Electronic Healthcare Record Databases 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.

Yellow fever vaccine usage in the United States and risk of neurotropic and viscerotropic disease: A retrospective cohort study using three healthcare databases

BackgroundYellow fever (YF) vaccines are highly effective and have a well-established safety profile despite the risk of rare serious adverse events (SAEs), vaccine-associated neurotropic (YEL-AND) and viscerotropic disease (YEL-AVD). This study aimed to describe US civilian YF vaccine usage, the population characteristics and pre-existing immunosuppressive medical conditions among those vaccinated, and to provide updated risk estimates of neurotropic and viscerotropic disease post-vaccination. MethodsA retrospective cohort study was conducted using de-identified patient information from Optum Electronic Healthcare Record (EHR) (2007–2019), Optum Clinformatics Data Mart (CDM) (2004–2019) and IBM MarketScan (2007–2019) databases. YF vaccine recipients were identified using relevant vaccination and procedural codes. Demographic characteristics and pre-existing medical conditions were described. Incidence proportions with 95% confidence intervals (CI) of neurotropic and viscerotropic diseases occurring ≤ 30 days post-vaccination, after exclusion of unlikely cases based on current clinical guidelines of YEL-AND and YEL-AVD, were calculated. ResultsA total of 92,205, 46,539 and 125,235 YF vaccine recipients were retrieved from Optum EHR, Optum CDM and IBM MarketScan databases, respectively. The majority of vaccine recipients were aged < 60 years (highest proportion aged 18–29 years) with a higher proportion of females overall. Few vaccine recipients (<1%) had conditions predisposing them to immunosuppression. Four non-fatal cases of neurotropic disease and zero cases of viscerotropic disease were identified. The incidence proportion of post-vaccination neurotropic disease was 1.41 (95% CI: 0.15–6.61) and 3.04 (95% CI: 0.86–8.11) per 100,000 vaccine recipients in Optum EHR and IBM MarketScan, respectively, with no events identified in Optum CDM. ConclusionsThis study provides updated insights into current YF vaccine usage in US civilian recipients and supports the safety profile of YF vaccines in US practice. The low frequency of pre-existing immunosuppressive medical conditions among vaccine recipients suggests good adherence to vaccination guidelines by healthcare practitioners. The risk of developing neurotropic and viscerotropic disease post-vaccination remains rare.

Validity and interpretation of spirometric recordings to diagnose COPD in UK primary care.

BackgroundThe diagnosis of COPD is dependent upon clinical judgment and confirmation of the presence of airflow obstruction using spirometry. Spirometry is now routinely available; however, spirometry incorrectly performed or interpreted can lead to misdiagnosis. We aimed to determine whether spirometry undertaken in primary care for patients suspected to have COPD was of sufficient quality and whether their spirometry was correctly interpreted.MethodsTwo chest physicians re-read all spirometric readings for both quality of the procedure and interpretation, received as a part of COPD validation studies using data from the Clinical Practice Research Datalink (CPRD). We then used logistic regression to investigate predictors of correct interpretation.ResultsSpirometry traces were obtained for 306 patients, of which 221 (72.2%) were conducted in primary care. Of those conducted in primary care, 98.6% (n=218) of spirometry traces were of adequate quality. Of those traces that were of adequate quality and conducted in primary care, and in whom a general practitioner (GP) diagnosis of COPD had been made, 72.5% (n=218) were consistent with obstruction. Historical records for asthma diagnosis significantly decreased odds of correct interpretation.ConclusionThe quality of the spirometry procedure undertaken in primary care is high. However, this was not reflected in the quality of interpretation, suggesting an unmet training in primary care. The quality of the spirometry procedure as demonstrated by spirometric tracings provides a re-assurance for the use of spirometric values available in the electronic health care record databases for research purposes.

The role of electronic healthcare record databases in paediatric drug safety surveillance: a retrospective cohort study.

Electronic healthcare record (EHR)-based surveillance systems are increasingly being developed to support early detection of safety signals. It is unknown what the power of such a system is for surveillance among children and adolescents. In this paper we provide estimates of the number and classes of drugs, and incidence rates (IRs) of events, that can be monitored in children and adolescents (0-18 years). Data were obtained from seven population-based EHR databases in Denmark, Italy, and the Netherlands during the period 1996-2010. We estimated the number of drugs for which specific adverse events can be monitored as a function of actual drug use, minimally detectable relative risk (RR) and IRs for 10 events. The population comprised 4 838 146 individuals (25 575 132 person years (PYs)), who were prescribed 2170 drugs (1 610 631 PYs drug-exposure). Half of the total drug-exposure in PYs was covered by only 18 drugs (0.8%). For a relatively frequent event like upper gastrointestinal bleeding there were 39 drugs for which an association with a RR ≥4, if present, could be investigated. The corresponding number of drugs was eight for a rare event like anaphylactic shock. Drug use in children is rare and shows little variation. The number of drugs with enough exposure to detect rare adverse events in children and adolescents within an EHR-based surveillance system such as EU-ADR is limited. Use of additional sources of paediatric drug exposure information and global collaboration are imperative in order to optimize EHR data for paediatric safety surveillance.

Signal Detection of Potentially Drug-Induced Acute Liver Injury in Children Using a Multi-Country Healthcare Database Network

Data mining in spontaneous reporting databases has shown that drug-induced liver injury is infrequently reported in children. Our objectives were to (i) identify drugs potentially associated with acute liver injury (ALI) in children and adolescents using electronic healthcare record (EHR) data; and (ii) to evaluate the significance and novelty of these associations. We identified potential cases of ALI during exposure to any prescribed/dispensed drug for individuals <18 years old from the EU-ADR network, which includes seven databases from three countries, covering the years 1996-2010. Several new methods for signal detection were applied to identify all statistically significant associations between drugs and ALI. A drug was considered statistically significantly associated with ALI, using all other time as a reference category, if the 95% CI lower band of the relative risk was >1 and in the presence of at least three exposed cases of ALI. Potentially new signals were distinguished from already known associations concerning ALI (whether in adults and/or in the paediatric population) through manual review of published literature and drug product labels. The study population comprised 4,838,146 individuals aged <18 years, who contributed an overall 25,575,132 person-years of follow-up. Within this population, we identified 1,015 potential cases of ALI. Overall, 20 positive drug-ALI associations were detected. The associations between ALI and domperidone, flunisolide and human insulin were considered as potentially new signals. Citalopram and cetirizine have been previously described as hepatotoxic in adults but not in children, while all remaining associations were already known in both adults and children. Data mining of multiple EHR databases for signal detection confirmed known associations between ALI and several drugs, and identified some potentially new signals in children that require further investigation through formal epidemiologic studies. This study shows that EHRs may complement traditional spontaneous reporting systems for signal detection and strengthening.

A Reference Standard for Evaluation of Methods for Drug Safety Signal Detection Using Electronic Healthcare Record Databases

The growing interest in using electronic healthcare record (EHR) databases for drug safety surveillance has spurred development of new methodologies for signal detection. Although several drugs have been withdrawn postmarketing by regulatory authorities after scientific evaluation of harms and benefits, there is no definitive list of confirmed signals (i.e. list of all known adverse reactions and which drugs can cause them). As there is no true gold standard, prospective evaluation of signal detection methods remains a challenge. Within the context of methods development and evaluation in the EU-ADR Project (Exploring and Understanding Adverse Drug Reactions by integrative mining of clinical records and biomedical knowledge), we propose a surrogate reference standard of drug-adverse event associations based on existing scientific literature and expert opinion. The reference standard was constructed for ten top-ranked events judged as important in pharmacovigilance. A stepwise approach was employed to identify which, among a list of drug-event associations, are well recognized (known positive associations) or highly unlikely ('negative controls') based on MEDLINE-indexed publications, drug product labels, spontaneous reports made to the WHO's pharmacovigilance database, and expert opinion. Only drugs with adequate exposure in the EU-ADR database network (comprising ≈60 million person-years of healthcare data) to allow detection of an association were considered. Manual verification of positive associations and negative controls was independently performed by two experts proficient in clinical medicine, pharmacoepidemiology and pharmacovigilance. A third expert adjudicated equivocal cases and arbitrated any disagreement between evaluators. Overall, 94 drug-event associations comprised the reference standard, which included 44 positive associations and 50 negative controls for the ten events of interest: bullous eruptions; acute renal failure; anaphylactic shock; acute myocardial infarction; rhabdomyolysis; aplastic anaemia/pancytopenia; neutropenia/agranulocytosis; cardiac valve fibrosis; acute liver injury; and upper gastrointestinal bleeding. For cardiac valve fibrosis, there was no drug with adequate exposure in the database network that satisfied the criteria for a positive association. A strategy for the construction of a reference standard to evaluate signal detection methods that use EHR has been proposed. The resulting reference standard is by no means definitive, however, and should be seen as dynamic. As knowledge on drug safety evolves over time and new issues in drug safety arise, this reference standard can be re-evaluated.

Using Electronic Health Care Records for Drug Safety Signal Detection

Drug safety monitoring relies primarily on spontaneous reporting, but electronic health care record databases offer a possible alternative for the detection of adverse drug reactions (ADRs). To evaluate the relative performance of different statistical methods for detecting drug-adverse event associations in electronic health care record data representing potential ADRs. Data from 7 databases across 3 countries in Europe comprising over 20 million subjects were used to compute the relative risk estimates for drug-event pairs using 10 different methods, including those developed for spontaneous reporting systems, cohort methods such as the longitudinal gamma poisson shrinker, and case-based methods such as case-control. The newly developed method "longitudinal evaluation of observational profiles of adverse events related to drugs" (LEOPARD) was used to remove associations likely caused by protopathic bias. Data from the different databases were combined by pooling of data, and by meta-analysis for random effects. A reference standard of known ADRs and negative controls was created to evaluate the performance of the method. The area under the curve of the receiver operator characteristic curve was calculated for each method, both with and without LEOPARD filtering. The highest area under the curve (0.83) was achieved by the combination of either longitudinal gamma poisson shrinker or case-control with LEOPARD filtering, but the performance between methods differed little. LEOPARD increased the overall performance, but flagged several known ADRs as caused by protopathic bias. Combinations of methods demonstrate good performance in distinguishing known ADRs from negative controls, and we assume that these could also be used to detect new drug safety signals.