Development and validation of a nomogram for predicting ADL outcomes in patients undergoing subacute stroke rehabilitation based on machine learning and standard bedside clinical data: a retrospective cohort study - Scorecard - MDSpire
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Development and validation of a nomogram for predicting ADL outcomes in patients undergoing subacute stroke rehabilitation based on machine learning and standard bedside clinical data: a retrospective cohort study
Clinical Scorecard: Creation and assessment of a predictive nomogram for ADL recovery in subacute stroke rehabilitation patients utilizing machine learning and standard clinical data: a retrospective cohort analysis
At a Glance
Category
Detail
Condition
Subacute Stroke Rehabilitation
Key Mechanisms
Predictive modeling using routine clinical data and machine learning techniques.
Target Population
Patients admitted to rehabilitation 7 to 30 days after first stroke.
Care Setting
Rehabilitation wards for subacute interventions.
Key Highlights
Developed a predictive model for ADL recovery at 3 months post-rehabilitation.
Key predictors include Braden score, baseline Barthel Index score, and age.
The model achieved an AUC of 0.866 in the validation cohort.
Guideline-Based Recommendations
Diagnosis
Assess ADL independence using Barthel Index (BI) score.
Management
Utilize the nomogram for individualized rehabilitation planning.
Monitoring & Follow-up
Monitor changes in ADL status at 3 months post-rehabilitation.
Risks
Consider individual variations in recovery potential.
Patient & Prescribing Data
Patients with first-ever ischemic or hemorrhagic stroke, aged ≥18 years.
Routine bedside assessments can inform rehabilitation strategies.
Clinical Best Practices
Incorporate predictive modeling tools in rehabilitation settings.
Use readily available clinical data for assessing rehabilitation outcomes.
