A. Banerjee

First name
A.
Last name
Banerjee
Davidson, J. A., Banerjee, A., Strongman, H., Herrett, E., Smeeth, L., Breuer, J., & Warren-Gash, C. (2023). Acute Cardiovascular Events After COVID-19 in England in 2020: A Self-Controlled Case Series Study. Clin Epidemiol, 15, 911-921. http://doi.org/10.2147/clep.s421062
Nakao, Y. M., Nakao, K., Nadarajah, R., Banerjee, A., Fonarow, G. C., Petrie, M. C., et al. (2023). Prognosis, characteristics, and provision of care for patients with the unspecified heart failure electronic health record phenotype: a population-based linked cohort study of 95262 individuals. Eclinicalmedicine, 63, 102164. http://doi.org/10.1016/j.eclinm.2023.102164
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
Koshiaris, C., Archer, L., Lay-Flurrie, S., Snell, K. I., Riley, R. D., Stevens, R., et al. (2023). Predicting the risk of acute kidney injury in primary care: derivation and validation of STRATIFY-AKI. Br J Gen Pract. http://doi.org/10.3399/bjgp.2022.0389
Sheppard, J. P., Koshiaris, C., Stevens, R., Lay-Flurrie, S., Banerjee, A., Bellows, B. K., et al. (2023). The association between antihypertensive treatment and serious adverse events by age and frailty: A cohort study. Plos Med, 20, e1004223. http://doi.org/10.1371/journal.pmed.1004223
Warren-Gash, C., Davidson, J. A., Strongman, H., Herrett, E., Smeeth, L., Breuer, J., & Banerjee, A. (2023). Severe COVID-19 outcomes by cardiovascular risk profile in England in 2020: a population-based cohort study. Lancet Reg Health Eur, 27, 100604. http://doi.org/10.1016/j.lanepe.2023.100604
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
Davidson, J. A., Banerjee, A., Douglas, I., Leyrat, C., Pebody, R., McDonald, H. I., et al. (2022). Primary prevention of acute cardiovascular events by influenza vaccination: an observational study. Eur Heart J. http://doi.org/10.1093/eurheartj/ehac737
Archer, L., Koshiaris, C., Lay-Flurrie, S., Snell, K. I. E., Riley, R. D., Stevens, R., et al. (2022). Development and external validation of a risk prediction model for falls in patients with an indication for antihypertensive treatment: retrospective cohort study. Bmj, 379, e070918. http://doi.org/10.1136/bmj-2022-070918
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