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

The British journal of general practice: the journal of the Royal College of General Practitioners

article

Ruby SM Tsang¹  Mark Joy¹  Heather Whitaker²  James P Sheppard¹  John Williams¹  Julian Sherlock¹  Nikhil Mayor³  Bernardo Meza-Torres¹  Elizabeth Button¹  Alice J Williams¹  Debasish Kar¹  Gayathri Delanerolle¹  Richard McManus¹  FD Richard Hobbs¹  Simon de Lusignan⁴ 

[1] Nuffield professor of primary care health sciences, Nuffield Department of Primary Care Health Sciences, University of Oxford;Public Health England;Royal Surrey NHS Foundation Trust;Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, director, Royal College of General Practitioners ,(RCGP) Research and Surveillance Centre

关键词: general practice;    medical record systems;    computerised;    mortality;    multimorbidity;    population surveillance;    Systematized Nomenclature of Medicine–Clinical Terms;   

DOI  :  10.3399/BJGP.2022.0235

学科分类：卫生学

来源: Royal College of General Practitioners

【 摘 要 】

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.

【 授权许可】

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