Random Forest machine learning model classified low versus high cardiorespiratory fitness post-subacute stroke with an AUC of 0.7905, outperforming other models in clinical utility.
Can machine learning models accurately classify concurrent cardiorespiratory fitness status in patients with subacute stroke using routinely collected clinical variables?
Machine learning models, particularly Random Forest, can accurately classify cardiorespiratory fitness in subacute stroke patients using routine clinical variables, supporting clinical triage when CPET is unavailable.
Effect estimate: AUC 0.7905
Reduced cardiopulmonary fitness (CRF) is common in subacute stroke and may limit functional recovery. Timely classification (risk stratification) of CRF status during the subacute phase may help guide rehabilitation when CPET is not readily available. 6 relevant features were selected using the Boruta algorithm. Subsequently, nine machine learning (ML) models were developed and evaluated, including logistic regression (LR), elastic net (EN), k-nearest neighbors (KNN), decision tree (DT), extreme gradient boosting (XGB), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and Light Gradient Boosting Machine (LightGBM). A total of 114 patients with subacute stroke were included in this study. Among the nine models developed for classifying CRF, RF, SVM, and EN demonstrated the highest discriminative performances, with areas under the receiver operating characteristic (ROC) curve (AUC) of 0.7900, 0.7952, and 0.7905, respectively. Of these models, RF exhibited the greatest clinical applicability. The most important features contributing to classification included age, FMA score, 6MWT distance, sex, lower extremity FMA score, and FAC. ML models—particularly RF—most accurately classify concurrent CRF status in subacute stroke using routinely collected variables, supporting clinical triage when CPET is unavailable.
Zhang et al. (Wed,) conducted a other in Adult patients with subacute stroke (1-6 months) and hemiplegia with Brunnstrom stage ≥3, functional ambulation category ≥2, adequate cognition and communication, without unstable cardiovascular or severe comorbidities (n=114). Random Forest-based machine learning model vs. Other machine learning models including SVM, elastic net, logistic regression, multilayer perceptron, LightGBM, decision tree, k-nearest neighbors, extreme gradient boosting was evaluated on Classification accuracy of concurrent cardiorespiratory fitness status defined by VO2peak threshold <15 mL/(kg·min) vs ≥15 mL/(kg·min) (AUC 0.7905). Random Forest machine learning model classified low versus high cardiorespiratory fitness post-subacute stroke with an AUC of 0.7905, outperforming other models in clinical utility.