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

Kostka, Kristin; Duarte‐Salles, Talita; Prats‐Uribe, Albert; Sena, Anthony G.; Pistillo, Andrea; Khalid, Sara; Lai, Lana Yh; Golozar, Asieh; Alshammari, Thamir M.; Dawoud, Dalia; Nyberg, Fredrik; Wilcox, Adam B.; Andryc, Alan; Williams, Andrew E.; Ostropolets, Anna; Areia, Carlos; Jung, Chi Young; Harle, Christopher A.; Reich, Christian; Blacketer, Clair; Moralès, Daniel; Dorr, David A.; Burn, Edward; Roel, Elena; Tan, Eng Hooi; Minty, Evan; DeFalco, Frank; Maeztu, Gabriel de; Lipori, Gigi; Alghoul, Heba; Zhu, Hong; Thomas, J. A. C.; Bian, Jiang; Park, Jimyung; Roldán, Jordi Martínez; Posada, Jose; Banda, Juan M.; Horcajada, Juan Pablo; Kohler, Julianna; Shah, Karishma; Natarajan, Karthik; Lynch, Kristine E.; Liu, Li; Schilling, Lisa M.; Recalde, Martina; Spotnitz, Matthew; Gong, Mengchun; Matheny, Michael E.; Valveny, Neus; Weiskopf, Nicole G.; Shah, Nigam H.; Alser, Osaid; Casajust, Paula; Park, Rae Woong; Schuff, Robert; Seager, Sarah; DuVall, Scott L.; You, Seng Chan; Song, Seok Young; Fernández-Bertolín, Sergio; Fortin, Stephen; Magoč, Tanja; Falconer, Thomas; Subbian, Vignesh; Huser, Vojtech; Ahmed, Waheed-Ul-Rahman; Carter, William; Guo, Yin; Galvan, Yankuic; He, Xiaowen; Hripcsak, George; Ryan, Patrick B.; Suchard, Marc A.; Prieto-Alhambra, Daniel

doi:10.2147/clep.s323292

Cited by 12 publications

(14 citation statements)

References 24 publications

(25 reference statements)

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“…Kostka et al 10) conducted a large-scale descriptive characterization study of 4.5 million coronavirus disease 2019 (COVID-19) cases using a federated network of CDM data partners in the USA, Europe (the Netherlands, Spain, the United Kingdom, Germany, France, and Italy), and Asia (South Korea and China). They noted similarities between the USA and Europe, but South Korea differed, with more women hospitalized than men.…”

Section: Characterizationmentioning

confidence: 99%

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

et al. 2022

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Author's summary A distributed research network refers to a research network wherein multiple institutions unite for joint research based on common data model wherein the structure and meaning of the data are standardized. Researchers can only send the analysis code to multiple institutions and get the summarized analysis results. Thus, researchers cannot see any of the individual patient data at any time, and no individual patient data can be leaked from the institutions. The Observational Health Data Sciences and Informatics research network standardized 928 million unique records or 12% of the world’s population from 41 countries.

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Section: Characterizationmentioning

confidence: 99%

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

et al. 2022

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“…Understanding of the clinical characteristics and responses to treatment of COVID-19 brought enormous value to clinicians when the trial-based evidence was sparse. 1 , 12 The large-scale real-world evidence generation network formed within the framework of OHDSI (Observational Health Data Sciences and Informatics) 1 has brought an innovative approach to coordinate data sources from different institutes, countries, and languages, aligned a cohort of over 4.5 million cases, and retrospectively described the unknown disease with strong representativeness on populations and regions (Europe, United States, South Korea, and China). OHDSI developed a comprehensive vocabulary system to incorporate data standards used in different countries and areas and implemented them in data processing and analytics.…”

Section: Data Standards Empower International Knowledge Discovery And...mentioning

confidence: 99%

“…It's important to point out that the data standards and their implementation in different countries and languages have enabled multi-national studies without inflicting concerns of data governance and original data leakage. Within the coordinating mechanisms organized by OHDSI, 1 TriNetX, 12 ICODA, 17 and other open-science networks, insights can be extracted, with an unprecedented scale and efficiency, from multiple independent databases around the world due to their common data model, vocabulary control, quality control, privacy protection mechanism and ethics standards.…”

Section: Data Standards Empower International Knowledge Discovery And...mentioning

confidence: 99%

“…SNOMED CT (Systematized Nomenclature of Medicine – Clinical Terms), LOINC (Logical Observation Identifiers Names and Codes), and RxNorm are among the top recommended terminology systems. 1 …”

Section: Reflection and Effort On Improving The Level Of Data Standar...mentioning

confidence: 99%

“…In the battle against the unprecedented pandemic of COVID-19 worldwide, biomedical informatics, especially data standards and data standardization, have played significant roles in multiple aspects in containment of the pandemic, including understanding disease mechanisms, 1 improving clinical care, 2 triaging resource needs, 3 advising policy-making, 4 implementing public health countermeasures, 5 enhancing technical innovation in syndromic surveillance, 6 developing vaccines, and enabling wide coverage of vaccination. 7 …”

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Data standards and standardization: The shortest plank of bucket for the COVID-19 containment

Gong

Jiao²,

Gong³

et al. 2022

The Lancet Regional Health - Western Pacific

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Risk of COVID-19 Diagnosis and Hospitalisation in Patients with Osteoarthritis or Back Pain Treated with Ibuprofen Compared to Other NSAIDs or Paracetamol: A Network Cohort Study

Xie

Brash

Turkmen

et al. 2023

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Objective We aimed to investigate whether ibuprofen use, compared with other non-selective non-steroidal anti-inflammatory drugs (ns-NSAIDs), cyclooxygenase-2 inhibitors (COX-2i) or paracetamol, increases the risk of coronavirus disease 2019 (COVID-19) diagnosis or hospitalisation. Design A prevalent user and active comparator cohort study. Setting Two US claims databases (Open Claims and PharMetrics Plus) mapped to the Observational Medical Outcomes Partnership Common Data Model. Participants Insured patients with a history of osteoarthritis or back pain and receiving ibuprofen, other ns-NSAIDs, COX-2i or paracetamol between 1 November, 2019 and 31 January, 2020 (study enrolment window 1) or between 1 February, 2020 and 31 October, 2020 (study enrolment window 2). Main Outcome Measures Large-scale propensity score matching and empirical calibration were used to minimise confounding. Incidence and hazard ratios of COVID-19 diagnosis and hospitalisation according to drug/s use were estimated and pooled in the same study period across data sources using a fixed-effects meta-analysis. Index treatment episode was the primary risk evaluation window, censored at the time of discontinuation. Results A total of 633,562 and 1,063,960 participants were included in periods 1 and 2, respectively, for the ibuprofen versus ns-NSAIDs comparison, 311,669 and 524,470 for ibuprofen versus COX-2i, and 492,002 and 878,598 for ibuprofen versus paracetamol. Meta-analyses of empirically calibrated hazard ratios revealed no significantly differential risk of COVID-19 outcomes in users of ibuprofen versus any of the other studied analgesic classes: hazard ratios were 1.13 (0.96–1.33) for the ibuprofen-ns-NSAIDs comparison, 1.03 (0.83–1.28) for the ibuprofen-COX-2i comparison and 1.13 (0.74–1.73) for ibuprofen-paracetamol comparison on COVID-19 diagnosis in the February 2020–October 2020 window. Similar hazard ratios were found on COVID-19 hospitalisation and across both study periods. Conclusions In patients with osteoarthritis or back pain, we found no differential risks of incident COVID-19 diagnosis or COVID-19 hospitalisation for ibuprofen users compared with other ns-NSAIDs, COX-2i or paracetamol. Our findings support regulatory recommendations that NSAIDs, including ibuprofen, should be prescribed as indicated in the same way as before the COVID-19 pandemic, especially for those who rely on ibuprofen or NSAIDs to manage chronic arthritis or musculoskeletal pain symptoms. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s40265-022-01822-z.

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

Cited by 12 publications

References 24 publications

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

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

Data standards and standardization: The shortest plank of bucket for the COVID-19 containment

Risk of COVID-19 Diagnosis and Hospitalisation in Patients with Osteoarthritis or Back Pain Treated with Ibuprofen Compared to Other NSAIDs or Paracetamol: A Network Cohort Study

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