Relationship Between Coronavirus Disease 2019 Vaccination Rates and Rare But Potentially Fatal Adverse Events: A Regression Discontinuity Analysis of Western Countries.

BACKGROUND: Owing to limited experience with the new vaccine platforms, discussion of vaccine safety is inevitable. However, media coverage of adverse events of special interest could influence the vaccination rate; thus, evaluating the outcomes of adverse events of special interest influencing vaccine administration is crucial. METHODS: We conducted regression discontinuity in time analysis to calculate the local average treatment effect (LATE) using datasets from Our World in Data and Johns Hopkins University Center for Systems Science and Engineering. For the United States, the United Kingdom, and Europe, the cutoff points were April 23rd and June 23rd, April 7th, and the 14th week of 2021, respectively. RESULTS: The LATE of the Advisory Committee on Immunization Practices (ACIP) meeting held on April 23rd was -0.249 for all vaccines, -0.133 (-0.189 to -0.076) for Pfizer, -0.064 (-0.115 to -0.012) for Moderna, and -0.038 (-0.047 to -0.030) for Johnson & Johnson. Discontinuities were observed for all three types of vaccines in the United States. The June 23rd meeting of the ACIP (mRNA vaccines and myocarditis) did not convene any discontinuities. Furthermore, there was no significant drop in the weekly average vaccination rates in Europe following the European Medicines Agency (EMA) statement on April 7th. Conversely, there was a significant drop in the first-dose vaccination rates in the United Kingdom related to the EMA report. The first-dose vaccination rate for all vaccines changed by -0.104 (-0.176 to -0.032). CONCLUSION: Although monitoring and reporting of adverse events of special interest are important, a careful approach towards public announcements is warranted.

Optimal Social Distancing Policy for COVID-19 Control in Korea: A Model-Based Analysis.

BACKGROUND: Since March 2020, when coronavirus disease 2019 (COVID-19) was declared a pandemic, many countries have applied unprecedented restrictive measures to contain the spread of the virus. This study aimed to explore the optimal social distancing policy for COVID-19 control in South Korea to safely reopen the society. METHODS: We developed an age-specific, deterministic compartment epidemic model to examine the COVID-19 control decision-making process, including the epidemiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between 1 July 2021 and 30 December 2022. The model consists of the natural history of COVID-19, testing performance, vaccinations, and social distancing enforcement measures to detect and control SARS-CoV-2. We modelled potential intervention scenarios with three distinct components: 1) social distancing duration and level; 2) testing intensity; and 3) vaccination uptake rate. The primary and secondary outcomes were COVID-19 incidence and prevalence of severe patients requiring intensive care unit (ICU) care. RESULTS: Four (or more) months of social distancing (that can reduce 40-60% transmission) may mitigate epidemic resurgence and ICU demand in the future and keep the cases below the capacity limit if the testing intensity and vaccination rate remain constant or increase by 20% (with respect to the current level). In contrast, two months of strict social distancing enforcement may also successfully mitigate future epidemic surge and ICU demand as long as testing intensity and vaccination rates are increased by 20%. CONCLUSION: In South Korea, given the relatively high vaccination coverage and low incidence, four or more months of social distancing enforcement can effectively mitigate epidemic resurgence after lifting the social distancing measures. In addition, increasing the testing intensity and vaccination rate may help reduce necessary social distancing levels and duration to prevent a future epidemic resurgence and mitigate social and economic damage.

COVID-19 infection inference with graph neural networks.

Infectious diseases spread rapidly, and epidemiological surveys are vital to detect high-risk transmitters and reduce transmission rates. To enhance efficiency and reduce the burden on epidemiologists, an automatic tool to assist with epidemiological surveys is necessary. This study aims to develop an automatic epidemiological survey to predict the influence of COVID-19-infected patients on future additional infections. To achieve this, the study utilized a dataset containing interaction information between confirmed cases, including contact order, contact times, and movement routes, as well as individual properties such as symptoms. Graph neural networks (GNNs) were used to incorporate interaction information and individual properties. Two variants of GNNs, graph convolutional and graph attention networks, were utilized, and the results showed that the graph-based models outperformed traditional machine learning models. For the area under the curve, the 2nd, 3rd, and 4th order spreading predictions showed higher performance by 0.200, 0.269, and 0.190, respectively. The results show that the contact information of an infected person is crucial data that can help predict whether that person will affect future infections. Our findings suggest that incorporating the relationships between an infected person and others can improve the effectiveness of an automatic epidemiological survey.

Model-based cost-effectiveness analysis of oral antivirals against SARS-CoV-2 in Korea.

OBJECTIVES: Many countries have authorized the emergency use of oral antiviral agents for patients with mild-to-moderate cases of coronavirus disease 2019 (COVID-19). We assessed the cost-effectiveness of these agents for reducing the number of severe COVID-19 cases and the burden on Korea's medical system. METHODS: Using an existing model, we estimated the number of people who would require hospital/intensive care unit (ICU) admission in Korea in 2022. The treatment scenarios included (1) all adult patients, (2) elderly patients only, and (3) adult patients with underlying diseases only, compared to standard care. Based on the current health system capacity, we calculated the incremental costs per severe case averted and hospital admission for each scenario. RESULTS: We estimated that 236,510 COVID-19 patients would require hospital/ICU admission in 2022 with standard care only. Nirmatrelvir/ritonavir (87% efficacy) was predicted to reduce this number by 80%, 24%, and 17% when targeting all adults, adults with underlying diseases, and elderly patients (25, 8, and 4%, respectively, for molnupiravir, with 30% efficacy). Nirmatrelvir/ritonavir use is likely to be cost-effective, with predicted costs of US$8,878, US$8,964, and US$1,454, per severe patient averted for the target groups listed above, respectively, while molnupiravir is likely to be less cost-effective, with costs of US$28,492, US$29,575, and US$7,915, respectively. CONCLUSIONS: In Korea, oral treatment using nirmatrelvir/ritonavir for symptomatic COVID-19 patients targeting elderly patients would be highly cost-effective and would substantially reduce the demand for hospital admission to below the capacity of the health system if targeted to all adult patients instead of standard care.

Eosinophilic Colitis that Presented with Subepithelial Tumor-like Lesions.

Eosinophilic colitis is a rare disease that is characterized by eosinophilic infiltration in the colon wall in symptomatic patients. Thus far, the epidemiology and pathophysiology of eosinophilic colitis have not been well defined, but the hypersensitivity response is likely to play a role in its pathogenesis. The clinical presentation of eosinophilic colitis is usually nonspecific and depends on the layer of the intestinal wall affected by the eosinophilic infiltrate. Eosinophilic colitis is diagnosed generally by exclusion, i.e., after all other causes of eosinophilic infiltration have been excluded. Although there is no consensus over its diagnostic criteria, the laboratory results and radiology and endoscopy findings can provide important diagnostic evidence. This paper reports a case of eosinophilic colitis presenting as subepithelial tumor-like lesions in a 41-year-old man with the chief complaints of abdominal pain and loose stools. The patient had no diseases and no food or drug allergies in his medical history. In general, the endoscopic findings of eosinophilic colitis can vary from a normal mucosa to frank ulcerations. In this case, however, endoscopy revealed subepithelial tumor-like lesions. The colon biopsy showed eosinophilic infiltration in the lamina propria. The patient was treated with steroids, and his symptoms regressed with no signs of relapse.

Case of Crohn's Disease Initially Misdiagnosed as Intestinal Tuberculosis Due to Active Pulmonary Tuberculosis.

Differentiating Crohn's disease (CD) from intestinal tuberculosis (TB) is a challenge. In patients suspected of having CD or intestinal TB compounded with active pulmonary TB in its early stages, clinicians often lean towards a diagnosis of intestinal TB. A 14-year-old female patient was admitted with symptoms of abdominal pain and diarrhea with hematochezia. Colonoscopy revealed a stricture of the ileocecal valve and scattered longitudinal ulcers. Initial chest radiography showed consolidation in the left lower lobe of the lung. Chest CT revealed branching nodular opacities and consolidation. The TB PCR of the bronchial washing fluid was positive. The patient was diagnosed with pulmonary and intestinal TB. The colonoscopy findings favored CD. Despite this, anti-tubercular therapy was initiated based on the radiology findings and PCR test. After treatment with anti-tubercular therapy, the patient's diarrhea and abdominal pain worsened despite the improvement observed on her chest radiography. Follow-up colonoscopy revealed aggravation of her ulcers. The patient was diagnosed with CD and treated with prednisolone and mesalazine. Her clinical condition improved, and follow-up colonoscopy showed significant improvement of the ulcers. This case highlights the need for caution in diagnosis and suggests that clinicians consider reevaluation in similar cases.

Analysis of Adverse Drug Reactions Identified in Nursing Notes Using Reinforcement Learning.

OBJECTIVES: Electronic Health Records (EHRs)-based surveillance systems are being actively developed for detecting adverse drug reactions (ADRs), but this is being hindered by the difficulty of extracting data from unstructured records. This study performed the analysis of ADRs from nursing notes for drug safety surveillance using the temporal difference method in reinforcement learning (TD learning). METHODS: Nursing notes of 8,316 patients (4,158 ADR and 4,158 non-ADR cases) admitted to Ajou University Hospital were used for the ADR classification task. A TD(λ) model was used to estimate state values for indicating the ADR risk. For the TD learning, each nursing phrase was encoded into one of seven states, and the state values estimated during training were employed for the subsequent testing phase. We applied logistic regression to the state values from the TD(λ) model for the classification task. RESULTS: The overall accuracy of TD-based logistic regression of 0.63 was comparable to that of two machine-learning methods (0.64 for a naïve Bayes classifier and 0.63 for a support vector machine), while it outperformed two deep learning-based methods (0.58 for a text convolutional neural network and 0.61 for a long short-term memory neural network). Most importantly, it was found that the TD-based method can estimate state values according to the context of nursing phrases. CONCLUSIONS: TD learning is a promising approach because it can exploit contextual, time-dependent aspects of the available data and provide an analysis of the severity of ADRs in a fully incremental manner.

Investigation of glass forming ability of Al-based metallic glasses by measuring vaporization enthalpy.

The analysis of the enthalpy changes for vaporization (ΔHvap) of Al-based metallic glass (MG) can provide insight into the origin of the MG's glass forming ability (GFA). The ΔHvap of three Al-based MGs, Al84.5 ± x(Y10Ni5.5)15.5 ± x, Al85 ± x(Y8Ni5Co2)15 ± x, and Al86 ± x(Y4.5Ni6Co2La1.5)14 ± x, (hereafter referred to as AYNx, AYNCx, and AYNCLx, respectively), is analyzed by measuring their weight losses below their glass transition temperatures. The relationship between ΔHvap and aluminum concentration exhibit minimum values in the range of 83-85 at.% of Al, and the ΔHvap increases, becoming saturated at 320-350 kJ/mol, as the percentage of Al deviates from this range. The depth of the enthalpy well, referring to the bottom of the parabolic graph of ΔHvap against the Al concentration, is proportional to the viscosity of clusters showing liquid-like behavior. The amount of weight loss is proportional to the concentration of these clusters. The cluster viscosity and concentration influences the overall viscosity of the MGs, and thus determines the GFA.

Comparative safety and effectiveness of alendronate versus raloxifene in women with osteoporosis.

Alendronate and raloxifene are among the most popular anti-osteoporosis medications. However, there is a lack of head-to-head comparative effectiveness studies comparing the two treatments. We conducted a retrospective large-scale multicenter study encompassing over 300 million patients across nine databases encoded in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The primary outcome was the incidence of osteoporotic hip fracture, while secondary outcomes were vertebral fracture, atypical femoral fracture (AFF), osteonecrosis of the jaw (ONJ), and esophageal cancer. We used propensity score trimming and stratification based on an expansive propensity score model with all pre-treatment patient characteritistcs. We accounted for unmeasured confounding using negative control outcomes to estimate and adjust for residual systematic bias in each data source. We identified 283,586 alendronate patients and 40,463 raloxifene patients. There were 7.48 hip fracture, 8.18 vertebral fracture, 1.14 AFF, 0.21 esophageal cancer and 0.09 ONJ events per 1,000 person-years in the alendronate cohort and 6.62, 7.36, 0.69, 0.22 and 0.06 events per 1,000 person-years, respectively, in the raloxifene cohort. Alendronate and raloxifene have a similar hip fracture risk (hazard ratio [HR] 1.03, 95% confidence interval [CI] 0.94-1.13), but alendronate users are more likely to have vertebral fractures (HR 1.07, 95% CI 1.01-1.14). Alendronate has higher risk for AFF (HR 1.51, 95% CI 1.23-1.84) but similar risk for esophageal cancer (HR 0.95, 95% CI 0.53-1.70), and ONJ (HR 1.62, 95% CI 0.78-3.34). We demonstrated substantial control of measured confounding by propensity score adjustment, and minimal residual systematic bias through negative control experiments, lending credibility to our effect estimates. Raloxifene is as effective as alendronate and may remain an option in the prevention of osteoporotic fracture.

Olmesartan is not associated with the risk of enteropathy: a Korean nationwide observational cohort study.

BACKGROUND/AIMS: Olmesartan, a widely used angiotensin II receptor blocker (ARB), has been linked to sprue-like enteropathy. No cases of olmesartan-associated enteropathy have been reported in Northeast Asia. We investigated the associations between olmesartan and other ARBs and the incidence of enteropathy in Korea. METHODS: Our retrospective cohort study used data from the Korean National Health Insurance Service to identify 108,559 patients (58,186 females) who were initiated on angiotensin converting enzyme inhibitors (ACEis), olmesartan, or other ARBs between January 2005 and December 2012. The incidences of enteropathy were compared among drug groups. Changes in body weight were compared after propensity score matching of patients in the ACEis and olmesartan groups. RESULTS: Among 108,559 patients, 31 patients were diagnosed with enteropathy. The incidences were 0.73, 0.24, and 0.37 per 1,000 persons, in the ACEis, olmesartan, and other ARBs groups, respectively. Adjusted rate ratios for enteropathy were: olmesartan, 0.33 (95% confidential interval [CI], 0.10 to 1.09; p = 0.070) and other ARBs, 0.34 (95% CI, 0.14 to 0.83; p = 0.017) compared to the ACEis group after adjustment for age, sex, income level, and various comorbidities. The post hoc analysis with matched cohorts revealed that the proportion of patients with significant weight loss did not differ between the ACEis and olmesartan groups. CONCLUSION: Olmesartan was not associated with intestinal malabsorption or significant body weight loss in the general Korean population. Additional large-scale prospective studies of the relationship between olmesartan and the incidence of enteropathy in the Asian population are needed.

Construction of an Electrocardiogram Database Including 12 Lead Waveforms.

OBJECTIVES: Electrocardiogram (ECG) data are important for the study of cardiovascular disease and adverse drug reactions. Although the development of analytical techniques such as machine learning has improved our ability to extract useful information from ECGs, there is a lack of easily available ECG data for research purposes. We previously published an article on a database of ECG parameters and related clinical data (ECG-ViEW), which we have now updated with additional 12-lead waveform information. METHODS: All ECGs stored in portable document format (PDF) were collected from a tertiary teaching hospital in Korea over a 23-year study period. We developed software which can extract all ECG parameters and waveform information from the ECG reports in PDF format and stored it in a database (meta data) and a text file (raw waveform). RESULTS: Our database includes all parameters (ventricular rate, PR interval, QRS duration, QT/QTc interval, P-R-T axes, and interpretations) and 12-lead waveforms (for leads I, II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5, and V6) from 1,039,550 ECGs (from 447,445 patients). Demographics, drug exposure data, diagnosis history, and laboratory test results (serum calcium, magnesium, and potassium levels) were also extracted from electronic medical records and linked to the ECG information. CONCLUSIONS: Electrocardiogram information that includes 12 lead waveforms was extracted and transformed into a form that can be analyzed. The description and programming codes in this case report could be a reference for other researchers to build ECG databases using their own local ECG repository.

Extracting information from free-text electronic patient records to identify practice-based evidence of the performance of coronary stents.

BACKGROUND AND OBJECTIVE: Percutaneous coronary intervention (PCI) using drug-eluting stents (DES) is an indispensable treatment for coronary artery disease. However, to evaluate the performance of various types of stents for PCI, numerous resources are required. We extracted clinical information from free-text records and, using practice-based evidence, compared the efficacy of various DES. MATERIALS AND METHODS: We developed a text mining tool based on regular expression and applied it to PCI reports stored in the electronic health records (EHRs) of Ajou University Hospital from 2010-2014. The PCI data were extracted from EHRs with a sensitivity of 0.996, a specificity of 1.000, and an F-measure of 0.995 when compared with a sample of 200 reports. Using these data, we compared the performance of stents by Kaplan-Meier analysis and the Cox hazard proportional regression. RESULTS: In the self-validation analysis comparing the first-generation to the second-generation DES, the second-generation DES was superior to the first-generation DES (hazard ratio [HR]: 0.423, 95% confidence interval [CI]: 0.284-0.630) in terms of target vessel revascularization (TVR), showing similar findings to the established results of previous studies. Among the second-generation DES, the biodegradable-polymer DES tended to be superior, with a risk of TVR (HR: 0.568, 95% CI: 0.281-1.147) falling below than that for the durable-polymer DES approximately 1 year after the index procedure. The Endeavor stent had the highest TVR risk among the newer generation DES (HR: 2.576, 95% CI: 1.273-5.210). CONCLUSIONS: In this study, we demonstrated how to construct a PCI data warehouse of PCI-related parameters obtained from free-text electronic records with high accuracy for use in the post surveillance of coronary stents in a time- and cost effective manner. Post surveillance of the practice based evidence in the PCI data warehouse indicated that the biodegradable-polymer DES might have a lower risk of TVR than the durable-polymer DES.

Conversion of National Health Insurance Service-National Sample Cohort (NHIS-NSC) Database into Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM).

It is increasingly necessary to generate medical evidence applicable to Asian people compared to those in Western countries. Observational Health Data Sciences a Informatics (OHDSI) is an international collaborative which aims to facilitate generating high-quality evidence via creating and applying open-source data analytic solutions to a large network of health databases across countries. We aimed to incorporate Korean nationwide cohort data into the OHDSI network by converting the national sample cohort into Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM). The data of 1.13 million subjects was converted to OMOP-CDM, resulting in average 99.1% conversion rate. The ACHILLES, open-source OMOP-CDM-based data profiling tool, was conducted on the converted database to visualize data-driven characterization and access the quality of data. The OMOP-CDM version of National Health Insurance Service-National Sample Cohort (NHIS-NSC) can be a valuable tool for multiple aspects of medical research by incorporation into the OHDSI research network.

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

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