Dataset Information

Prediction of caregiver burden in amyotrophic lateral sclerosis: a machine learning approach using random forests applied to a cohort study.

ABSTRACT: OBJECTIVES: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. DESIGN: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. SETTING AND PARTICIPANTS: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. RESULTS: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. CONCLUSION: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.

SUBMITTER: Antoniadi AM

PROVIDER: S-EPMC7050406 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prediction of caregiver burden in amyotrophic lateral sclerosis: a machine learning approach using random forests applied to a cohort study.

Antoniadi Anna Markella AM Galvin Miriam M Heverin Mark M Hardiman Orla O Mooney Catherine C

BMJ open 20200228 2

<h4>Objectives</h4>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 ...[more]

PMID: 32114464

Similar Datasets

Project description:Objective: The heterogeneity of amyotrophic lateral sclerosis (ALS) survival duration, which varies from <1 year to >10 years, challenges clinical decisions and trials. Utilizing data from 801 deceased ALS patients, we: (1) assess the underlying complex relationships among common clinical ALS metrics; (2) identify which clinical ALS metrics are the "best" survival predictors and how their predictive ability changes as a function of disease progression. Methods: Analyses included examination of relationships within the raw data as well as the construction of interactive survival regression and classification models (generalized linear model and random forests model). Dimensionality reduction and feature clustering enabled decomposition of clinical variable contributions. Thirty-eight metrics were utilized, including Medical Research Council (MRC) muscle scores; respiratory function, including forced vital capacity (FVC) and FVC % predicted, oxygen saturation, negative inspiratory force (NIF); the Revised ALS Functional Rating Scale (ALSFRS-R) and its activities of daily living (ADL) and respiratory sub-scores; body weight; onset type, onset age, gender, and height. Prognostic random forest models confirm the dominance of patient age-related parameters decline in classifying survival at thresholds of 30, 60, 90, and 180 days and 1, 2, 3, 4, and 5 years. Results: Collective prognostic insight derived from the overall investigation includes: multi-dimensionality of ALSFRS-R scores suggests cautious usage for survival forecasting; upper and lower extremities independently degenerate and are autonomous from respiratory decline, with the latter associating with nearer-to-death classifications; height and weight-based metrics are auxiliary predictors for farther-from-death classifications; sex and onset site (limb, bulbar) are not independent survival predictors due to age co-correlation. Conclusion: The dimensionality and fluctuating predictors of ALS survival must be considered when developing predictive models for clinical trial development or in-clinic usage. Additional independent metrics and possible revisions to current metrics, like the ALSFRS-R, are needed to capture the underlying complexity needed for population and personalized forecasting of survival.

Dataset Information

Prediction of caregiver burden in amyotrophic lateral sclerosis: a machine learning approach using random forests applied to a cohort study.

Publications

Prediction of caregiver burden in amyotrophic lateral sclerosis: a machine learning approach using random forests applied to a cohort study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets