17
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12874-024-02175-9.

          Related collections

          Most cited references16

          • Record: found
          • Abstract: found
          • Article: not found

          A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

          The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study.

            Long-term disorders are the main challenge facing health-care systems worldwide, but health systems are largely configured for individual diseases rather than multimorbidity. We examined the distribution of multimorbidity, and of comorbidity of physical and mental health disorders, in relation to age and socioeconomic deprivation. In a cross-sectional study we extracted data on 40 morbidities from a database of 1,751,841 people registered with 314 medical practices in Scotland as of March, 2007. We analysed the data according to the number of morbidities, disorder type (physical or mental), sex, age, and socioeconomic status. We defined multimorbidity as the presence of two or more disorders. 42·2% (95% CI 42·1-42·3) of all patients had one or more morbidities, and 23·2% (23·08-23·21) were multimorbid. Although the prevalence of multimorbidity increased substantially with age and was present in most people aged 65 years and older, the absolute number of people with multimorbidity was higher in those younger than 65 years (210,500 vs 194,996). Onset of multimorbidity occurred 10-15 years earlier in people living in the most deprived areas compared with the most affluent, with socioeconomic deprivation particularly associated with multimorbidity that included mental health disorders (prevalence of both physical and mental health disorder 11·0%, 95% CI 10·9-11·2% in most deprived area vs 5·9%, 5·8%-6·0% in least deprived). The presence of a mental health disorder increased as the number of physical morbidities increased (adjusted odds ratio 6·74, 95% CI 6·59-6·90 for five or more disorders vs 1·95, 1·93-1·98 for one disorder), and was much greater in more deprived than in less deprived people (2·28, 2·21-2·32 vs 1·08, 1·05-1·11). Our findings challenge the single-disease framework by which most health care, medical research, and medical education is configured. A complementary strategy is needed, supporting generalist clinicians to provide personalised, comprehensive continuity of care, especially in socioeconomically deprived areas. Scottish Government Chief Scientist Office. Copyright © 2012 Elsevier Ltd. All rights reserved.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Toxicity and response criteria of the Eastern Cooperative Oncology Group.

                Bookmark

                Author and article information

                Contributors
                hh504@medschl.cam.ac.uk
                Journal
                BMC Med Res Methodol
                BMC Med Res Methodol
                BMC Medical Research Methodology
                BioMed Central (London )
                1471-2288
                20 March 2024
                20 March 2024
                2024
                : 24
                : 71
                Affiliations
                [1 ]Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, ( https://ror.org/013meh722) Cambridge, UK
                [2 ]Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, ( https://ror.org/013meh722) Cambridge, UK
                [3 ]Department of Behavioural Science and Health, Institute of Epidemiology and Healthcare, University College London, ( https://ror.org/02jx3x895) London, UK
                [4 ]Faculty of Medicine, University Vita-Salute San Raffaele, Milan, ( https://ror.org/01gmqr298) Via Olgettina 58, Milan, Italy
                [5 ]National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, ( https://ror.org/013meh722) Cambridge, UK
                [6 ]Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, ( https://ror.org/013meh722) Cambridge, UK
                Article
                2175
                10.1186/s12874-024-02175-9
                10953059
                38509467
                47b71aa4-513f-4a18-9e31-9289b46d0e57
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 18 July 2023
                : 6 February 2024
                Funding
                Funded by: International Alliance for Cancer Early Detection
                Award ID: ACEDFR3_0620I135PR007
                Award ID: ACEDFR3_0620I135PR007
                Award ID: ACEDFR3_0620I135PR007
                Award Recipient :
                Funded by: CRUK International Alliance for Cancer Early Detection
                Award ID: EDDAPA-2022/100001
                Award ID: EDDAPA-2022/100002
                Award Recipient :
                Funded by: Cancer Research UK
                Award ID: EDDPMA-May22\100062
                Award ID: C18081/A18180
                Award ID: PPRPGM-Nov20\100002
                Award Recipient :
                Funded by: Cancer Research UK - Early Detection and Diagnosis Committee
                Award ID: EDDCPJT\100018
                Award Recipient :
                Funded by: National Institute of Health Research Advanced Fellowship
                Award ID: NIHR300861
                Award Recipient :
                Funded by: British Heart Foundation
                Award ID: RG/13/13/30194; RG/18/13/33946
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100018956, NIHR Cambridge Biomedical Research Centre;
                Award ID: BRC-1215-20014
                Award Recipient :
                Funded by: BHF-Turing Cardiovascular Data Science Award
                Award ID: BCDSA\100005
                Award Recipient :
                Funded by: BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking
                Award ID: 116074
                Award Recipient :
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Medicine
                multimorbidity,uk biobank,external validation,electronic health records,primary care records

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content157

                Most referenced authors588