Centre for Academic Primary Care (Payne), Population Health Sciences, University of Bristol, Bristol, UK; Primary Care Unit (Mendonca, Saunders, Edwards, Roland), Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Cambridge, UK; RAND Corporation (Elliott), Santa Monica, Calif.; Research Department of Primary Care and Population Health (Marshall), University College London Medical School, Royal Free Campus, London, UK
Centre for Academic Primary Care (Payne), Population Health Sciences, University of Bristol, Bristol, UK; Primary Care Unit (Mendonca, Saunders, Edwards, Roland), Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Cambridge, UK; RAND Corporation (Elliott), Santa Monica, Calif.; Research Department of Primary Care and Population Health (Marshall), University College London Medical School, Royal Free Campus, London, UK.
CMAJ. 2020 Feb 3;192(5):E107-E114. doi: 10.1503/cmaj.190757.
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 ( = 300 000) and validation ( = 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.
医疗服务未能应对多种疾病的压力。需要改进衡量多种疾病的方法，以便进行研究、规划服务和分配资源。
我们对 37 种疾病与 3 种关键结局（初级保健咨询、非计划性住院、死亡）在 1 年和 5 年时的关联进行建模。我们从英国临床实践研究数据链接中提取了开发（=300000）和验证（=150000）样本。我们通过平均单独结局评分的标准化权重来构建一般结局的多种疾病评分。我们比较了该评分与 Charlson 合并症指数的表现。
包含所有 37 种疾病的模型可作为一般医生咨询（C 指数 0.732，95%置信区间[CI] 0.731-0.734）、非计划性住院（C 指数 0.742，95% CI 0.737-0.747）和 1 年死亡率（C 指数 0.912，95% CI 0.905-0.918）的可接受预测因子。简化为具有最大综合患病率/权重的 20 种疾病的模型具有相似的预测能力（C 指数分别为 0.727、95% CI 0.725-0.728；0.738、95% CI 0.732-0.743；0.910、95% CI 0.904-0.917）。它们对咨询和死亡的 5 年结局的预测也相似（C 指数分别为 0.735、95% CI 0.734-0.736，0.889、95% CI 0.885-0.892），但对住院的预测效果较差（C 指数为 0.708，95% CI 0.705-0.712）。一般结局评分的表现与针对特定结局的模型相似。这些模型在咨询（C 指数 0.691，95% CI 0.690-0.693）和住院（C 指数 0.703，95% CI 0.697-0.709）方面的表现明显优于基于 Charlson 合并症指数的模型，在死亡率方面的表现也相似（C 指数 0.907，95% CI 0.900-0.914）。
剑桥多种疾病评分稳健可靠，既可以针对特定的健康结局进行调整，也可以不进行调整。它将对规划临床服务、分配资源的决策者和寻求考虑多种疾病影响的研究人员具有重要价值。
