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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 06291naa a2200889 4500 | |
| 001 | oai:gup.ub.gu.se/313937 | |
| 003 | SwePub | |
| 008 | 240528s2022 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://gup.ub.gu.se/publication/3139372 URI |
| 024 | 7 | a https://doi.org/10.1186/s12874-022-01505-z2 DOI |
| 040 | a (SwePub)gu | |
| 041 | a eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Williams, R. D.4 aut |
| 245 | 1 0 | a Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network |
| 264 | c 2022-01-30 | |
| 264 | 1 | b Springer Science and Business Media LLC,c 2022 |
| 520 | a Background: We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient’s risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. Methods: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. Results: Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69–0.81, COVER-I: 0.73–0.91, and COVER-F: 0.72–0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. Conclusions: This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use. © 2022, The Author(s). | |
| 650 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Hälsovetenskapx Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi0 (SwePub)303022 hsv//swe |
| 650 | 7 | a MEDICAL AND HEALTH SCIENCESx Health Sciencesx Public Health, Global Health, Social Medicine and Epidemiology0 (SwePub)303022 hsv//eng |
| 653 | a COVID-19 | |
| 653 | a Patient-level prediction modelling | |
| 653 | a Risk score | |
| 700 | 1 | a Markus, A. F.4 aut |
| 700 | 1 | a Yang, C.4 aut |
| 700 | 1 | a Duarte-Salles, T.4 aut |
| 700 | 1 | a DuVall, S. L.4 aut |
| 700 | 1 | a Falconer, T.4 aut |
| 700 | 1 | a Jonnagaddala, J.4 aut |
| 700 | 1 | a Kim, C.4 aut |
| 700 | 1 | a Rho, Y.4 aut |
| 700 | 1 | a Williams, A. E.4 aut |
| 700 | 1 | a Machado, A. A.4 aut |
| 700 | 1 | a An, M. H.4 aut |
| 700 | 1 | a Aragón, M.4 aut |
| 700 | 1 | a Areia, C.4 aut |
| 700 | 1 | a Burn, E.4 aut |
| 700 | 1 | a Choi, Y. H.4 aut |
| 700 | 1 | a Drakos, I.4 aut |
| 700 | 1 | a Abrahão, M. T. F.4 aut |
| 700 | 1 | a Fernández-Bertolín, S.4 aut |
| 700 | 1 | a Hripcsak, G.4 aut |
| 700 | 1 | a Kaas-Hansen, B. S.4 aut |
| 700 | 1 | a Kandukuri, P. L.4 aut |
| 700 | 1 | a Kors, J. A.4 aut |
| 700 | 1 | a Kostka, K.4 aut |
| 700 | 1 | a Liaw, S. T.4 aut |
| 700 | 1 | a Lynch, K. E.4 aut |
| 700 | 1 | a Machnicki, G.4 aut |
| 700 | 1 | a Matheny, M. E.4 aut |
| 700 | 1 | a Morales, D.4 aut |
| 700 | 1 | a Nyberg, Fredrik,d 1961u Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för samhällsmedicin och folkhälsa,Institute of Medicine, School of Public Health and Community Medicine4 aut0 (Swepub:gu)xnybef |
| 700 | 1 | a Park, R. W.4 aut |
| 700 | 1 | a Prats-Uribe, A.4 aut |
| 700 | 1 | a Pratt, N.4 aut |
| 700 | 1 | a Rao, G.4 aut |
| 700 | 1 | a Reich, C. G.4 aut |
| 700 | 1 | a Rivera, M.4 aut |
| 700 | 1 | a Seinen, T.4 aut |
| 700 | 1 | a Shoaibi, A.4 aut |
| 700 | 1 | a Spotnitz, M. E.4 aut |
| 700 | 1 | a Steyerberg, E. W.4 aut |
| 700 | 1 | a Suchard, M. A.4 aut |
| 700 | 1 | a You, S. C.4 aut |
| 700 | 1 | a Zhang, L.4 aut |
| 700 | 1 | a Zhou, L.4 aut |
| 700 | 1 | a Ryan, P. B.4 aut |
| 700 | 1 | a Prieto-Alhambra, D.4 aut |
| 700 | 1 | a Reps, J. M.4 aut |
| 700 | 1 | a Rijnbeek, P. R.4 aut |
| 710 | 2 | a Göteborgs universitetb Institutionen för medicin, avdelningen för samhällsmedicin och folkhälsa4 org |
| 773 | 0 | t BMC Medical Research Methodologyd : Springer Science and Business Media LLCg 22:1q 22:1x 1471-2288 |
| 856 | 4 | u https://bmcmedresmethodol.biomedcentral.com/track/pdf/10.1186/s12874-022-01505-z |
| 856 | 4 8 | u https://gup.ub.gu.se/publication/313937 |
| 856 | 4 8 | u https://doi.org/10.1186/s12874-022-01505-z |
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