Fraccaro Paolo, Kontopantelis Evangelos, Sperrin Matthew, Peek Niels, Mallen Christian, Urban Philip, Buchan Iain E, Mamas Mamas A
Health eResearch Centre, Farr Institute for Health Informatics Research NIHR Greater Manchester Primary Care Patient Safety Translational Research Centre, Institute of Population Health NIHR School for Primary Care Research, University of Manchester, Manchester Research Institute for Primary Care & Health Sciences, Arthritis Research UK Primary Care Centre, Keele University, Keele, Staffordshire, United Kingdom Cardiovascular Department, Hôpital de La Tour, Geneva, Switzerland Keele Cardiovascular Research Group, Keele University Stoke-on-Trent and Royal Stoke Hospital, University Hospital North Midlands, Stoke-on-Trent, United Kingdom.
Medicine (Baltimore). 2016 Oct;95(43):e4973. doi: 10.1097/MD.0000000000004973.
Multimorbidity is common among older people and presents a major challenge to health systems worldwide. Metrics of multimorbidity are, however, crude: focusing on measuring comorbid conditions at single time-points rather than reflecting the longitudinal and additive nature of chronic conditions. In this paper, we explore longitudinal comorbidity metrics and their value in predicting mortality.Using linked primary and secondary care data, we conducted a retrospective cohort study on adults in Salford, UK from 2005 to 2014 (n = 287,459). We measured multimorbidity with the Charlson Comorbidity Index (CCI) and quantified its changes in various time windows. We used survival models to assess the relationship between CCI changes and mortality, controlling for gender, age, baseline CCI, and time-dependent CCI. Goodness-of-fit was assessed with the Akaike Information Criterion and discrimination with the c-statistic.Overall, 15.9% patients experienced a change in CCI after 10 years, with a mortality rate of 19.8%. The model that included gender and time-dependent age, CCI, and CCI change across consecutive time windows had the best fit to the data but equivalent discrimination to the other time-dependent models. The absolute CCI score gave a constant hazard ratio (HR) of around 1.3 per unit increase, while CCI change afforded greater prognostic impact, particularly when it occurred in shorter time windows (maximum HR value for the 3-month time window, with 1.63 and 95% confidence interval 1.59-1.66).Change over time in comorbidity is an important but overlooked predictor of mortality, which should be considered in research and care quality management.
多重疾病在老年人中很常见，给全球卫生系统带来了重大挑战。然而，多重疾病的衡量指标很粗略：侧重于在单个时间点测量共病状况，而不是反映慢性病的纵向和累加性质。在本文中，我们探讨了纵向共病指标及其在预测死亡率方面的价值。利用初级和二级医疗保健的关联数据，我们对英国索尔福德2005年至2014年的成年人进行了一项回顾性队列研究（n = 287,459）。我们用查尔森共病指数（CCI）来衡量多重疾病，并量化其在不同时间窗口的变化。我们使用生存模型来评估CCI变化与死亡率之间的关系，同时控制性别、年龄、基线CCI和随时间变化的CCI。用赤池信息准则评估拟合优度，用c统计量评估区分度。总体而言，15.9%的患者在10年后CCI发生了变化，死亡率为19.8%。包含性别以及随时间变化的年龄、CCI和连续时间窗口内CCI变化的模型对数据的拟合度最佳，但与其他随时间变化的模型区分度相当。CCI的绝对得分每增加一个单位，危险比（HR）约为1.3且保持不变，而CCI变化具有更大的预后影响，特别是在较短时间窗口内发生变化时（3个月时间窗口的最大HR值为1.63，95%置信区间为1.59 - 1.66）。共病随时间的变化是死亡率的一个重要但被忽视的预测因素，在研究和医疗质量管理中应予以考虑。
