Development and Verification of Time-Series Deep Learning for Drug-Induced Liver Injury Detection in Patients Taking Angiotensin II Receptor Blockers: A Multicenter Distributed Research Network Approach / 대한의료정보학회지

Objectives@#The objective of this study was to develop and validate a multicenter-based, multi-model, time-series deep learning model for predicting drug-induced liver injury (DILI) in patients taking angiotensin receptor blockers (ARBs). The study leveraged a national-level multicenter approach, utilizing electronic health records (EHRs) from six hospitals in Korea. @*Methods@#A retrospective cohort analysis was conducted using EHRs from six hospitals in Korea, comprising a total of 10,852 patients whose data were converted to the Common Data Model. The study assessed the incidence rate of DILI among patients taking ARBs and compared it to a control group. Temporal patterns of important variables were analyzed using an interpretable timeseries model. @*Results@#The overall incidence rate of DILI among patients taking ARBs was found to be 1.09%. The incidence rates varied for each specific ARB drug and institution, with valsartan having the highest rate (1.24%) and olmesartan having the lowest rate (0.83%). The DILI prediction models showed varying performance, measured by the average area under the receiver operating characteristic curve, with telmisartan (0.93), losartan (0.92), and irbesartan (0.90) exhibiting higher classification performance. The aggregated attention scores from the models highlighted the importance of variables such as hematocrit, albumin, prothrombin time, and lymphocytes in predicting DILI. @*Conclusions@#Implementing a multicenter-based timeseries classification model provided evidence that could be valuable to clinicians regarding temporal patterns associated with DILI in ARB users. This information supports informed decisions regarding appropriate drug use and treatment strategies.

Applying the OMOP Common Data Model to Facilitate Benefit-Risk Assessments of Medicinal Products Using Real-World Data from Singapore and South Korea / 대한의료정보학회지

Objectives@#The aim of this study was to characterize the benefits of converting Electronic Medical Records (EMRs) to a common data model (CDM) and to assess the potential of CDM-converted data to rapidly generate insights for benefit-risk assessments in post-market regulatory evaluation and decisions. @*Methods@#EMRs from January 2013 to December 2016 were mapped onto the Observational Medical Outcomes Partnership-CDM (OMOP-CDM) schema. Vocabulary mappings were applied to convert source data values into OMOP-CDM-endorsed terminologies. Existing analytic codes used in a prior OMOP-CDM drug utilization study were modified to conduct an illustrative analysis of oral anticoagulants used for atrial fibrillation in Singapore and South Korea, resembling a typical benefit-risk assessment. A novel visualization is proposed to represent the comparative effectiveness, safety and utilization of the drugs. @*Results@#Over 90% of records were mapped onto the OMOP-CDM. The CDM data structures and analytic code templates simplified the querying of data for the analysis. In total, 2,419 patients from Singapore and South Korea fulfilled the study criteria, the majority of whom were warfarin users. After 3 months of follow-up, differences in cumulative incidence of bleeding and thromboembolic events were observable via the proposed visualization, surfacing insights as to the agent of preference in a given clinical setting, which may meaningfully inform regulatory decision-making. @*Conclusions@#While the structure of the OMOP-CDM and its accessory tools facilitate real-world data analysis, extending them to fulfil regulatory analytic purposes in the post-market setting, such as benefit-risk assessments, may require layering on additional analytic tools and visualization techniques.

Can we trust the prediction model? Demonstrating the importance of external validation by investigating the COVID-19 Vulnerability (C-19) Index across an international network of observational healthcare datasets

BackgroundSARS-CoV-2 is straining healthcare systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate between patients requiring hospitalization and those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision making during the pandemic. However, the model is at high risk of bias according to the Prediction model Risk Of Bias ASsessment Tool and has not been externally validated. MethodsWe followed the OHDSI framework for external validation to assess the reliability of the C-19 model. We evaluated the model on two different target populations: i) 41,381 patients that have SARS-CoV-2 at an outpatient or emergency room visit and ii) 9,429,285 patients that have influenza or related symptoms during an outpatient or emergency room visit, to predict their risk of hospitalization with pneumonia during the following 0 to 30 days. In total we validated the model across a network of 14 databases spanning the US, Europe, Australia and Asia. FindingsThe internal validation performance of the C-19 index was a c-statistic of 0.73 and calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data the model obtained c-statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US and South Korean datasets respectively. The calibration was poor with the model under-estimating risk. When validated on 12 datasets containing influenza patients across the OHDSI network the c-statistics ranged between 0.40-0.68. InterpretationThe results show that the discriminative performance of the C-19 model is low for influenza cohorts, and even worse amongst COVID-19 patients in the US, Spain and South Korea. These results suggest that C-19 should not be used to aid decision making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.

Seek COVER: Development and validation of a personalized risk calculator for COVID-19 outcomes in an international network

ObjectiveTo develop and externally validate COVID-19 Estimated Risk (COVER) scores that quantify a patients risk of hospital admission (COVER-H), requiring intensive services (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis. MethodsWe analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries. We developed and validated 3 scores using 6,869,127 patients with a general practice, emergency room, or outpatient visit with diagnosed influenza or flu-like symptoms any time prior to 2020. The scores were validated on patients with confirmed or suspected COVID-19 diagnosis across five databases from South Korea, Spain and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death iii) death in the 30 days after index date. ResultsOverall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved high performance in influenza. When transported to COVID-19 cohorts, the AUC ranges were, COVER-H: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration was overall acceptable. ConclusionsA 9-predictor model performs well for COVID-19 patients for predicting hospitalization, intensive services and fatality. The models could aid in providing reassurance for low risk patients and shield high risk patients from COVID-19 during de-confinement to reduce the virus impact on morbidity and mortality.

Clinical Outcomes of Antiplatelets Combined with Statins in Patients with Ischemic Heart Disease

BACKGROUND@#Patients with cardiovascular risks are recommended to use statins and antiplatelet agents to prevent major cerebrocardiovascular events (MACCE). Antiplatelet agents also possess anti-inflammatory and antioxidant effects, in addition to their inhibitory activity on platelets. The differences in clinical outcomes in ischemic heart disease (IHD) based on the type of antiplatelet therapy combined with statin treatment were investigated in this study.@*METHODS@#We conducted a retrospective cohort study using electronic medical records of IHD patients from January 2010 to December 2014 at Ajou University Hospital. Patients on combination therapy of antiplatelet drugs and statins were grouped based on antiplatelet drug types: clopidogrel, cilostazol, or sarpogrelate. Propensity score matching was applied to balance the baseline of the groups of clopidogrel vs. cilostazol and the groups of clopidogrel vs. sarpogrelate. The incidence and risk of MACCE as primary outcomes were assessed between the groups of antiplatelet drugs.@*RESULTS@#Among the approximately 128,500 patients with IHD, 1,049 patients had taken a combination therapy of statin and antiplatelet agents. The cohorts of patients administered clopidogrel, cilostazol, or sarpogrelate were 906, 79, and 64, respectively. The incidence of MACCE was not significantly different among the cohorts (p=0.58), and there were no differences between clopidogrel vs. cilostazol (p=0.72) or clopidogrel vs. sarpogrelate (p=1.00) after propensity score matching.@*CONCLUSION@#There was no difference in the incidence of MACCE based on the type of antiplatelet drug (clopidogrel, cilostazol, or sarpogrelate) in combination with a statin in patients with IHD.

Influence of Uncertainty and Uncertainty Appraisal on Quality of Life in Prostate Cancer Patients after Prostatectomy

PURPOSE: The purpose of this study was to determine the impact of uncertainty and uncertainty appraisal on quality of life (QoL) among prostate cancer patients after prostatectomy. METHODS: A descriptive correlational study was conducted with 117 participants at a hospital in S city from October 1 to December 31, 2016. Data were analyzed using descriptive statistics, t-test, ANOVA, Pearson's correlation coefficients and stepwise multiple regression using the IBM SPSS/WIN 21.0 program. RESULTS: According to a multiple regression model of the factors affecting QoL among prostate cancer patients after the operation, 61% of variance (F=13.92, p<.001) was explained by metastasis, recurrence, monthly income, uncertainty, uncertainty danger appraisal, and uncertainty opportunity appraisal. And the most influential factor in the QoL was uncertainty danger appraisal (β=-.37, p<.001). CONCLUSION: This study demonstrated that QoL was influenced by uncertainty, uncertainty appraisal and personal characteristics. Prostate cancer patients following prostatectomy should be provided with tailored training to improve their uncertainty opportunity appraisal. Also the educational program for reducing their uncertainty should be developed and provided to patients.