Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT.

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE), AUC, precision and F1-score achieved by our method are 91.79%, 93.05%, 89.95%, 96.35%, 93.10% and 93.07%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.

Validity of diagnoses of respiratory diseases recorded in a Japanese administrative database.

BACKGROUND: Validating the information recorded in administrative databases is essential. However, no study has comprehensively validated the accuracy of Japanese Diagnosis Procedure Combination (DPC) data on various respiratory diseases. Therefore, this study aimed to evaluate the validity of diagnoses of respiratory diseases in the DPC database. METHODS: We conducted chart reviews of 400 patients hospitalized in the departments of respiratory medicine in two acute-care hospitals in Tokyo, between April 1, 2019 and March 31, 2021, and used them as reference standards. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of DPC data on 25 respiratory diseases were determined. RESULTS: Sensitivity ranged from 22.2% (aspiration pneumonia) to 100% (chronic eosinophilic pneumonia and malignant pleural mesothelioma) and was <50% for eight diseases, while specificity was >90% for all diseases. PPV ranged from 40.0% (aspiration pneumonia) to 100% (coronavirus disease 2019, bronchiectasis, chronic eosinophilic pneumonia, pulmonary hypertension, squamous cell carcinoma, small cell carcinoma, lung cancer of other histological types, and malignant pleural mesothelioma) and was >80% for 16 diseases. Except for chronic obstructive pulmonary disease (82.9%) and interstitial pneumonia (other than idiopathic pulmonary fibrosis) (85.4%), NPV was >90% for all diseases. These validity indices were similar in both hospitals. CONCLUSIONS: The validity of diagnoses of respiratory diseases in the DPC database was high in general, thereby providing an important basis for future studies.

Healthcare costs and mortality associated with serious fluoroquinolone-related adverse reactions.

The aim of this study was to estimate healthcare costs and mortality associated with serious fluoroquinolone-related adverse reactions in Finland from 2008 to 2019. Serious adverse reaction types were identified from the Finnish Pharmaceutical Insurance Pool's pharmaceutical injury claims and the Finnish Medicines Agency's Adverse Reaction Register. A decision tree model was built to predict costs and mortality associated with serious adverse drug reactions (ADR). Severe clostridioides difficile infections, severe cutaneous adverse reactions, tendon ruptures, aortic ruptures, and liver injuries were included as serious adverse drug reactions in the model. Direct healthcare costs of a serious ADR were based on the number of reimbursed fluoroquinolone prescriptions from the Social Insurance Institution of Finland's database. Sensitivity analyses were conducted to address parameter uncertainty. A total of 1 831 537 fluoroquinolone prescriptions were filled between 2008 and 2019 in Finland, with prescription numbers declining 40% in recent years. Serious ADRs associated with fluoroquinolones lead to estimated direct healthcare costs of 501 938 402 €, including 11 405 ADRs and 3,884 deaths between 2008 and 2019. The average mortality risk associated with the use of fluoroquinolones was 0.21%. Severe clostridioides difficile infections were the most frequent, fatal, and costly serious ADRs associated with the use of fluoroquinolones. Although fluoroquinolones continue to be generally well-tolerated antimicrobials, serious adverse reactions cause long-term impairment to patients and high healthcare costs. Therefore, the risks and benefits should be weighed carefully in antibiotic prescription policies, as well as with individual patients.

Using an Administrative and Clinical Database to Determine the Early Spread of COVID-19 at the US Department of Veterans Affairs during the Beginning of the 2019-2020 Flu Season: A Retrospective Longitudinal Study.

BACKGROUND: Previous studies examining the early spread of COVID-19 have used influenza-like illnesses (ILIs) to determine the early spread of COVID-19. We used COVID-19 case definition to identify COVID-like symptoms (CLS) independently of other influenza-like illnesses (ILIs). METHODS: Using data from Emergency Department (ED) visits at VA Medical Centers in CA, TX, and FL, we compared weekly rates of CLS, ILIs, and non-influenza ILIs encounters during five consecutive flu seasons (2015-2020) and estimated the risk of developing each illness during the first 23 weeks of the 2019-2020 season compared to previous seasons. RESULTS: Patients with CLS were significantly more likely to visit the ED during the first 23 weeks of the 2019-2020 compared to prior seasons, while ED visits for influenza and non-influenza ILIs did not differ substantially. Adjusted CLS risk was significantly lower for all seasons relative to the 2019-2020 season: RR15-16 = 0.72, 0.75, 0.72; RR16-17 = 0.81, 0.77, 0.79; RR17-18 = 0.80, 0.89, 0.83; RR18-19 = 0.82, 0.96, 0.81, in CA, TX, and FL, respectively. CONCLUSIONS: The observed increase in ED visits for CLS indicates the likely spread of COVID-19 in the US earlier than previously reported. VA data could potentially help identify emerging infectious diseases and supplement existing syndromic surveillance systems.

Multimorbidity and adverse events of special interest associated with Covid-19 vaccines in Hong Kong.

Prior research using electronic health records for Covid-19 vaccine safety monitoring typically focuses on specific disease groups and excludes individuals with multimorbidity, defined as ≥2 chronic conditions. We examine the potential additional risk of adverse events 28 days after the first dose of CoronaVac or Comirnaty imposed by multimorbidity. Using a territory-wide public healthcare database with population-based vaccination records in Hong Kong, we analyze a retrospective cohort of patients with chronic conditions. Thirty adverse events of special interest according to the World Health Organization are examined. In total, 883,416 patients are included and 2,807 (0.3%) develop adverse events. Results suggest vaccinated patients have lower risks of adverse events than unvaccinated individuals, multimorbidity is associated with increased risks regardless of vaccination, and the association of vaccination with adverse events is not modified by multimorbidity. To conclude, we find no evidence that multimorbidity imposes extra risks of adverse events following Covid-19 vaccination.

Development of an affective database made of interactive virtual environments.

Despite the great potential of Virtual Reality (VR) to arouse emotions, there are no VR affective databases available as it happens for pictures, videos, and sounds. In this paper, we describe the validation of ten affective interactive Virtual Environments (VEs) designed to be used in Virtual Reality. These environments are related to five emotions. The testing phase included using two different experimental setups to deliver the overall experience. The setup did not include any immersive VR technology, because of the ongoing COVID-19 pandemic, but the VEs were designed to run on stereoscopic visual displays. We collected measures related to the participants' emotional experience based on six discrete emotional categories plus neutrality and we included an assessment of the sense of presence related to the different experiences. The results showed how the scenarios can be differentiated according to the emotion aroused. Finally, the comparison between the two experimental setups demonstrated high reliability of the experience and strong adaptability of the scenarios to different contexts of use.

The European Bioinformatics Institute (EMBL-EBI) in 2021.

The European Bioinformatics Institute (EMBL-EBI) maintains a comprehensive range of freely available and up-to-date molecular data resources, which includes over 40 resources covering every major data type in the life sciences. This year's service update for EMBL-EBI includes new resources, PGS Catalog and AlphaFold DB, and updates on existing resources, including the COVID-19 Data Platform, trRosetta and RoseTTAfold models introduced in Pfam and InterPro, and the launch of Genome Integrations with Function and Sequence by UniProt and Ensembl. Furthermore, we highlight projects through which EMBL-EBI has contributed to the development of community-driven data standards and guidelines, including the Recommended Metadata for Biological Images (REMBI), and the BioModels Reproducibility Scorecard. Training is one of EMBL-EBI's core missions and a key component of the provision of bioinformatics services to users: this year's update includes many of the improvements that have been developed to EMBL-EBI's online training offering.

Scientometric analysis of medical publications during COVID-19 pandemic: the twenty-twenty research boom.

INTRODUCTION: There was significant surge in the academic publications after the onset of COVID-19 outbreak. The aim of this study was to scientometrically analyze all the medical publications on COVID-19 in 2020 as well as the top 100 cited articles. EVIDENCE ACQUISITION: We performed a search of the "Web of Science" database using the keywords "COVID," and "corona" on December 20, 2020. EVIDENCE SYNTHESIS: Our search retrieved a total of 45,420 articles on the topic COVID-19 in the year 2020. Corresponding authors from 143 countries contributed to these articles. The highest number of articles were contributed by corresponding authors from the USA (N.=10299), whereas 50 articles in the top 100 cited articles had corresponding authors from China. Among the top 100 cited, the majority were published from Huazhong University of Science and Technology in China (N.=37). New England Journal of Medicine had the maximum impact (h-index of 57), closely followed by Lancet (h-index=55). CONCLUSIONS: Scientific publications amount on COVID-19 disease grew at an astonishing pace during 2020. We caution the readers that this rapidity of publication could have missed out on the rigorous review process and the scientific basis of the methods followed.

Automated Diagnosis of Chest X-Ray for Early Detection of COVID-19 Disease.

In March 2020, the World Health Organization announced the COVID-19 pandemic, its dangers, and its rapid spread throughout the world. In March 2021, the second wave of the pandemic began with a new strain of COVID-19, which was more dangerous for some countries, including India, recording 400,000 new cases daily and more than 4,000 deaths per day. This pandemic has overloaded the medical sector, especially radiology. Deep-learning techniques have been used to reduce the burden on hospitals and assist physicians for accurate diagnoses. In our study, two models of deep learning, ResNet-50 and AlexNet, were introduced to diagnose X-ray datasets collected from many sources. Each network diagnosed a multiclass (four classes) and a two-class dataset. The images were processed to remove noise, and a data augmentation technique was applied to the minority classes to create a balance between the classes. The features extracted by convolutional neural network (CNN) models were combined with traditional Gray-level Cooccurrence Matrix (GLCM) and Local Binary Pattern (LBP) algorithms in a 1-D vector of each image, which produced more representative features for each disease. Network parameters were tuned for optimum performance. The ResNet-50 network reached accuracy, sensitivity, specificity, and Area Under the Curve (AUC) of 95%, 94.5%, 98%, and 97.10%, respectively, with the multiclasses (COVID-19, viral pneumonia, lung opacity, and normal), while it reached accuracy, sensitivity, specificity, and AUC of 99%, 98%, 98%, and 97.51%, respectively, with the binary classes (COVID-19 and normal).

Toward a systematic conflict resolution framework for ontologies.

BACKGROUND: The ontology authoring step in ontology development involves having to make choices about what subject domain knowledge to include. This may concern sorting out ontological differences and making choices between conflicting axioms due to limitations in the logic or the subject domain semantics. Examples are dealing with different foundational ontologies in ontology alignment and OWL 2 DL's transitive object property versus a qualified cardinality constraint. Such conflicts have to be resolved somehow. However, only isolated and fragmented guidance for doing so is available, which therefore results in ad hoc decision-making that may not be the best choice or forgotten about later. RESULTS: This work aims to address this by taking steps towards a framework to deal with the various types of modeling conflicts through meaning negotiation and conflict resolution in a systematic way. It proposes an initial library of common conflicts, a conflict set, typical steps toward resolution, and the software availability and requirements needed for it. The approach was evaluated with an actual case of domain knowledge usage in the context of epizootic disease outbreak, being avian influenza, and running examples with COVID-19 ontologies. CONCLUSIONS: The evaluation demonstrated the potential and feasibility of a conflict resolution framework for ontologies.

Developing a Recognition System for Diagnosing Melanoma Skin Lesions Using Artificial Intelligence Algorithms.

In recent years, computerized biomedical imaging and analysis have become extremely promising, more interesting, and highly beneficial. They provide remarkable information in the diagnoses of skin lesions. There have been developments in modern diagnostic systems that can help detect melanoma in its early stages to save the lives of many people. There is also a significant growth in the design of computer-aided diagnosis (CAD) systems using advanced artificial intelligence. The purpose of the present research is to develop a system to diagnose skin cancer, one that will lead to a high level of detection of the skin cancer. The proposed system was developed using deep learning and traditional artificial intelligence machine learning algorithms. The dermoscopy images were collected from the PH2 and ISIC 2018 in order to examine the diagnose system. The developed system is divided into feature-based and deep leaning. The feature-based system was developed based on feature-extracting methods. In order to segment the lesion from dermoscopy images, the active contour method was proposed. These skin lesions were processed using hybrid feature extractions, namely, the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) methods to extract the texture features. The obtained features were then processed using the artificial neural network (ANNs) algorithm. In the second system, the convolutional neural network (CNNs) algorithm was applied for the efficient classification of skin diseases; the CNNs were pretrained using large AlexNet and ResNet50 transfer learning models. The experimental results show that the proposed method outperformed the state-of-art methods for HP2 and ISIC 2018 datasets. Standard evaluation metrics like accuracy, specificity, sensitivity, precision, recall, and F-score were employed to evaluate the results of the two proposed systems. The ANN model achieved the highest accuracy for PH2 (97.50%) and ISIC 2018 (98.35%) compared with the CNN model. The evaluation and comparison, proposed systems for classification and detection of melanoma are presented.

Tocilizumab in COVID-19: a study of adverse drug events reported in the WHO database.

BACKGROUND: Elevated inflammatory cytokines in Coronavirus disease 2019 (COVID-19) affect the lungs leading to pneumonitis with a poor prognosis. Tocilizumab, a type of humanized monoclonal antibody antagonizing interleukin-6 receptors, is currently utilized to treat COVID-19. The present study reviews tocilizumab adverse drug events (ADEs) reported in the World Health Organization (WHO) pharmacovigilance database. RESEARCH DESIGN AND METHODS: All suspected ADEs associated with tocilizumab between April to August 2020 were analyzed based on COVID-19 patients' demographic and clinical variables, and severity of involvement of organ system. RESULTS: A total of 1005 ADEs were reported among 513 recipients. The majority of the ADEs (46.26%) were reported from 18-64 years, were males and reported spontaneously. Around 80%, 20%, and 64% were serious, fatal, and administered intravenously, respectively. 'Injury, Poisoning, and Procedural Complications' remain as highest (35%) among categorized ADEs. Neutropenia, hypofibrinogenemia were common hematological ADEs. The above 64 years was found to have significantly lower odds than of below 45 years. In comparison, those in the European Region have substantially higher odds compared to the Region of Americas. CONCLUSION: Neutropenia, superinfections, reactivation of latent infections, hepatitis, and cardiac abnormalities were common ADEs observed that necessitate proper monitoring and reporting.

Characteristics of systematic reviews evaluating treatments for COVID-19 registered in PROSPERO.

Characteristics and research collaboration of registered systematic reviews (SRs) on treatment modalities for coronavirus disease-2019 (COVID-19) remain unclear. This study analysed research collaboration, interventions and outcome measures in registered SRs on COVID-19 treatments and pointed out the relevant problems. PROSPERO (international prospective register of systematic reviews) was searched for SRs on COVID-19 treatments as of 2 June 2020. Excel 2016 was used for descriptive analyses of the extracted data. VOSviewer 1.6.14 software was used to generate network maps for collaborations between countries and institutions. A total of 189 SRs were included, which were registered by 301 institutions from 39 countries. China (69, 36.50%) exhibited the highest output. Cooperation between countries was not close enough. As an institution, the Chengdu University of Traditional Chinese Medicine (7, 3.70%) had the highest output. There was close cooperation between institutions. Interventions included antiviral therapy (81, 42.86%), respiratory support (16, 8.47%), circulatory support (11, 5.82%), plasma therapy for convalescent patients (11, 5.82%), immunotherapy (9, 4.76%), TCM (traditional Chinese medicine) treatment (9, 4.76%), rehabilitation treatment (5, 2.65%), anti-inflammatory treatment (16, 8.47%) and other treatments (31, 16.40%). Concerning antiviral therapy (81, 42.86%), the most commonly used antiviral agents were chloroquine/hydroxychloroquine (26, 13.76%), followed by remdesivir (12, 6.35%), lobinavir/ritonavir (11, 5.82%), favipiravir (5, 2.65%), ribavirin (5, 2.65%), interferon (5, 2.65%), abiron (4, 2.12%) and abidor (4, 2.12%). The most frequently used primary and secondary outcomes were the mortality rate (92, 48.68%) and hospital stay length (48, 25.40%), respectively. The expression of the outcomes was not standardised. Many COVID-19 SRs on treatment modalities have been registered, with a low completion rate. Although there was some collaboration between countries and institutions in the currently registered SRs on treatment modalities for COVID-19 on PROSPERO, cooperation between countries should be further enhanced. More attention should be directed towards identifying deficiencies of outcome measures, and the standardisation of results should be maximised.

A COVID-19-ready public health surveillance system: The Food and Drug Administration's Sentinel System.

The US Food and Drug Administration's Sentinel System was established in 2009 to use routinely collected electronic health data for improving the national capability to assess post-market medical product safety. Over more than a decade, Sentinel has become an integral part of FDA's surveillance capabilities and has been used to conduct analyses that have contributed to regulatory decisions. FDA's role in the COVID-19 pandemic response has necessitated an expansion and enhancement of Sentinel. Here we describe how the Sentinel System has supported FDA's response to the COVID-19 pandemic. We highlight new capabilities developed, key data generated to date, and lessons learned, particularly with respect to working with inpatient electronic health record data. Early in the pandemic, Sentinel developed a multi-pronged approach to support FDA's anticipated data and analytic needs. It incorporated new data sources, created a rapidly refreshed database, developed protocols to assess the natural history of COVID-19, validated a diagnosis-code based algorithm for identifying patients with COVID-19 in administrative claims data, and coordinated with other national and international initiatives. Sentinel is poised to answer important questions about the natural history of COVID-19 and is positioned to use this information to study the use, safety, and potentially the effectiveness of medical products used for COVID-19 prevention and treatment.

Determinants of COVID-19 Mortality in Patients With Cancer From a Community Oncology Practice in Brazil.

PURPOSE: The COVID-19 pandemic remains a public health emergency of global concern. Determinants of mortality in the general population are now clear, but specific data on patients with cancer remain limited, particularly in Latin America. MATERIALS AND METHODS: A longitudinal multicenter cohort study of patients with cancer and confirmed COVID-19 from Oncoclínicas community oncology practice in Brazil was conducted. The primary end point was all-cause mortality after isolation of the SARS-CoV-2 by Real-Time Polymerase Chain Reaction (RT-PCR) in patients initially diagnosed in an outpatient environment. We performed univariate and multivariable logistic regression analysis and recursive partitioning modeling to define the baseline clinical determinants of death in the overall population. RESULTS: From March 29 to July 4, 2020, 198 patients with COVID-19 were prospectively registered in the database, of which 167 (84%) had solid tumors and 31 (16%) had hematologic malignancies. Most patients were on active systemic therapy or radiotherapy (77%), largely for advanced or metastatic disease (64%). The overall mortality rate was 16.7% (95% CI, 11.9 to 22.7). In univariate models, factors associated with death after COVID-19 diagnosis were age ≥ 60 years, current or former smoking, coexisting comorbidities, respiratory tract cancer, and management in a noncurative setting (P < .05). In multivariable logistic regression and recursive partitioning modeling, only age, smoking history, and noncurative disease setting remained significant determinants of mortality, ranging from 1% in cancer survivors under surveillance or (neo)adjuvant therapy to 60% in elderly smokers with advanced or metastatic disease. CONCLUSION: Mortality after COVID-19 in patients with cancer is influenced by prognostic factors that also affect outcomes of the general population. Fragile patients and smokers are entitled to active preventive measures to reduce the risk of SARS-CoV-2 infection and close monitoring in the case of exposure or COVID-19-related symptoms.

The RSNA International COVID-19 Open Radiology Database (RICORD).

The coronavirus disease 2019 (COVID-19) pandemic is a global health care emergency. Although reverse-transcription polymerase chain reaction testing is the reference standard method to identify patients with COVID-19 infection, chest radiography and CT play a vital role in the detection and management of these patients. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making. However, inadequate availability of a diverse annotated data set has limited the performance and generalizability of existing models. To address this unmet need, the RSNA and Society of Thoracic Radiology collaborated to develop the RSNA International COVID-19 Open Radiology Database (RICORD). This database is the first multi-institutional, multinational, expert-annotated COVID-19 imaging data set. It is made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. Pixel-level volumetric segmentation with clinical annotations was performed by thoracic radiology subspecialists for all COVID-19-positive thoracic CT scans. The labeling schema was coordinated with other international consensus panels and COVID-19 data annotation efforts, the European Society of Medical Imaging Informatics, the American College of Radiology, and the American Association of Physicists in Medicine. Study-level COVID-19 classification labels for chest radiographs were annotated by three radiologists, with majority vote adjudication by board-certified radiologists. RICORD consists of 240 thoracic CT scans and 1000 chest radiographs contributed from four international sites. It is anticipated that RICORD will ideally lead to prediction models that can demonstrate sustained performance across populations and health care systems.

Suitability of databases in the Asia-Pacific for collaborative monitoring of vaccine safety.

INTRODUCTION: Information regarding availability of electronic healthcare databases in the Asia-Pacific region is critical for planning vaccine safety assessments particularly, as COVID-19 vaccines are introduced. This study aimed to identify data sources in the region, potentially suitable for vaccine safety surveillance. This manuscript is endorsed by the International Society for Pharmacoepidemiology (ISPE). METHODS: Nineteen countries targeted for database reporting were identified using published country lists and review articles. Surveillance capacity was assessed using two surveys: a 9-item introductory survey and a 51-item full survey. Survey questions related to database characteristics, covariate and health outcome variables, vaccine exposure characteristics, access and governance, and dataset linkage capability. Other questions collated research/regulatory applications of the data and local publications detailing database use for research. RESULTS: Eleven databases containing vaccine-specific information were identified across 8 countries. Databases were largely national in coverage (8/11, 73%), encompassed all ages (9/11, 82%) with population size from 1.4 to 52 million persons. Vaccine exposure information varied particularly for standardized vaccine codes (5/11, 46%), brand (7/11, 64%) and manufacturer (5/11, 46%). Outcome data were integrated with vaccine data in 6 (55%) databases and available via linkage in 5 (46%) databases. Data approval processes varied, impacting on timeliness of data access. CONCLUSIONS: Variation in vaccine data availability, complexities in data access including, governance and data release approval procedures, together with requirement for data linkage for outcome information, all contribute to the challenges in building a distributed network for vaccine safety assessment in the Asia-Pacific and globally. Common data models (CDMs) may help expedite vaccine safety research across the region.

A Systematic Review and Meta-Analysis of Cancer Patients Affected by a Novel Coronavirus.

BACKGROUND: Cancer patients with coronavirus disease 2019 (COVID-19) have been reported to have double the case fatality rate of the general population. METHODS: A systematic search of PubMed, Embase, and Cochrane Central was done for studies on cancer patients with COVID-19. Pooled proportions were calculated for categorical variables. Odds ratio (OR) and forest plots (random-effects model) were constructed for both primary and secondary outcomes. RESULTS: This systematic review of 38 studies and meta-analysis of 181 323 patients from 26 studies included 23 736 cancer patients. Our meta-analysis shows that cancer patients with COVID-19 have a higher likelihood of death (n = 165 980, OR = 2.54, 95% confidence interval [CI] = 1.47 to 4.42), which was largely driven by mortality among patients in China. Cancer patients were more likely to be intubated. Among cancer subtypes, the mortality was highest in hematological malignancies (n = 878, OR = 2.39, 95% CI = 1.17 to 4.87) followed by lung cancer (n = 646, OR = 1.83, 95% CI = 1.00 to 3.37). There was no association between receipt of a particular type of oncologic therapy and mortality. Our study showed that cancer patients affected by COVID-19 are a decade older than the normal population and have a higher proportion of comorbidities. There was insufficient data to assess the association of COVID-19-directed therapy and survival outcomes in cancer patients. CONCLUSION: Cancer patients with COVID-19 disease are at increased risk of mortality and morbidity. A more nuanced understanding of the interaction between cancer-directed therapies and COVID-19-directed therapies is needed. This will require uniform prospective recording of data, possibly in multi-institutional registry databases.

COVID-19 International Collaborative Research by the Health Insurance Review and Assessment Service Using Its Nationwide Real-world Data: Database, Outcomes, and Implications.

This article aims to introduce the inception and operation of the COVID-19 International Collaborative Research Project, the world's first coronavirus disease 2019 (COVID-19) open data project for research, along with its dataset and research method, and to discuss relevant considerations for collaborative research using nationwide real-world data (RWD). COVID-19 has spread across the world since early 2020, becoming a serious global health threat to life, safety, and social and economic activities. However, insufficient RWD from patients was available to help clinicians efficiently diagnose and treat patients with COVID-19, or to provide necessary information to the government for policy-making. Countries that saw a rapid surge of infections had to focus on leveraging medical professionals to treat patients, and the circumstances made it even more difficult to promptly use COVID-19 RWD. Against this backdrop, the Health Insurance Review and Assessment Service (HIRA) of Korea decided to open its COVID-19 RWD collected through Korea's universal health insurance program, under the title of the COVID-19 International Collaborative Research Project. The dataset, consisting of 476 508 claim statements from 234 427 patients (7590 confirmed cases) and 18 691 318 claim statements of the same patients for the previous 3 years, was established and hosted on HIRA's in-house server. Researchers who applied to participate in the project uploaded analysis code on the platform prepared by HIRA, and HIRA conducted the analysis and provided outcome values. As of November 2020, analyses have been completed for 129 research projects, which have been published or are in the process of being published in prestigious journals.