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预测爱尔兰老龄化纵向研究(TILDA)中的死亡率:四年指数的制定及与国际指标的比较。

Predicting mortality in The Irish Longitudinal Study on Ageing (TILDA): development of a four-year index and comparison with international measures.

机构信息

Centre for Health Policy and Management, Trinity College Dublin, 3-4 Foster PlaceDublin 2, Dublin, Ireland.

The Irish Longitudinal Study On Ageing, Trinity College Dublin, Dublin, Ireland.

出版信息

BMC Geriatr. 2022 Jun 21;22(1):510. doi: 10.1186/s12877-022-03196-z.

Abstract

OBJECTIVES

We aimed to replicate existing international (US and UK) mortality indices using Irish data. We developed and validated a four-year mortality index for adults aged 50 + in Ireland and compared performance with these international indices. We then extended this model by including additional predictors (self-report and healthcare utilization) and compared its performance to our replication model.

METHODS

Eight thousand one hundred seventy-four participants in The Irish Longitudinal Study on Ageing were split for development (n = 4,121) and validation (n = 4,053). Six baseline predictor categories were examined (67 variables total): demographics; cardiovascular-related illness; non-cardiovascular illness; health and lifestyle variables; functional variables; self-report (wellbeing and social connectedness) and healthcare utilization. We identified variables independently associated with four-year mortality in the development cohort and attached these variables a weight according to strength of association. We summed the weights to calculate a single index score for each participant and evaluated predicted accuracy in the validation cohort.

RESULTS

Our final 14-predictor (extended) model assigned risk points for: male (1pt); age (65-69: 2pts; 70-74: 4 pts; 75-79: 4pts; 80-84: 6pts; 85 + : 7pts); heart attack (1pt); cancer (3pts); smoked past age 30 (2pts); difficulty walking 100 m (2pts); difficulty using the toilet (3pts); difficulty lifting 10lbs (1pts); poor self-reported health (1pt); and hospital admission in previous year (1pt). Index discrimination was strong (ROC area = 0.78).

DISCUSSION

Our index is predictive of four-year mortality in community-dwelling older Irish adults. Comparisons with the international indices show that our 12-predictor (replication) model performed well and suggests that generalisability is high. Our 14-predictor (extended) model showed modest improvements compared to the 12-predictor model.

摘要

目的

我们旨在使用爱尔兰数据复制现有的国际(美国和英国)死亡率指数。我们为爱尔兰 50 岁及以上的成年人开发并验证了一个四年死亡率指数,并将其与这些国际指数进行了比较。然后,我们通过纳入其他预测因素(自我报告和医疗保健利用情况)扩展了该模型,并将其性能与我们的复制模型进行了比较。

方法

8174 名参与爱尔兰老龄化纵向研究的参与者被分为开发组(n=4121)和验证组(n=4053)。共检查了六个基线预测因素类别(总计 67 个变量):人口统计学;心血管相关疾病;非心血管疾病;健康和生活方式变量;功能变量;自我报告(幸福感和社交联系)和医疗保健利用情况。我们确定了与开发队列中四年死亡率独立相关的变量,并根据关联强度为这些变量分配权重。我们为每个参与者计算了一个单一的指数得分,并在验证队列中评估了预测准确性。

结果

我们的最终 14 个预测因素(扩展)模型为以下情况分配风险点:男性(1 分);年龄(65-69 岁:2 分;70-74 岁:4 分;75-79 岁:4 分;80-84 岁:6 分;85 岁及以上:7 分);心脏病发作(1 分);癌症(3 分);30 岁后吸烟(2 分);100 米行走困难(2 分);使用厕所困难(3 分);提举 10 磅重物困难(1 分);自我报告健康状况差(1 分);以及前一年住院(1 分)。指数的区分度很强(ROC 面积=0.78)。

讨论

我们的指数可以预测爱尔兰社区居住的老年成年人的四年死亡率。与国际指数的比较表明,我们的 12 个预测因素(复制)模型表现良好,表明可推广性较高。与 12 个预测因素模型相比,我们的 14 个预测因素(扩展)模型略有改进。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/440f/9215089/d8810cdfe9e8/12877_2022_3196_Fig1_HTML.jpg

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