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1.
Sci Rep ; 11(1): 24108, 2021 12 16.
Article in English | MEDLINE | ID: covidwho-1585796

ABSTRACT

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.


Subject(s)
Arousal/physiology , COVID-19/psychology , Databases, Factual/statistics & numerical data , Emotions/physiology , SARS-CoV-2/isolation & purification , Virtual Reality , Adult , COVID-19/epidemiology , COVID-19/virology , Emotions/classification , Empathy , Female , Humans , Male , Pandemics/prevention & control , Photic Stimulation/methods , Reproducibility of Results , SARS-CoV-2/physiology , Young Adult
2.
Comput Math Methods Med ; 2021: 6919483, 2021.
Article in English | MEDLINE | ID: covidwho-1484105

ABSTRACT

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).


Subject(s)
COVID-19/diagnostic imaging , Deep Learning , SARS-CoV-2 , Tomography, X-Ray Computed/methods , Algorithms , Computational Biology , Databases, Factual/statistics & numerical data , Diagnosis, Computer-Assisted/methods , Diagnosis, Computer-Assisted/statistics & numerical data , Early Diagnosis , Humans , Lung/diagnostic imaging , Neural Networks, Computer , Pandemics , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/statistics & numerical data
3.
J Biomed Semantics ; 12(1): 15, 2021 08 09.
Article in English | MEDLINE | ID: covidwho-1350153

ABSTRACT

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.


Subject(s)
Biological Ontologies/statistics & numerical data , Computational Biology/statistics & numerical data , Information Storage and Retrieval/statistics & numerical data , Semantic Web , Semantics , Vocabulary, Controlled , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/virology , Computational Biology/methods , Databases, Factual/statistics & numerical data , Epidemics/prevention & control , Humans , Information Storage and Retrieval/methods , Logic , SARS-CoV-2/physiology
4.
Comput Math Methods Med ; 2021: 9998379, 2021.
Article in English | MEDLINE | ID: covidwho-1314186

ABSTRACT

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.


Subject(s)
Diagnosis, Computer-Assisted/methods , Melanoma/diagnostic imaging , Skin Neoplasms/diagnostic imaging , Algorithms , Artificial Intelligence , Computational Biology , Databases, Factual/statistics & numerical data , Deep Learning , Dermoscopy , Diagnosis, Computer-Assisted/statistics & numerical data , Early Detection of Cancer/methods , Early Detection of Cancer/statistics & numerical data , Humans , Image Enhancement/methods , Image Interpretation, Computer-Assisted/methods , Image Interpretation, Computer-Assisted/statistics & numerical data , Neural Networks, Computer , Skin Diseases/classification , Skin Diseases/diagnostic imaging
5.
Expert Opin Drug Saf ; 20(9): 1125-1136, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1286513

ABSTRACT

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.


Subject(s)
Adverse Drug Reaction Reporting Systems/statistics & numerical data , Antibodies, Monoclonal, Humanized/adverse effects , COVID-19/drug therapy , Pharmacovigilance , Adolescent , Adult , Age Distribution , Aged , Antibodies, Monoclonal, Humanized/administration & dosage , Databases, Factual/statistics & numerical data , Female , Humans , Male , Middle Aged , Sex Distribution , World Health Organization , Young Adult
6.
Epidemiol Infect ; 149: e146, 2021 06 16.
Article in English | MEDLINE | ID: covidwho-1270642

ABSTRACT

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.


Subject(s)
COVID-19/therapy , Databases, Factual/statistics & numerical data , Antiviral Agents/therapeutic use , Humans , Internationality , Intersectoral Collaboration , SARS-CoV-2 , Systematic Reviews as Topic , Treatment Outcome
7.
Pharmacoepidemiol Drug Saf ; 30(7): 827-837, 2021 07.
Article in English | MEDLINE | ID: covidwho-1192592

ABSTRACT

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.


Subject(s)
COVID-19/therapy , Health Information Management/organization & administration , Product Surveillance, Postmarketing/methods , Public Health Surveillance/methods , United States Food and Drug Administration/organization & administration , Antiviral Agents/therapeutic use , COVID-19/epidemiology , COVID-19/virology , COVID-19 Vaccines/administration & dosage , COVID-19 Vaccines/adverse effects , Communicable Disease Control/legislation & jurisprudence , Databases, Factual/statistics & numerical data , Electronic Health Records/statistics & numerical data , Health Policy , Humans , Pandemics/prevention & control , Pandemics/statistics & numerical data , United States/epidemiology , United States Food and Drug Administration/legislation & jurisprudence
8.
JCO Glob Oncol ; 7: 46-55, 2021 01.
Article in English | MEDLINE | ID: covidwho-1154054

ABSTRACT

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.


Subject(s)
COVID-19/mortality , Cancer Survivors/statistics & numerical data , Neoplasms/mortality , SARS-CoV-2/isolation & purification , Adult , Aged , Aged, 80 and over , Brazil/epidemiology , COVID-19/diagnosis , COVID-19/virology , COVID-19 Nucleic Acid Testing/statistics & numerical data , Cause of Death , Databases, Factual/statistics & numerical data , Female , Frailty/epidemiology , Humans , Longitudinal Studies , Male , Medical Oncology/statistics & numerical data , Middle Aged , Neoplasms/complications , Prognosis , Prospective Studies , RNA, Viral/isolation & purification , Risk Assessment/statistics & numerical data , Risk Factors , SARS-CoV-2/genetics , Smoking/epidemiology , Young Adult
9.
Radiology ; 299(1): E204-E213, 2021 04.
Article in English | MEDLINE | ID: covidwho-1147215

ABSTRACT

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.


Subject(s)
COVID-19/diagnostic imaging , Databases, Factual/statistics & numerical data , Global Health/statistics & numerical data , Lung/diagnostic imaging , Tomography, X-Ray Computed/methods , Humans , Internationality , Radiography, Thoracic , Radiology , SARS-CoV-2 , Societies, Medical , Tomography, X-Ray Computed/statistics & numerical data
10.
Pharmacoepidemiol Drug Saf ; 30(7): 843-857, 2021 07.
Article in English | MEDLINE | ID: covidwho-1103356

ABSTRACT

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.


Subject(s)
COVID-19 Vaccines/adverse effects , COVID-19/prevention & control , Health Information Interoperability , Pharmacoepidemiology/methods , Product Surveillance, Postmarketing/methods , Asia/epidemiology , COVID-19/epidemiology , COVID-19/immunology , COVID-19/virology , COVID-19 Vaccines/administration & dosage , Databases, Factual/statistics & numerical data , Electronic Health Records/statistics & numerical data , Geography , Humans , International Cooperation , Pacific Islands/epidemiology , Pharmacoepidemiology/organization & administration , Pharmacovigilance , Product Surveillance, Postmarketing/statistics & numerical data , SARS-CoV-2/immunology
11.
J Prev Med Public Health ; 54(1): 8-16, 2021 Jan.
Article in English | MEDLINE | ID: covidwho-1097326

ABSTRACT

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.


Subject(s)
COVID-19/prevention & control , Insurance Carriers/statistics & numerical data , Internationality , COVID-19/transmission , Databases, Factual/statistics & numerical data , Humans , Outcome Assessment, Health Care/standards , Outcome Assessment, Health Care/statistics & numerical data , Quality of Health Care/standards , Quality of Health Care/statistics & numerical data , Republic of Korea
13.
J Med Libr Assoc ; 109(1): 75-83, 2021 Jan 01.
Article in English | MEDLINE | ID: covidwho-1060095

ABSTRACT

Objective: There are concerns about nonscientific and/or unclear information on the coronavirus disease 2019 (COVID-19) that is available on the Internet. Furthermore, people's ability to understand health information varies and depends on their skills in reading and interpreting information. This study aims to evaluate the readability and creditability of websites with COVID-19-related information. Methods: The search terms "coronavirus," "COVID," and "COVID-19" were input into Google. The websites of the first thirty results for each search term were evaluated in terms of their credibility and readability using the Health On the Net Foundation code of conduct (HONcode) and Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), Gunning Fog, and Flesch Reading Ease Score (FRE) scales, respectively. Results: The readability of COVID-19-related health information on websites was suitable for high school graduates or college students and, thus, was far above the recommended readability level. Most websites that were examined (87.2%) had not been officially certified by HONcode. There was no significant difference in the readability scores of websites with and without HONcode certification. Conclusion: These results suggest that organizations should improve the readability of their websites and provide information that more people can understand. This could lead to greater health literacy, less health anxiety, and the provision of better preventive information about the disease.


Subject(s)
COVID-19/nursing , Comprehension , Consumer Health Information/methods , Data Accuracy , Databases, Factual/statistics & numerical data , Health Literacy/methods , Internet , Self Care/methods , Adult , Aged , Aged, 80 and over , COVID-19/physiopathology , Female , Humans , Male , Middle Aged , SARS-CoV-2
14.
JAMA Netw Open ; 3(12): e2030072, 2020 12 01.
Article in English | MEDLINE | ID: covidwho-1051185

ABSTRACT

Importance: Resource limitations because of pandemic or other stresses on infrastructure necessitate the triage of time-sensitive care, including cancer treatments. Optimal time to treatment is underexplored, so recommendations for which cancer treatments can be deferred are often based on expert opinion. Objective: To evaluate the association between increased time to definitive therapy and mortality as a function of cancer type and stage for the 4 most prevalent cancers in the US. Design, Setting, and Participants: This cohort study assessed treatment and outcome information from patients with nonmetastatic breast, prostate, non-small cell lung (NSCLC), and colon cancers from 2004 to 2015, with data analyzed January to March 2020. Data on outcomes associated with appropriate curative-intent surgical, radiation, or medical therapy were gathered from the National Cancer Database. Exposures: Time-to-treatment initiation (TTI), the interval between diagnosis and therapy, using intervals of 8 to 60, 61 to 120, 121 to 180, and greater than 180 days. Main Outcomes and Measures: 5-year and 10-year predicted all-cause mortality. Results: This study included 2 241 706 patients (mean [SD] age 63 [11.9] years, 1 268 794 [56.6%] women, 1 880 317 [83.9%] White): 1 165 585 (52.0%) with breast cancer, 853 030 (38.1%) with prostate cancer, 130 597 (5.8%) with NSCLC, and 92 494 (4.1%) with colon cancer. Median (interquartile range) TTI by cancer was 32 (21-48) days for breast, 79 (55-117) days for prostate, 41 (27-62) days for NSCLC, and 26 (16-40) days for colon. Across all cancers, a general increase in the 5-year and 10-year predicted mortality was associated with increasing TTI. The most pronounced mortality association was for colon cancer (eg, 5 y predicted mortality, stage III: TTI 61-120 d, 38.9% vs. 181-365 d, 47.8%), followed by stage I NSCLC (5 y predicted mortality: TTI 61-120 d, 47.4% vs 181-365 d, 47.6%), while survival for prostate cancer was least associated (eg, 5 y predicted mortality, high risk: TTI 61-120 d, 12.8% vs 181-365 d, 14.1%), followed by breast cancer (eg, 5 y predicted mortality, stage I: TTI 61-120 d, 11.0% vs. 181-365 d, 15.2%). A nonsignificant difference in treatment delays and worsened survival was observed for stage II lung cancer patients-who had the highest all-cause mortality for any TTI regardless of treatment timing. Conclusions and Relevance: In this cohort study, for all studied cancers there was evidence that shorter TTI was associated with lower mortality, suggesting an indirect association between treatment deferral and mortality that may not become evident for years. In contrast to current pandemic-related guidelines, these findings support more timely definitive treatment for intermediate-risk and high-risk prostate cancer.


Subject(s)
Antineoplastic Protocols , Breast Neoplasms , Colonic Neoplasms , Lung Neoplasms , Prostatic Neoplasms , Time-to-Treatment , Aged , Breast Neoplasms/mortality , Breast Neoplasms/pathology , Breast Neoplasms/therapy , COVID-19/epidemiology , COVID-19/prevention & control , Cohort Studies , Colonic Neoplasms/mortality , Colonic Neoplasms/pathology , Colonic Neoplasms/therapy , Databases, Factual/statistics & numerical data , Female , Humans , Lung Neoplasms/mortality , Lung Neoplasms/pathology , Lung Neoplasms/therapy , Male , Middle Aged , Mortality , Neoplasm Staging , Prognosis , Prostatic Neoplasms/mortality , Prostatic Neoplasms/pathology , Prostatic Neoplasms/therapy , Time-to-Treatment/standards , Time-to-Treatment/statistics & numerical data , United States/epidemiology
15.
Intern Emerg Med ; 16(6): 1487-1496, 2021 09.
Article in English | MEDLINE | ID: covidwho-1008090

ABSTRACT

The factors that predispose an individual to a higher risk of death from COVID-19 are poorly understood. The goal of the study was to identify factors associated with risk of death among patients with COVID-19. This is a retrospective cohort study of people with laboratory-confirmed SARS-CoV-2 infection from February to May 22, 2020. Data retrieved for this study included patient sociodemographic data, baseline comorbidities, baseline treatments, other background data on care provided in hospital or primary care settings, and vital status. Main outcome was deaths until June 29, 2020. In the multivariable model based on nursing home residents, predictors of mortality were being male, older than 80 years, admitted to a hospital for COVID-19, and having cardiovascular disease, kidney disease or dementia while taking anticoagulants or lipid-lowering drugs at baseline was protective. The AUC was 0.754 for the risk score based on this model and 0.717 in the validation subsample. Predictors of death among people from the general population were being male and/or older than 60 years, having been hospitalized in the month before admission for COVID-19, being admitted to a hospital for COVID-19, having cardiovascular disease, dementia, respiratory disease, liver disease, diabetes with organ damage, or cancer while being on anticoagulants was protective. The AUC was 0.941 for this model's risk score and 0.938 in the validation subsample. Our risk scores could help physicians identify high-risk groups and establish preventive measures and better follow-up for patients at high risk of dying.ClinicalTrials.gov Identifier: NCT04463706.


Subject(s)
COVID-19/mortality , Databases, Factual/statistics & numerical data , Nursing Homes/statistics & numerical data , Aged , Aged, 80 and over , Comorbidity , Female , Humans , Male , Middle Aged , Prognosis , Retrospective Studies , Risk Factors , Survival Rate
16.
Arch Dis Child Fetal Neonatal Ed ; 106(3): 327-329, 2021 May.
Article in English | MEDLINE | ID: covidwho-940788

ABSTRACT

The reduction in the use of neonatal intensive care units (NICUs) during the COVID-19 outbreak has been reported, but whether this phenomenon is widespread across countries is unclear. Using a large-scale inpatient database in Japan, we analysed the intensive neonatal care volume and the number of preterm births for weeks 10-17 vs weeks 2-9 (during and before the outbreak) of 2020 with adjustment for the trends during the same period of 2019. We found statistically significant reductions in the numbers of NICU admissions (adjusted incidence rate ratio (aIRR), 0.76; 95% CI, 0.65 to 0.89) and neonatal resuscitations (aIRR, 0.37; 95% CI, 0.25 to 0.55) during the COVID-19 outbreak. Along with the decrease in the intensive neonatal care volume, preterm births before 34 gestational weeks (aIRR, 0.71) and between 34 0/7 and 36 6/7 gestational weeks (aIRR, 0.85) also showed a significant reduction. Further studies about the mechanism of this phenomenon are warranted.


Subject(s)
COVID-19 , Intensive Care Units, Neonatal/statistics & numerical data , Intensive Care, Neonatal , Patient Acceptance of Health Care/statistics & numerical data , Premature Birth , COVID-19/epidemiology , COVID-19/prevention & control , Databases, Factual/statistics & numerical data , Female , Gestational Age , Humans , Infant, Newborn , Infant, Premature , Intensive Care, Neonatal/methods , Intensive Care, Neonatal/statistics & numerical data , Intensive Care, Neonatal/trends , Japan/epidemiology , Neonatology/statistics & numerical data , Neonatology/trends , Patient Admission/statistics & numerical data , Pregnancy , Premature Birth/epidemiology , Premature Birth/therapy , Resuscitation/statistics & numerical data , SARS-CoV-2
17.
Nucleic Acids Res ; 49(D1): D1507-D1514, 2021 01 08.
Article in English | MEDLINE | ID: covidwho-920714

ABSTRACT

Europe PMC (https://europepmc.org) is a database of research articles, including peer reviewed full text articles and abstracts, and preprints - all freely available for use via website, APIs and bulk download. This article outlines new developments since 2017 where work has focussed on three key areas: (i) Europe PMC has added to its core content to include life science preprint abstracts and a special collection of full text of COVID-19-related preprints. Europe PMC is unique as an aggregator of biomedical preprints alongside peer-reviewed articles, with over 180 000 preprints available to search. (ii) Europe PMC has significantly expanded its links to content related to the publications, such as links to Unpaywall, providing wider access to full text, preprint peer-review platforms, all major curated data resources in the life sciences, and experimental protocols. The redesigned Europe PMC website features the PubMed abstract and corresponding PMC full text merged into one article page; there is more evident and user-friendly navigation within articles and to related content, plus a figure browse feature. (iii) The expanded annotations platform offers ∼1.3 billion text mined biological terms and concepts sourced from 10 providers and over 40 global data resources.


Subject(s)
Biological Science Disciplines/statistics & numerical data , COVID-19/prevention & control , Data Curation/statistics & numerical data , Data Mining/statistics & numerical data , Databases, Factual/statistics & numerical data , PubMed , SARS-CoV-2/isolation & purification , Biological Science Disciplines/methods , Biomedical Research/methods , Biomedical Research/statistics & numerical data , COVID-19/epidemiology , COVID-19/virology , Data Curation/methods , Data Mining/methods , Epidemics , Europe , Humans , Internet , SARS-CoV-2/physiology
18.
Endocr Metab Immune Disord Drug Targets ; 21(4): 586-591, 2021.
Article in English | MEDLINE | ID: covidwho-895217

ABSTRACT

COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19 from the explicit data based on optimal ARIMA model estimators. Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and the Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to the number of autoregressive terms, d refers to the number of times the series has to be differenced before it becomes stationary, and q refers to the number of moving average terms. Results obtained from the ARIMA model showed a significant decrease in cases in Australia; a stable case for China and rising cases have been observed in other countries. This study predicted the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.


Subject(s)
COVID-19/epidemiology , Data Analysis , Databases, Factual/statistics & numerical data , Internationality , Pandemics/statistics & numerical data , Forecasting/methods , Humans , Models, Statistical , Pandemics/prevention & control
19.
Am Heart J ; 232: 105-115, 2021 02.
Article in English | MEDLINE | ID: covidwho-893406

ABSTRACT

Morbidity and mortality associated with COVID-19 has increased exponentially, and patients with cardiovascular (CV) disease are at risk for poor outcomes. Several lines of evidence suggest a potential role for CV therapies in COVID-19 treatment. Characteristics of clinical trials of CV therapies related to COVID-19 registered on ClinicalTrials.gov have not been described. METHODS: ClinicalTrials.gov was queried on August 7, 2020 for COVID-19 related trials. Studies evaluating established CV drugs, other fibrinolytics (defibrotide), and extracorporeal membrane oxygenation were included. Studies evaluating anti-microbial, convalescent plasma, non-colchicine anti-inflammatory, and other therapies were excluded. Trial characteristics were tabulated from study-specific entries. RESULTS: A total of 2,935 studies related to COVID-19 were registered as of August 7, 2020. Of these, 1,645 were interventional studies, and the final analytic cohort consisted of 114 studies evaluating 10 CV therapeutic categories. Antithrombotics (32.5%; n = 37) were most commonly evaluated, followed by pulmonary vasodilators (14.0%; n = 16), renin-angiotensin-aldosterone system-related therapies (12.3%; n = 14), and colchicine (8.8%; n = 10). Trials evaluating multiple CV therapy categories and CV therapies in combination with non-CV therapies encompassed 4.4% (n = 5) and 9.6% (n = 11) of studies, respectively. Most studies were designed for randomized allocation (87.7%; n = 100), enrollment of less than 1000 participants (86.8%; n = 99), single site implementation (55.3%; n = 63), and had a primary outcome of mortality or a composite including mortality (56.1%; n = 64). Most study populations consisted of patients hospitalized with COVID-19 (81.6%; n = 93). At the time of database query, 28.9% (n = 33) of studies were not yet recruiting and the majority were estimated to be completed after December 2020 (67.8%; n = 78). Most lead sponsors were located in North America (43.9%; n = 50) or Europe (36.0%; n = 41). CONCLUSIONS: A minority (7%) of clinical trials related to COVID-19 registered on ClinicalTrials.gov plan to evaluate CV therapies. Of CV therapy studies, most were planned to be single center, enroll less than 1000 inpatients, sponsored by European or North American academic institutions, and estimated to complete after December 2020. Collectively, these findings underscore the need for a network of sites with a platform protocol for rapid evaluation of multiple therapies and generalizability to inform clinical care and health policy for COVID-19 moving forward.


Subject(s)
COVID-19/drug therapy , Cardiovascular Diseases/drug therapy , Clinical Trials as Topic/statistics & numerical data , National Library of Medicine (U.S.) , Registries/statistics & numerical data , SARS-CoV-2 , COVID-19/complications , COVID-19/mortality , Cardiovascular Agents/therapeutic use , Cardiovascular Diseases/complications , Cardiovascular Diseases/mortality , Colchicine/therapeutic use , Combined Modality Therapy/statistics & numerical data , Databases, Factual/statistics & numerical data , Extracorporeal Membrane Oxygenation/statistics & numerical data , Fibrinolytic Agents/therapeutic use , Humans , Hydroxymethylglutaryl-CoA Reductase Inhibitors/therapeutic use , Hypoglycemic Agents/therapeutic use , Patient Participation/statistics & numerical data , Randomized Controlled Trials as Topic/statistics & numerical data , Renin-Angiotensin System , Treatment Outcome , United States , Vasodilator Agents/therapeutic use
20.
Eur Rev Med Pharmacol Sci ; 24(19): 10286-10292, 2020 10.
Article in English | MEDLINE | ID: covidwho-890964

ABSTRACT

OBJECTIVE: The wildfire allied environmental pollution is highly toxic and can cause significant wide-ranging damage to the regional environment, weather conditions, and it can facilitate the transmission of microorganisms and diseases. The present study aims to investigate the effect of wildfire allied pollutants, particulate matter (PM-2.5 µm), and carbon monoxide (CO) on the dynamics of daily cases and deaths due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in San Francisco, USA. MATERIALS AND METHODS: For this study, we selected San Francisco, one of the regions affected by the wildfires allied pollution in California, USA. The data on the COVID-19 pandemic in San Francisco, including daily new cases and new deaths were recorded from Worldometer Web. The daily environmental pollutants particulate matter (PM-2.5 µm) and carbon monoxide (CO) were recorded from the metrological web "BAAQMD". The daily cases, deaths, particulate matter (PM-2.5 µm) and carbon monoxide were documented from the date of the occurrence of the first case of (SARS-CoV-2) in San Francisco, CA, USA, from March 20, 2020 to Sept 16, 2020. RESULTS: The results revealed a significant positive correlation between the environmental pollutants particulate matter (PM2.5 µm) and the number of daily cases (r=0.203, p=0.007), cumulative cases (r=0.567, p<0.001) and cumulative deaths (r=0.562, p<0.001); whereas the PM2.5 µm and daily deaths had no relationship (r=-0.015, p=0.842). In addition, CO was also positively correlated with cumulative cases (r=0.423, p<0.001) and cumulative deaths (r=0.315, p<0.001), however, CO had no correlation with the number of daily cases (r=0.134, p=0.075) and daily deaths (r=0.030, p=0.693). In San Francisco, one micrometer (µg/m3) increase in PM2.5 caused an increase in the daily cases, cumulative cases and cumulative deaths of SARS-COV-2 by 0.5%, 0.9% and 0.6%, respectively. Moreover, with a 1 part per million (ppm) increase in carbon monoxide level, the daily number of cases, cumulative cases and cumulative deaths increased by 5%, 9.3% and 5.3%, respectively. On the other hand, CO and daily deaths had no significant relationship. CONCLUSIONS: The wildfire allied pollutants, particulate matter PM-2.5µm and CO have a positive association with an increased number of SARS-COV-2 daily cases, cumulative cases and cumulative deaths in San Francisco. The metrological, disaster management and health officials must implement the necessary policies and assist in planning to minimize the wildfire incidences, environmental pollution and COVID-19 pandemic both at regional and international levels.


Subject(s)
COVID-19/epidemiology , COVID-19/mortality , Carbon Monoxide/adverse effects , Environmental Pollution/statistics & numerical data , Wildfires/mortality , Wildfires/statistics & numerical data , Atmosphere/chemistry , Databases, Factual/statistics & numerical data , Environmental Pollution/analysis , Female , Humans , Male , Pandemics/statistics & numerical data , Particulate Matter/analysis , SARS-CoV-2 , San Francisco/epidemiology
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