Spiros Denaxas

Background Incident events of cardiovascular diseases (CVDs) are heterogenous and may result in different mortality risks. Such evidence may help inform patient and physician decisions in CVD prevention and risk factor management. Aims This study aimed to determine the extent to which incident events of common CVD show heterogeneous associations with subsequent mortality risk in the general population. Methods and results Based on England-wide linked electronic health records, we established a cohort of 1 310 518 people ≥30 years of age initially free of CVD and followed up for non-fatal events of 12 common CVD and cause-specific mortality. The 12 CVDs were considered as time-varying exposures in Cox’s proportional hazards models to estimate hazard rate ratios (HRRs) with 95% confidence intervals (CIs). Over the median follow-up of 4.2 years (2010–16), 81 516 non-fatal CVD, 10 906 cardiovascular deaths, and 40 843 non-cardiovascular deaths occurred. All 12 CVDs were associated with increased risk of cardiovascular mortality, with HRR (95% CI) ranging from 1.67 (1.47–1.89) for stable angina to 7.85 (6.62–9.31) for haemorrhagic stroke. All 12 CVDs were also associated with increased non-cardiovascular and all-cause mortality risk but to a lesser extent: HRR (95% CI) ranged from 1.10 (1.00–1.22) to 4.55 (4.03–5.13) and from 1.24 (1.13–1.35) to 4.92 (4.44–5.46) for transient ischaemic attack and sudden cardiac arrest, respectively. Conclusion Incident events of 12 common CVD show significant adverse and markedly differential associations with subsequent cardiovascular, non-cardiovascular, and all-cause mortality risk in the general population.

Objective The coronavirus disease 2019 (COVID-19) pandemic has demonstrated the value of real-world data for public health research. International federated analyses are crucial for informing policy makers. Common data models (CDMs) are critical for enabling these studies to be performed efficiently. Our objective was to convert the UK Biobank, a study of 500 000 participants with rich genetic and phenotypic data to the Observational Medical Outcomes Partnership (OMOP) CDM. Materials and Methods We converted UK Biobank data to OMOP CDM v. 5.3. We transformedparticipant research data on diseases collected at recruitment and electronic health records (EHRs) from primary care, hospitalizations, cancer registrations, and mortality from providers in England, Scotland, and Wales. We performed syntactic and semantic validations and compared comorbidities and risk factors between source and transformed data. Results We identified 502 505 participants (3086 with COVID-19) and transformed 690 fields (1 373 239 555 rows) to the OMOP CDM using 8 different controlled clinical terminologies and bespoke mappings. Specifically, we transformed self-reported noncancer illnesses 946 053 (83.91% of all source entries), cancers 37 802 (70.81%), medications 1 218 935 (88.25%), and prescriptions 864 788 (86.96%). In EHR, we transformed 13 028 182 (99.95%) hospital diagnoses, 6 465 399 (89.2%) procedures, 337 896 333 primary care diagnoses (CTV3, SNOMED-CT), 139 966 587 (98.74%) prescriptions (dm+d) and 77 127 (99.95%) deaths (ICD-10). We observed good concordance across demographic, risk factor, and comorbidity factors between source and transformed data. Discussion and Conclusion Our study demonstrated that the OMOP CDM can be successfully leveraged to harmonize complex large-scale biobanked studies combining rich multimodal phenotypic data. Our study uncovered several challenges when transforming data from questionnaires to the OMOP CDM which require further research. The transformed UK Biobank resource is a valuable tool that can enable federated research, like COVID-19 studies.