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. This study aims to develop and validate a modified version of the Cambridge Multimorbidity Score in an extended age range, using clinical terms which are routinely used in electronic health records across the world (SNOMED CT). Methods and Findings: We curated new variables describing 37 health conditions and modelled the associations between these and 1-year mortality risk using the Cox proportional hazard model in a development dataset (n=300,000). We then developed two simplified models – 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). Our 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. Conclusions: This modified version of the Cambridge Multimorbidity Score allows reliable estimation using clinical terms which can be applied internationally across multiple healthcare settings.