S. Denaxas

First name
S.
Last name
Denaxas
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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Jordan, K. P., Rathod-Mistry, T., Bailey, J., Chen, Y., Clarson, L., Denaxas, S., et al. (2022). Long-Term Cardiovascular Risk and Management of Patients Recorded in Primary Care With Unattributed Chest Pain: An Electronic Health Record Study. J Am Heart Assoc, 11, e023146. http://doi.org/10.1161/jaha.121.023146
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Clarke, C. S., Williamson, E., Denaxas, S., Carpenter, J. R., Thomas, M., Blackshaw, H., et al. (2022). Observational retrospective study calculating health service costs of patients receiving surgery for chronic rhinosinusitis in England, using linked patient-level primary and secondary care electronic data. Bmj Open, 12, e055603. http://doi.org/10.1136/bmjopen-2021-055603
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