A. Banerjee

First name
A.
Last name
Banerjee
. Y. Wong, A., Warren-Gash, C., Bhaskaran, K., Leyrat, C., Banerjee, A., Smeeth, L., & Douglas, I. J. (2024). Potential interactions between medications for rate control and direct oral anticoagulants: population-based cohort and case-crossover study. Heart Rhythm. http://doi.org/10.1016/j.hrthm.2024.06.033
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Mizani, M. A., Dashtban, A., Pasea, L., Zeng, Q., Khunti, K., Valabhji, J., et al. (2024). Identifying subtypes of type 2 diabetes mellitus with machine learning: development, internal validation, prognostic validation and medication burden in linked electronic health records in 420 448 individuals. Bmj Open Diabetes Res Care, 12. http://doi.org/10.1136/bmjdrc-2024-004191
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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
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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
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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
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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
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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
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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
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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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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
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