Objective Electronic health records are increasingly utilized for observational and clinical research. Identification of cohorts using electronic health records is an important step in this process. Previous studies largely focused on the methods of cohort selection, but there is little evidence on the impact of underlying vocabularies and mappings between vocabularies used for cohort selection. We aim to compare the cohort selection performance using Australian Medicines Terminology to Anatomical Therapeutic Chemical (ATC) mappings from 2 different sources. These mappings were taken from the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) and the Pharmaceutical Benefits Scheme (PBS) schedule. Materials and Methods We retrieved patients from the electronic Practice Based Research Network data repository using 3 ATC classification groups (A10, N02A, N06A). The retrieved patients were further verified manually and pooled to form a reference standard which was used to assess the accuracy of mappings using precision, recall, and F measure metrics. Results The OMOP-CDM mappings identified 2.6%, 15.2%, and 24.4% more drugs than the PBS mappings in the A10, N02A and N06A groups respectively. Despite this, the PBS mappings generally performed the same in cohort selection as OMOP-CDM mappings except for the N02A Opioids group, where a significantly greater number of patients were retrieved. Both mappings exhibited variable recall, but perfect precision, with all drugs found to be correctly identified. Conclusion We found that 1 of the 3 ATC groups had a significant difference and this affected cohort selection performance. Our findings highlighted that underlying terminology mappings can greatly impact cohort selection accuracy. Clinical researchers should carefully evaluate vocabulary mapping sources including methodologies used to develop those mappings.

Background Shared decision making (SDM) is important when considering whether an older patient with advanced CKD should be managed with dialysis or conservative kidney management (CKM). Physicians may find these conversations difficult because of the relative paucity of data on patients managed without dialysis. Methods This prospective observational study was conducted in a unit supported by a multidisciplinary Kidney Supportive Care (KSC) program, in a cohort of 580 patients (280 CKM and 230 dialysis) ≥65-years-old with CKD Stages IV and V. Survival was evaluated using logistic regression and cox-proportional-hazard models. Linear mixed models were utilised to assess symptoms over time. Results CKM patients were older (mean 84 vs. 74-years-old.; p < 0.001) and almost 2-fold more likely to have ≥ 3 comorbidities (p < 0.001). The median survival of CKM patients was lower compared to dialysis from all time-points: 14months (Interquartile range [IQR] 6–32) vs. 53(IQR 28–103) from decision of treatment modality or dialysis-start-date (p < 0.001); 15(IQR 7–34) vs. 64(IQR 30–103) months from the time eGFR ≤ 15ml/min/1.73m2 (p < 0.001); and 8(IQR 3–18) vs. 49(19–101) months from eGFR ≤ 10ml/min/1.73m2. 59% of CKM patients reported an improvement in symptoms by their third KSC-clinic visit (p < 0.001). The rate of unplanned hospitalisation was 2-fold higher in the dialysis cohort. Conclusions CKM patients survive a median of 14 months from the time of modality choice and have a lower rate of hospitalisation than dialysis patients. Although the symptom burden in advanced CKD is high, most elderly CKM patients managed through an integrated KSC program can achieve improvement in their symptoms over time. These data might help with SDM.