National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy; Laboratory of Healthcare Research and Pharmacoepidemiology, Unit of Biostatistics, Epidemiology and Public Health, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy.
National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy; Laboratory of Healthcare Research and Pharmacoepidemiology, Unit of Biostatistics, Epidemiology and Public Health, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy.
J Clin Epidemiol. 2019 Dec;116:62-71. doi: 10.1016/j.jclinepi.2019.08.009. Epub 2019 Aug 28.
The aim of this study was to develop a new population-based risk stratification tool (Chronic Related Score [CReSc]) for predicting 5-year mortality and other outcomes.
The score included 31 conditions selected from a list of 65 candidates whose weights were assigned according to the Cox model coefficients. The model was built from a sample of 5.4 million National Health Service (NHS) beneficiaries from the Italian Lombardy Region and applied to the remaining 2.7 million NHS beneficiaries. Predictive performance was assessed by discrimination and calibration. CReSc ability in predicting secondary endpoints (i.e., hospital admissions and health care costs) was investigated. Finally, the relationship between CReSc and income was considered.
Among individuals aged 50-85 years, CReSc performance showed (1) an area under the receiver operating characteristic curve of 0.730, (2) an improved reclassification from 44% to 52% with respect to other scores, and (3) a remarkable calibration. A trend toward increasing rates of all the considered endpoints as CReSc increases was observed. Compared with individuals on low-intermediate income, NHS beneficiaries on high income showed better CReSc profile.
We developed a risk stratification tool able to predict mortality, costs, and hospital admissions. The application of CReSc may generate clinically and operationally important effects.
本研究旨在开发一种新的基于人群的风险分层工具(慢性相关评分[CReSc]),用于预测 5 年死亡率和其他结局。
该评分纳入了从 65 种候选条件中选出的 31 种条件,其权重根据 Cox 模型系数分配。该模型基于意大利伦巴第地区 540 万国家卫生服务(NHS)受益人的样本构建,并应用于剩余的 270 万 NHS 受益人。通过区分度和校准来评估预测性能。研究了 CReSc 预测次要结局(即住院和医疗保健费用)的能力。最后,还考虑了 CReSc 与收入之间的关系。
在 50-85 岁人群中,CReSc 的表现为:(1)受试者工作特征曲线下面积为 0.730;(2)与其他评分相比,重新分类的能力从 44%提高到 52%;(3)具有显著的校准度。观察到随着 CReSc 的增加,所有考虑的结局的发生率呈上升趋势。与中低收入的 NHS 受益人相比,高收入的 NHS 受益人具有更好的 CReSc 评分。
我们开发了一种能够预测死亡率、成本和住院的风险分层工具。CReSc 的应用可能会产生重要的临床和操作效果。