External Validation of the Early Prediction of Functional Outcome After Stroke Prediction Model for Independent Gait at 3 Months After Stroke

External Validation and Update of Minimal Important Change in the 6-Minute Walk Test in Hospitalized Patients With Subacute Stroke

OBJECTIVE

To investigate the external validation of the previously reported minimal important change (MIC) in the 6-minute walk test (6MWT) and update it for patients with subacute stroke hospitalized in rehabilitation unit.

DESIGN

Longitudinal study.

SETTING

Rehabilitation unit of a neurosurgical hospital.

PARTICIPANTS

One hundred and seven patients with subacute stroke.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

The 6MWT, modified Rankin Scale (mRS), Functional Ambulation Categories (FAC), and Functional Independence Measure (FIM) were assessed at 30 (baseline) and 60 (follow-up) days after stroke onset. Patients were divided into 2 groups according to improvements of mRS by ≥1, FAC by ≥1, or FIM by ≥22. The change in the 6MWT between baseline and follow-up was calculated and patients were divided into 2 groups according to improvements of 6MWT by ≥71 m. External validation was performed using likelihood ratio (LR) between change of 6MWT by ≥71 m and improvement of mRS. An LR+ of >2.0 and LR- of <0.5 was considered valid. The new MIC of the 6MWT was calculated for the mRS, FAC, and FIM using the receiver operating characteristic curve (MICROC) and adjusted predictive modeling method (MICadjusted).

RESULTS

No external validation was achieved (LR+ of 1.41, LR- of 0.77). The MICROC values for mRS, FAC, and FIM were 22.0, 69.0, and 22.0 m, respectively. The MICadjusted values for the mRS, FAC, and FIM were 68.7, 63.1, and 83.1 m, respectively. Only the MIC of the 6MWT for FAC was validated.

CONCLUSIONS

The previously reported MIC of the 6MWT was not suitable for patients with subacute stroke hospitalized in rehabilitation units; however, the newly determined MIC was useful.

Multiple imputation integrated to machine learning: predicting post-stroke recovery of ambulation after intensive inpatient rehabilitation

Good data quality is vital for personalising plans in rehabilitation. Machine learning (ML) improves prognostics but integrating it with Multiple Imputation (MImp) for dealing missingness is an unexplored field. This work aims to provide post-stroke ambulation prognosis, integrating MImp with ML, and identify the prognostic influential factors. Stroke survivors in intensive rehabilitation were enrolled. Data on demographics, events, clinical, physiotherapy, and psycho-social assessment were collected. An independent ambulation at discharge, using the Functional Ambulation Category scale, was the outcome. After handling missingness using MImp, ML models were optimised, cross-validated, and tested. Interpretability techniques analysed predictor contributions. Pre-MImp, the dataset included 54.1% women, 79.2% ischaemic patients, median age 80.0 (interquartile range: 15.0). Post-MImp, 368 non-ambulatory patients on 10 imputed datasets were used for training, 80 for testing. The random forest (the validation best-performing algorithm) obtained 75.5% aggregated balanced accuracy on the test set. The main predictors included modified Barthel index, Fugl-Meyer assessment/motricity index, short physical performance battery, age, Charlson comorbidity index/cumulative illness rating scale, and trunk control test. This is among the first studies applying ML, together with MImp, to predict ambulation recovery in post-stroke rehabilitation. This pipeline reliably exploits the potential of incomplete datasets for healthcare prognosis, identifying relevant predictors.

Predicting Recovery of Independent Walking After Stroke: A Systematic Review

ABSTRACT

Patients recovering from a stroke experience reduced participation, especially when they are limited in daily activities involving walking. Understanding the recovery of independent walking, can be used by clinicians in the decision-making process during rehabilitation, resulting in more personalized stroke rehabilitation. Therefore, it is necessary to gain insight in predicting the recovery of independent walking in patients after stroke. This systematic review provided an overview of current evidence about prognostic models and its performance to predict recovery of independent walking after stroke. Therefore, MEDLINE, CINAHL, and Embase were searched for all relevant studies in English and Dutch. Descriptive statistics, study methods, and model performance were extracted and divided into two categories: subacute phase and chronic phase. This resulted in 16 articles that fulfilled all the search criteria, which included 30 prognostic models. Six prognostic models showed an excellent performance (area under the curve value and/or overall accuracy ≥0.90). The model of Smith et al. (2017) showed highest overall accuracy (100%) in predicting independent walking in the subacute phase after stroke ( Neurorehabil Neural Repair 2017;31(10-11):955-64.). Recovery of independent walking can be predicted in the subacute and chronic phase after stroke. However, proper external validation and the applicability in clinical practice of identified prognostic models are still lacking.

Biomarkers of Motor Outcomes After Stroke

Predicting motor outcomes after stroke based on clinical judgment alone is often inaccurate and can lead to inefficient and inequitable allocation of rehabilitation resources. Prediction tools are being developed so that clinicians can make evidence-based, accurate, and reproducible prognoses for individual patients. Biomarkers of corticospinal tract structure and function can improve prediction tool performance, particularly for patients with initially moderate to severe motor impairment. Being able to make accurate predictions for individual patients supports rehabilitation planning and communication with patients and families.