Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network

BACKGROUND

Predictive models show promise in healthcare, but their successful deployment is challenging due to limited generalizability. Current external validation often focuses on model performance with restricted feature use from the original training data, lacking insights into their suitability at external sites. Our study introduces an innovative methodology for evaluating features during both the development phase and the validation, focusing on creating and validating predictive models for post-surgery patient outcomes with improved generalizability.

METHODS

Electronic health records (EHRs) from 4 countries (United States, United Kingdom, Finland, and Korea) were mapped to the OMOP Common Data Model (CDM), 2008-2019. Machine learning (ML) models were developed to predict post-surgery prolonged opioid use (POU) risks using data collected 6 months before surgery. Both local and cross-site feature selection methods were applied in the development and external validation datasets. Models were developed using Observational Health Data Sciences and Informatics (OHDSI) tools and validated on separate patient cohorts.

RESULTS

Model development included 41 929 patients, 14.6% with POU. The external validation included 31 932 (UK), 23 100 (US), 7295 (Korea), and 3934 (Finland) patients with POU of 44.2%, 22.0%, 15.8%, and 21.8%, respectively. The top-performing model, Lasso logistic regression, achieved an area under the receiver operating characteristic curve (AUROC) of 0.75 during local validation and 0.69 (SD = 0.02) (averaged) in external validation. Models trained with cross-site feature selection significantly outperformed those using only features from the development site through external validation (P < .05).

CONCLUSIONS

Using EHRs across four countries mapped to the OMOP CDM, we developed generalizable predictive models for POU. Our approach demonstrates the significant impact of cross-site feature selection in improving model performance, underscoring the importance of incorporating diverse feature sets from various clinical settings to enhance the generalizability and utility of predictive healthcare models.

Association between pre-diagnostic serum uric acid levels in patients with newly diagnosed epilepsy and conversion rate to drug-resistant epilepsy within 5 years: A common data model analysis

PURPOSE

Drug-resistant epilepsy (DRE) poses a significant challenge in epilepsy management, and reliable biomarkers for identifying patients at risk of DRE are lacking. This study aimed to investigate the association between serum uric acid (UA) levels and the conversion rate to DRE.

METHODS

A retrospective cohort study was conducted using a common data model database. The study included patients newly diagnosed with epilepsy, with prediagnostic serum UA levels within a six-month window. Patients were categorized into hyperUA (≥7.0 mg/dL), normoUA (<7.0 and >2.0 mg/dL), and hypoUA (≤2.0 mg/dL) groups based on their prediagnostic UA levels. The outcome was the conversion rate to DRE within five years of epilepsy diagnosis.

RESULTS

The study included 5,672 patients with epilepsy and overall conversion rate to DRE was 19.4%. The hyperUA group had a lower DRE conversion rate compared to the normoUA group (HR: 0.81 [95% CI: 0.69-0.96]), while the hypoUA group had a higher conversion rate (HR: 1.88 [95% CI: 1.38-2.55]).

CONCLUSIONS

Serum UA levels have the potential to serve as a biomarker for identifying patients at risk of DRE, indicating a potential avenue for novel therapeutic strategies aimed at preventing DRE conversion.

Explainable multimodal prediction of treatment-resistance in patients with depression leveraging brain morphometry and natural language processing

Although 20 % of patients with depression receiving treatment do not achieve remission, predicting treatment-resistant depression (TRD) remains challenging. In this study, we aimed to develop an explainable multimodal prediction model for TRD using structured electronic medical record data, brain morphometry, and natural language processing. In total, 247 patients with a new depressive episode were included. TRD-predictive models were developed based on the combination of following parameters: selected tabular dataset features, independent components-map weightings from brain T1-weighted magnetic resonance imaging (MRI), and topic probabilities from clinical notes. All models applied the extreme gradient boosting (XGBoost) algorithm via five-fold cross-validation. The model using all data sources showed the highest area under the receiver operating characteristic of 0.794, followed by models that used combined brain MRI and structured data, brain MRI and clinical notes, clinical notes and structured data, brain MRI only, structured data only, and clinical notes only (0.770, 0.762, 0.728, 0.703, 0.684, and 0.569, respectively). Classifications of TRD were driven by several predictors, such as previous exposure to antidepressants and antihypertensive medications, sensorimotor network, default mode network, and somatic symptoms. Our findings suggest that a combination of clinical data with neuroimaging and natural language processing variables improves the prediction of TRD.

Inter hospital external validation of interpretable machine learning based triage score for the emergency department using common data model

Emergency departments (ED) are complex, triage is a main task in the ED to prioritize patient with limited medical resources who need them most. Machine learning (ML) based ED triage tool, Score for Emergency Risk Prediction (SERP), was previously developed using an interpretable ML framework with single center. We aimed to develop SERP with 3 Korean multicenter cohorts based on common data model (CDM) without data sharing and compare performance with inter-hospital validation design. This retrospective cohort study included all adult emergency visit patients of 3 hospitals in Korea from 2016 to 2017. We adopted CDM for the standardized multicenter research. The outcome of interest was 2-day mortality after the patients' ED visit. We developed each hospital SERP using interpretable ML framework and validated inter-hospital wisely. We accessed the performance of each hospital's score based on some metrics considering data imbalance strategy. The study population for each hospital included 87,670, 83,363 and 54,423 ED visits from 2016 to 2017. The 2-day mortality rate were 0.51%, 0.56% and 0.65%. Validation results showed accurate for inter hospital validation which has at least AUROC of 0.899 (0.858-0.940). We developed multicenter based Interpretable ML model using CDM for 2-day mortality prediction and executed Inter-hospital external validation which showed enough high accuracy.

Applicable Machine Learning Model for Predicting Contrast-induced Nephropathy Based on Pre-catheterization Variables

Objective Contrast agents used for radiological examinations are an important cause of acute kidney injury (AKI). We developed and validated a machine learning and clinical scoring prediction model to stratify the risk of contrast-induced nephropathy, considering the limitations of current classical and machine learning models. Methods This retrospective study included 38,481 percutaneous coronary intervention cases from 23,703 patients in a tertiary hospital. We divided the cases into development and internal test sets (8:2). Using the development set, we trained a gradient boosting machine prediction model (complex model). We then developed a simple model using seven variables based on variable importance. We validated the performance of the models using an internal test set and tested them externally in two other hospitals. Results The complex model had the best area under the receiver operating characteristic (AUROC) curve at 0.885 [95% confidence interval (CI) 0.876-0.894] in the internal test set and 0.837 (95% CI 0.819-0.854) and 0.850 (95% CI 0.781-0.918) in two different external validation sets. The simple model showed an AUROC of 0.795 (95% CI 0.781-0.808) in the internal test set and 0.766 (95% CI 0.744-0.789) and 0.782 (95% CI 0.687-0.877) in the two different external validation sets. This was higher than the value in the well-known scoring system (Mehran criteria, AUROC=0.67). The seven precatheterization variables selected for the simple model were age, known chronic kidney disease, hematocrit, troponin I, blood urea nitrogen, base excess, and N-terminal pro-brain natriuretic peptide. The simple model is available at http://52.78.230.235:8081/Conclusions We developed an AKI prediction machine learning model with reliable performance. This can aid in bedside clinical decision making.

Incidence of mental health diagnoses during the COVID-19 pandemic: a multinational network study

AIMS

Population-wide restrictions during the COVID-19 pandemic may create barriers to mental health diagnosis. This study aims to examine changes in the number of incident cases and the incidence rates of mental health diagnoses during the COVID-19 pandemic.

METHODS

By using electronic health records from France, Germany, Italy, South Korea and the UK and claims data from the US, this study conducted interrupted time-series analyses to compare the monthly incident cases and the incidence of depressive disorders, anxiety disorders, alcohol misuse or dependence, substance misuse or dependence, bipolar disorders, personality disorders and psychoses diagnoses before (January 2017 to February 2020) and after (April 2020 to the latest available date of each database [up to November 2021]) the introduction of COVID-related restrictions.

RESULTS

A total of 629,712,954 individuals were enrolled across nine databases. Following the introduction of restrictions, an immediate decline was observed in the number of incident cases of all mental health diagnoses in the US (rate ratios (RRs) ranged from 0.005 to 0.677) and in the incidence of all conditions in France, Germany, Italy and the US (RRs ranged from 0.002 to 0.422). In the UK, significant reductions were only observed in common mental illnesses. The number of incident cases and the incidence began to return to or exceed pre-pandemic levels in most countries from mid-2020 through 2021.

CONCLUSIONS

Healthcare providers should be prepared to deliver service adaptations to mitigate burdens directly or indirectly caused by delays in the diagnosis and treatment of mental health conditions.

Real-world treatment trajectories of adults with newly diagnosed asthma or COPD

BACKGROUND

There is a lack of knowledge on how patients with asthma or chronic obstructive pulmonary disease (COPD) are globally treated in the real world, especially with regard to the initial pharmacological treatment of newly diagnosed patients and the different treatment trajectories. This knowledge is important to monitor and improve clinical practice.

METHODS

This retrospective cohort study aims to characterise treatments using data from four claims (drug dispensing) and four electronic health record (EHR; drug prescriptions) databases across six countries and three continents, encompassing 1.3 million patients with asthma or COPD. We analysed treatment trajectories at drug class level from first diagnosis and visualised these in sunburst plots.

RESULTS

In four countries (USA, UK, Spain and the Netherlands), most adults with asthma initiate treatment with short-acting ß2 agonists monotherapy (20.8%-47.4% of first-line treatments). For COPD, the most frequent first-line treatment varies by country. The largest percentages of untreated patients (for asthma and COPD) were found in claims databases (14.5%-33.2% for asthma and 27.0%-52.2% for COPD) from the USA as compared with EHR databases (6.9%-15.2% for asthma and 4.4%-17.5% for COPD) from European countries. The treatment trajectories showed step-up as well as step-down in treatments.

CONCLUSION

Real-world data from claims and EHRs indicate that first-line treatments of asthma and COPD vary widely across countries. We found evidence of a stepwise approach in the pharmacological treatment of asthma and COPD, suggesting that treatments may be tailored to patients' needs.

Comparative estimation of the effects of antihypertensive medications on schizophrenia occurrence: a multinational observational cohort study

BACKGROUND

The association between antihypertensive medication and schizophrenia has received increasing attention; however, evidence of the impact of antihypertensive medication on subsequent schizophrenia based on large-scale observational studies is limited. We aimed to compare the schizophrenia risk in large claims-based US and Korea cohort of patients with hypertension using angiotensin-converting enzyme (ACE) inhibitors versus those using angiotensin receptor blockers (ARBs) or thiazide diuretics.

METHODS

Adults aged 18 years who were newly diagnosed with hypertension and received ACE inhibitors, ARBs, or thiazide diuretics as first-line antihypertensive medications were included. The study population was sub-grouped based on age (> 45 years). The comparison groups were matched using a large-scale propensity score (PS)-matching algorithm. The primary endpoint was incidence of schizophrenia.

RESULTS

5,907,522; 2,923,423; and 1,971,549 patients used ACE inhibitors, ARBs, and thiazide diuretics, respectively. After PS matching, the risk of schizophrenia was not significantly different among the groups (ACE inhibitor vs. ARB: summary hazard ratio [HR] 1.15 [95% confidence interval, CI, 0.99-1.33]; ACE inhibitor vs. thiazide diuretics: summary HR 0.91 [95% CI, 0.78-1.07]). In the older subgroup, there was no significant difference between ACE inhibitors and thiazide diuretics (summary HR, 0.91 [95% CI, 0.71-1.16]). The risk for schizophrenia was significantly higher in the ACE inhibitor group than in the ARB group (summary HR, 1.23 [95% CI, 1.05-1.43]).

CONCLUSIONS

The risk of schizophrenia was not significantly different between the ACE inhibitor vs. ARB and ACE inhibitor vs. thiazide diuretic groups. Further investigations are needed to determine the risk of schizophrenia associated with antihypertensive drugs, especially in people aged > 45 years.

Real-World Effectiveness of Statin Therapy in Adult Asthma

BACKGROUND

Blood lipids affect airway inflammation in asthma. Although several studies have suggested anti-inflammatory effects of statins on asthmatic airways, further studies are needed to clarify the long-term effectiveness of statins on asthma control and whether they are an effective treatment option.

OBJECTIVE

To evaluate the long-term effectiveness of statins in the chronic management of adult asthma in real-world practice.

METHODS

Electronic medical record data spanning 28 years, collected from the Ajou University Medical Center in Korea, were used to conduct a retrospective study. Clinical outcomes were compared between patients with asthma who had maintained statin use (the statin group) and those not taking statins, whose blood lipid tests were always normal (the non-statin group). We performed propensity score matching and calculated hazard ratios with 95% CIs using the Cox proportional hazards model. Severe asthma exacerbation was the primary outcome; asthma exacerbation, asthma-related hospitalization, and new-onset type 2 diabetes mellitus and hypertension were secondary outcomes.

RESULTS

After 1:1 propensity score matching, the statin and non-statin groups each included 545 adult patients with asthma. The risk of severe asthma exacerbations and asthma exacerbations was significantly lower in the statin group than in the non-statin group (hazard ratios [95% CI] = 0.57 [0.35-0.90] and 0.71 [0.52-0.96], respectively). There were no significant differences in the risk of asthma-related hospitalization or new-onset type 2 diabetes mellitus or hypertension between groups (0.76 [0.53-1.09], 2.33 [0.94-6.59], and 1.71 [0.95-3.17], respectively).

CONCLUSION

Statin use is associated with a lower risk of asthma exacerbation, with better clinical outcomes in adult asthma.

Perioperative adverse cardiac events and mortality after non-cardiac surgery: a multicenter study

BACKGROUND

Perioperative adverse cardiac events (PACE), a composite of myocardial infarction, coronary revascularization, congestive heart failure, arrhythmic attack, acute pulmonary embolism, cardiac arrest, and stroke during 30-day postoperative period, is associated with long-term mortality, but with limited clinical evidence. We compared long-term mortality with PACE using data from nationwide multicenter electronic health records.

METHODS

Data from 7 hospitals, converted to Observational Medical Outcomes Partnership Common Data Model, were used. We extracted records of 277,787 adult patients over 18 years old undergoing non-cardiac surgery for the first time at the hospital and had medical records for more than 180 days before surgery. We performed propensity score matching and then an aggregated meta‑analysis.

RESULTS

After 1:4 propensity score matching, 7,970 patients with PACE and 28,807 patients without PACE were matched. The meta‑analysis showed that PACE was associated with higher one-year mortality risk (hazard ratio [HR]: 1.33, 95% CI [1.10, 1.60], P = 0.005) and higher three-year mortality (HR: 1.18, 95% CI [1.01, 1.38], P = 0.038). In subgroup analysis, the risk of one-year mortality by PACE became greater with higher-risk surgical procedures (HR: 1.20, 95% CI [1.04, 1.39], P = 0.020 for low-risk surgery; HR: 1.69, 95% CI [1.45, 1.96], P < 0.001 for intermediate-risk; and HR: 2.38, 95% CI [1.47, 3.86], P = 0.034 for high-risk).

CONCLUSIONS

A nationwide multicenter study showed that PACE was significantly associated with increased one-year mortality. This association was stronger in high-risk surgery, older, male, and chronic kidney disease subgroups. Further studies to improve mortality associated with PACE are needed.

Prediction Models for Readmission Using Home Healthcare Notes and OMOP-CDM

This study developed readmission prediction models using Home Healthcare (HHC) documents via natural language processing (NLP). An electronic health record of Ajou University Hospital was used to develop prediction models (A reference model using only structured data, and an NLP-enriched model with structured and unstructured data). Among 573 patients, 63 were readmitted to the hospital. Five topics were extracted from HHC documents and improved the model performance (AUROC 0.740).

A De-Identification Model for Korean Clinical Notes: Using Deep Learning Models

To extract information from free-text in clinical records due to the patient's protected health information PHI in the records pre-processing of de-identification is required. Therefore we aimed to identify PHI list and fine-tune the deep learning BERT model for developing de-identification model. The result of fine-tuning the model is strict F1 score of 0.924. Due to the convinced score the model can be used for the development of a de-identification model.

Multi-Institutional Collaborative Research Using Ophthalmic Medical Image Data Standardized by Radiology Common Data Model (R-CDM)

Observational Medical Outcome Partners - Common Data Model (OMOP-CDM) is an international standard model for standardizing electronic medical record data. However, unstructured data such as medical image data which is beyond the scope of standardization by the current OMOP-CDM is difficult to be used in multi-institutional collaborative research. Therefore, we developed the Radiology-CDM (R-CDM) which standardizes medical imaging data. As a proof of concept, 737,500 Optical Coherence Tomography (OCT) data from two tertiary hospitals in South Korea is standardized in the form of R-CDM. The relationship between chronic disease and retinal thickness was analyzed by using the R-CDM. Central macular thickness and retinal nerve fiber layer (RNFL) thickness were significantly thinner in the patients with hypertension compared to the control cohort. It is meaningful in that multi-institutional collaborative research using medical image data and clinical data simultaneously can be conducted very efficiently.

RHEA: Real-World Observational Health Data Exploration Application

We developed a standardized framework named RHEA to represent longitudinal status of patient with cancer. RHEA generates a dashboard to visualize patients' data in the Observational Medical Outcomes Partnership-Common Data Model format. The generated dashboard consists of three main parts for providing the macroscopic characteristics of the patient: 1) cohort-level visualization, 2) individual-level visualization and 3) cohort generation.

Assessing the potential of ChatGPT for psychodynamic formulations in psychiatry: An exploratory study

Although there were several attempts to apply ChatGPT (Generative Pre-Trained Transformer) to medicine, little is known about therapeutic applications in psychiatry. In this exploratory study, we aimed to evaluate the characteristics and appropriateness of the psychodynamic formulations created by ChatGPT. Along with a case selected from the psychoanalytic literature, input prompts were designed to include different levels of background knowledge. These included naïve prompts, keywords created by ChatGPT, keywords created by psychiatrists, and psychodynamic concepts from the literature. The psychodynamic formulations generated from the different prompts were evaluated by five psychiatrists from different institutions. We next conducted further tests in which instructions on the use of different psychodynamic models were added to the input prompts. The models used were ego psychology, self-psychology, and object relations. The results from naïve prompts and psychodynamic concepts were rated as appropriate by most raters. The psychodynamic concept prompt output was rated the highest. Interrater agreement was statistically significant. The results from the tests using instructions in different psychoanalytic theories were also rated as appropriate by most raters. They included key elements of the psychodynamic formulation and suggested interpretations similar to the literature. These findings suggest potential of ChatGPT for use in psychiatry.

Safety outcomes of antipsychotics classes in drug-naïve patients with first-episode schizophrenia: A nationwide cohort study in South Korea

INTRODUCTION

Given the similar efficacies across antipsychotic medications for schizophrenia, understanding their safety profiles, particularly concerning receptor-binding differences, is crucial for optimal drug selection, especially for patients with first episode schizophrenia. We aimed to compare the safety outcomes of second-generation antipsychotics.

METHODS

We conducted a retrospective cohort study with new user active comparator design using a nationwide claims database in South Korea. Participants were drug-naïve adult patients with first-episode schizophrenia. Three representative drugs with different pharmacologic profiles were compared: risperidone, olanzapine, and aripiprazole. Propensity scores were used to match the study groups, and the Cox proportional hazard model was used to calculate hazard ratios. Sensitivity analyses were performed in various epidemiological settings. Seventeen safety outcomes, including neuropsychiatric, cardiometabolic and gastrointestinal events, were assessed, with upper-respiratory-tract infection as a negative control outcome.

RESULTS

A total of 1044, 2078, and 3634 participants were matched for olanzapine vs. risperidone, olanzapine vs. aripiprazole, and risperidone vs. aripiprazole comparisons, respectively. For parkinsonism, there was a significant difference in outcomes between the risperidone and aripiprazole groups (HR 1.80 [95% CI 1.13-2.91]), with consistent sensitivity analysis results. There were no significant differences in other neuropsychiatry outcomes or in the risk of cardiometabolic and gastrointestinal outcomes between any of the comparative group pairs.

CONCLUSIONS

The risk of drug-induced parkinsonism was significantly higher with risperidone than with aripiprazole. Although olanzapine is known for its metabolic risk, there were no significant differences in risk between the other pairs.

Effect of statin use on head and neck cancer prognosis in a multicenter study using a Common Data Model

Few studies have found an association between statin use and head and neck cancer (HNC) outcomes. We examined the effect of statin use on HNC recurrence using the converted Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM) in seven hospitals between 1986 and 2022. Among the 9,473,551 eligible patients, we identified 4669 patients with HNC, of whom 398 were included in the target cohort, and 4271 were included in the control cohort after propensity score matching. A Cox proportional regression model was used. Of the 4669 patients included, 398 (8.52%) previously received statin prescriptions. Statin use was associated with a reduced rate of 3- and 5-year HNC recurrence compared to propensity score-matched controls (risk ratio [RR], 0.79; 95% confidence interval [CI], 0.61-1.03; and RR 0.89; 95% CI 0.70-1.12, respectively). Nevertheless, the association between statin use and HNC recurrence was not statistically significant. A meta-analysis of recurrence based on subgroups, including age subgroups, showed similar trends. The results of this propensity-matched cohort study may not provide a statistically significant association between statin use and a lower risk of HNC recurrence. Further retrospective studies using nationwide claims data and prospective studies are warranted.

Translation of Machine Learning-Based Prediction Algorithms to Personalised Empiric Antibiotic Selection: A Population-Based Cohort Study

BACKGROUND

Prediction of antibiotic non-susceptibility based on patient characteristics and clinical status may support selection of empiric antibiotics for suspected hospital-acquired urinary tract infections (HA-UTIs).

METHODS

Prediction models were developed to predict non-susceptible results of eight antibiotic susceptibility tests ordered for suspected HA-UTI. Eligible patients were those with urine culture and susceptibility test results after 48 hours of admission between 2010-2021. Patient demographics, diagnosis, prescriptions, exposure to multidrug-resistant organisms, transfer history, and a daily calculated antibiogram were used as predictors. Lasso logistic regression (LLR), extreme gradient boosting (XGB), random forest, and stacked ensemble methods were used for development. Parsimonious models were also developed for clinical utility. Discrimination was assessed using the area under the receiver operating characteristic curve (AUROC).

RESULTS

In 10 474 suspected HA-UTI cases, the mean age was 62.1 ± 16.2 years and 48.1% were male. Non-susceptibility prediction for ampicillin/sulbactam, cefepime, ciprofloxacin, imipenem, piperacillin/tazobactam, and trimethoprim/sulfamethoxazole performed best using the stacked ensemble (AUROC 76.9, 76.1, 77.0, 80.6, 76.1, and 76.5, respectively). The model for ampicillin performed best with LLR (AUROC 73.4). Extreme gradient boosting only performed best for gentamicin (AUROC 66.9). In the parsimonious models, the LLR yielded the highest AUROC for ampicillin, ampicillin/sulbactam, cefepime, gentamicin, and trimethoprim/sulfamethoxazole (AUROC 70.6, 71.8, 73.0, 65.9, and 73.0, respectively). The model for ciprofloxacin performed best with XGB (AUROC 70.3), while the model for imipenem performed best in the stacked ensemble (AUROC 71.3). A personalised application using the parsimonious models was publicly released.

CONCLUSIONS

Prediction models for antibiotic non-susceptibility were developed to support empiric antibiotic selection for HA-UTI.

Scalable Infrastructure Supporting Reproducible Nationwide Healthcare Data Analysis toward FAIR Stewardship

Transparent and FAIR disclosure of meta-information about healthcare data and infrastructure is essential but has not been well publicized. In this paper, we provide a transparent disclosure of the process of standardizing a common data model and developing a national data infrastructure using national claims data. We established an Observational Medical Outcome Partnership (OMOP) common data model database for national claims data of the Health Insurance Review and Assessment Service of South Korea. To introduce a data openness policy, we built a distributed data analysis environment and released metadata based on the FAIR principle. A total of 10,098,730,241 claims and 56,579,726 patients' data were converted as OMOP common data model. We also built an analytics environment for distributed research and made the metadata publicly available. Disclosure of this infrastructure to researchers will help to eliminate information inequality and contribute to the generation of high-quality medical evidence.

Impact of Trimetazidine on the Incident Heart Failure After Coronary Artery Revascularization

ABSTRACT

Abnormal myocardial metabolism is a common pathophysiological process underlying ischemic heart disease and heart failure (HF). Trimetazidine is an antianginal agent with a unique mechanism of action that regulates myocardial energy metabolism and might have a beneficial effect in preventing HF in patients undergoing myocardial revascularization. We aimed to evaluate the potential benefit of trimetazidine in preventing incident hospitalization for HF after myocardial revascularization. Using the common data model, we identified patients without prior HF undergoing myocardial revascularization from 8 hospital databases in Korea. To compare clinical outcomes using trimetazidine, database-level hazard ratios (HRs) were estimated using large-scale propensity score matching for each database and pooled using a random-effects model. The primary outcome was incident hospitalization for HF. The secondary outcome of interest was major adverse cardiac events (MACEs). After propensity score matching, 6724 and 11,211 patients were allocated to trimetazidine new-users and nonusers, respectively. There was no significant difference in the incidence of hospitalization for HF between the 2 groups (HR: 1.08, 95% confidence interval [CI], 0.88-1.31; P = 0.46). The risk of MACE also did not differ between the 2 groups (HR: 1.07, 95% CI, 0.98-1.16; P = 0.15). In conclusion, the use of trimetazidine did not reduce the risk of hospitalization for HF or MACE in patients undergoing myocardial revascularization. Therefore, the role of trimetazidine in contemporary clinical practice cannot be expanded beyond its current role as an add-on treatment for symptomatic angina.

Impact of symptomatic menopausal transition on the occurrence of depression, anxiety, and sleep disorders: A real-world multi-site study

BACKGROUND

The menopause transition is a vulnerable period that can be associated with changes in mood and cognition. The present study aimed to investigate whether a symptomatic menopausal transition increases the risks of depression, anxiety, and sleep disorders.

METHODS

This population-based, retrospective cohort study analysed data from five electronic health record databases in South Korea. Women aged 45-64 years with and without symptomatic menopausal transition were matched 1:1 using propensity-score matching. Subgroup analyses were conducted according to age and use of hormone replacement therapy (HRT). A primary analysis of 5-year follow-up data was conducted, and an intention-to-treat analysis was performed to identify different risk windows over 5 or 10 years. The primary outcome was first-time diagnosis of depression, anxiety, and sleep disorder. We used Cox proportional hazard models and a meta-analysis to calculate the summary hazard ratio (HR) estimates across the databases.

RESULTS

Propensity-score matching resulted in a sample of 17,098 women. Summary HRs for depression (2.10; 95% confidence interval [CI] 1.63-2.71), anxiety (1.64; 95% CI 1.01-2.66), and sleep disorders (1.47; 95% CI 1.16-1.88) were higher in the symptomatic menopausal transition group. In the subgroup analysis, the use of HRT was associated with an increased risk of depression (2.21; 95% CI 1.07-4.55) and sleep disorders (2.51; 95% CI 1.25-5.04) when compared with non-use of HRT.

CONCLUSIONS

Our findings suggest that women with symptomatic menopausal transition exhibit an increased risk of developing depression, anxiety, and sleep disorders. Therefore, women experiencing a symptomatic menopausal transition should be monitored closely so that interventions can be applied early.

Ranitidine Use and Incident Cancer in a Multinational Cohort

Importance

Ranitidine, the most widely used histamine-2 receptor antagonist (H2RA), was withdrawn because of N-nitrosodimethylamine impurity in 2020. Given the worldwide exposure to this drug, the potential risk of cancer development associated with the intake of known carcinogens is an important epidemiological concern.

Objective

To examine the comparative risk of cancer associated with the use of ranitidine vs other H2RAs.

Design, Setting, and Participants

This new-user active comparator international network cohort study was conducted using 3 health claims and 9 electronic health record databases from the US, the United Kingdom, Germany, Spain, France, South Korea, and Taiwan. Large-scale propensity score (PS) matching was used to minimize confounding of the observed covariates with negative control outcomes. Empirical calibration was performed to account for unobserved confounding. All databases were mapped to a common data model. Database-specific estimates were combined using random-effects meta-analysis. Participants included individuals aged at least 20 years with no history of cancer who used H2RAs for more than 30 days from January 1986 to December 2020, with a 1-year washout period. Data were analyzed from April to September 2021.

Exposure

The main exposure was use of ranitidine vs other H2RAs (famotidine, lafutidine, nizatidine, and roxatidine).

Main Outcomes and Measures

The primary outcome was incidence of any cancer, except nonmelanoma skin cancer. Secondary outcomes included all cancer except thyroid cancer, 16 cancer subtypes, and all-cause mortality.

Results

Among 1 183 999 individuals in 11 databases, 909 168 individuals (mean age, 56.1 years; 507 316 [55.8%] women) were identified as new users of ranitidine, and 274 831 individuals (mean age, 58.0 years; 145 935 [53.1%] women) were identified as new users of other H2RAs. Crude incidence rates of cancer were 14.30 events per 1000 person-years (PYs) in ranitidine users and 15.03 events per 1000 PYs among other H2RA users. After PS matching, cancer risk was similar in ranitidine compared with other H2RA users (incidence, 15.92 events per 1000 PYs vs 15.65 events per 1000 PYs; calibrated meta-analytic hazard ratio, 1.04; 95% CI, 0.97-1.12). No significant associations were found between ranitidine use and any secondary outcomes after calibration.

Conclusions and Relevance

In this cohort study, ranitidine use was not associated with an increased risk of cancer compared with the use of other H2RAs. Further research is needed on the long-term association of ranitidine with cancer development.

Use of eye tracking to improve the identification of attention-deficit/hyperactivity disorder in children

Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder of childhood. Although it requires timely detection and intervention, existing continuous performance tests (CPTs) have limited efficacy. Research suggests that eye movement could offer important diagnostic information for ADHD. This study aimed to compare the performance of eye-tracking with that of CPTs, both alone and in combination, and to evaluate the effect of medication on eye movement and CPT outcomes. We recruited participants into an ADHD group and a healthy control group between July 2021 and March 2022 from among children aged 6-10 years (n = 30 per group). The integration of eye-tracking with CPTs produced higher values for the area under the receiver operating characteristic (AUC, 0.889) compared with using CPTs only (AUC, 0.769) for identifying patients with ADHD. The use of eye-tracking alone showed higher performance compare with the use of CPTs alone (AUC of EYE: 0.856, AUC of CPT: 0.769, p = 0.029). Follow-up analysis revealed that most eye-tracking and CPT indicators improved significantly after taking an ADHD medication. The use of eye movement scales could be used to differentiate children with ADHD, with the possibility that integrating eye movement scales and CPTs could improve diagnostic precision.

Long-term clinical outcomes of aspirin-exacerbated respiratory disease: Real-world data from an adult asthma cohort

BACKGROUND

Aspirin-exacerbated respiratory disease (AERD) is a phenotype of severe asthma, but its disease course has not been well documented compared with that of aspirin-tolerant asthma (ATA).

OBJECTIVES

This study aimed to investigate the long-term clinical outcomes between AERD and ATA.

METHODS

AERD patients were identified by the diagnostic code and positive bronchoprovocation test in a real-world database. Longitudinal changes in lung function, blood eosinophil/neutrophil counts, and annual numbers of severe asthma exacerbations (AEx) were compared between the AERD and the ATA groups. Within a year after baseline, two or more severe AEx events indicated severe AERD, whereas less than two AEx events indicated nonsevere AERD.

RESULTS

Among asthmatics, 353 had AERD in which 166 and 187 patients had severe and nonsevere AERD, respectively, and 717 had ATA. AERD patients had significantly lower FEV1%, higher blood neutrophil counts, and higher sputum eosinophils (%) (all p < .05) as well as higher levels of urinary LTE4 and serum periostin, and lower levels of serum myeloperoxidase and surfactant protein D (all p < .01) than those with ATA. In a 10-year follow-up, the severe AERD group maintained lower FEV1% with more severe AEs than the nonsevere AERD group.

CONCLUSION AND CLINICAL RELEVANCE

We demonstrated that AERD patients presented poorer long-term clinical outcomes than ATA patients in real-world data analyses.

Privacy-Preserving Federated Model Predicting Bipolar Transition in Patients With Depression: Prediction Model Development Study

BACKGROUND

Mood disorder has emerged as a serious concern for public health; in particular, bipolar disorder has a less favorable prognosis than depression. Although prompt recognition of depression conversion to bipolar disorder is needed, early prediction is challenging due to overlapping symptoms. Recently, there have been attempts to develop a prediction model by using federated learning. Federated learning in medical fields is a method for training multi-institutional machine learning models without patient-level data sharing.

OBJECTIVE

This study aims to develop and validate a federated, differentially private multi-institutional bipolar transition prediction model.

METHODS

This retrospective study enrolled patients diagnosed with the first depressive episode at 5 tertiary hospitals in South Korea. We developed models for predicting bipolar transition by using data from 17,631 patients in 4 institutions. Further, we used data from 4541 patients for external validation from 1 institution. We created standardized pipelines to extract large-scale clinical features from the 4 institutions without any code modification. Moreover, we performed feature selection in a federated environment for computational efficiency and applied differential privacy to gradient updates. Finally, we compared the federated and the 4 local models developed with each hospital's data on internal and external validation data sets.

RESULTS

In the internal data set, 279 out of 17,631 patients showed bipolar disorder transition. In the external data set, 39 out of 4541 patients showed bipolar disorder transition. The average performance of the federated model in the internal test (area under the curve [AUC] 0.726) and external validation (AUC 0.719) data sets was higher than that of the other locally developed models (AUC 0.642-0.707 and AUC 0.642-0.699, respectively). In the federated model, classifications were driven by several predictors such as the Charlson index (low scores were associated with bipolar transition, which may be due to younger age), severe depression, anxiolytics, young age, and visiting months (the bipolar transition was associated with seasonality, especially during the spring and summer months).

CONCLUSIONS

We developed and validated a differentially private federated model by using distributed multi-institutional psychiatric data with standardized pipelines in a real-world environment. The federated model performed better than models using local data only.

Safety outcomes of selective serotonin reuptake inhibitors in adolescent attention-deficit/hyperactivity disorder with comorbid depression: the ASSURE study

BACKGROUND

Attention deficit-hyperactivity disorder (ADHD) is related to depressive disorder, and adolescents with both present poor outcomes. However, evidence for the safety of concomitantly using a methylphenidate (MPH) and a selective serotonin reuptake inhibitor (SSRI) among adolescent ADHD patients is limited, a literature gap aimed to address through this investigation.

METHODS

We conducted a new-user cohort study using a nationwide claims database in South Korea. We identified a study population as adolescents who were diagnosed both ADHD and depressive disorder. MPH-only users were compared with patients who prescribed both a SSRI and a MPH. Fluoxetine and escitalopram users were also compared to find a preferable treatment option. Thirteen outcomes including neuropsychiatric, gastrointestinal, and other events were assessed, taking respiratory tract infection as a negative control outcome. We matched the study groups using a propensity score and used the Cox proportional hazard model to calculate the hazard ratio. Subgroup and sensitivity analyses were conducted in various epidemiologic settings.

RESULTS

The risks of all the outcomes between the MPH-only and SSRI groups were not significantly different. Regarding SSRI ingredients, the risk of tic disorder was significantly lower in the fluoxetine group than the escitalopram group [HR 0.43 (0.25-0.71)]. However, there was no significant difference in other outcomes between the fluoxetine and escitalopram groups.

CONCLUSION

The concomitant use of MPHs and SSRIs showed generally safe profiles in adolescent ADHD patients with depression. Most of the differences between fluoxetine and escitalopram, except those concerning tic disorder, were not significant.

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

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.

Impact of concomitant cardiovascular medications on overall survival in patients with liver cirrhosis

OBJECTIVES OF THE ARTICLE

Liver cirrhosis is the end-stage liver disease associated with poor prognosis and cardiovascular comorbidity could significantly impact mortality of cirrhotic patients. We conducted a large, retrospective study to investigate the survival impact of cardiovascular co-medications in patients with liver cirrhosis.

MATERIALS AND METHODS

A study-specific R package was processed on the local databases of partner institutions within the Observational Health Data Sciences and Informatics consortium, namely Columbia University, New York City (NYC), USA and Ajou University School of Medicine (AUSOM), South Korea. Patients with cirrhosis diagnosed between 2000 and 2020 were included. Final analysis of the anonymous survival data was performed at Medical Faculty Mannheim, Heidelberg University.

RESULTS

We investigated a total of 32,366 patients with liver cirrhosis. Our data showed that administration of antiarrhythmics amiodarone or digoxin presented as a negative prognostic indicator (p = 0.000 in both cohorts). Improved survival was associated with angiotensin-converting enzyme inhibitor ramipril (p = 0.005 in NYC cohort, p = 0.075 in AUSOM cohort) and angiotensin II receptor blocker losartan (p = 0.000 in NYC cohort, p = 0.005 in AUSOM cohort). Non-selective beta blocker carvedilol was associated with a survival advantage in the NYC (p = 0.000) cohort but not in the AUSOM cohort (p = 0.142). Patients who took platelet inhibitor clopidogrel had a prolonged overall survival compared to those without (p = 0.000 in NYC cohort, p = 0.003 in AUSOM cohort).

CONCLUSION

Concomitant cardiovascular medications are associated with distinct survival difference in cirrhotic patients. Multidisciplinary management is needed for a judicious choice of proper cardiovascular co-medications in cirrhotic patients.

Efficacy of Co-Medications in Patients with Alcoholic Liver Disease

BACKGROUND

Alcoholic liver disease (ALD) is still increasing and leads to acute liver injury but also liver cirrhosis and subsequent complications such as liver failure or hepatocellular carcinoma (HCC). As most patients fail to achieve alcohol abstinence, it is essential to identify alternative treatment options in order to improve the outcome of ALD patients.

METHODS

Evaluating two large cohorts of patients with ALD from the USA and Korea with a total of 12,006 patients, we investigated the effect on survival of aspirin, metformin, metoprolol, dopamine, and dobutamine drugs in patients with ALD between 2000 and 2020. Patient data were obtained through the "The Observational Health Data Sciences and Informatics consortium," an open-source, multi-stakeholder, and interdisciplinary collaborative effort.

RESULTS

The use of aspirin (p = 0.000, p = 0.000), metoprolol (p = 0.002, p = 0.000), and metformin (p = 0.000, p = 0.000) confers a survival benefit for both AUSOM- and NY-treated cohorts. Need of catecholamines dobutamine (p = 0.000, p = 0.000) and dopamine (p = 0.000, p = 0.000) was strongly indicative of poor survival. β-Blocker treatment with metoprolol (p = 0.128, p = 0.196) or carvedilol (p = 0.520, p = 0.679) was not shown to be protective in any of the female subgroups.

CONCLUSION

Overall, our data fill a large gap in long-term, real-world data on patients with ALD, confirming an impact of metformin, acetylsalicylic acid, and β-blockers on ALD patient's survival. However, gender and ethnic background lead to diverse efficacy in those patients.

Machine learning-based prediction model for postoperative delirium in non-cardiac surgery

BACKGROUND

Postoperative delirium is a common complication that is distressing. This study aimed to demonstrate a prediction model for delirium.

METHODS

Among 203,374undergoing non-cardiac surgery between January 2011 and June 2019 at Samsung Medical Center, 2,865 (1.4%) were diagnosed with postoperative delirium. After comparing performances of machine learning algorithms, we chose variables for a prediction model based on an extreme gradient boosting algorithm. Using the top five variables, we generated a prediction model for delirium and conducted an external validation. The Kaplan-Meier and Cox survival analyses were used to analyse the difference of delirium occurrence in patients classified as a prediction model.

RESULTS

The top five variables selected for the postoperative delirium prediction model were age, operation duration, physical status classification, male sex, and surgical risk. An optimal probability threshold in this model was estimated to be 0.02. The area under the receiver operating characteristic (AUROC) curve was 0.870 with a 95% confidence interval of 0.855-0.885, and the sensitivity and specificity of the model were 0.76 and 0.84, respectively. In an external validation, the AUROC was 0.867 (0.845-0.877). In the survival analysis, delirium occurred more frequently in the group of patients predicted as delirium using an internal validation dataset (p < 0.001).

CONCLUSION

Based on machine learning techniques, we analyzed a prediction model of delirium in patients who underwent non-cardiac surgery. Screening for delirium based on the prediction model could improve postoperative care. The working model is provided online and is available for further verification among other populations.

TRIAL REGISTRATION

KCT 0006363.

Association of 5α-Reductase Inhibitor Prescription With Bladder Cancer Progression in Males in South Korea

Importance

The antiandrogenic effect of the 5α-reductase inhibitor (5-ARI) has been investigated for its role in preventing male-predominant cancers. Although 5-ARI has been widely associated with prostate cancer, its association with urothelial bladder cancer (BC), another cancer experienced predominantly by males, has been less explored.

Objective

To assess the association between 5-ARI prescription prior to BC diagnosis and reduced risk of BC progression.

Design, Setting, and Participants

This cohort study analyzed patient claims data from the Korean National Health Insurance Service database. The nationwide cohort included all male patients with BC diagnosis in this database from January 1, 2008, to December 31, 2019. Propensity score matching was conducted to balance the covariates between 2 treatment groups: α-blocker only group and 5-ARI plus α-blocker group. Data were analyzed from April 2021 to March 2023.

Exposure

Newly dispensed prescriptions of 5-ARIs at least 12 months prior to cohort entry (BC diagnosis), with a minimum of 2 prescriptions filled.

Main Outcomes and Measures

The primary outcomes were the risks of bladder instillation and radical cystectomy, and the secondary outcome was all-cause mortality. To compare the risk of outcomes, the hazard ratio (HR) was estimated using a Cox proportional hazards regression model and difference in restricted mean survival time analysis.

Results

The study cohort initially included 22 845 males with BC. After propensity score matching, 5300 patients each were assigned to the α-blocker only group (mean [SD] age, 68.3 [8.8] years) and 5-ARI plus α-blocker group (mean [SD] age, 67.8 [8.6] years). Compared with the α-blocker only group, the 5-ARI plus α-blocker group had a lower risk of mortality (adjusted HR [AHR], 0.83; 95% CI, 0.75-0.91), bladder instillation (crude HR, 0.84; 95% CI, 0.77-0.92), and radical cystectomy (AHR, 0.74; 95% CI, 0.62-0.88). The differences in restricted mean survival time were 92.6 (95% CI, 25.7-159.4) days for all-cause mortality, 88.1 (95% CI, 25.2-150.9) days for bladder instillation, and 68.0 (95% CI, 31.6-104.3) days for radical cystectomy. The incidence rates per 1000 person-years were 85.59 (95% CI, 80.53-90.88) for bladder instillation and 19.57 (95% CI, 17.41-21.91) for radical cystectomy in the α-blocker only group and 66.43 (95% CI, 62.22-70.84) for bladder instillation and 13.56 (95% CI, 11.86-15.45) for radical cystectomy in the 5-ARI plus α-blocker group.

Conclusions and relevance

Results of this study suggest an association between prediagnostic prescription of 5-ARI and reduced risk of BC progression.

Feasibility Study of Federated Learning on the Distributed Research Network of OMOP Common Data Model

OBJECTIVES

Since protecting patients' privacy is a major concern in clinical research, there has been a growing need for privacy-preserving data analysis platforms. For this purpose, a federated learning (FL) method based on the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) was implemented, and its feasibility was demonstrated.

METHODS

We implemented an FL platform on FeederNet, which is a distributed clinical data analysis platform based on the OMOP CDM in Korea. We trained it through an artificial neural network (ANN) using data from patients who received steroid prescriptions or injections, with the aim of predicting the occurrence of side effects depending on the prescribed dose. The ANN was trained using the FL platform with the OMOP CDMs of Kyung Hee University Medical Center (KHMC) and Ajou University Hospital (AUH).

RESULTS

The area under the receiver operating characteristic curves (AUROCs) for predicting bone fracture, osteonecrosis, and osteoporosis using only data from each hospital were 0.8426, 0.6920, and 0.7727 for KHMC and 0.7891, 0.7049, and 0.7544 for AUH, respectively. In contrast, when using FL, the corresponding AUROCs were 0.8260, 0.7001, and 0.7928 for KHMC and 0.7912, 0.8076, and 0.7441 for AUH, respectively. In particular, FL led to a 14% improvement in performance for osteonecrosis at AUH.

CONCLUSIONS

FL can be performed with the OMOP CDM, and FL often shows better performance than using only a single institution's data. Therefore, research using OMOP CDM has been expanded from statistical analysis to machine learning so that researchers can conduct more diverse research.

Rates of Antipsychotic Drug Prescribing Among People Living With Dementia During the COVID-19 Pandemic

Importance

Concerns have been raised that the use of antipsychotic medication for people living with dementia might have increased during the COVID-19 pandemic.

Objective

To examine multinational trends in antipsychotic drug prescribing for people living with dementia before and during the COVID-19 pandemic.

Design, Setting, and Participants

This multinational network cohort study used electronic health records and claims data from 8 databases in 6 countries (France, Germany, Italy, South Korea, the UK, and the US) for individuals aged 65 years or older between January 1, 2016, and November 30, 2021. Two databases each were included for South Korea and the US.

Exposures

The introduction of population-wide COVID-19 restrictions from April 2020 to the latest available date of each database.

Main Outcomes and Measures

The main outcomes were yearly and monthly incidence of dementia diagnosis and prevalence of people living with dementia who were prescribed antipsychotic drugs in each database. Interrupted time series analyses were used to quantify changes in prescribing rates before and after the introduction of population-wide COVID-19 restrictions.

Results

A total of 857 238 people with dementia aged 65 years or older (58.0% female) were identified in 2016. Reductions in the incidence of dementia were observed in 7 databases in the early phase of the pandemic (April, May, and June 2020), with the most pronounced reduction observed in 1 of the 2 US databases (rate ratio [RR], 0.30; 95% CI, 0.27-0.32); reductions were also observed in the total number of people with dementia prescribed antipsychotic drugs in France, Italy, South Korea, the UK, and the US. Rates of antipsychotic drug prescribing for people with dementia increased in 6 databases representing all countries. Compared with the corresponding month in 2019, the most pronounced increase in 2020 was observed in May in South Korea (Kangwon National University database) (RR, 2.11; 95% CI, 1.47-3.02) and June in the UK (RR, 1.96; 95% CI, 1.24-3.09). The rates of antipsychotic drug prescribing in these 6 databases remained high in 2021. Interrupted time series analyses revealed immediate increases in the prescribing rate in Italy (RR, 1.31; 95% CI, 1.08-1.58) and in the US Medicare database (RR, 1.43; 95% CI, 1.20-1.71) after the introduction of COVID-19 restrictions.

Conclusions and Relevance

This cohort study found converging evidence that the rate of antipsychotic drug prescribing to people with dementia increased in the initial months of the COVID-19 pandemic in the 6 countries studied and did not decrease to prepandemic levels after the acute phase of the pandemic had ended. These findings suggest that the pandemic disrupted the care of people living with dementia and that the development of intervention strategies is needed to ensure the quality of care.

Association between impaired glucose metabolism and long-term prognosis at the time of diagnosis of depression: Impaired glucose metabolism as a promising biomarker proposed through a machine-learning approach

BACKGROUND

Predicting the course of depression is necessary for personalized treatment. Impaired glucose metabolism (IGM) was introduced as a promising depression biomarker, but no consensus was made. This study aimed to predict IGM at the time of depression diagnosis and examine the relationship between long-term prognosis and predicted results.

METHODS

Clinical data were extracted from four electronic health records in South Korea. The study population included patients with depression, and the outcome was IGM within 1 year. One database was used to develop the model using three algorithms. External validation was performed using the best algorithm across the three databases. The area under the curve (AUC) was calculated to determine the model's performance. Kaplan-Meier and Cox survival analyses of the risk of hospitalization for depression as the long-term outcome were performed. A meta-analysis of the long-term outcome was performed across the four databases.

RESULTS

A prediction model was developed using the data of 3,668 people, with an AUC of 0.781 with least absolute shrinkage and selection operator (LASSO) logistic regression. In the external validation, the AUCs were 0.643, 0.610, and 0.515. Through the predicted results, survival analysis and meta-analysis were performed; the hazard ratios of risk of hospitalization for depression in patients predicted to have IGM was 1.20 (95% confidence interval [CI] 1.02-1.41, p = 0.027) at a 3-year follow-up.

CONCLUSIONS

We developed prediction models for IGM occurrence within a year. The predicted results were related to the long-term prognosis of depression, presenting as a promising IGM biomarker related to the prognosis of depression.

Risks of complicated acute appendicitis in patients with psychiatric disorders

BACKGROUND

Acute appendicitis often presents with vague abdominal pain, which fosters diagnostic challenges to clinicians regarding early detection and proper intervention. This is even more problematic with individuals with severe psychiatric disorders who have reduced sensitivity to pain due to long-term or excessive medication use or disturbed bodily sensation perceptions. This study aimed to determine whether psychiatric disorder, psychotropic prescription, and treatment compliance increase the risks of complicated acute appendicitis.

METHODS

The diagnosis records of acute appendicitis from four university hospitals in Korea were investigated from 2002 to 2020. A total of 47,500 acute appendicitis-affected participants were divided into groups with complicated and uncomplicated appendicitis to determine whether any of the groups had more cases of psychiatric disorder diagnoses. Further, the ratio of complicated compared to uncomplicated appendicitis in the mentally ill group was calculated regarding psychotropic dose, prescription duration, and treatment compliance.

RESULTS

After adjusting for age and sex, presence of psychotic disorder (odds ratio [OR]: 1.951; 95% confidence interval [CI]: 1.218-3.125), and bipolar disorder (OR: 2.323; 95% CI: 1.194-4.520) was associated with a higher risk of having complicated appendicitis compared with absence of psychiatric disorders. Patients who are taking high-daily-dose antipsychotics, regardless of prescription duration, show high complicated appendicitis risks; High-dose antipsychotics for < 1 year (OR: 1.896, 95% CI: 1.077-3.338), high-dose antipsychotics for 1-5 years (OR: 1.930, 95% CI: 1.144-3.256). Poor psychiatric outpatient compliance was associated with a high risk of complicated appendicitis (OR: 1.664, 95% CI: 1.014-2.732).

CONCLUSIONS

This study revealed a close relationship in the possibility of complicated appendicitis in patients with severe psychiatric disorders, including psychotic and bipolar disorders. The effect on complicated appendicitis was more remarkable by the psychiatric disease entity itself than by psychotropic prescription patterns. Good treatment compliance and regular visit may reduce the morbidity of complicated appendicitis in patients with psychiatric disorders.

Establishment of an International Evidence Sharing Network Through Common Data Model for Cardiovascular Research

A retrospective observational study is one of the most widely used research methods in medicine. However, evidence postulated from a single data source likely contains biases such as selection bias, information bias, and confounding bias. Acquiring enough data from multiple institutions is one of the most effective methods to overcome the limitations. However, acquiring data from multiple institutions from many countries requires enormous effort because of financial, technical, ethical, and legal issues as well as standardization of data structure and semantics. The Observational Health Data Sciences and Informatics (OHDSI) research network standardized 928 million unique records or 12% of the world's population into a common structure and meaning and established a research network of 453 data partners from 41 countries around the world. OHDSI is a distributed research network wherein researchers do not own or directly share data but only analyzed results. However, sharing evidence without sharing data is difficult to understand. In this review, we will look at the basic principles of OHDSI, common data model, distributed research networks, and some representative studies in the cardiovascular field using the network. This paper also briefly introduces a Korean distributed research network named FeederNet.

Long-term methylphenidate use for children and adolescents with attention deficit hyperactivity disorder and risk for depression, conduct disorder, and psychotic disorder: a nationwide longitudinal cohort study in South Korea

BACKGROUND

Methylphenidate (MPH) is the most frequently prescribed medication for the treatment of attention deficit hyperactivity disorder (ADHD). However, the safety of its long-term use remain unclear. In particular, real-world evidence of long-term MPH treatment regarding the risk of depression, conduct disorders, and psychotic disorders in children and adolescents is needed. This study aimed to compare the risks of depression, conduct disorder, and psychotic disorder between long- and short-term MPH treatments in children and adolescents.

METHODS

This population-based cohort study used a nationwide claims database of all patients with ADHD in South Korea. Patients aged less than 18 years who were prescribed MPH were included in the study. Long- and short-term MPH were defined as > 1 year, and < 1 year, respectively. Overall, the risk of developing depressive disorder, conduct disorder and oppositional defiant disorder (ODD), and psychotic disorder were investigated. A 1:2 propensity score matching was used to balance the cohorts, and the Cox proportional hazards model was used to evaluate the safety of MPH.

RESULTS

We identified 1309 long-term and 2199 short-term MPH users. Long-term MPH use was associated with a significantly lower risk of depressive (hazard ratio [HR], 0.70 [95% confidence interval [CI] 0.55-0.88]) and conduct disorders and ODD (HR, 0.52 [95% CI 0.38-0.73]) than short-term MPH use. Psychotic disorder was not significantly associated with long-term MPH use (hazard ratio [HR], 0.83 [95% confidence interval [CI] 0.52-1.32]).

CONCLUSIONS

Our findings suggest that long-term MPH use may be associated with a decreased risk of depression, conduct disorders and ODD. Moreover, the long-term use of MPH does not increase the risk of psychotic disorders. Long-term MPH administration may be considered as a favourable treatment strategy for children and adolescents with ADHD regarding depressive, conduct, and psychotic disorders.

Real-world treatment intensities and pathways of macular edema following retinal vein occlusion in Korea from Common Data Model in ophthalmology

Despite many studies, optimal treatment sequences or intervals are still questionable in retinal vein occlusion (RVO) macular edema. The aim of this study was to examine the real-world treatment patterns of RVO macular edema. A retrospective analysis of the Observational Medical Outcomes Partnership Common Data Model, a distributed research network, of four large tertiary referral centers (n = 9,202,032) identified 3286 eligible. We visualized treatment pathways (prescription volume and treatment sequence) with sunburst and Sankey diagrams. We calculated the average number of intravitreal injections per patient in the first and second years to evaluate the treatment intensities. Bevacizumab was the most popular first-line drug (80.9%), followed by triamcinolone (15.1%) and dexamethasone (2.28%). Triamcinolone was the most popular drug (8.88%), followed by dexamethasone (6.08%) in patients who began treatment with anti-vascular endothelial growth factor (VEGF) agents. The average number of all intravitreal injections per person decreased in the second year compared with the first year. The average number of injections per person in the first year increased throughout the study. Bevacizumab was the most popular first-line drug and steroids were considered the most common as second-line drugs in patients first treated with anti-VEGF agents. Intensive treatment patterns may cause an increase in intravitreal injections.

Effectiveness of Maintenance and Reliever Therapy Using Inhaled Corticosteroid–Formoterol in Asthmatics

BACKGROUND

Real-world evidence on the effectiveness of maintenance and reliever therapy (MART) using inhaled corticosteroids plus long-acting beta-2 agonist (ICS-LABA) is sparse.

OBJECTIVE

This study aimed to evaluate the clinical effectiveness of MART (ICS-formoterol) by comparing its effectiveness with that of ICS-LABA plus as-needed short-acting beta-2 agonist (SABA) in adult asthmatics.

METHODS

We retrospectively retrieved data from the medical records of the Ajou University Medical Center, Korea, to compare clinical outcomes between patients treated with MART (the MART group) and those treated with ICS-LABA plus SABA (the non-MART group). Propensity score matching was performed and hazard ratios (HRs) with 95% confidence intervals were calculated using the Cox proportional hazards model. Severe asthma exacerbation (SAEx) was the primary end point, and asthma exacerbation (AEx), hospitalization, and pneumonia were secondary end points. Corticosteroid requirement was also analyzed.

RESULTS

After propensity score matching, the MART and the non-MART groups included 231 and 512 adult asthmatics, respectively. The risk of SAEx and AEx was significantly lower in the MART group than in the non-MART group (HR [95% CI] 0.39 [0.18-0.77] and 0.61 [0.37-0.99], respectively). There was no significant difference in hospitalization and pneumonia risk between the 2 groups (HR [95% CI] 0.88 [0.55-1.37] and 0.63 [0.03-4.51], respectively). Corticosteroid requirements were lower in the MART group than in the non-MART group (median [interquartile range], 190.0 [97.9-420.0] and 411.0 [143.0-833.0] mg/person-year, respectively; P < .01).

CONCLUSIONS

The MART strategy of ICS-formoterol was associated with lower risk of AEx and reduced corticosteroid requirement.

Impact of pitavastatin on new-onset diabetes mellitus compared to atorvastatin and rosuvastatin: a distributed network analysis of 10 real-world databases

BACKGROUND

Statin treatment increases the risk of new-onset diabetes mellitus (NODM); however, data directly comparing the risk of NODM among individual statins is limited. We compared the risk of NODM between patients using pitavastatin and atorvastatin or rosuvastatin using reliable, large-scale data.

METHODS

Data of electronic health records from ten hospitals converted to the Observational Medical Outcomes Partnership Common Data Model (n = 14,605,368 patients) were used to identify new users of pitavastatin, atorvastatin, or rosuvastatin (atorvastatin + rosuvastatin) for ≥ 180 days without a previous history of diabetes or HbA1c level ≥ 5.7%. We conducted a cohort study using Cox regression analysis to examine the hazard ratio (HR) of NODM after propensity score matching (PSM) and then performed an aggregate meta-analysis of the HR.

RESULTS

After 1:2 PSM, 10,238 new pitavastatin users (15,998 person-years of follow-up) and 18,605 atorvastatin + rosuvastatin users (33,477 person-years of follow-up) were pooled from 10 databases. The meta-analysis of the HRs demonstrated that pitavastatin resulted in a significantly reduced risk of NODM than atorvastatin + rosuvastatin (HR 0.72; 95% CI 0.59-0.87). In sub-analysis, pitavastatin was associated with a lower risk of NODM than atorvastatin or rosuvastatin after 1:1 PSM (HR 0.69; CI 0.54-0.88 and HR 0.74; CI 0.55-0.99, respectively). A consistently low risk of NODM in pitavastatin users was observed when compared with low-to-moderate-intensity atorvastatin + rosuvastatin users (HR 0.78; CI 0.62-0.98).

CONCLUSIONS

In this retrospective, multicenter active-comparator, new-user, cohort study, pitavastatin reduced the risk of NODM compared with atorvastatin or rosuvastatin.

Cycle-consistent adversarial networks improves generalizability of radiomics model in grading meningiomas on external validation

The heterogeneity of MRI is one of the major reasons for decreased performance of a radiomics model on external validation, limiting the model’s generalizability and clinical application. We aimed to establish a generalizable radiomics model to predict meningioma grade on external validation through leveraging Cycle-Consistent Adversarial Networks (CycleGAN). In this retrospective study, 257 patients with meningioma were included in the institutional training set. Radiomic features (n = 214) were extracted from T2-weighted (T2) and contrast-enhanced T1 (T1C) images. After radiomics feature selection, extreme gradient boosting classifiers were developed. The models were validated in the external validation set consisting of 61 patients with meningiomas. To reduce the gap in generalization associated with the inter-institutional heterogeneity of MRI, the smaller image set style of the external validation was translated into the larger image set style of the institutional training set using CycleGAN. On external validation before CycleGAN application, the performance of the combined T2 and T1C models showed an area under the curve (AUC), accuracy, and F1 score of 0.77 (95% confidence interval 0.63–0.91), 70.7%, and 0.54, respectively. After applying CycleGAN, the performance of the combined T2 and T1C models increased, with an AUC, accuracy, and F1 score of 0.83 (95% confidence interval 0.70–0.97), 73.2%, and 0.59, respectively. Quantitative metrics (by Fréchet Inception Distance) showed that CycleGAN can decrease inter-institutional image heterogeneity while preserving predictive information. In conclusion, leveraging CycleGAN may be helpful to increase the generalizability of a radiomics model in differentiating meningioma grade on external validation.

Electrocardiographic biomarker based on machine learning for detecting overt hyperthyroidism

Aims

Although overt hyperthyroidism adversely affects a patient's prognosis, thyroid function tests (TFTs) are not routinely conducted. Furthermore, vague symptoms of hyperthyroidism often lead to hyperthyroidism being overlooked. An electrocardiogram (ECG) is a commonly used screening test, and the association between thyroid function and ECG is well known. However, it is difficult for clinicians to detect hyperthyroidism through subtle ECG changes. For early detection of hyperthyroidism, we aimed to develop and validate an electrocardiographic biomarker based on a deep learning model (DLM) for detecting hyperthyroidism.

Methods and results

This multicentre retrospective cohort study included patients who underwent ECG and TFTs within 24 h. For model development and internal validation, we obtained 174 331 ECGs from 113 194 patients. We extracted 48 648 ECGs from 33 478 patients from another hospital for external validation. Using 500 Hz raw ECG, we developed a DLM with 12-lead, 6-lead (limb leads, precordial leads), and single-lead (lead I) ECGs to detect overt hyperthyroidism. We calculated the model's performance on the internal and external validation sets using the area under the receiver operating characteristic curve (AUC). The AUC of the DLM using a 12-lead ECG was 0.926 (0.913-0.94) for internal validation and 0.883(0.855-0.911) for external validation. The AUC of DLMs using six and a single-lead were in the range of 0.889-0.906 for internal validation and 0.847-0.882 for external validation.

Conclusion

We developed a DLM using ECG for non-invasive screening of overt hyperthyroidism. We expect this model to contribute to the early diagnosis of diseases and improve patient prognosis.

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

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.

DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models

Linear mixed models are commonly used in healthcare-based association analyses for analyzing multi-site data with heterogeneous site-specific random effects. Due to regulations for protecting patients’ privacy, sensitive individual patient data (IPD) typically cannot be shared across sites. We propose an algorithm for fitting distributed linear mixed models (DLMMs) without sharing IPD across sites. This algorithm achieves results identical to those achieved using pooled IPD from multiple sites (i.e., the same effect size and standard error estimates), hence demonstrating the lossless property. The algorithm requires each site to contribute minimal aggregated data in only one round of communication. We demonstrate the lossless property of the proposed DLMM algorithm by investigating the associations between demographic and clinical characteristics and length of hospital stay in COVID-19 patients using administrative claims from the UnitedHealth Group Clinical Discovery Database. We extend this association study by incorporating 120,609 COVID-19 patients from 11 collaborative data sources worldwide.

A lossless, one-shot and privacy-preserving distributed algorithm was revealed for fitting linear mixed models on multi-site data. The algorithm was applied to a study of 120,609 COVID-19 patients using only minimal aggregated data from each of 14 sites.

Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS

Purpose

Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.

Patients and Methods

We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services.

Results

We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed.

Conclusion

We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.

Analysis of Dual Combination Therapies Used in Treatment of Hypertension in a Multinational Cohort

IMPORTANCE

More than 1 billion adults have hypertension globally, of whom 70% cannot achieve their hypertension control goal with monotherapy alone. Data are lacking on clinical use patterns of dual combination therapies prescribed to patients who escalate from monotherapy.

OBJECTIVE

To investigate the most common dual combinations prescribed for treatment escalation in different countries and how treatment use varies by age, sex, and history of cardiovascular disease.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study used data from 11 electronic health record databases that cover 118 million patients across 8 countries and regions between January 2000 and December 2019. Included participants were adult patients (ages ≥18 years) who newly initiated antihypertensive dual combination therapy after escalating from monotherapy. There were 2 databases included for 3 countries: the Iqvia Longitudinal Patient Database (LPD) Australia and Electronic Practice-based Research Network 2019 linked data set from South Western Sydney Local Health District (ePBRN SWSLHD) from Australia, Ajou University School of Medicine (AUSOM) and Kyung Hee University Hospital (KHMC) databases from South Korea, and Khoo Teck Puat Hospital (KTPH) and National University Hospital (NUH) databases from Singapore. Data were analyzed from June 2020 through August 2021.

EXPOSURES

Treatment with dual combinations of the 4 most commonly used antihypertensive drug classes (angiotensin-converting enzyme inhibitor [ACEI] or angiotensin receptor blocker [ARB]; calcium channel blocker [CCB]; β-blocker; and thiazide or thiazide-like diuretic).

MAIN OUTCOMES AND MEASURES

The proportion of patients receiving each dual combination regimen, overall and by country and demographic subgroup.

RESULTS

Among 970 335 patients with hypertension who newly initiated dual combination therapy included in the final analysis, there were 11 494 patients from Australia (including 9291 patients in Australia LPD and 2203 patients in ePBRN SWSLHD), 6980 patients from South Korea (including 6029 patients in Ajou University and 951 patients in KHMC), 2096 patients from Singapore (including 842 patients in KTPH and 1254 patients in NUH), 7008 patients from China, 8544 patients from Taiwan, 103 994 patients from France, 76 082 patients from Italy, and 754 137 patients from the US. The mean (SD) age ranged from 57.6 (14.8) years in China to 67.7 (15.9) years in the Singapore KTPH database, and the proportion of patients by sex ranged from 24 358 (36.9%) women in Italy to 408 964 (54.3%) women in the US. Among 12 dual combinations of antihypertensive drug classes commonly used, there were significant variations in use across country and patient subgroup. For example starting an ACEI or ARB monotherapy followed by a CCB (ie, ACEI or ARB + CCB) was the most commonly prescribed combination in Australia (698 patients in ePBRN SWSLHD [31.7%] and 3842 patients in Australia LPD [41.4%]) and Singapore (216 patients in KTPH [25.7%] and 439 patients in NUH [35.0%]), while in South Korea, CCB + ACEI or ARB (191 patients in KHMC [20.1%] and 1487 patients in Ajou University [24.7%]), CCB + β-blocker (814 patients in Ajou University [13.5%] and 217 patients in KHMC [22.8%]), and ACEI or ARB + CCB (147 patients in KHMC [15.5%] and 1216 patients in Ajou University [20.2%]) were the 3 most commonly prescribed combinations. The distribution of 12 dual combination therapies were significantly different by age and sex in almost all databases. For example, use of ACEI or ARB + CCB varied from 873 of 3737 patients ages 18 to 64 years (23.4%) to 343 of 2292 patients ages 65 years or older (15.0%) in South Korea's Ajou University database (P for database distribution by age < .001), while use of ACEI or ARB + CCB varied from 2121 of 4718 (44.8%) men to 1721 of 4549 (37.7%) women in Australian LPD (P for drug combination distributions by sex < .001).

CONCLUSIONS AND RELEVANCE

In this study, large variation in the transition between monotherapy and dual combination therapy for hypertension was observed across countries and by demographic group. These findings suggest that future research may be needed to investigate what dual combinations are associated with best outcomes for which patients.

Network modeling of anxiety and psychological characteristics on suicidal behavior: Cross-sectional study

BACKGROUND

Suicide is influenced by complex interactions among various psychopathological features. We aimed to examine the relationship between suicide risk and psychological risk factors such as defense mechanisms, personality, and anxiety.

METHODS

We established a psychiatric database by utilizing the Observational Medical Outcomes Partnership Common Data Model. We conducted a 1:1 propensity score matching with age, sex, and depression severity, and identified a sample (n = 258) with two groups: those with suicidal behavior and those with non-suicidal behavior. Using principal component analysis, we extracted nine psychological scales and applied network analysis to compare relationships among psychological factors between the two groups.

RESULTS

Patients with non-suicidal behaviors showed associations between trait anxiety and defense mechanisms, while those with suicidal behaviors did not. For patients with suicidal ideation there was an association between somatization and trait anxiety. Patients with suicide attempts showed associations between paranoia and dissociation connected to trait anxiety.

LIMITATIONS

Longitudinal research is required to fully observe transitions from suicidal ideation to attempts and recurrent suicidal events. In addition, these networks may not generalize suicidal behaviors because the group participants are not homogeneous. Lastly, data from self-report questionnaires limits the reliability of responses.

CONCLUSIONS

We presented important new insights on suicidal behavior by estimating psychological networks. Patients with non-suicidal behavior may exhibit discrete relationships between defense mechanisms and anxiety, compared to those with suicidal behavior. Patients with suicidal ideation and suicide attempts may show distinct associations between anxiety and psychopathological features.