Development of a modified Cambridge Multimorbidity Score for use with SNOMED CT: an observational English primary care sentinel network study

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.

Keywords: Systematized Nomenclature of Medicine–Clinical Terms; general practice; medical record systems, computerised; mortality; multimorbidity; population surveillance.

Figure 1.

Study index dates and study start dates for the development and validation cohorts (see main text for more detailed description).

Figure 2.

Observed–predicted calibration curve for the 21-condition model using 2000 bootstrap samples. Mean 0.008 (standard error = 0.9). Quantile 0.01. Black = observed. Blue = optimism corrected. Grey = ideal.