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Implementation and external validation of the Cambridge Multimorbidity Score in the UK Biobank cohort
Hannah Harrison1, Samantha Ip2,3, Cristina Renzi4,5
1Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK. hh504@medschl.cam.ac.uk.
Insights
The Cambridge Multimorbidity Scores (CMS) show reasonable discrimination for death, primary care use, and cancer in UK Biobank. Recalibration is needed for accurate predictions in different populations.
Area of Science:
- Multimorbidity research
- Health informatics
- Epidemiology
Background:
- Managing patients with multiple chronic conditions is a growing healthcare challenge.
- Multimorbidity measures are crucial for clinical management and resource allocation.
- Cambridge Multimorbidity Scores (CMS) offer potential using primary care records but require external validation.
Purpose of the Study:
- To validate the Cambridge Multimorbidity Scores (CMS) in a large, independent cohort (UK Biobank).
- To assess the predictive performance of CMS for mortality, primary care consultation rates, and cancer diagnosis.
- To compare CMS performance against a simple count of conditions.
Main Methods:
- Validated CMS in 111,898 UK Biobank participants with primary care data not overlapping with the CPRD development cohort.
- Mapped 37 conditions from CMS to UK Biobank coding frameworks.
- Utilized calibration plots and discrimination measures (C-index) for validation, analyzing variations by age and sex.
Main Results:
- CMS demonstrated poor calibration in the UK Biobank cohort across all outcomes.
- Discrimination for primary care consultations was similar to the development study (C-index: 0.67).
- Discrimination for death was lower than the development study (0.69 vs. 0.89), while cancer diagnosis showed adequate discrimination (0.64).
Conclusions:
- CMS show reasonable discrimination for death, primary care consultations, and cancer diagnosis in UK Biobank.
- Recalibration of CMS is essential for accurate predictions in populations with different characteristics than the development cohort.
- Validated resources, including condition codelists and implementation code, are publicly available.
Background:
Patients 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.
Methods:
The 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.
Results:
For 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.
Conclusions:
In 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.
