Will you please stop with this fucking useless research on predicting failure to recover! And just do what survivors want! EXACT 100% RECOVERY PROTOCOLS! I 'd have you all fired.
Hope you're OK with that when you are the 1 in 4 per WHO that has a stroke, you'll want full recovery, this doesn't get you there.
Predicting patient-reported outcome of activities of daily living in stroke rehabilitation: a machine learning study
Journal of NeuroEngineering and Rehabilitation volume 20, Article number: 25 (2023)
Abstract
Background
Machine Learning is increasingly used to predict rehabilitation outcomes in stroke in the context of precision rehabilitation and patient-centered care. However, predictors for patient-centered outcome measures for activities and participation in stroke rehabilitation requires further investigation.
Methods
This study retrospectively analyzed data collected for our previous studies from 124 participants. Machine Learning models were built to predict postintervention improvement of patient-reported outcome measures of daily activities (i.e, the Motor Activity Log and the Nottingham Extended Activities of Daily Living) and participation (i.e, the Activities of Daily Living domain of the Stroke Impact Scale). Three groups of 18 potential predictors were included: patient demographics, stroke characteristics, and baseline assessment scores that encompass all three domains under the framework of International Classification of Functioning, Disability and Health. For each target variable, classification models were built with four algorithms, logistic regression, k-nearest neighbors, support vector machine, and random forest, and with all 18 potential predictors and the most important predictors identified by feature selection.
Results
Predictors for the four target variables partially overlapped. For all target variables, their own baseline scores were among the most important predictors. Upper-limb motor function and selected demographic and stroke characteristics were also among the important predictors across the target variables. For the four target variables, prediction accuracies of the best-performing models with 18 features ranged between 0.72 and 0.96. Those of the best-performing models with fewer features ranged between 0.72 and 0.84.
Conclusions
Our findings support the feasibility of using Machine Learning for the prediction of stroke rehabilitation outcomes. The study was the first to use Machine Learning to identify important predictors for postintervention improvement on four patient-reported outcome measures of activities and participation in chronic stroke. The study contributes to precision rehabilitation and patient-centered care, and the findings may provide insights into the identification of patients that are likely to benefit from stroke rehabilitation.
Background
Stroke is a leading cause of disability that requires long-term post-stroke care and rehabilitation [1]. Along the course, patients and family and the care team are required to make multiple clinical decisions. Clinical decision making in rehabilitation benefits from accurate predictions of prognosis, which prompts research that investigates predictors for stroke-rehabilitation outcomes.
Two recent trends in rehabilitation are precision rehabilitation and patient-centered care. Clinical decision making in the context of precision rehabilitation involves identifying the characteristics of patients who would likely benefit from rehabilitation programs. Machine learning (ML) is increasingly used for the task of understanding predictors for rehabilitation outcomes by the construction of models that can predict outcomes when given new data. ML is a branch of artificial intelligence that uses algorithms to find patterns in the input data and generate models to predict target variables. Through pattern-finding, the models identify the most important “features,” or potential predictors, for the “target,” or the predicted variable. The advantages of ML include its ability to take a large amount of features at once, to conduct multidimensional data analyses, and to learn from the data without substantial a priori knowledge about the features [2].
In stroke rehabilitation, studies have investigated the feasibility of ML models for the prediction of postintervention outcomes. Most studies focused on patients in the subacute stage. The predicted outcome measures in these studies represent the three domains of the World Health Organization’s International Classification of Functioning, Disability and Health (ICF) [3], and range from measures of motor function, including the Ten-Meter Walk Test, Six-Minute Walk Test, and Berg Balance Scale [4], to measures of activities and participation, including the Barthel Index [5, 6], the modified Rankin Scale [7,8,9,10], the Functional Independence Measure (FIM) [4], and patients’ discharge placement [11, 12]. However, few ML predictive studies on chronic stroke investigated the postintervention outcomes [13,14,15,16]. To our knowledge, two studies investigated postintervention improvements in upper-limb (UL) motor function measured by the Fugl-Meyer Assessment Upper Extremity subscale (FMA-UE) [13, 14] or lower-limb motor function measured by step threshold [16]. One study used the Stroke Impact Scale (SIS), a measure in the ICF domain of activities and participation. Studies using ML remains scarce on the prediction of postintervention improvements, especially in measures of the ICF domains of activities and participation, for chronic stroke.
The other recent trend in medicine and rehabilitation, patient-centered care, aims at engaging the patients, family, and caregivers in the clinical decision-making process. To achieve this goal, patient-reported outcome measures (PROMs) for activities and participation should be incorporated in the assessment in addition to therapist-rated and impairment-level measures. However, most of the existing ML predictive studies on stroke rehabilitation outcomes investigated therapist-rated outcome measures such as the Barthel Index [5, 6] and the FIM [4] for the acute and subacute stages. In the chronic stage, earlier reports studied the FMA-UE [13, 14], and one recent study investigated SIS [15] as the concept of PROMs emerges. There is still a need to expand our knowledge of the relevance of ML predictive models to include more commonly used PROMs of activities and participation.
Another common practice found in the literature has been the inclusion of only one predicted outcome measure. However, given the heterogeneous nature of the stroke population, including multiple predicted outcome measures in research studies was recommended [4]. In fact, most therapists use multiple assessment tools to quantify related but distinct aspects of body functions, activities, and participation in clinical practice. For example, the Motor Activity Log (MAL) [17] and the Nottingham Extended Activities of Daily Living (NEADL) [18, 19] are commonly used patient-reported assessment tools of activities, and the SIS [20] has been widely used to measure function of participation.
The MAL was designed to measure the use of the affected upper-limb in basic activities of daily living (ADL). Patients are asked to rate how much (amount of use; MAL-AOU) and how well (quality of movement; MAL-QOM) they use the affected arm for a number of given ADL. The NEADL measures instrumental ADL and assesses functional independence in community living. The SIS measures patients’ health-related quality of life and includes items for participation; one of its domains is Activities of Daily Living (SIS-ADL). Assessing multiple outcome measures to provide multifaceted clinical information about potential prognosis could empower the patients and their families to make appropriate decisions that are most relevant and meaningful to the patient. However, most predictive studies only reported one outcome measure. There is a need to expand the repertoire of outcome measures in research studies to meet clinical applications.
This study used ML to build predictive models to predict postintervention outcomes and identify the most important predictors for these outcome measures in stroke rehabilitation. We have expanded on previous findings to use multiple PROMs for activities and participation in consideration of clinical applications and recent trends in stroke rehabilitation.
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