Toward precision stroke rehabilitation: an integrated causal machine learning and clinician feedback approach

To identify causal mechanisms driving variations in the impact of rehabilitation treatments on stroke survivors' independence improvement during rehabilitation inpatient stays.

Iterative cycles of clinical input and causal machine learning (causal ML) were employed toward the goal of identifying relevant heterogeneous treatment effects. Data were from stroke patients (n = 484) seeking to improve independence during inpatient rehabilitation, where treatments provided included sessions (eg, physical therapy) and medication administration.

We find heterogeneity in rehabilitation treatment effects for a number of patient subgroups. Patient subgroups found to have the most heterogeneity in treatment effects were those with a bilateral involvement stroke location and those with diabetes. In a small minority of cases, we also observe heterogeneous treatment effects for those of older age, males versus females, and stroke location on either the right or left side of the brain. In regard to therapies, those related to mental health (ie, psychotherapy and spiritual/chaplaincy) had the most positive uplift in independence outcomes by the end of inpatient rehabilitation stays.

Stroke survivors have varying responses to stroke rehabilitation treatments. We show that heterogeneous treatment effects are indeed present in rehabilitation. Identification of specific mechanisms, such as stroke location and provisioning of mental health services, is made possible through the use of causal ML applied to observational data in stroke rehabilitation.

Causal ML can help to identify the mechanisms driving independence outcome variation. However, the large number of effects discovered and the small size of many effects make clinician feedback of paramount importance. Use of causal ML with clinician feedback throughout the process improves identification of appropriate measures and selection of relevant results.