Added Value of One-Month Clinical Data in Predicting Chronic-Stage Motor Function After Ischemic Stroke

Vollständiger Abstract

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Background/Objectives: This study aimed to develop machine learning models—specifically logistic regression (LR), random forest (RF), and deep neural network (DNN) models—using initial and 1-month post-stroke clinical data to predict 6-month upper and lower extremity motor functional outcomes in patients with ischemic stroke. Additionally, we sought to evaluate the potential improvement in discriminative performance and clinical utility achieved by integrating 1-month reassessment data. Methods: We analyzed retrospective cohort data from 353 patients with ischemic stroke. Two prediction models were constructed: (1) Model 1, which used only early-stage clinical data, and (2) Model 2, which incorporated both early-stage and 1-month post-stroke clinical data. Model performance and clinical utility were evaluated using the area under the receiver operating characteristic curve (ROC-AUC), DeLong’s test, calibration analysis, decision curve analysis (DCA), and variable importance analysis. Results: Although Model 2, which incorporated 1-month data, generally showed an upward trend in discriminative performance across all models for both upper and lower extremity prediction compared to Model 1, a statistically significant improvement was observed only in the LR model for upper extremity prediction (test AUC increased from 0.889 to 0.990; ΔAUC = +0.102, p = 0.037). For all other models—including the RF and DNN models for the upper extremity, as well as all lower extremity prediction models—the observed increases in AUC did not reach statistical significance according to DeLong’s test. In calibration analyses, the LR model exhibited the most stable calibration for both extremities. In DCA, Model 2 generally yielded a higher net benefit across most threshold probability ranges compared to Model 1 than Model 1 across most threshold probability ranges. Variable importance analysis indicated a shift in the primary contributing variables from initial motor evoked potential parameters in Model 1 to 1-month clinical functional measures in Model 2. Conclusions: Models integrating 1-month reassessment data showed a tendency toward improved discriminative performance compared to those relying solely on initial data. However, as this study was based on a limited sample from a single institution and instability was observed in certain models, external validation using larger, multicenter cohorts is necessary before generalizing these findings.