Re-evaluating physiological indicators for all-cause mortality.
Re-evaluating physiological indicators for all-cause mortality.
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DOI：
10.1016/s2666-7568(21)00228-2
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发表时间：
2021-10
影响因子：
13.1
通讯作者：
Terrera, Graciela Muniz
中科院分区：
文献类型：
--
作者：
Clouston, Sean;Terrera, Graciela Muniz
老年学研究一直侧重于为临床决策确定适当的基准，并改进对健康状况不佳风险可能增加的个人的检测。几十年来，研究人员依赖于少量的分析工具，包括线性模型(例如，线性和逻辑回归或生存模型)，通过评估这些标记与死亡风险之间的关联，积累了关于适当生物标记的大量信息。这项研究表明，收缩压超过130毫米汞柱的患者1或高密度脂蛋白胆固醇浓度非常低(≤30 mg/dL)的患者死亡风险增加。然而，许多研究人员坚持认为，应该以怀疑的态度看待这种截止点，因为模型假设的实现并不总是得到充分的评估或描述。3临床决策、研究方案和健康行为指南通常依赖于既定的界限。4因此，当商定的截止日期不能复制或改变时，老年人有效护理计划和治疗的知识和战略可能会改变。与此同时，随着传染病、饥荒和创伤对生命过程中健康结果和死亡风险的影响降低，越来越明显的是，几项临床检查的平均分数随着时间的推移而发生变化。5同时，人们对以界定健康标准为依据的早期干预越来越感兴趣，因为早期干预有可能改善阿尔茨海默病和相关痴呆症等与年龄有关的疾病的长期结果。尽管有这些担忧，但很少使用新的人群重新检查临界值，导致诊断标准可能依赖于过时的临界值。在《柳叶刀》的《健康长寿》中，Vy Kim Nguyen和他的同事们挑战了一大套生理指标的现有临床阈值及其与全因死亡率的关系。他们使用了一个大型的标准数据库(n=47266名成年人)，使用数据驱动的方法和Cox比例风险模型，调查了通常假设的线性模型与不同的非线性模型相比，在描述27个生理指标和死亡率之间的关系时，有多好。25(93%)
Geriatric research has consistently focused on the identification of appropriate benchmarks for clinical decision making and the improved detection of individuals who might be at increased risk of poor health. Over decades, researchers have relied on a small number of analytical tools, including linear models (eg, linear and logistic regression or survival modelling) to accumulate a wealth of information about appropriate biomarkers by assessing the associations between these markers and risk of death. This research has shown that patients who have systolic blood pressure exceeding 130 mm Hg, 1 or who have very low HDL cholesterol concentrations (≤ 30 mg/dL) have an increased risk of mortality. 2 Yet many researchers insist that such cutoffs should be viewed with scepticism, because the fulfilment of modelling assumptions are not always fully evaluated or described. 3Clinical decision making, research protocols, and guidelines for healthy behaviours often rely on established cutoffs. 4 As a result, knowledge and strategies for effective care plans and therapeutic treatment of older adults could change when agreed upon cutoffs cannot be replicated or they change. At the same time, it is increasingly clear that average scores on several clinical examinations have shifted over time, as the impact of infectious disease, famine, and trauma on health outcomes and mortality risk across the life course has decreased. 5 Concurrently, there is increasing interest in early interventions based on cutoffs used to define healthy standards, as early intervention has the potential to improve long-term outcomes for agerelated diseases including Alzheimer’s disease and related dementias. Despite these concerns, cutoffs are rarely re-examined using novel populations, resulting in diagnostic criteria that might rely on outdated cutoffs. In The Lancet Healthy Longevity, Vy Kim Nguyen and colleagues6 challenged existing clinical thresholds for a large set of physiological indicators and their association with all-cause mortality. They used a large normative database (n= 47 266 adults) to investigate how well commonly assumed linear models characterise the association between 27 physiological indicators and mortality, when compared with different nonlinear models, using a data-driven approach and Cox proportional hazards modelling. 25 (93%) of