Bicknell Ross, Lim Wen Kwang, Maier Andrea B, LoGiuidice Dina
Department of Medicine and Aged Care, @AgeMelbourne, Melbourne Health-Royal Melbourne Hospital, University of Melbourne, 6 North Main Building, Royal Melbourne Hospital, 300 Grattan Street, Parkville, Victoria 3050 Australia.
Department of Human Movement Sciences, @AgeAmsterdam, Amsterdam Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Diagn Progn Res. 2020 Oct 7;4:17. doi: 10.1186/s41512-020-00085-0. eCollection 2020.
For residential aged care facility (RACF) residents with dementia, lack of prognostic guidance presents a significant challenge for end of life care planning. In an attempt to address this issue, models have been developed to assess mortality risk for people with advanced dementia, predominantly using long-term care minimum data set (MDS) information from the USA. A limitation of these models is that the information contained within the MDS used for model development was not collected for the purpose of identifying prognostic factors. The models developed using MDS data have had relatively modest ability to discriminate mortality risk and are difficult to apply outside the MDS setting. This study will aim to develop a model to estimate 6- and 12-month mortality risk for people with dementia from prognostic indicators recorded during usual clinical care provided in RACFs in Australia.
A secondary analysis will be conducted for a cohort of people with dementia from RACFs participating in a cluster-randomized trial of a palliative care education intervention (IMPETUS-D). Ten prognostic indicator variables were identified based on a literature review of clinical features associated with increased mortality for people with dementia living in RACFs. Variables will be extracted from RACF files at baseline and mortality measured at 6 and 12 months after baseline data collection. A multivariable logistic regression model will be developed for 6- and 12-month mortality outcome measures using backwards elimination with a fractional polynomial approach for continuous variables. Internal validation will be undertaken using bootstrapping methods. Discrimination of the model for 6- and 12-month mortality will be presented as receiver operating curves with c statistics. Calibration curves will be presented comparing observed and predicted event rates for each decile of risk as well as flexible calibration curves derived using loess-based functions.
The model developed in this study aims to improve clinical assessment of mortality risk for people with dementia living in RACFs in Australia. Further external validation in different populations will be required before the model could be developed into a tool to assist with clinical decision-making in the future.
对于患有痴呆症的老年护理机构（RACF）居民而言，缺乏预后指导给临终护理规划带来了重大挑战。为解决这一问题，已开发出多种模型来评估晚期痴呆症患者的死亡风险，主要使用来自美国的长期护理最小数据集（MDS）信息。这些模型的一个局限性在于，用于模型开发的MDS中所包含的信息并非为识别预后因素而收集。使用MDS数据开发的模型在区分死亡风险方面能力相对有限，且难以在MDS环境之外应用。本研究旨在基于澳大利亚RACF日常临床护理期间记录的预后指标，开发一个模型来估计痴呆症患者6个月和12个月的死亡风险。
将对参与姑息治疗教育干预（IMPETUS-D）整群随机试验的RACF痴呆症患者队列进行二次分析。基于对与RACF中痴呆症患者死亡率增加相关的临床特征的文献综述，确定了10个预后指标变量。变量将从基线时的RACF文件中提取，并在基线数据收集后6个月和12个月测量死亡率。将使用向后消除法和连续变量的分数多项式方法，为6个月和12个月的死亡率结局指标开发多变量逻辑回归模型。将使用自抽样法进行内部验证。6个月和12个月死亡率模型的区分度将以带有c统计量的受试者工作特征曲线呈现。校准曲线将展示每个风险十分位数的观察事件率和预测事件率的比较，以及使用基于局部加权回归散点平滑法（loess）函数得出的灵活校准曲线。
本研究中开发的模型旨在改善对澳大利亚RACF中痴呆症患者死亡风险的临床评估。在该模型能够发展成为未来协助临床决策的工具之前，还需要在不同人群中进行进一步的外部验证。
