Accuracy of identifying incident stroke cases from linked health care data in UK Biobank

Rannikmäe, Kristiina; Ngoh, Kenneth; Bush, Kathryn; Salman, Rustam Al-Shahi; Doubal, Fergus; Flaig, Robin; Henshall, David; Hutchison, Aidan; Nolan, John J.; Osborne, Scott; Samarasekera, Neshika; Schnier, Christian; Whiteley, William; Wilkinson, Tim; Wilson, Kirsty; Woodfield, Rebecca; Zhang, Qiuli; Allen, Naomi E.; Sudlow, Cathie

doi:10.1212/wnl.0000000000009924

Cited by 30 publications

(43 citation statements)

References 12 publications

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“…1 Selection of included UK Biobank (UKB) participants. GP general practitioner, NHS National Health Service; Information for code validation refers to the participant having any information on the hospital electronic patient record system to allow an expert stroke physician to confirm or reject the accuracy of the coded diagnosis [ 2 ] …”

Section: Methodsmentioning

confidence: 99%

“…It is an open access resource, established to facilitate research into the determinants of a wide range of health outcomes [ 1 ]. Disease outcomes are ascertained primarily via linkages to routinely collected coded national administrative health datasets [ 2 ], enabling the identification of a broad range of disease phenotypes with sufficient accuracy for many research studies [ 2 – 4 ]. However, these coded data are often incomplete and less accurate when it comes to identifying specific disease subtypes [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…Disease outcomes are ascertained primarily via linkages to routinely collected coded national administrative health datasets [ 2 ], enabling the identification of a broad range of disease phenotypes with sufficient accuracy for many research studies [ 2 – 4 ]. However, these coded data are often incomplete and less accurate when it comes to identifying specific disease subtypes [ 2 , 3 ]. For example, up to 40% of participants with a stroke code in hospital, death record or primary care administrative data in UKB do not have a code specifying their stroke subtype, even though review of the full text medical records shows that a stroke subtype was known in over 99% of cases [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, these coded data are often incomplete and less accurate when it comes to identifying specific disease subtypes [ 2 , 3 ]. For example, up to 40% of participants with a stroke code in hospital, death record or primary care administrative data in UKB do not have a code specifying their stroke subtype, even though review of the full text medical records shows that a stroke subtype was known in over 99% of cases [ 2 ]. Further, among subtype specific codes, hemorrhagic stroke codes may have precision as low as 42% [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…For example, up to 40% of participants with a stroke code in hospital, death record or primary care administrative data in UKB do not have a code specifying their stroke subtype, even though review of the full text medical records shows that a stroke subtype was known in over 99% of cases [ 2 ]. Further, among subtype specific codes, hemorrhagic stroke codes may have precision as low as 42% [ 2 ]. This will be a limitation for many researchers since stroke is a heterogeneous disease, and genetic and environmental risk factors to date have been found to be very subtype specific.…”

Section: Introductionmentioning

confidence: 99%

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Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke

Rannikmäe¹,

Tominey

et al. 2021

BMC Med Inform Decis Mak

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Background Better phenotyping of routinely collected coded data would be useful for research and health improvement. For example, the precision of coded data for hemorrhagic stroke (intracerebral hemorrhage [ICH] and subarachnoid hemorrhage [SAH]) may be as poor as < 50%. This work aimed to investigate the feasibility and added value of automated methods applied to clinical radiology reports to improve stroke subtyping. Methods From a sub-population of 17,249 Scottish UK Biobank participants, we ascertained those with an incident stroke code in hospital, death record or primary care administrative data by September 2015, and ≥ 1 clinical brain scan report. We used a combination of natural language processing and clinical knowledge inference on brain scan reports to assign a stroke subtype (ischemic vs ICH vs SAH) for each participant and assessed performance by precision and recall at entity and patient levels. Results Of 225 participants with an incident stroke code, 207 had a relevant brain scan report and were included in this study. Entity level precision and recall ranged from 78 to 100%. Automated methods showed precision and recall at patient level that were very good for ICH (both 89%), good for SAH (both 82%), but, as expected, lower for ischemic stroke (73%, and 64%, respectively), suggesting coded data remains the preferred method for identifying the latter stroke subtype. Conclusions Our automated method applied to radiology reports provides a feasible, scalable and accurate solution to improve disease subtyping when used in conjunction with administrative coded health data. Future research should validate these findings in a different population setting.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke

Rannikmäe¹,

Tominey

et al. 2021

BMC Med Inform Decis Mak

Self Cite

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Association of Cardiovascular Health With Life Expectancy Free of Cardiovascular Disease, Diabetes, Cancer, and Dementia in UK Adults

Wang

et al. 2023

JAMA Intern Med

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ImportanceThe average life expectancy has increased substantially in the past few decades in most industrialized countries; however, not all of the increased life expectancy is being spent in optimal health, especially among individuals with low socioeconomic status.ObjectiveTo quantify the associations between levels of cardiovascular health (CVH), estimated by the American Heart Association’s Life’s Essential 8 (LE8) metrics, with life expectancy free of major chronic disease, including cardiovascular disease (CVD), diabetes, cancer, and dementia, in UK adults.Design, Setting, and ParticipantsThis cohort study included 135 199 adults in the UK Biobank study who were initially free of major chronic disease and had complete data on LE8 metrics. Data analyses were performed in August 2022.ExposuresCardiovascular health levels, as estimated by LE8 score. The LE8 score, which consists of 8 components: (1) diet, (2) physical activity, (3) tobacco/nicotine exposure, (4) sleep, (5) body mass index, (6) non–high-density lipoprotein cholesterol, (7) blood glucose, and (8) blood pressure. The CVH level was evaluated at baseline and categorized into low (LE8 score &lt;50), moderate (LE8 score ≥50 but &lt;80), and high (LE8 score ≥80) levels.Main Outcomes and MeasuresThe primary outcome was the life expectancy free of 4 major chronic diseases (CVD, diabetes, cancer, and dementia).ResultsOf the 135 199 adults (44.7% men; mean [SD] age, 55.4 [7.9] years) included in the study, a total of 4712, 48 955, and 6748 men had low, moderate, and high CVH levels, respectively, and the corresponding numbers for women were 3661, 52 192, and 18 931. At age 50 years, the estimated disease-free years were 21.5 (95% CI, 21.0-22.0), 25.5 (95% CI, 25.4-25.6), and 28.4 (95% CI, 27.8-29.0) for men with low, moderate, and high CVH levels, respectively; the corresponding estimated disease-free years at age 50 years for women were 24.2 (95% CI, 23.5-24.8), 30.5 (95% CI, 30.4-30.6), and 33.6 (95% CI, 33.1-34.0). Equivalently, men with moderate or high CVH levels lived on average 4.0 (95% CI, 3.4-4.5) or 6.9 (95% CI, 6.1-7.7) longer years free of chronic disease, respectively, at age 50 years, compared with men with low CVH levels. The corresponding longer years lived free of disease for women were 6.3 (95% CI, 5.6-7.0) or 9.4 (95% CI, 8.5-10.2). For participants with high CVH level, there was not a statistically significant difference in disease-free life expectancy between participants with low and other socioeconomic status.Conclusions and RelevanceIn this cohort study, a high level of CVH, evaluated using the LE8 metrics, was associated with longer life expectancy free of major chronic diseases and may contribute to narrowing socioeconomic health inequalities in both men and women.

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Integration of Real‐World Data and Genetics to Support Target Identification and Validation

Davitte

Stott-Miller

Ehm

et al. 2021

Clin Pharma and Therapeutics

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Even modest improvements in the probability of success of selecting drug targets which are ultimately approved can substantially reduce the costs of research and development. Drug targets with human genetic evidence of disease association are twice as likely to lead to approved drugs. A key enabler of identifying and validating these genetically validated targets is access to association results from genome-wide genotyping, whole-exome sequencing, and whole-genome sequencing studies with observable traits (often diseases) across large numbers of individuals. Today, linkage between genotype and real-world data (RWD) provides significant opportunities to not only increase the statistical power of genome-wide association studies by ascertaining additional cases for diseases of interest, but also to improve diversity and coverage of association studies across the disease phenome. As RWD-genetics linked resources continue to grow in diversity of participants, breadth of data captured, length of observation, and number of participants, there is a greater need to leverage the experience of RWD experts, clinicians, and highly experienced geneticists together to understand which lessons and frameworks from general research using RWD sources are relevant to improve genetics-driven drug discovery and development. This paper describes new challenges and opportunities for phenotypes enabled by diverse RWD sources, considerations in the use of RWD phenotypes for disease gene identification across the disease phenome, and challenges and opportunities in leveraging RWD phenotypes in target validation. The paper concludes with views on the future directions for phenotype development using RWD, and key questions requiring further research and development to advance this nascent field.

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Accuracy of identifying incident stroke cases from linked health care data in UK Biobank

Cited by 30 publications

References 12 publications

Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke

Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke

Association of Cardiovascular Health With Life Expectancy Free of Cardiovascular Disease, Diabetes, Cancer, and Dementia in UK Adults

Integration of Real‐World Data and Genetics to Support Target Identification and Validation

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