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1. Integrating real-world data from Brazil and Pakistan into the OMOP common data model and standardized health analytics framework to characterize COVID-19 in the Global South

Author

Junior, Elzo Pereira Pinto, primary, Normando, Priscilla, additional, Flores-Ortiz, Renzo, additional, Afzal, Muhammad Usman, additional, Jamil, Muhammad Asaad, additional, Bertolin, Sergio Fernandez, additional, Oliveira, Vinícius de Araújo, additional, Martufi, Valentina, additional, de Sousa, Fernanda, additional, Bashir, Amir, additional, Burn, Edward, additional, Ichihara, Maria Yury, additional, Barreto, Maurício L, additional, Salles, Talita Duarte, additional, Prieto-Alhambra, Daniel, additional, Hafeez, Haroon, additional, and Khalid, Sara, additional

Published
2022
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3. Integrating real-world data from Brazil and Pakistan into the OMOP common data model and standardized health analytics framework to characterize COVID-19 in the Global South.

Author

Junior, Elzo Pereira Pinto, Normando, Priscilla, Flores-Ortiz, Renzo, Afzal, Muhammad Usman, Jamil, Muhammad Asaad, Bertolin, Sergio Fernandez, Oliveira, Vinícius de Araújo, Martufi, Valentina, Sousa, Fernanda de, Bashir, Amir, Burn, Edward, Ichihara, Maria Yury, Barreto, Maurício L, Salles, Talita Duarte, Prieto-Alhambra, Daniel, Hafeez, Haroon, Khalid, Sara, and de Sousa, Fernanda

Abstract

Objectives: The aim of this work is to demonstrate the use of a standardized health informatics framework to generate reliable and reproducible real-world evidence from Latin America and South Asia towards characterizing coronavirus disease 2019 (COVID-19) in the Global South.Materials and Methods: Patient-level COVID-19 records collected in a patient self-reported notification system, hospital in-patient and out-patient records, and community diagnostic labs were harmonized to the Observational Medical Outcomes Partnership common data model and analyzed using a federated network analytics framework. Clinical characteristics of individuals tested for, diagnosed with or tested positive for, hospitalized with, admitted to intensive care unit with, or dying with COVID-19 were estimated.Results: Two COVID-19 databases covering 8.3 million people from Pakistan and 2.6 million people from Bahia, Brazil were analyzed. 109 504 (Pakistan) and 921 (Brazil) medical concepts were harmonized to Observational Medical Outcomes Partnership common data model. In total, 341 505 (4.1%) people in the Pakistan dataset and 1 312 832 (49.2%) people in the Brazilian dataset were tested for COVID-19 between January 1, 2020 and April 20, 2022, with a median [IQR] age of 36 [25, 76] and 38 (27, 50); 40.3% and 56.5% were female in Pakistan and Brazil, respectively. 1.2% percent individuals in the Pakistan dataset had Afghan ethnicity. In Brazil, 52.3% had mixed ethnicity. In agreement with international findings, COVID-19 outcomes were more severe in men, elderly, and those with underlying health conditions.Conclusions: COVID-19 data from 2 large countries in the Global South were harmonized and analyzed using a standardized health informatics framework developed by an international community of health informaticians. This proof-of-concept study demonstrates a potential open science framework for global knowledge mobilization and clinical translation for timely response to healthcare needs in pandemics and beyond. [ABSTRACT FROM AUTHOR]

Published
2023
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4. Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study

Author

Reps, Jenna M, Kim, Chungsoo, Williams, Ross D, Markus, Aniek F, Yang, Cynthia, Salles, Talita Duarte, Falconer, Thomas, Jonnagaddala, Jitendra, Williams, Andrew, Fernández-Bertolín, Sergio, DuVall, Scott L, Kostka, Kristin, Rao, Gowtham, Shoaibi, Azza, Ostropolets, Anna, Spotnitz, Matthew E, Zhang, Lin, Casajust, Paula, Steyerberg, Ewout W, Nyberg, Fredrik, Kaas-Hansen, Benjamin Skov, Choi, Young Hwa, Morales, Daniel, Liaw, Siaw-Teng, Abrahão, Maria Tereza Fernandes, Areia, Carlos, Matheny, Michael E, Aragón, María, Park, Rae Woong, Hripcsak, George, Reich, Christian G, Suchard, Marc A, You, Seng Chan, Ryan, Patrick B, Prieto-Alhambra, Daniel, Rijnbeek, Peter R, Reps, Jenna M, Kim, Chungsoo, Williams, Ross D, Markus, Aniek F, Yang, Cynthia, Salles, Talita Duarte, Falconer, Thomas, Jonnagaddala, Jitendra, Williams, Andrew, Fernández-Bertolín, Sergio, DuVall, Scott L, Kostka, Kristin, Rao, Gowtham, Shoaibi, Azza, Ostropolets, Anna, Spotnitz, Matthew E, Zhang, Lin, Casajust, Paula, Steyerberg, Ewout W, Nyberg, Fredrik, Kaas-Hansen, Benjamin Skov, Choi, Young Hwa, Morales, Daniel, Liaw, Siaw-Teng, Abrahão, Maria Tereza Fernandes, Areia, Carlos, Matheny, Michael E, Aragón, María, Park, Rae Woong, Hripcsak, George, Reich, Christian G, Suchard, Marc A, You, Seng Chan, Ryan, Patrick B, Prieto-Alhambra, Daniel, and Rijnbeek, Peter R

Abstract

BACKGROUND: SARS-CoV-2 is straining healthcare systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate between patients requiring hospitalization and those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision making during the pandemic. However, the model is at high risk of bias according to the "prediction model risk of bias assessment" criteria and has not been externally validated.OBJECTIVE: Externally validate the C-19 index across a range of healthcare settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases.METHODS: We followed the OHDSI framework for external validation to assess the reliability of the C-19 model. We evaluated the model on two different target populations: i) 41,381 patients that have SARS-CoV-2 at an outpatient or emergency room visit and ii) 9,429,285 patients that have influenza or related symptoms during an outpatient or emergency room visit, to predict their risk of hospitalization with pneumonia during the following 0 to 30 days. In total we validated the model across a network of 14 databases spanning the US, Europe, Australia and Asia.RESULTS: The internal validation performance of the C-19 index was a c-statistic of 0.73 and calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data the model obtained c-statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US and South Korean datasets respectively. The calibration was poor with the model under-estimating risk. When validated on 12 datasets containing influenza patients across the OHDSI network the c-statistics ranged between 0.40-0.68.CONCLUSIONS: The results show that the discrimi

Published
2021

5. Can we trust the prediction model? Demonstrating the importance of external validation by investigating the COVID-19 Vulnerability (C-19) Index across an international network of observational healthcare datasets

Author

Reps, Jenna M., primary, Kim, Chungsoo, additional, Williams, Ross D., additional, Markus, Aniek F., additional, Yang, Cynthia, additional, Salles, Talita Duarte, additional, Falconer, Thomas, additional, Jonnagaddala, Jitendra, additional, Williams, Andrew, additional, Fernández-Bertolín, Sergio, additional, DuVall, Scott L., additional, Kostka, Kristin, additional, Rao, Gowtham, additional, Shoaibi, Azza, additional, Ostropolets, Anna, additional, Spotnitz, Matthew E, additional, Zhang, Lin, additional, Casajust, Paula, additional, Steyerberg, Ewout W., additional, Nyberg, Fredrik, additional, Kaas-Hansen, Benjamin Skov, additional, Choi, Young Hwa, additional, Morales, Daniel, additional, Liaw, Siaw-Teng, additional, Fernandes Abrahão, Maria Tereza, additional, Areia, Carlos, additional, Matheny, Michael E., additional, Aragón, María, additional, Park, Rae Woong, additional, Hripcsak, George, additional, Reich, Christian G., additional, Suchard, Marc A., additional, You, Seng Chan, additional, Ryan, Patrick B., additional, Prieto-Alhambra, Daniel, additional, and Rijnbeek, Peter R., additional

Published
2020
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6. Seek COVER: Development and validation of a personalized risk calculator for COVID-19 outcomes in an international network

Author

Williams, Ross D., primary, Markus, Aniek F., additional, Yang, Cynthia, additional, Salles, Talita Duarte, additional, DuVall, Scott L., additional, Falconer, Thomas, additional, Jonnagaddala, Jitendra, additional, Kim, Chungsoo, additional, Rho, Yeunsook, additional, Williams, Andrew, additional, Alberga, Amanda, additional, An, Min Ho, additional, Aragón, María, additional, Areia, Carlos, additional, Burn, Edward, additional, Choi, Young Hwa, additional, Drakos, Iannis, additional, Fernandes Abrahão, Maria Tereza, additional, Fernández-Bertolín, Sergio, additional, Hripcsak, George, additional, Kaas-Hansen, Benjamin Skov, additional, Kandukuri, Prasanna L, additional, Kors, Jan A., additional, Kostka, Kristin, additional, Liaw, Siaw-Teng, additional, Lynch, Kristine E., additional, Machnicki, Gerardo, additional, Matheny, Michael E., additional, Morales, Daniel, additional, Nyberg, Fredrik, additional, Park, Rae Woong, additional, Prats-Uribe, Albert, additional, Pratt, Nicole, additional, Rao, Gowtham, additional, Reich, Christian G., additional, Rivera, Marcela, additional, Seinen, Tom, additional, Shoaibi, Azza, additional, Spotnitz, Matthew E, additional, Steyerberg, Ewout W., additional, Suchard, Marc A., additional, You, Seng Chan, additional, Zhang, Lin, additional, Zhou, Lili, additional, Ryan, Patrick B., additional, Prieto-Alhambra, Daniel, additional, Reps, Jenna M., additional, and Rijnbeek, Peter R., additional

Published
2020
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7. [P4-341]: LEVELS OF BLOOD PRESSURE, BODY MASS INDEX AND TOTAL SERUM CHOLESTEROL AT DIFFERENT TIME POINTS PRIOR TO DEMENTIA DIAGNOSIS: A CASE CONTROL STUDY OF OVER 28 MILLION ELECTRONIC HEALTH RECORDS FROM THE EMIF EHR DATA RESOURCE

Author

Perera, Gayan, primary, Gungabissoon, Usha, additional, Alexander, Myriam, additional, Ansel, David, additional, Avillach, Paul, additional, Salles, Talita Duarte, additional, Gordon, Mark Forrest, additional, Nevado-Holgado, Alejo J., additional, Novak, Gerald P., additional, Pasqua, Alessandro, additional, Pedersen, Lars, additional, Ponjoan, Anna, additional, Rijnbeek, Peter, additional, Van Der Lei, Johan, additional, and Stewart, Robert, additional

Published
2017
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8. LEVELS OF BLOOD PRESSURE, BODY MASS INDEX AND TOTAL SERUM CHOLESTEROL AT DIFFERENT TIME POINTS PRIOR TO DEMENTIA DIAGNOSIS: A CASE CONTROL STUDY OF OVER 28 MILLION ELECTRONIC HEALTH RECORDS FROM THE EMIF EHR DATA RESOURCE

Author

Perera, Gayan, Gungabissoon, Usha, Alexander, Myriam, Ansel, David, Avillach, Paul, Salles, Talita Duarte, Gordon, Mark Forrest, Nevado-Holgado, Alejo J., Novak, Gerald P., Pasqua, Alessandro, Pedersen, Lars, Ponjoan, Anna, Rijnbeek, Peter, Van Der Lei, Johan, and Stewart, Robert

Published
2017
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