitalization in Korean medical institutions is significant, despite the fact that the development and spending in ICT infrastructure and services provided by individual institutions imposes a significant cost. This illustrates that, in the face of a pandemic, strong government backing and policymaking are essential to activate ICT-based medical services and efficiently use medical data.ope

Machine Learning Model for the Prediction of Hemorrhage in Intensive Care Units

Objectives: Early hemorrhage detection in intensive care units (ICUs) enables timely intervention and reduces the risk of irreversible outcomes. In this study, we aimed to develop a machine learning model to predict hemorrhage by learning the patterns of continuously changing, real-world clinical data. Methods: We used the Medical Information Mart for Intensive Care databases (MIMIC-III and MIMIC-IV). A recurrent neural network was used to predict severe hemorrhage in the ICU. We developed three machine learning models with an increasing number of input features and levels of complexity: model 1 (11 features), model 2 (18 features), and model 3 (27 features). MIMIC-III was used for model training, and MIMIC-IV was split for internal validation. Using the model with the highest performance, external verification was performed using data from a subgroup extracted from the eICU Collaborative Research Database. Results: We included 5,670 ICU admissions, with 3,150 in the training set and 2,520 in the internal test set. A positive correlation was found between model complexity and performance. As a measure of performance, three models developed with an increasing number of features showed area under the receiver operating characteristic (AUROC) curve values of 0.61-0.94 according to the range of input data. In the subgroup extracted from the eICU database for external validation, an AUROC value of 0.74 was observed. Conclusions: Machine learning models that rely on real clinical data can be used to predict patients at high risk of bleeding in the ICU.ope

Machine Learning Model for Classifying the Results of Fetal Cardiotocography Conducted in High-Risk Pregnancies

Purpose: Fetal well-being is usually assessed via fetal heart rate (FHR) monitoring during the antepartum period. However, the interpretation of FHR is a complex and subjective process with low reliability. This study developed a machine learning model that can classify fetal cardiotocography results as normal or abnormal. Materials and methods: In total, 17492 fetal cardiotocography results were obtained from Ajou University Hospital and 100 fetal cardiotocography results from Czech Technical University and University Hospital in Brno. Board-certified physicians then reviewed the fetal cardiotocography results and labeled 1456 of them as gold-standard; these results were used to train and validate the model. The remaining results were used to validate the clinical effectiveness of the model with the actual outcome. Results: In a test dataset, our model achieved an area under the receiver operating characteristic curve (AUROC) of 0.89 and area under the precision-recall curve (AUPRC) of 0.73 in an internal validation dataset. An average AUROC of 0.73 and average AUPRC of 0.40 were achieved in the external validation dataset. Fetus abnormality score, as calculated from the continuous fetal cardiotocography results, was significantly associated with actual clinical outcomes [intrauterine growth restriction: odds ratio, 3.626 (p=0.031); Apgar score 1 min: odds ratio, 9.523 (p<0.001), Apgar score 5 min: odds ratio, 11.49 (p=0.001), and fetal distress: odds ratio, 23.09 (p<0.001)]. Conclusion: The machine learning model developed in this study showed precision in classifying FHR signals. This suggests that the model can be applied to medical devices as a screening tool for monitoring fetal status.ope

Modeling Brain Volume Using Deep Learning-Based Physical Activity Features in Patients With Dementia

There is a proven correlation between the severity of dementia and reduced brain volumes. Several studies have attempted to use activity data to estimate brain volume as a means of detecting reduction early; however, raw activity data are not directly interpretable and are unstructured, making them challenging to utilize. Furthermore, in the previous research, brain volume estimates were limited to total brain volume and the investigators were unable to detect reductions in specific regions of the brain that are typically used to characterize disease progression. We aimed to evaluate volume prediction of 116 brain regions through activity data obtained combining time-frequency domain- and unsupervised deep learning-based feature extraction methods. We developed a feature extraction model based on unsupervised deep learning using activity data from the National Health and Nutrition Examination Survey (NHANES) dataset (n = 14,482). Then, we applied the model and the time-frequency domain feature extraction method to the activity data of the Biobank Innovations for chronic Cerebrovascular disease With ALZheimer's disease Study (BICWALZS) datasets (n = 177) to extract activity features. Brain volumes were calculated from the brain magnetic resonance imaging of the BICWALZS dataset and anatomically subdivided into 116 regions. Finally, we fitted linear regression models to estimate each regional volume of the 116 brain areas based on the extracted activity features. Regression models were statistically significant for each region, with an average correlation coefficient of 0.990 ± 0.006. In all brain regions, the correlation was > 0.964. Particularly, regions of the temporal lobe that exhibit characteristic atrophy in the early stages of Alzheimer's disease showed the highest correlation (0.995). Through a combined deep learning-time-frequency domain feature extraction method, we could extract activity features based solely on the activity dataset, without including clinical variables. The findings of this study indicate the possibility of using activity data for the detection of neurological disorders such as Alzheimer's disease.ope

Data-driven drug-induced QT prolongation surveillance using adverse reaction signals derived from 12-lead and continuous electrocardiogram data

Drug-induced QT prolongation is one of the most common side effects of drug use and can cause fatal outcomes such as sudden cardiac arrest. This study adopts the data-driven approach to assess the QT prolongation risk of all the frequently used drugs in a tertiary teaching hospital using both standard 12-lead ECGs and intensive care unit (ICU) continuous ECGs. We used the standard 12-lead ECG results (n = 1,040,752) measured in the hospital during 1994-2019 and the continuous ECG results (n = 4,835) extracted from the ICU's patient-monitoring devices during 2016-2019. Based on the drug prescription frequency, 167 drugs were analyzed using 12-lead ECG data under the case-control study design and 60 using continuous ECG data under the retrospective cohort study design. Whereas the case-control study yielded the odds ratio, the cohort study generated the hazard ratio for each candidate drug. Further, we observed the possibility of inducing QT prolongation in 38 drugs in the 12-lead ECG analysis and 7 drugs in the continuous ECG analysis. The seven drugs (vasopressin, vecuronium, midazolam, levetiracetam, ipratropium bromide, nifedipine, and chlorpheniramine) that showed a significantly higher risk of QT prolongation in the continuous ECG analysis were also identified in the 12-lead ECG data analysis. The use of two different ECG sources enabled us to confidently assess drug-induced QT prolongation risk in clinical practice. In this study, seven drugs showed QT prolongation risk in both study designs.ope