Electronic Health Records

Vestesson, E. M., De Corte, K. L. A., Crellin, E., Ledger, J., Bakhai, M., & Clarke, G. M. (2023). Consultation Rate and Mode by Deprivation in English General Practice From 2018 to 2022: Population-Based Study. Jmir Public Health Surveill, 9, e44944. http://doi.org/10.2196/44944
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
Little, D. J., Arnold, M., Hedman, K., Sun, P., Haque, S. A., & James, G. (2023). Rates of adverse clinical events in patients with chronic kidney disease: analysis of electronic health records from the UK clinical practice research datalink linked to hospital data. Bmc Nephrol, 24, 91. http://doi.org/10.1186/s12882-023-03119-z
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
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
Jordan, K. P., Rathod-Mistry, T., van der Windt, D. A., Bailey, J., Chen, Y., Clarson, L., et al. (2023). Determining cardiovascular risk in patients with unattributed chest pain in UK primary care: an electronic health record study. Eur J Prev Cardiol. http://doi.org/10.1093/eurjpc/zwad055
Nadarajah, R., Wu, J., Hogg, D., Raveendra, K., Nakao, Y. M., Nakao, K., et al. (2023). Prediction of short-term atrial fibrillation risk using primary care electronic health records. Heart. http://doi.org/10.1136/heartjnl-2022-322076
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
Ling, S., Zaccardi, F., Issa, E., Davies, M. J., Khunti, K., & Brown, K. (2023). Inequalities in cancer mortality trends in people with type 2 diabetes: 20 year population-based study in England. Diabetologia. http://doi.org/10.1007/s00125-022-05854-8
Jani, M., Yimer, B. B., Selby, D., Lunt, M., Nenadic, G., & Dixon, W. G. (2023). "Take up to eight tablets per day": Incorporating free-text medication instructions into a transparent and reproducible process for preparing drug exposure data for pharmacoepidemiology. Pharmacoepidemiol Drug Saf. http://doi.org/10.1002/pds.5595