Comparison of identifiable and non-identifiable data linkage: health technology assessment of MitraClip using registry, administrative and mortality datasets
Objectives The UK MitraClip registry was commissioned by National Health Service (NHS) England to assess real-world outcomes from percutaneous mitral valve repair for mitral regurgitation using a new technology, MitraClip. This study aimed to determine longitudinal patient outcomes by linking to rou...
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| Format: | Article |
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BMJ Publishing Group
2021-03-01
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| Series: | BMJ Health & Care Informatics |
| Online Access: | https://informatics.bmj.com/content/28/1/e100223.full |
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| author | Hannah Patrick Helen Cole Kim Keltie Paola Cognigni Sam Gross Samuel Urwin Julie Burn Lee Berry Andrew Sims |
| author_facet | Hannah Patrick Helen Cole Kim Keltie Paola Cognigni Sam Gross Samuel Urwin Julie Burn Lee Berry Andrew Sims |
| author_sort | Hannah Patrick |
| collection | DOAJ |
| description | Objectives The UK MitraClip registry was commissioned by National Health Service (NHS) England to assess real-world outcomes from percutaneous mitral valve repair for mitral regurgitation using a new technology, MitraClip. This study aimed to determine longitudinal patient outcomes by linking to routine datasets: Hospital Episode Statistics (HES) Admitted Patient Care (APC) and Office of National Statistics.Methods Two methods of linkage were compared, using identifiable (NHS number, date of birth, postcode, gender) and non-identifiable data (hospital trust, age in years, admission, discharge and operation dates, operation and diagnosis codes). Outcome measures included: matching success, patient demographics, all-cause mortality and subsequent cardiac intervention.Results A total of 197 registry patients were eligible for matching with routine administrative data. Using identifiable linkage, a total of 187 patients (94.9%) were matched with the HES APC dataset. However, 21 matched individuals (11.2%) had inconsistencies across the datasets (eg, different gender) and were subsequently removed, leaving 166 (84.3%) for analysis. Using non-identifiable data linkage, a total of 170 patients (86.3%) were uniquely matched with the HES APC dataset.Baseline patient characteristics were not significantly different between the two methods of data linkage. The total number of deaths (all causes) identified from identifiable and non-identifiable linkage methods was 37 and 40, respectively, and the difference in subsequent cardiac interventions identified between the two methods was negligible.Conclusions Patients from a bespoke clinical procedural registry were matched to routine administrative data using identifiable and non-identifiable methods with equivalent matching success rates, similar baseline characteristics and similar 2-year outcomes. |
| format | Article |
| id | doaj-art-592bfbe18e5d4af6a0866e56d6cc07db |
| institution | OA Journals |
| issn | 2632-1009 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Health & Care Informatics |
| spelling | doaj-art-592bfbe18e5d4af6a0866e56d6cc07db2025-08-20T02:12:02ZengBMJ Publishing GroupBMJ Health & Care Informatics2632-10092021-03-0128110.1136/bmjhci-2020-100223Comparison of identifiable and non-identifiable data linkage: health technology assessment of MitraClip using registry, administrative and mortality datasetsHannah Patrick0Helen Cole1Kim Keltie2Paola Cognigni3Sam Gross4Samuel Urwin5Julie Burn6Lee Berry7Andrew Sims8Observational Data Unit, National Institute for Health and Care Excellence, London, London, UKThe Northern Health Science Alliance, Manchester, UKNorthern Medical Physics and Clinical Engineering, Newcastle Upon Tyne Hospitals NHS Trust, Newcastle Upon Tyne, Tyne and Wear, UKNorthern Medical Physics and Clinical Engineering, Newcastle Upon Tyne Hospitals NHS Trust, Newcastle Upon Tyne, Tyne and Wear, UKData Management Services, NHS Digital, Leeds, Leeds, UKTranslational and Clinical Research Institute, University of Newcastle upon Tyne, Newcastle upon Tyne, UKNorthern Medical Physics and Clinical Engineering, Newcastle Upon Tyne Hospitals NHS Trust, Newcastle Upon Tyne, Tyne and Wear, UKObservational Data Unit, National Institute for Health and Care Excellence, London, London, UKNorthern Medical Physics and Clinical Engineering, Newcastle Upon Tyne Hospitals NHS Trust, Newcastle Upon Tyne, Tyne and Wear, UKObjectives The UK MitraClip registry was commissioned by National Health Service (NHS) England to assess real-world outcomes from percutaneous mitral valve repair for mitral regurgitation using a new technology, MitraClip. This study aimed to determine longitudinal patient outcomes by linking to routine datasets: Hospital Episode Statistics (HES) Admitted Patient Care (APC) and Office of National Statistics.Methods Two methods of linkage were compared, using identifiable (NHS number, date of birth, postcode, gender) and non-identifiable data (hospital trust, age in years, admission, discharge and operation dates, operation and diagnosis codes). Outcome measures included: matching success, patient demographics, all-cause mortality and subsequent cardiac intervention.Results A total of 197 registry patients were eligible for matching with routine administrative data. Using identifiable linkage, a total of 187 patients (94.9%) were matched with the HES APC dataset. However, 21 matched individuals (11.2%) had inconsistencies across the datasets (eg, different gender) and were subsequently removed, leaving 166 (84.3%) for analysis. Using non-identifiable data linkage, a total of 170 patients (86.3%) were uniquely matched with the HES APC dataset.Baseline patient characteristics were not significantly different between the two methods of data linkage. The total number of deaths (all causes) identified from identifiable and non-identifiable linkage methods was 37 and 40, respectively, and the difference in subsequent cardiac interventions identified between the two methods was negligible.Conclusions Patients from a bespoke clinical procedural registry were matched to routine administrative data using identifiable and non-identifiable methods with equivalent matching success rates, similar baseline characteristics and similar 2-year outcomes.https://informatics.bmj.com/content/28/1/e100223.full |
| spellingShingle | Hannah Patrick Helen Cole Kim Keltie Paola Cognigni Sam Gross Samuel Urwin Julie Burn Lee Berry Andrew Sims Comparison of identifiable and non-identifiable data linkage: health technology assessment of MitraClip using registry, administrative and mortality datasets BMJ Health & Care Informatics |
| title | Comparison of identifiable and non-identifiable data linkage: health technology assessment of MitraClip using registry, administrative and mortality datasets |
| title_full | Comparison of identifiable and non-identifiable data linkage: health technology assessment of MitraClip using registry, administrative and mortality datasets |
| title_fullStr | Comparison of identifiable and non-identifiable data linkage: health technology assessment of MitraClip using registry, administrative and mortality datasets |
| title_full_unstemmed | Comparison of identifiable and non-identifiable data linkage: health technology assessment of MitraClip using registry, administrative and mortality datasets |
| title_short | Comparison of identifiable and non-identifiable data linkage: health technology assessment of MitraClip using registry, administrative and mortality datasets |
| title_sort | comparison of identifiable and non identifiable data linkage health technology assessment of mitraclip using registry administrative and mortality datasets |
| url | https://informatics.bmj.com/content/28/1/e100223.full |
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