Oud F M M, Schut M C, Spies P E, van der Zaag-Loonen H J, de Rooij S E, Abu-Hanna A, van Munster B C
Department of Geriatrics and Centre of Excellence for Old Age Medicine, Gelre Ziekenhuizen Apeldoorn and Zutphen, the Netherlands; Department of Internal Medicine, University Medical Centre Groningen, Groningen, the Netherlands.
Department of Medical Informatics, Amsterdam University Medical Centers, Location AMC, Amsterdam, the Netherlands.
Arch Gerontol Geriatr. 2022 Nov-Dec;103:104774. doi: 10.1016/j.archger.2022.104774. Epub 2022 Jul 8.
Capturing frailty using a quick tool has proven to be challenging. We hypothesise that this is due to the complex interactions between frailty domains. We aimed to identify these interactions and assess whether adding interactions between domains improves mortality predictability.
In this retrospective cohort study, we selected all patients aged 70 or older who were admitted to one Dutch hospital between April 2015 and April 2016. Patient characteristics, frailty screening (using VMS (Safety Management System), a screening tool used in Dutch hospital care), length of stay, and mortality within three months were retrospectively collected from electronic medical records. To identify predictive interactions between the frailty domains, we constructed a classification tree with mortality as the outcome using five variables: the four VMS-domains (delirium risk, fall risk, malnutrition, physical impairment) and their sum. To determine if any domain interactions were predictive for three-month mortality, we performed a multivariable logistic regression analysis.
We included 4,478 patients. (median age: 79 years; maximum age: 101 years; 44.8% male) The highest risk for three-month mortality included patients that were physically impaired and malnourished (23% (95%-CI 19.0-27.4%)). Subgroups had comparable three-month mortality risks based on different domains: malnutrition without physical impairment (15.2% (96%-CI 12.4-18.6%)) and physical impairment and delirium risk without malnutrition (16.3% (95%-CI 13.7-19.2%)).
We showed that taking interactions between domains into account improves the predictability of three-month mortality risk. Therefore, when screening for frailty, simply adding up domains with a cut-off score results in loss of valuable information.
事实证明,使用快速工具来识别衰弱具有挑战性。我们推测这是由于衰弱各领域之间存在复杂的相互作用。我们旨在确定这些相互作用,并评估增加领域间的相互作用是否能提高死亡率预测能力。
在这项回顾性队列研究中,我们选取了2015年4月至2016年4月期间入住一家荷兰医院的所有70岁及以上患者。从电子病历中回顾性收集患者特征、衰弱筛查(使用VMS(安全管理系统),一种荷兰医院护理中使用的筛查工具)、住院时间和三个月内的死亡率。为了识别衰弱各领域之间的预测性相互作用,我们构建了一棵以死亡率为结果的分类树,使用五个变量:四个VMS领域(谵妄风险、跌倒风险、营养不良、身体功能障碍)及其总和。为了确定是否有任何领域间相互作用可预测三个月死亡率,我们进行了多变量逻辑回归分析。
我们纳入了4478名患者。(中位年龄:79岁;最大年龄:101岁;44.8%为男性)三个月死亡率最高的风险人群包括身体功能障碍和营养不良的患者(23%(95%置信区间19.0 - 27.4%))。基于不同领域的亚组具有相当的三个月死亡风险:无身体功能障碍的营养不良患者(15.2%(96%置信区间12.4 - 18.6%))以及无营养不良的身体功能障碍和谵妄风险患者(16.3%(95%置信区间13.7 - 19.2%))。
我们表明,考虑领域间的相互作用可提高三个月死亡风险的预测能力。因此,在筛查衰弱时,简单地将各领域分数相加会导致有价值信息的丢失。