Journal Article

Development and validation of the Cambridge Multimorbidity Score

Payne, Rupert A., Marshall, Martin, Edwards, Duncan A., Saunders, Catherine L., Mendonca, Silvia C., Elliott, Marc N., Roland, Martin

2020

Overview

Health services have failed to respond to the pressures of multimorbidity. Improved measures of multimorbidity are needed for conducting research, planning services and allocating resources. We modelled the association between 37 morbidities and 3 key outcomes (primary care consultations, unplanned hospital admission, death) at 1 and 5 years. We extracted development (n = 300 000) and validation (n = 150 000) samples from the UK Clinical Practice Research Datalink. We constructed a general-outcome multimorbidity score by averaging the standardized weights of the separate outcome scores. We compared performance with the Charlson Comorbidity Index. Models that included all 37 conditions were acceptable predictors of general practitioner consultations (C-index 0.732, 95% confidence interval [CI] 0.731–0.734), unplanned hospital admission (C-index 0.742, 95% CI 0.737–0.747) and death at 1 year (C-index 0.912, 95% CI 0.905–0.918). Models reduced to the 20 conditions with the greatest combined prevalence/weight showed similar predictive ability (C-indices 0.727, 95% CI 0.725–0.728; 0.738, 95% CI 0.732–0.743; and 0.910, 95% CI 0.904–0.917, respectively). They also predicted 5-year outcomes similarly for consultations and death (C-indices 0.735, 95% CI 0.734–0.736, and 0.889, 95% CI 0.885–0.892, respectively) but performed less well for admissions (C-index 0.708, 95% CI 0.705–0.712). The performance of the general-outcome score was similar to that of the outcome-specific models. These models performed significantly better than those based on the Charlson Comorbidity Index for consultations (C-index 0.691, 95% CI 0.690–0.693) and admissions (C-index 0.703, 95% CI 0.697–0.709) and similarly for mortality (C-index 0.907, 95% CI 0.900–0.914). The Cambridge Multimorbidity Score is robust and can be either tailored or not tailored to specific health outcomes. It will be valuable to those planning clinical services, policymakers allocating resources and researchers seeking to account for the effect of multimorbidity.

• Publisher: Elsevier Inc,Joule Inc,CMA Impact Inc,CMA Impact, Inc
• Subject: Adult / Age / Aged / Aged, 80 and over / Clinical medicine / Comorbidity / Comparative analysis / Confidence intervals / Datasets / Death / Electronic Health Records / Female / General practitioners / Health / Health care reform / Hospital admission and discharge / Humans / Internal Medicine / Male / Medical research / Middle Aged / Morbidity / Mortality / Mortality - trends / Multimorbidity / Outcome and Process Assessment, Health Care - methods / Patient Admission - statistics & numerical data / Patients / Predictive Value of Tests / Primary care / Primary Health Care - methods / Proportional Hazards Models / Referral and Consultation - statistics & numerical data / Resource allocation / Retrospective Studies / Statistical analysis / United Kingdom / Young Adult