Department of Internal Medicine, Section on Pulmonology, Critical Care, Allergy & Immunologic Diseases, Wake Forest University School of Medicine, 2 Watlington Hall, 1 Medical Center Boulevard, Winston-Salem, NC, 27157, USA.
Informatics and Analytics, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, USA.
BMC Geriatr. 2024 Nov 29;24(1):982. doi: 10.1186/s12877-024-05567-0.
New or worsening cognitive impairment or dementia is common in older adults following an episode of critical illness, and screening post-discharge is recommended for those at increased risk. There is a need for prediction models of post-ICU cognitive impairment to guide delivery of screening and support resources to those in greatest need. We sought to develop and internally validate a machine learning model for new cognitive impairment or dementia in older adults after critical illness using electronic health record (EHR) data.
Our cohort included patients > 60 years of age admitted to a large academic health system ICU in North Carolina between 2015 and 2021. Patients were included in the cohort if they were admitted to the ICU for ≥ 48 h with ≥ 2 ambulatory visits prior to hospitalization and at least one visit in the post-discharge year. We used a machine learning model, oblique random survival forests (ORSF), to examine the multivariable association of 54 structured data elements available by 3 months after discharge with incident diagnoses of cognitive impairment or dementia over 1-year.
In this cohort of 8,299 adults, 22% died and 4.9% were diagnosed with dementia or cognitive impairment within one year. The ORSF model showed reasonable discrimination (c-statistic = 0.83) and stability with little difference in the model's c-statistic across time.
Machine learning using readily available EHR data can predict new cognitive impairment or dementia at 1-year post-ICU discharge in older adults with acceptable accuracy. Further studies are needed to understand how this tool may impact screening for cognitive impairment in the post-discharge period.
重症疾病后，老年人中常见新的或恶化的认知障碍或痴呆，建议对高危人群进行出院后筛查。需要开发和内部验证一种基于电子健康记录 (EHR) 数据的重症监护病房后认知障碍的机器学习预测模型，以指导向最需要的人提供筛查和支持资源。
我们的队列包括 2015 年至 2021 年期间在北卡罗来纳州一家大型学术医疗系统 ICU 住院的年龄大于 60 岁的患者。如果患者 ICU 住院时间≥48 小时，在住院前有≥2 次门诊就诊，并且在出院后至少有一次就诊，则将患者纳入队列。我们使用机器学习模型，斜向随机生存森林 (ORSF)，来检查 54 个结构化数据元素在出院后 3 个月与 1 年内新发认知障碍或痴呆的诊断之间的多变量关联。
在这个由 8299 名成年人组成的队列中，22%的患者死亡，4.9%的患者在一年内被诊断为痴呆或认知障碍。ORSF 模型显示出合理的区分度（c 统计量=0.83）和稳定性，模型的 c 统计量在不同时间点差异不大。
使用易于获得的 EHR 数据进行机器学习可以以可接受的准确性预测老年人在重症监护病房出院后 1 年内新的认知障碍或痴呆。需要进一步的研究来了解该工具如何影响出院后认知障碍的筛查。
