Prediction of the functional outcome of intensive inpatient rehabilitation after stroke using machine learning methods

 Do you not understand, prognosis is completely useless for stroke survivors? It does nothing to get them recovered. There are a lot of mentors and senior researchers that need to be re-educated on the purpose of stroke research. The only goal in stroke is 100% recovery; not biomarkers, prediction, prognosis or other useless shit! I'd fire all of you for incompetence!

Prediction of the functional outcome of intensive inpatient rehabilitation after stroke using machine learning methods

• Silvia Campagnini,
• Alessandro Sodero,
• Marco Baccini,
• Bahia Hakiki,
• Antonello Grippo,
• Claudio Macchi,
• Andrea Mannini &
• Francesca Cecchi 

Scientific Reports volume 15, Article number: 16083 (2025) Cite this article

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Abstract

An accurate and reliable functional prognosis is vital to stroke patients addressing rehabilitation, to their families, and healthcare providers(NO ITS NOT! PROGNOSIS DOES NOTHING FOR RECOVERY! ARE YOU THAT BLITHERINGLY STUPID?). This study aimed at developing and validating externally patient-wise prognostic models of the global functional outcome at discharge from intensive inpatient post-acute rehabilitation after stroke, based on a standardized comprehensive multidimensional assessment performed at admission to rehabilitation. Patients addressing intensive inpatient rehabilitation pathways within 30 days from stroke were prospectively enrolled in two consecutive multisite studies. Demographics, description of the event, clinical/functional, and psycho-social data were collected. The outcome of interest was disability in basic daily living activities at discharge, measured by the modified Barthel Index (mBI). Machine learning-based prognostic models were developed, internally cross-validated, and externally validated. Interpretability techniques were applied for the analysis of predictors. 385 patients were considered, 220 (165) for training (external test) sets. A 50.9% (55.8%) of women, 79.5% (80.0%) of ischemic, and a median [interquartile range- IQR] age of 80.0[15.0] (79.0[17.0]) were registered. The Support Vector Machine obtained the best validation performances and a median absolute error [IQR] on discharge mBI estimation of 11.5[15.0] and 9.2[13.0] points on the internal and external testing, respectively. The baseline variables providing the main contributions to the predictions were mBI, motor upper-limb score, age, and cognitive screening score. We achieved a solution to support the formulation of a functional prognosis at intensive rehabilitation admission. The interpretability analysis confirms the relevance of easily collected motor and cognitive dataat admission and of the patient’s age.

Trial registration: Prospectively registered on ClinicalTrials.gov (registration numbers RIPS NCT03866057, STRATEGY NCT05389878).