L. Pasea

First name
L.
Last name
Pasea
Banerjee, A., Dashtban, A., Chen, S., Pasea, L., Thygesen, J. H., Fatemifar, G., et al. (2023). Identifying subtypes of heart failure from three electronic health record sources with machine learning: an external, prognostic, and genetic validation study. Lancet Digit Health, 5, e370-e379. http://doi.org/10.1016/s2589-7500(23)00065-1
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
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
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
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
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
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
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
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
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