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Implementation and external validation of the Cambridge Multimorbidity Score in the UK Biobank cohort

Published version
Peer-reviewed

Repository DOI


Change log

Authors

Harrison, Hannah 
Ip, Samantha 
Renzi, Cristina 
Li, Yangfan 
Barclay, Matthew 

Abstract

jats:titleAbstract</jats:title>jats:sec jats:titleBackground</jats:title> jats:pPatients with multiple conditions present a growing challenge for healthcare provision. Measures of multimorbidity may support clinical management, healthcare resource allocation and accounting for the health of participants in purpose-designed cohorts. The recently developed Cambridge Multimorbidity scores (CMS) have the potential to achieve these aims using primary care records, however, they have not yet been validated outside of their development cohort.</jats:p> </jats:sec>jats:sec jats:titleMethods</jats:title> jats:pThe CMS, developed in the Clinical Research Practice Dataset (CPRD), were validated in UK Biobank participants whose data is not available in CPRD (the cohort used for CMS development) with available primary care records (n = 111,898). This required mapping of the 37 pre-existing conditions used in the CMS to the coding frameworks used by UK Biobank data providers. We used calibration plots and measures of discrimination to validate the CMS for two of the three outcomes used in the development study (death and primary care consultation rate) and explored variation by age and sex. We also examined the predictive ability of the CMS for the outcome of cancer diagnosis. The results were compared to an unweighted count score of the 37 pre-existing conditions.</jats:p> </jats:sec>jats:sec jats:titleResults</jats:title> jats:pFor all three outcomes considered, the CMS were poorly calibrated in UK Biobank. We observed a similar discriminative ability for the outcome of primary care consultation rate to that reported in the development study (C-index: 0.67 (95%CI:0.66–0.68) for both, 5-year follow-up); however, we report lower discrimination for the outcome of death than the development study (0.69 (0.68–0.70) and 0.89 (0.88–0.90) respectively). Discrimination for cancer diagnosis was adequate (0.64 (0.63–0.65)). The CMS performs favourably to the unweighted count score for death, but not for the outcomes of primary care consultation rate or cancer diagnosis.</jats:p> </jats:sec>jats:sec jats:titleConclusions</jats:title> jats:pIn the UK Biobank, CMS discriminates reasonably for the outcomes of death, primary care consultation rate and cancer diagnosis and may be a valuable resource for clinicians, public health professionals and data scientists. However, recalibration will be required to make accurate predictions when cohort composition and risk levels differ substantially from the development cohort. The generated resources (including codelists for the conditions and code for CMS implementation in UK Biobank) are available online.</jats:p> </jats:sec>

Description

Keywords

Primary care records, Multimorbidity, External validation, UK Biobank, Electronic health records

Journal Title

BMC Medical Research Methodology

Conference Name

Journal ISSN

1471-2288

Volume Title

24

Publisher

Springer Science and Business Media LLC
Sponsorship
International Alliance for Cancer Early Detection (ACEDFR3_0620I135PR007, ACEDFR3_0620I135PR007, ACEDFR3_0620I135PR007)
CRUK International Alliance for Cancer Early Detection (EDDAPA-2022/100001, EDDAPA-2022/100002)
Cancer Research UK (EDDPMA-May22\100062, C18081/A18180, PPRPGM-Nov20\100002)
Cancer Research UK - Early Detection and Diagnosis Committee (EDDCPJT\100018)
National Institute of Health Research Advanced Fellowship (NIHR300861)
British Heart Foundation (RG/13/13/30194)
NIHR Cambridge Biomedical Research Centre (BRC-1215-20014)
BHF-Turing Cardiovascular Data Science Award (BCDSA\100005)
BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking (116074)