UCD School of Computer Science, University College Dublin, Dublin, Ireland.
FutureNeuro SFI Research Centre, Royal College of Surgeons in Ireland, Dublin, Ireland.
BMJ Open. 2020 Feb 28;10(2):e033109. doi: 10.1136/bmjopen-2019-033109.
Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disease that is characterised by the rapid degeneration of upper and lower motor neurons and has a fatal trajectory 3-4 years from symptom onset. Due to the nature of the condition patients with ALS require the assistance of informal caregivers whose task is demanding and can lead to high feelings of burden. This study aims to predict caregiver burden and identify related features using machine learning techniques.
This included demographic and socioeconomic information, quality of life, anxiety and depression questionnaires, for patients and carers, resource use of patients and clinical information. The method used for prediction was the Random forest algorithm.
This study investigates a cohort of 90 patients and their primary caregiver at three different time-points. The patients were attending the National ALS/Motor Neuron Disease Multidisciplinary Clinic at Beaumont Hospital, Dublin.
The caregiver's quality of life and psychological distress were the most predictive features of burden (0.92 sensitivity and 0.78 specificity). The most predictive features for Clinical Decision Support model were associated with the weekly caregiving duties of the primary caregiver as well as their age and health and also the patient's physical functioning and age of onset. However, this model had a lower sensitivity and specificity score (0.84 and 0.72, respectively). The ability of patients without gastrostomy to cut food and handle utensils was also highly predictive of burden in this study. Generally, our models are better in predicting the high-risk category, and we suggest that information related to the caregiver's quality of life and psychological distress is required.
This work demonstrates a proof of concept of an informatics solution to identifying caregivers at risk of burden that could be incorporated into future care pathways.
肌萎缩侧索硬化症（ALS）是一种罕见的神经退行性疾病，其特征是上下运动神经元迅速退化，从症状出现到死亡的轨迹为 3-4 年。由于该病的性质，ALS 患者需要非正规护理人员的协助，他们的工作任务繁重，可能会带来很高的负担感。本研究旨在使用机器学习技术预测护理人员的负担并识别相关特征。
本研究包括患者和护理人员的人口统计学和社会经济信息、生活质量、焦虑和抑郁问卷、患者的资源使用情况以及临床信息。用于预测的方法是随机森林算法。
本研究调查了 90 名患者及其主要护理人员在三个不同时间点的情况。患者在都柏林 Beaumont 医院的国家 ALS/运动神经元疾病多学科诊所就诊。
护理人员的生活质量和心理困扰是负担的最具预测性特征（敏感性为 0.92，特异性为 0.78）。用于临床决策支持模型的最具预测性特征与主要护理人员每周的护理工作以及他们的年龄和健康状况以及患者的身体功能和发病年龄有关。然而，该模型的敏感性和特异性得分较低（分别为 0.84 和 0.72）。患者是否能够进行切食物和使用餐具的能力也高度预测了本研究中的负担。一般来说，我们的模型更擅长预测高危人群，我们建议需要与护理人员的生活质量和心理困扰相关的信息。
这项工作证明了一种信息学解决方案的概念验证，可用于识别有负担风险的护理人员，该解决方案可纳入未来的护理路径中。
