A methodology to infer continuous periods of EHR data collection increased follow-up for around 40% of participants with multiple registration records by a mean of 3.8 years.
A novel methodology for handling primary care EHR data in cohort studies successfully maximizes longitudinal follow-up without sacrificing phenotyping accuracy.
Primary care EHR data are often of clinical importance to cohort studies however they require careful handling. Challenges include determining the periods during which EHR data were collected. Participants are typically censored when they deregister from a medical practice, however, cohort studies wish to follow participants longitudinally including those that change practice. Using UK Biobank as an exemplar, we developed methodology to infer continuous periods of data collection and maximize follow-up in longitudinal studies. This resulted in longer follow-up for around 40% of participants with multiple registration records (mean increase of 3.8 years from the first study visit). The approach did not sacrifice phenotyping accuracy when comparing agreement between self-reported and EHR data. A diabetes mellitus case study illustrates how the algorithm supports longitudinal study design and provides further validation. We use UK Biobank data, however, the tools provided can be used for other conditions and studies with minimal alteration.
Darke et al. (Tue,) conducted a other in Longitudinal studies using primary care EHR data. Methodology to infer continuous periods of data collection was evaluated on Increase in follow-up duration. A methodology to infer continuous periods of EHR data collection increased follow-up for around 40% of participants with multiple registration records by a mean of 3.8 years.