Abstract
Background People with multiple health conditions are more likely to have poorer health outcomes and greater care and service needs; a reliable measure of multimorbidity would inform management strategies and resource allocation.
Aim To develop and validate a modified version of the Cambridge Multimorbidity Score in an extended age range, using clinical terms that are routinely used in electronic health records across the world (Systematized Nomenclature of Medicine — Clinical Terms, SNOMED CT).
Design and setting Observational study using diagnosis and prescriptions data from an English primary care sentinel surveillance network between 2014 and 2019.
Method In this study new variables describing 37 health conditions were curated and the associations modelled between these and 1-year mortality risk using the Cox proportional hazard model in a development dataset (n = 300 000). Two simplified models were then developed — a 20-condition model as per the original Cambridge Multimorbidity Score and a variable reduction model using backward elimination with Akaike information criterion as the stopping criterion. The results were compared and validated for 1-year mortality in a synchronous validation dataset (n = 150 000), and for 1-year and 5-year mortality in an asynchronous validation dataset (n = 150 000).
Results The final variable reduction model retained 21 conditions, and the conditions mostly overlapped with those in the 20-condition model. The model performed similarly to the 37- and 20-condition models, showing high discrimination and good calibration following recalibration.
Conclusion This modified version of the Cambridge Multimorbidity Score allows reliable estimation using clinical terms that can be applied internationally across multiple healthcare settings.
Aim To develop and validate a modified version of the Cambridge Multimorbidity Score in an extended age range, using clinical terms that are routinely used in electronic health records across the world (Systematized Nomenclature of Medicine — Clinical Terms, SNOMED CT).
Design and setting Observational study using diagnosis and prescriptions data from an English primary care sentinel surveillance network between 2014 and 2019.
Method In this study new variables describing 37 health conditions were curated and the associations modelled between these and 1-year mortality risk using the Cox proportional hazard model in a development dataset (n = 300 000). Two simplified models were then developed — a 20-condition model as per the original Cambridge Multimorbidity Score and a variable reduction model using backward elimination with Akaike information criterion as the stopping criterion. The results were compared and validated for 1-year mortality in a synchronous validation dataset (n = 150 000), and for 1-year and 5-year mortality in an asynchronous validation dataset (n = 150 000).
Results The final variable reduction model retained 21 conditions, and the conditions mostly overlapped with those in the 20-condition model. The model performed similarly to the 37- and 20-condition models, showing high discrimination and good calibration following recalibration.
Conclusion This modified version of the Cambridge Multimorbidity Score allows reliable estimation using clinical terms that can be applied internationally across multiple healthcare settings.
| Original language | English |
|---|---|
| Pages (from-to) | e435-e442 |
| Number of pages | 8 |
| Journal | British Journal of General Practice |
| Volume | 73 |
| Issue number | 731 |
| DOIs | |
| Publication status | Published - 25 May 2023 |
Bibliographical note
Funding Information:The UK Health Security Agency (UKHSA) is the principal funder of the Oxford–Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC). While no specific funding was allocated for this project, a single multimorbidity measure was required by UKHSA. James P Sheppard is funded by the Wellcome Trust/Royal Society via a Sir Henry Dale Fellowship (reference: 211182/Z/18/Z). James P Sheppard also receives funding via a National Institute for Health Research (NIHR) Oxford Biomedical Research Centre Senior Fellowship. This publication presents independent research supported by the NIHR. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care.
Funding Information:
The authors acknowledge patients who do not opt out of data sharing, and practices who share data with Oxford–RCGP RSC. They also acknowledge EMIS, TPP, In Practice Systems, and Wellbeing for facilitating pseudonymisation of clinical data as close to source as possible.
Publisher Copyright:
© 2023 Royal College of General Practitioners. All rights reserved.