Development and validation of the Cambridge Multimorbidity Score
Rupert A. Payne, Silvia C. Mendonca, Marc N. Elliott, Catherine L. Saunders, Duncan A. Edwards, Martin Marshall and Martin Roland
BACKGROUND: 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. METHODS: 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. RESULTS: 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). INTERPRETATION: 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. Patients with multiple long-term health conditions are commonly seen by clinicians in generalist and specialist settings.1,2 Services and policies have failed to respond to the pressures that multimorbidity places on primary and secondary care. These pressures are driven by the aging population, by policies that promote rapid access over longer consultations and continuity of care, and by single-disease guidelines and performance targets, which lead to overprescribing without addressing the priorities of the patients themselves.3,4 Several approaches have been used to quantify multimorbidity. Simple counts of conditions show a clear association with various outcomes, including primary care utilization, unplanned hospital admission and death.5,6 Weighted approaches allow for differences in the strength of association between specific morbidities and a given outcome, as is the case for the Charlson Comorbidity Index, a composite morbidity score with condition weightings based on mortality.7 Although its performance has exceeded that of several other metrics,4 clinical practice has advanced considerably since its development in the 1980s, and the high weightings of particular conditions have been questioned.8 A further problem with such indices is that weightings are generally based on a specific outcome such as death, and the indices may not predict other outcomes. The lists of conditions are also problematic. A minimum list of 12 conditions has been proposed.9 However, a limited list may fail to capture important health problems, and comprehensive lists such as the Adjusted Clinical Groups (ACG) system may be challenging to implement. The aim of the current study was to develop and validate a transparent, simple measure of multimorbidity based on data from United Kingdom general practitioner (GP) records and weighted on different clinical outcomes, for use in future studies of multimorbidity and for resource allocation.