S. Denaxas

First name
S.
Last name
Denaxas
Voss, E. A., Shoaibi, A., Lai, Y. H., Blacketer, C., Alshammari, T., Makadia, R., et al. (2023). Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study. Eclinicalmedicine, 58, 101932. http://doi.org/10.1016/j.eclinm.2023.101932
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Jordan, K. P., Rathod-Mistry, T., van der Windt, D. A., Bailey, J., Chen, Y., Clarson, L., et al. (2023). Determining cardiovascular risk in patients with unattributed chest pain in UK primary care: an electronic health record study. Eur J Prev Cardiol. http://doi.org/10.1093/eurjpc/zwad055
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Dashtban, A., Mizani, M. A., Pasea, L., Denaxas, S., Corbett, R., Mamza, J. B., et al. (2023). Identifying subtypes of chronic kidney disease with machine learning: development, internal validation and prognostic validation using linked electronic health records in 350,067 individuals. Ebiomedicine, 89, 104489. http://doi.org/10.1016/j.ebiom.2023.104489
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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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Kuan, V., Denaxas, S., Patalay, P., Nitsch, D., Mathur, R., Gonzalez-Izquierdo, A., et al. (2022). Identifying and visualising multimorbidity and comorbidity patterns in patients in the English National Health Service: a population-based study. Lancet Digit Health. http://doi.org/10.1016/s2589-7500(22)00187-x
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Rapsomaniki, E., Shah, A., Perel, P., Denaxas, S., George, J., Nicholas, O., et al. (2014). Prognostic models for stable coronary artery disease based on electronic health record cohort of 102 023 patients. Eur Heart J, 35, 844-52. http://doi.org/10.1093/eurheartj/eht533
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. Y. Tan, Y., Papez, V., Chang, W. H., Mueller, S. H., Denaxas, S., & Lai, A. G. (2022). Comparing clinical trial population representativeness to real-world populations: an external validity analysis encompassing 43 895 trials and 5 685 738 individuals across 989 unique drugs and 286 conditions in England. Lancet Healthy Longev. http://doi.org/10.1016/s2666-7568(22)00186-6
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Elkheder, M., Gonzalez-Izquierdo, A., Arfeen, Q. U., Kuan, V., Lumbers, R. T., Denaxas, S., & Shah, A. D. (2022). Translating and evaluating historic phenotyping algorithms using SNOMED CT. J Am Med Inform Assoc. http://doi.org/10.1093/jamia/ocac158
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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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Chung, S. C., Providencia, R., Sofat, R., Pujades-Rodriguez, M., Torralbo, A., Fatemifar, G., et al. (2022). Incidence, morbidity, mortality and disparities in dementia: A population linked electronic health records study of 4.3 million individuals. Alzheimers Dement. http://doi.org/10.1002/alz.12635
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