L. Pasea

First name
L.
Last name
Pasea
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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Chung, S. C., Pujades-Rodriguez, M., Duyx, B., Denaxas, S. C., Pasea, L., Hingorani, A., et al. (2018). Time spent at blood pressure target and the risk of death and cardiovascular diseases. PLoS One. http://doi.org/10.1371/journal.pone.0202359
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Gho, J., Schmidt, A. F., Pasea, L., Koudstaal, S., Pujades-Rodriguez, M., Denaxas, S., et al. (2018). An electronic health records cohort study on heart failure following myocardial infarction in England: incidence and predictors. BMJ Open. http://doi.org/10.1136/bmjopen-2017-018331
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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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Katsoulis, M., Pasea, L., Lai, A. G., Dobson, R. J. B., Denaxas, S., Hemingway, H., & Banerjee, A. (2020). Obesity during the COVID-19 pandemic: both cause of high risk and potential effect of lockdown? A population-based electronic health record study. Public Health. http://doi.org/10.1016/j.puhe.2020.12.003
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Lai, A. G., Pasea, L., Banerjee, A., Hall, G., Denaxas, S., Chang, W. H., et al. (2020). Estimated impact of the COVID-19 pandemic on cancer services and excess 1-year mortality in people with cancer and multimorbidity: near real-time data on cancer care, cancer deaths and a population-based cohort study. BMJ Open. http://doi.org/10.1136/bmjopen-2020-043828
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Banerjee, A., Pasea, L., Harris, S., Gonzalez-Izquierdo, A., Torralbo, A., Shallcross, L., et al. (2020). Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study. Lancet. http://doi.org/10.1016/s0140-6736(20)30854-0
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Banerjee, A., Chen, S., Pasea, L., Lai, A. G., Katsoulis, M., Denaxas, S., et al. (2021). Excess deaths in people with cardiovascular diseases during the COVID-19 pandemic. Eur J Prev Cardiol. http://doi.org/10.1093/eurjpc/zwaa155
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Katsoulis, M., Lai, A. G., Diaz-Ordaz, K., Gomes, M., Pasea, L., Banerjee, A., et al. (2021). Identifying adults at high-risk for change in weight and BMI in England: a longitudinal, large-scale, population-based cohort study using electronic health records. Lancet Diabetes Endocrinol. http://doi.org/10.1016/s2213-8587(21)00207-2
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