Determining the predictive value of Read codes to identify congenital cardiac malformations in the UK Clinical Practice Research Datalink

Hammad, Tarek A.; Margulis, Andrea V.; Ding, Yu; Strazzeri, Marian M.; Epperly, Holly

doi:10.1002/pds.3511

Cited by 13 publications

(20 citation statements)

References 11 publications

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“…Read codes are a coded dictionary of clinical terms that is widely used in the UK National Health Services by both the primary and secondary care sectors. Algorithms based on Read codes that define various diagnoses have been validated previously . These validation efforts help ensure the quality and credibility of the studies done in CPRD.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the validity of clinical codes to identify cataract and glaucoma in the UK Clinical Practice Research Datalink

Kang

Pinheiro

Hammad

et al. 2014

Pharmacoepidemiol Drug Saf

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Section: Introductionmentioning

confidence: 99%

“…Algorithms based on Read codes that define various diagnoses have been validated previously. [4][5][6] These validation efforts help ensure the quality and credibility of the studies done in CPRD.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the validity of clinical codes to identify cataract and glaucoma in the UK Clinical Practice Research Datalink

Kang

Pinheiro

Hammad

et al. 2014

Pharmacoepidemiol Drug Saf

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“…The time of diagnoses based on Read code entry into primary care systems has been reported as later, on average, than dates of actual diagnosis by general practitioners. 17 The cross mapping of Read to ICD-10 is vulnerable to discrepancies due to multiple Read codes matching one ICD-10 code. We accounted for this by performing a clinical review, assigning the most appropriate Read code to ICD-10 match.…”

Section: Discussionmentioning

confidence: 99%

Improving case ascertainment of congenital anomalies: findings from a prospective birth cohort with detailed primary care record linkage

Bishop

Small

Mason

et al. 2017

bmjpo

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BackgroundCongenital anomalies (CAs) are a common cause of infant death and disability. We linked children from a large birth cohort to a routine primary care database to detect CA diagnoses from birth to age 5 years. There could be evidence of underreporting by CA registries as they estimate that only 2% of CA registrations occur after age 1 year.MethodsCA cases were identified by linking children from a prospective birth cohort to primary care records. CAs were classified according to the European Surveillance of CA guidelines. We calculated rates of CAs by using a bodily system group for children aged 0 to <5 years, together with risk ratios (RRs) with 95% CIs for maternal risk factors.ResultsRoutinely collected primary care data increased the ascertainment of children with CAs from 432.9 per 10 000 live births under 1 year to 620.6 per 10 000 live births under 5 years. Consanguinity was a risk factor for Pakistani mothers (multivariable RR 1.87, 95% CI 1.46 to 2.83), and maternal age >34 years was a risk factor for mothers of other ethnicities (multivariable RR 2.19, 95% CI 1.36 to 3.54). Education was associated with a lower risk (multivariable RR 0.78, 95% CI 0.62 to 0.98).Conclusion98% of UK CA registrations relate to diagnoses made in the first year of life. Our data suggest that this leads to incomplete case ascertainment with a further 30% identified after age 1 year in our study. Risk factors for CAs identified up to age 1 year persist up to 5 years. National registries should consider using routine data linkage to provide more complete case ascertainment after infancy.

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“…In a UK Clinical Practice Research Datalink (CPRD) database study, 14 a high (93%) positive predictive value of Read Codes for congenital cardiac malformations registered between 1996 and 2010 was found. However, 31% of cases had a different event date, including 10% that differed no more than 30 days.…”

Section: Discussionmentioning

confidence: 99%

“…Despite these developments, there are indications that (coded) disease registry in primary care is still suboptimal, 13 14 even for important diagnoses such as cancer. 15 The literature describing the quality of data in primary care however is limited.…”

Section: Introductionmentioning

confidence: 99%

Do GPs know their patients with cancer? Assessing the quality of cancer registration in Dutch primary care: a cross-sectional validation study

et al. 2016

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ObjectivesTo assess the quality of cancer registry in primary care.Design and settingA cross-sectional validation study using linked data from primary care electronic health records (EHRs) and the Netherlands Cancer Registry (NCR).Population290 000 patients, registered with 120 general practitioners (GPs), from 50 practice centres in the Utrecht area, the Netherlands, in January 2013.InterventionLinking the EHRs of all patients in the Julius General Practitioners’ Network database at an individual patient level to the full NCR (∼1.7 million tumours between 1989 and 2011), to determine the proportion of matching cancer diagnoses. Full-text EHR extraction and manual analysis for non-matching diagnoses.Main outcome measuresProportions of matching and non-matching breast, lung, colorectal and prostate cancer diagnoses between 2007 and 2011, stratified by age category, cancer type and EHR system. Differences in year of diagnosis between the EHR and the NCR. Reasons for non-matching diagnoses.ResultsIn the Primary Care EHR, 60.6% of cancer cases were registered and coded in accordance with the NCR. Of the EHR diagnoses, 48.9% were potentially false positive (not registered in the NCR). Results differed between EHR systems but not between age categories or cancer types. The year of diagnosis corresponded in 80.6% of matching coded diagnoses. Adding full-text EHR analysis improved results substantially. A national disease registry (the NCR) proved incomplete.ConclusionsEven though GPs do know their patients with cancer, only 60.6% are coded in concordance with the NCR. Reusers of coded EHR data should be aware that 40% of cases can be missed, and almost half can be false positive. The type of EHR system influences registration quality. If full-text manual EHR analysis is used, only 10% of cases will be missed and 20% of cases found will be wrong. EHR data should only be reused with care.

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Determining the predictive value of Read codes to identify congenital cardiac malformations in the UK Clinical Practice Research Datalink

Cited by 13 publications

References 11 publications

Evaluating the validity of clinical codes to identify cataract and glaucoma in the UK Clinical Practice Research Datalink

Evaluating the validity of clinical codes to identify cataract and glaucoma in the UK Clinical Practice Research Datalink

Improving case ascertainment of congenital anomalies: findings from a prospective birth cohort with detailed primary care record linkage

Do GPs know their patients with cancer? Assessing the quality of cancer registration in Dutch primary care: a cross-sectional validation study

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