Using nationwide ‘big data’ from linked electronic health records to help improve outcomes in cardiovascular diseases: 33 studies using methods from epidemiology, informatics, economics and social science in the ClinicAl disease research using LInked Bespoke studies and Electronic health Records (CALIBER) programme

Abstract

BackgroundElectronic health records (EHRs), when linked across primary and secondary care and curated for research use, have the potential to improve our understanding of care quality and outcomes.ObjectiveTo evaluate new opportunities arising from linked EHRs for improving quality of care and outcomes for patients at risk of or with coronary disease across the patient journey.DesignEpidemiological cohort, health informatics, health economics and ethnographic approaches were used.Setting230 NHS hospitals and 226 general practices in England and Wales.ParticipantsUp to 2 million initially healthy adults, 100,000 people with stable coronary artery disease (SCAD) and up to 300,000 patients with acute coronary syndrome.Main outcome measuresQuality of care, fatal and non-fatal cardiovascular disease (CVD) events.Data platform and methodsWe created a novel research platform [ClinicAl disease research using LInked Bespoke studies and Electronic health Records (CALIBER)] based on linkage of four major sources of EHR data in primary care and national registries. We carried out 33 complementary studies within the CALIBER framework. We developed a web-based clinical decision support system (CDSS) in hospital chest pain clinics. We established a novel consented prognostic clinical cohort of SCAD patients.ResultsCALIBER was successfully established as a valid research platform based on linked EHR data in nearly 2 million adults with > 600 EHR phenotypes implemented on the web portal (seehttps://caliberresearch.org/portal). Despite national guidance, key opportunities for investigation and treatment were missed across the patient journey, resulting in a worse prognosis for patients in the UK compared with patients in health systems in other countries. Our novel, contemporary, high-resolution studies showed heterogeneous associations for CVD risk factors across CVDs. The CDSS did not alter the decision-making behaviour of clinicians in chest pain clinics. Prognostic models using real-world data validly discriminated risk of death and events, and were used in cost-effectiveness decision models.ConclusionsEmerging ‘big data’ opportunities arising from the linkage of records at different stages of a patient’s journey are vital to the generation of actionable insights into the diagnosis, risk stratification and cost-effective treatment of people at risk of, or with, CVD.Future workThe vast majority of NHS data remain inaccessible to research and this hampers efforts to improve efficiency and quality of care and to drive innovation. We propose three priority directions for further research. First, there is an urgent need to ‘unlock’ more detailed data within hospitals for the scale of the UK’s 65 million population. Second, there is a need for scaled approaches to using EHRs to design and carry out trials, and interpret the implementation of trial results. Third, large-scale, disease agnostic genetic and biological collections linked to such EHRs are required in order to deliver precision medicine and to innovate discovery.Study registrationCALIBER studies are registered as follows: study 2 – NCT01569139, study 4 – NCT02176174 and NCT01164371, study 5 – NCT01163513, studies 6 and 7 – NCT01804439, study 8 – NCT02285322, and studies 26–29 – NCT01162187. Optimising the Management of Angina is registered as Current Controlled Trials ISRCTN54381840.FundingThe National Institute for Health Research (NIHR) Programme Grants for Applied Research programme (RP-PG-0407-10314) (all 33 studies) and additional funding from the Wellcome Trust (study 1), Medical Research Council Partnership grant (study 3), Servier (study 16), NIHR Research Methods Fellowship funding (study 19) and NIHR Research for Patient Benefit (study 33).

Big data from electronic health records for early and late translational cardiovascular research: challenges and potential

“… 103 For example patients in whom blood pressure declines over time, without diagnosed heart failure, have a worse survival than those whose blood pressure remains stable. 104 Using all available data points across data modalities combined with machine learning or Bayesian network models may further add to prediction. 105–107 …”

Big Data’s Role in Precision Public Health

“…This enables researchers to link a mélange of unstructured disease and outcome data ( 158 , 159 ). In their 2017 study, Harry Hemingway, in their completion of 33 studies using linked data with a total population of two million patients, said “Our findings clearly show that research using one of the NHS greatest assets—its data—is vital to innovate improvements in disease prevention, to make earlier diagnoses and to give the best treatments” ( 160 ). The inclusion of data variety increases the number of independent variables; one novel variable—or a combination of as yet uncompared variables—could end up being significant in defining relevant precision subpopulations ( 161 , 162 ).…”

Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability

“…Additional enrichment information in the form of linked medical records and supplementary knowledge bases are necessary for achieving this aim. LinkedEHR has presented a good approach to partially resolve the data veracity problem, by identifying and building a common platform for primary and secondary data [19], leading to actionable insights into diagnosis, risk stratification and treatment [30]. Yet another key factor to consider here is the fact, that high volume does not always translate to veracity.…”