| Summary: | <p><strong>Background</strong> 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.
<p><strong>Aim</strong> 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).
<p><strong>Design and setting</strong> Observational study using diagnosis and prescriptions data from an English primary care sentinel surveillance network between 2014 and 2019.
<p><strong>Method</strong> 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 (<i>n</i> = 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 (<i>n</i> = 150 000), and for 1-year and 5-year mortality in an asynchronous validation dataset (<i>n</i> = 150 000).
<p><strong>Results</strong> 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.
<p><strong>Conclusion</strong> This modified version of the Cambridge Multimorbidity Score allows reliable estimation using clinical terms that can be applied internationally across multiple healthcare settings.
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