Department of General Practice, Department of General Practice, Amsterdam UMC, Meibergdreef 9 (Location AMC), Amsterdam 1105AZ, the Netherlands; Amsterdam Public Health research institute (Aging & Later Life), Meibergdreef 9 (Location AMC), Amsterdam 1105AZ, the Netherlands.
Amsterdam Public Health research institute (Aging & Later Life), Meibergdreef 9 (Location AMC), Amsterdam 1105AZ, the Netherlands; Department of Medicine for Older People, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam 1081HV, the Netherlands.
Ageing Res Rev. 2024 Nov;101:102525. doi: 10.1016/j.arr.2024.102525. Epub 2024 Oct 3.
As complexity and comorbidities increase with age, the increasing number of community-dwelling older adults poses a challenge to healthcare professionals in making trade-offs between beneficial and harmful treatments, monitoring deteriorating patients and resource allocation. Mortality predictions may help inform these decisions. So far, a systematic overview on the characteristics of currently existing mortality prediction models, is lacking.
To provide a systematic overview and assessment of mortality prediction models for the community-dwelling older population.
A systematic search of terms related to predictive modelling and older adults was performed until March 1st, 2024, in four databases. We included studies developing multivariable all-cause mortality prediction models for community-dwelling older adults (aged ≥65 years). Data extraction followed the CHARMS Checklist and Quality assessment was performed with the PROBAST tool.
A total of 22 studies involving 38 unique mortality prediction models were included, of which 14 models were based on a cumulative deficit-based frailty index and 9 on machine learning. C-statistics of the models ranged from 0.60 to 0.93 for all studies versus 0.61-0.78 when a frailty index was used. Eight models reached c-statistics higher than 0.8 and reported calibration. The most used variables in all models were demographics, symptoms, diagnoses and physical functioning. Five studies accounting for eleven models had a high risk of bias.
Some mortality prediction models showed promising results for use in practice and most studies were of sufficient quality. However, more uniform methodology and validation studies are needed for clinical implementation.
随着年龄的增长，复杂性和合并症的增加，越来越多的社区居住的老年人给医疗保健专业人员在权衡有益和有害治疗、监测病情恶化的患者和资源分配方面带来了挑战。死亡率预测可能有助于做出这些决策。到目前为止，缺乏对现有死亡率预测模型特征的系统综述。
提供社区居住老年人死亡率预测模型的系统综述和评估。
直到 2024 年 3 月 1 日，我们在四个数据库中使用与预测建模和老年人相关的术语进行了系统搜索。我们纳入了为社区居住老年人（年龄≥65 岁）开发多变量全因死亡率预测模型的研究。数据提取遵循 CHARMS 清单，使用 PROBAST 工具进行质量评估。
共纳入 22 项研究，涉及 38 个独特的死亡率预测模型，其中 14 个模型基于累积缺陷型衰弱指数，9 个基于机器学习。所有研究的模型 C 统计量范围为 0.60 至 0.93，而使用衰弱指数时为 0.61 至 0.78。有 8 个模型的 C 统计量高于 0.8 并报告了校准。所有模型中最常用的变量是人口统计学、症状、诊断和身体功能。五项研究涵盖了十一个模型，存在较高的偏倚风险。
一些死亡率预测模型在实践中显示出有前景的结果，并且大多数研究具有足够的质量。然而，需要更多统一的方法学和验证研究来进行临床实施。
