C. Sudlow

First name

C.

Last name

Sudlow

Mizani, M. A., Dashtban, A., Pasea, L., Lai, A. G., Thygesen, J., Tomlinson, C., et al. (2022). Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19-a data-driven retrospective cohort study. J R Soc Med, 1410768221131897. http://doi.org/10.1177/01410768221131897

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Dashtban, A., Mizani, M. A., Denaxas, S., Nitsch, D., Quint, J., Corbett, R., et al. (2022). A retrospective cohort study measured predicting and validating the impact of the COVID-19 pandemic in individuals with chronic kidney disease. Kidney Int. http://doi.org/10.1016/j.kint.2022.05.015

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Eastwood, S. V., Mathur, R., Atkinson, M., Brophy, S., Sudlow, C., Flaig, R., et al. (2016). Algorithms for the Capture and Adjudication of Prevalent and Incident Diabetes in UK Biobank. PLoS One. http://doi.org/10.1371/journal.pone.0162388

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Brown, A., Kirichek, O., Balkwill, A., Reeves, G., Beral, V., Sudlow, C., et al. (2016). Comparison of dementia recorded in routinely collected hospital admission data in England with dementia recorded in primary care. Emerg Themes Epidemiol. http://doi.org/10.1186/s12982-016-0053-z

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Pujades-Rodriguez, M., Assi, V., Gonzalez-Izquierdo, A., Wilkinson, T., Schnier, C., Sudlow, C., et al. (2018). The diagnosis, burden and prognosis of dementia: A record-linkage cohort study in England. PLoS One. http://doi.org/10.1371/journal.pone.0199026

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Denaxas, S., Gonzalez-Izquierdo, A., Direk, K., Fitzpatrick, N. K., Fatemifar, G., Banerjee, A., et al. (2019). UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER. J Am Med Inform Assoc. http://doi.org/10.1093/jamia/ocz105

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