Development and validation of prediction model to estimate 10-year risk of all-cause mortality using modern statistical learning methods: a large population-based cohort study and external validation.
Development and validation of prediction model to estimate 10-year risk of all-cause mortality using modern statistical learning methods: a large population-based cohort study and external validation.
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DOI：
10.1186/s12874-020-01204-7
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发表时间：
2021-01-06
影响因子：
4
通讯作者：
Steptoe A
中科院分区：
文献类型：
--
作者：
Ajnakina O;Agbedjro D;McCammon R;Faul J;Murray RM;Stahl D;Steptoe A
在人口日益老龄化的背景下，迫切需要开发一个稳健的预测模型来估计个体全因死亡的绝对风险，以便相关的评估和干预措施能够有针对性地进行。该研究的目的是推导、评估和验证（内部和外部）风险预测模型，以便快速估计未来 10 年全因死亡的绝对风险。模型开发的数据来自英国老龄化纵向研究，该研究包括 9154 名年龄在 50-75 岁之间的人口代表，其中 1240 人（13.5%）在 10 年随访期间死亡。使用 Harrell 的乐观校正程序进行内部验证；外部验证是使用健康与退休研究 (HRS) 进行的，该研究是对居住在美国的 50 岁以上成年人进行的一项具有全国代表性的纵向调查。采用最小绝对收缩和选择算子正则化的 Cox 比例风险模型（其中基于重复交叉验证选择优化参数）用于变量选择和模型拟合。在开发和验证队列中确定了校准、区分、敏感性和特异性的测量。该模型选择了 13 个全因死亡率的预后因素，包括人口特征、健康合并症、生活方式和认知功能等信息。内部验证的模型具有良好的区分能力（c指数=0.74）、特异性（72.5%）和敏感性（73.0%）。经过外部验证，模型的预测精度保持在临床可接受的范围内（c指数=0.69，校准斜率β=0.80，特异性=71.5%，灵敏度=70.6%）。我们的模型的主要局限性有两个：1）它可能不适用于疗养院和其他机构人群，2）它是在以白人为主的队列中开发和验证的。一种新的预测模型已经开发出来并经过外部验证，该模型可以量化普通人群未来 10 年全因死亡的绝对风险。它具有良好的预测准确性，并且基于各种护理和研究环境中可用的变量。该模型可以帮助识别老年人全因死亡的高风险，以进行进一步的评估或干预。在线版本包含可在 10.1186/s12874-020-01204-7 获取的补充材料。
In increasingly ageing populations, there is an emergent need to develop a robust prediction model for estimating an individual absolute risk for all-cause mortality, so that relevant assessments and interventions can be targeted appropriately. The objective of the study was to derive, evaluate and validate (internally and externally) a risk prediction model allowing rapid estimations of an absolute risk of all-cause mortality in the following 10 years. For the model development, data came from English Longitudinal Study of Ageing study, which comprised 9154 population-representative individuals aged 50–75 years, 1240 (13.5%) of whom died during the 10-year follow-up. Internal validation was carried out using Harrell’s optimism-correction procedure; external validation was carried out using Health and Retirement Study (HRS), which is a nationally representative longitudinal survey of adults aged ≥50 years residing in the United States. Cox proportional hazards model with regularisation by the least absolute shrinkage and selection operator, where optimisation parameters were chosen based on repeated cross-validation, was employed for variable selection and model fitting. Measures of calibration, discrimination, sensitivity and specificity were determined in the development and validation cohorts. The model selected 13 prognostic factors of all-cause mortality encompassing information on demographic characteristics, health comorbidity, lifestyle and cognitive functioning. The internally validated model had good discriminatory ability (c-index=0.74), specificity (72.5%) and sensitivity (73.0%). Following external validation, the model’s prediction accuracy remained within a clinically acceptable range (c-index=0.69, calibration slope β=0.80, specificity=71.5% and sensitivity=70.6%). The main limitation of our model is twofold: 1) it may not be applicable to nursing home and other institutional populations, and 2) it was developed and validated in the cohorts with predominately white ethnicity. A new prediction model that quantifies absolute risk of all-cause mortality in the following 10-years in the general population has been developed and externally validated. It has good prediction accuracy and is based on variables that are available in a variety of care and research settings. This model can facilitate identification of high risk for all-cause mortality older adults for further assessment or interventions. The online version contains supplementary material available at 10.1186/s12874-020-01204-7.
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影响因子：
3.7
作者：
Idler, Ellen;Blevins, John;Hogue, Carol
通讯作者：
Hogue, Carol
影响因子：
3
作者：
Bosworth, HB;Siegler, IC;Mark, DB
通讯作者：
Mark, DB
DOI：
10.1016/s1470-2045(14)71116-7
发表时间：
2015-04
期刊：
The Lancet. Oncology
影响因子：
--
作者：
Balachandran VP;Gonen M;Smith JJ;DeMatteo RP
通讯作者：
DeMatteo RP
DOI：
10.1136/bmj.c6624
发表时间：
2010-12-09
期刊：
BMJ (Clinical research ed.)
影响因子：
--
作者：
Hippisley-Cox J;Coupland C;Robson J;Brindle P
通讯作者：
Brindle P
影响因子：
5
作者：
Hajat, Anjum;Kaufman, Jay S.;Thomas, James C.
通讯作者：
Thomas, James C.