Care Electronic Health Record Data Research Articles

P183 Establishing the epidemiology of rheumatoid arthritis, psoriatic arthritis, and axial spondyloarthritis in England using primary care electronic health record data

Background/AimsThe substantial personal and socioeconomic costs associated with rheumatoid arthritis (RA), psoriatic arthritis (PsA), and axial spondyloarthritis (SpA) make understanding their epidemiology crucial. The Clinical Practice Research Datalink (Aurum) is an electronic healthcare record (EHR) database, containing primary care records from ∼20% of English practices (>13 million patients currently registered). To determine RA/PsA/axial SpA epidemiology using EHR data, validated methods need to be applied to ascertain patients with these diagnoses. To address this, we updated and applied approaches validated in other primary care EHR databases in Aurum and described the annual incidence/point-prevalence of RA/PsA/axial SpA alongside patient characteristics (providing indirect evidence of coding accuracy).MethodsDiagnosis and synthetic disease-modifying anti-rheumatic drug (DMARD) prescription code lists were constructed, and pre-defined approaches for ascertaining patients with RA/axial SpA/PsA applied. The annual incidence and point-prevalence of RA/PsA/axial SpA were calculated from 2004-2020. Samples were stratified by age/gender, and mean age and gender/ethnic-group relative frequencies described. The study was approved by the CPRD Independent Scientific Advisory Committee (reference 20_000244).ResultsFrom 2004-2019 the point-prevalence of RA/PsA increased annually, peaking in 2019 (RA 7.79/1,000; PsA 2.87/1,000) then falling slightly. From 2004-2020 the point-prevalence of axial SpA increased annually (except in 2018/2019), peaking in 2020 (1.13/1,000). Annual RA incidence was higher between 2013-2019 (when included in the Quality Outcomes Framework, ranging 0.491 to 0.521/1,000 person-years) than 2004-2012 (ranging 0.345 to 0.400/1,000 person-years). The annual incidence of PsA and axial SpA increased from 2006 (0.108 to a peak of 0.172/1,000 person-years) and 2010 (0.025 to a peak of 0.045/1,000 person-years), respectively. These years were when new disease classification criteria were introduced. Marked falls in the annual incidence of RA, PsA and axial SpA between 2019 and 2020 were seen, reducing by 40.1%, 67.4% and 38.1%, respectively, reflecting the impact of the COVID-19 pandemic on arthritis diagnoses. Stratifying incidence/prevalence by age/gender broadly showed expected patterns (although the incidence of axial SpA/PsA in women increased over time), and the mean age and gender proportions followed those previously reported.ConclusionThe approaches we used to determine patients with RA, PsA, and axial SpA in Aurum led to incidence/prevalence estimates broadly consistent with published studies, and patient characteristics as would be expected. These data support the potential of the Aurum-updated ascertainment approaches for use in further studies of RA, PsA and axial SpA.Disclosure I. Scott: None. R. Whittle: None. J. Bailey: None. H. Twohig: None. S. Hider: None. C. Mallen: None. S. Muller: None. K. Jordan: None.

Risk Predictors and Symptom Features of Long COVID Within a Broad Primary Care Patient Population Including Both Tested and Untested Patients.

IntroductionSymptoms may persist after the initial phases of COVID-19 infection, a phenomenon termed long COVID. Current knowledge on long COVID has been mostly derived from test-confirmed and hospitalized COVID-19 patients. Data are required on the burden and predictors of long COVID in a broader patient group, which includes both tested and untested COVID-19 patients in primary care.MethodsThis is an observational study using data from Platform C19, a quality improvement program-derived research database linking primary care electronic health record data (EHR) with patient-reported questionnaire information. Participating general practices invited consenting patients aged 18–85 to complete an online questionnaire since 7th August 2020. COVID-19 self-diagnosis, clinician-diagnosis, testing, and the presence and duration of symptoms were assessed via the questionnaire. Patients were considered present with long COVID if they reported symptoms lasting ≥4 weeks. EHR and questionnaire data up till 22nd January 2021 were extracted for analysis. Multivariable regression analyses were conducted comparing demographics, clinical characteristics, and presence of symptoms between patients with long COVID and patients with shorter symptom duration.ResultsLong COVID was present in 310/3151 (9.8%) patients with self-diagnosed, clinician-diagnosed, or test-confirmed COVID-19. Only 106/310 (34.2%) long COVID patients had test-confirmed COVID-19. Risk predictors of long COVID were age ≥40 years (adjusted Odds Ratio [AdjOR]=1.49 [1.05–2.17]), female sex (adjOR=1.37 [1.02–1.85]), frailty (adjOR=2.39 [1.29–4.27]), visit to A&E (adjOR=4.28 [2.31–7.78]), and hospital admission for COVID-19 symptoms (adjOR=3.22 [1.77–5.79]). Aches and pain (adjOR=1.70 [1.21–2.39]), appetite loss (adjOR=3.15 [1.78–5.92]), confusion and disorientation (adjOR=2.17 [1.57–2.99]), diarrhea (adjOR=1.4 [1.03–1.89]), and persistent dry cough (adjOR=2.77 [1.94–3.98]) were symptom features statistically more common in long COVID.ConclusionThis study reports the factors and symptom features predicting long COVID in a broad primary care population, including both test-confirmed and the previously missed group of COVID-19 patients.

Quality of life and mental health in breast cancer survivors compared with non-cancer controls: a study of patient-reported outcomes in the United Kingdom

PurposeThere is limited high-quality evidence on quality of life, anxiety, and depressive symptoms in breast cancer survivors and women with no history of cancer. We aimed to address this by comparing patient-reported outcomes between breast cancer survivors and women with no history of breast cancer.MethodsBreast cancer survivors and women with no prior cancer were selected from the UK Clinical Practice Research Datalink GOLD primary care database, which includes population-based primary care electronic health record data. Breast cancer survivors and controls were frequency matched by age and primary care practice. Outcomes were assessed with validated instruments via postal questionnaire. Linear and logistic regression models were fitted to estimate adjusted associations between breast cancer survivorship and outcomes.ResultsA total of 356 breast cancer survivors (8.1 years post diagnosis) and 252 women with no prior cancer participated in the study. Compared with non-cancer controls, breast cancer survivors had poorer QoL in the domains of cognitive problems (adjusted β (aβ) = 1.4, p = 0.01), sexual function (aβ = 1.7, p = 0.02) and fatigue (aβ = 1.3, p = 0.01), but no difference in negative feelings, positive feelings, pain, or social avoidance. Breast cancer survivors had higher odds of borderline-probable anxiety (score ≥ 8) (adjusted OR = 1.47, 95%CI:1.15–1.87), but no differences in depression. Advanced stage at diagnosis and chemotherapy treatment were associated with poorer QoL.ConclusionsCompared with women with no history of cancer, breast cancer survivors report more problems with cognition, sexual function, fatigue, and anxiety, particularly where their cancer was advanced and/or treated with chemotherapy.Implications for Cancer SurvivorsBreast cancer survivors with more advanced disease and/or treated with chemotherapy should be closely monitored and, when possible, offered evidence-based intervention for fatigue, cognitive dysfunction, and sexual problems.

Raised inflammatory markers as a predictor of one-year mortality: A cohort study using primary care electronic health record data

Raised inflammatory markers as a predictor of one-year mortality: A cohort study using primary care electronic health record data

Measuring the prevalence of 60 health conditions in older Australians in residential aged care with electronic health records: a retrospective dynamic cohort study

BackgroundThe number of older Australians using aged care services is increasing, yet there is an absence of reliable data on their health. Multimorbidity in this population has not been well described. A clear picture of the health status of people using aged care is essential for informing health practice and policy to support evidence-based, equitable, high-quality care. Our objective was to describe the health status of older Australians living in residential aged care facilities (RACFs) and develop a model for monitoring health conditions using data from electronic health record systems.MethodsUsing a dynamic retrospective cohort of 9436 RACF residents living in 68 RACFs in New South Wales and the Australian Capital Territory from 2014 to 2017, we developed an algorithm to identify residents’ conditions using aged care funding assessments, medications administered, and clinical notes from their facility electronic health record (EHR). We generated age- and sex-specific prevalence estimates for 60 health conditions. Agreement between conditions recorded in aged care funding assessments and those documented in residents’ EHRs was evaluated using Cohen’s kappa. Cluster analysis was used to describe combinations of health conditions (multimorbidity) occurring among residents.ResultsUsing all data sources, 93% of residents had some form of circulatory disease, with hypertension the most common (62%). Most residents (93%) had a mental or behavioural disorder, including dementia (58%) or depression (54%). For most conditions, EHR data identified approximately twice the number of people with the condition compared to aged care funding assessments. Agreement between data sources was highest for multiple sclerosis, Huntington’s disease, and dementia. The cluster analysis identified seven groups with distinct combinations of health conditions and demographic characteristics and found that the most complex cluster represented a group of residents that had on average the longest lengths of stay in residential care.ConclusionsThe prevalence of many health conditions among RACF residents in Australia is underestimated in previous reports. Aged care EHR data have the potential to be used to better understand the complex health needs of this vulnerable population and can help fill the information gaps needed for population health surveillance and quality monitoring.

Approaches for combining primary care electronic health record data from multiple sources: a systematic review of observational studies

ObjectiveTo identify observational studies which used data from more than one primary care electronic health record (EHR) database, and summarise key characteristics including: objective and rationale for using multiple data...

Strategies to Promote ResiliencY (SPRY): a randomised embedded multifactorial adaptative platform (REMAP) clinical trial protocol to study interventions to improve recovery after surgery in high-risk patients

IntroductionAs the population ages, there is interest in strategies to promote resiliency, especially for frail patients at risk of its complications. The physiological stress of surgery in high-risk individuals has...

Dementia recognition, diagnosis, and treatment in the UK, 1997–2017: a change-point analysis

BackgroundOver the past decade, numerous national dementia policies and incentive schemes have been introduced in the UK. However, the effects of these initiatives are challenging to assess because of their overlapping nature. We aimed to assess the potential effects of policies on dementia diagnosis, monitoring, and treatment. MethodsWe used UK primary care electronic health record data from the Clinical Practice Research Datalink (CPRD), available through the CALIBER resource, combined with data-driven change-point analyses. We estimated the monthly incidence of dementia diagnoses, dementia-monitoring events, or dementia-related drug prescriptions among patients older than 65 years in CPRD from Jan 1, 1997, to Jan 1, 2017. We compared hypothesis-driven and data-driven approaches to estimate the locations of change-points: an interrupted time-series regression with a step-and-level change impact model versus a continuous model; and a Pruned Exact Linear Time (PELT), a search method based on an optimal partitioning algorithm. FindingsWe identified 356 234 patients that matched the study inclusion criteria and analysed 19 policies or schemes. Across Alzheimer's, vascular dementia, cognitive and memory impairment, and dementia tests, the interrupted time-series approach consistently identified change-points at the introduction of the Quality Outcomes Framework (April, 2006), the Commissioning for Quality and Innovation, the first Prime Minister's Challenge, and the Dementia Enhanced Service (approximately March, 2012). Although the Quality Outcomes Framework change-point marked an expected rise in the incidence of these dementia-related events, the change-point in March, 2012, marked a sharp decrease in incidence. No other change-point locations were concordant among the different models used. InterpretationElectronic health records allow us to explore complex policy interventions, over long periods, at a national scale. Our findings, however, showed that rates of diagnostic, monitoring, or treatment events associated with dementia did not always align to national interventions and the location of change-points varied considerably by method. The temporary potential effects of policies are likely to be minimal compared with the intensifying decrease in dementia incidence. Further research in data-driven methods in the context of overlapping policies is required. FundingNone.

Convergent validity of the electronic frailty index.

the electronic frailty index (eFI) has been developed and validated using routine primary care electronic health record data. The focus of the original big data study was on predictive validity as a form of criterion validation. Convergent validity is a subtype of construct validity and considered a core component of the validity of a test. to investigate convergent validity between the eFI and research standard frailty measures. cross-sectional validation study using data from the Community Ageing Research 75+ (CARE 75+) cohort. multi-site UK community-based cohort study. three hundred fifty-three community-dwelling older people (median age 80 years, IQR 77-84), excluding care home residents and people in the terminal stage of life. Median eFI score of participants was 0.22 (IQR 0.14-0.31). convergent validities between the eFI and: a research standard frailty index (FI); the phenotype model of frailty; Clinical Frailty Scale (CFS) and Edmonton Frail Scale were assessed using scatter plots and Spearman's rank tests to estimate correlation coefficients (Spearman's rho, ρ) and 95% confidence intervals. results indicate strong correlation between the eFI and both the research standard FI (ρ = 0.68, 95% CI 0.62-0.74) and Edmonton Frail Scale (ρ = 0.63, 95% CI 0.57-0.69). There was evidence for moderate correlation between the eFI and both the CFS (ρ = 0.59, 95% CI 0.49-0.65) and phenotype model (ρ = 0.51, 95% CI 0.42-0.59). This study provides evidence for convergent validity of the eFI, a core component of test validity.

Underrecording of osteoarthritis in United Kingdom primary care electronic health record data.

PurposePrimary care electronic health records are increasingly used to estimate the occurrence of osteoarthritis (OA). We aimed to estimate the extent and trend over time of underrecording of severe OA patients in UK primary care electronic health records using first primary total hip and knee replacements (THR/TKR) – >90% of which are performed for OA – as the reference population.Patients and methodsWe identified patients with a first primary THR or TKR recorded in the UK Clinical Practice Research Datalink between 2000 and 2015. We then searched for a diagnostic/problem code for OA up to 10 years prior to THR/TKR using 3 definitions: “diagnosed OA (joint-specific),” “diagnosed OA (any joint),” “clinical OA” (diagnosed OA or relevant peripheral joint pain symptom code).ResultsAmong 34,299 THR patients identified, 28.1%, 53.4%, and 74.4% had a prior record of diagnosed OA (hip), diagnosed OA (any), and clinical OA, respectively. Among 47,588 TKR patients, the corresponding figures were, 25.5% (diagnosed OA [knee]), 43.7%, and 74.8%. In the UK Clinical Practice Research Datalink, the proportion of patients with prior recorded OA decreased between 2000 and 2015.ConclusionAn increasing trend of underrecording of OA or joint pain among patients with THR or TKR (severe OA patients) between 2000 and 2015 was identified. An underestimate health care demand could be derived based on consultation incidence and prevalence of OA from electronic health record data that relies on osteoarthritis diagnostic codes. Further studies are warranted to investigate the validity of OA or joint pain recorded in primary care settings, which might be used to correct the consultation incidence and prevalence of OA.

Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse.

BackgroundEnormous amounts of data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use these data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems.ObjectiveIn this paper, we discuss whether data that are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the data are fit for another purpose, and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the use and reuse of these type of data.MethodsThis paper is based on the authors’ experience as users of electronic health records data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records.ResultsWe first describe the different stages in the processing of electronic health record data, as well as the different purposes for which these data are used. Given the different data processing steps and purposes, we then discuss the possible mechanisms for each individual data processing step that can generate biased outcomes. We identified 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature.ConclusionsThere are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses data that were recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated data collection process and environmental influences that can affect the quality of the data. Our stepwise, actor- and purpose-oriented approach may help to identify these possible sources of bias. Unless data quality issues are better understood and unless adequate controls are embedded throughout the data lifecycle, data-driven health care will not live up to its expectations. We need a data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda.

Survival analysis using primary care electronic health record data: A systematic review of the literature.

An emerging body of research involves observational studies in which survival analysis is applied to data obtained from primary care electronic health records (EHRs). This systematic review of these studies examined the utility of using this approach. An electronic literature search of the Scopus, PubMed, Web of Science, CINAHL, and Cochrane databases was conducted. Search terms and exclusion criteria were chosen to select studies where survival analysis was applied to the data extracted wholly from EHRs used in primary care medical practice. A total of 46 studies that met the inclusion criteria for the systematic review were examined. All were published within the past decade (2005-2014) with a majority ( n = 26, 57%) being published between 2012 and 2014. Even though citation rates varied from nil to 628, over half ( n = 27, 59%) of the studies were cited 10 times or more. The median number of subjects was 18,042 with five studies including over 1,000,000 patients. Of the included studies, 35 (76%) were published in specialty journals and 11 (24%) in general medical journals. The many conditions studied largely corresponded well with conditions important to general practice. Survival analysis applied to primary care electronic medical data is a research approach that has been frequently used in recent times. The utility of this approach was demonstrated by the ability to produce research with large numbers of subjects, across a wide range of conditions and with the potential of a high impact. Importantly, primary care data were thus available to inform primary care practice.

Using primary care electronic health record data for comparative effectiveness research: experience of data quality assessment and preprocessing in The Netherlands

Details of data quality and how quality issues were solved have not been reported in published comparative effectiveness studies using electronic health record data. We developed a conceptual framework of data quality assessment and preprocessing and apply it to a study comparing angiotensin-converting enzyme inhibitors with angiotensin receptor blockerss on renal function decline in diabetes patients. The framework establishes a line of thought to identify and act on data issues. The core concept is to evaluate whether data are fit-for-use for research tasks. Possible quality problems are listed through specific signal detections, and verified whether they are true problems. Optimal solutions are selected for the identified problems. This framework can be used in observational studies to improve validity of results.

Primary Care Medication Safety Surveillance with Integrated Primary and Secondary Care Electronic Health Records: A Cross-Sectional Study.

IntroductionThe extent of preventable medication-related hospital admissions and medication-related issues in primary care is significant enough to justify developing decision support systems for medication safety surveillance. The prerequisite for such systems is defining a relevant set of medication safety-related indicators and understanding the influence of both patient and general practice characteristics on medication prescribing and monitoring.ObjectiveThe aim of the study was to investigate the feasibility of linked primary and secondary care electronic health record data for surveillance of medication safety, examining not only prescribing but also monitoring, and associations with patient- and general practice-level characteristics.MethodsA cross-sectional study was conducted using linked records of patients served by one hospital and over 50 general practices in Salford, UK. Statistical analysis consisted of mixed-effects logistic models, relating prescribing safety indicators to potential determinants.ResultsThe overall prevalence (proportion of patients with at least one medication safety hazard) was 5.45 % for prescribing indicators and 7.65 % for monitoring indicators. Older patients and those on multiple medications were at higher risk of prescribing hazards, but at lower risk of missed monitoring. The odds of missed monitoring among all patients were 25 % less for males, 50 % less for patients in practices that provide general practitioner training, and threefold higher in practices serving the most deprived compared with the least deprived areas. Practices with more prescribing hazards did not tend to show more monitoring issues.ConclusionsSystematic collection, collation, and analysis of linked primary and secondary care records produce plausible and useful information about medication safety for a health system. Medication safety surveillance systems should pay close attention to patient age and polypharmacy with respect to both prescribing and monitoring failures; treat prescribing and monitoring as different statistical processes, rather than a combined measure of prescribing safety; and audit the socio-economic equity of missed monitoring.Electronic supplementary materialThe online version of this article (doi:10.1007/s40264-015-0304-x) contains supplementary material, which is available to authorized users.