A Nomogram for Individualized Prediction of Calf Muscular Vein Thrombosis in Stroke Patients During Rehabilitation: A Retrospective Study

Objectives: To develop a nomogram for predicting calf muscle veins thrombosis (CMVT) in stroke patients during rehabilitation. Methods: We enrolled 360 stroke patients from the Rehabilitation Medicine Center from December 2015 to February 2019. Of the participants, 123 were included in the CMVT group and 237 in the no CMVT group. The least absolute shrinkage and selection operator (LASSO) regression model was applied to optimize feature selection for the model. Multivariable logistic regression analysis was applied to construct a predictive model. Performance and clinical utility of the nomogram were generated using the Harrell's concordance index, calibration curve, and decision curve analysis (DCA). Results: Age, Brunnstrom stage (lower extremity), D-dimer, and antiplatelet therapy were associated with the occurrence of CMVT. The prediction nomogram showed satisfactory performance with a concordance index of 0.718 (95% CI: 0.663-0.773) in internal verification. The Hosmer-Lemeshow test, P = .217, suggested that the model was of goodness-of-fit. In addition, the DCA demonstrated that the CMVT nomogram had a good clinical net benefit. Conclusions: We developed a nomogram that could help clinicians identify high-risk groups of CMVT in stroke patients during rehabilitation for early intervention.

The Distance Covered in Field Tests is more Explained by Walking Capacity than by Cardiorespiratory Fitness after Stroke

OBJECTIVES

To investigate if the distance covered in the Six-Minute Walk Test (6MWT) and in the Incremental Shuttle Walk Test (ISWT) is most strongly explained by walking capacity or cardiorespiratory fitness (CRF) measures in individuals after chronic stroke.

MATERIALS AND METHODS

This is a cross-sectional study. Individuals after chronic stroke aged at least 20 years old and able to walk at least 10 minutes independently were included. The distance covered (meters) in the 6 MWT and ISWT (dependent variables), comfortable and fast gait speed obtained by the 10 m walk test (10 mWT) (walking capacity measures; independent variables) and peak oxygen consumption (VO_2peak; CRF measure; independent variable) (ml.kg^-1.min^-1) obtained by the cardiopulmonary exercise test (CPET) were obtained. Linear regression analyses were performed (α = 5%).

RESULTS

Fifty individuals (mean age of 55±12 years and mean time after stroke of 67±74 months) were included. Comfortable and fast gait speeds were the variables that most strongly explained the distance covered in the field tests: 6MWT (R² = 0.614, β = 0.784, p < 0.001 and R² = 0.615, β = 0.778, p < 0.001, respectively) and ISWT (R² = 0.450, β = 0.671, p < 0.001 and R² = 0.456, β = 0.746, p < 0.001, respectively). On the other hand, for the VO_2peak, the following models were generated: 6MWT (R² = 0.280, β = 0.530, p < 0.001) and ISWT (R² = 0.154, β = 0.393, p = 0.005)_. CONCLUSIONS: The distance covered in the field tests (6MWT and ISWT) is more suitable to support inferences about the walking capacity than about the CRF of individuals after chronic stroke.

Interaction between locomotion and three subcategories for patients with stroke demonstrating fewer than 37 points on the total functional independence measure upon admission to the recovery ward

[Purpose] To investigate the interaction between locomotion and improvements in performing self-care. [Participants and Methods] We retrospectively analyzed 930 patients with stroke who were registered in the Japanese Rehabilitation Database. We performed a correlation analysis to evaluate the relationships among all the collected data. Then, hierarchical multiple regression analysis was performed using the self-care motor score of the Functional Independent Measure (FIM) as the dependent variable. “Model 1” used two independent variables (National Institute of Health Stroke and Rankin Scale), “model 2” used two independent variables (locomotion gain and gain of an item with the closest coefficient correlation added to model 1), and “model 3” used a mean-centering value, which was added to model 2. R² values were calculated using a simple slope analysis. [Results] Locomotion showed an interaction with three self-care activities. The R² changes in models 1 and 2 (ΔR²) were significant for dressing upper body (ΔR²=0.001), bowel management (ΔR²=0.006), and toileting (ΔR²=0.006). The results of the simple slope analysis were significant. [Conclusion] Locomotion demonstrated an interaction with various activities for improving self-care. There were varying degrees of improvement in self-care despite a uniform improvement in the degree of locomotion. Therefore, locomotion interaction should be considered for each intervention that targets activities of daily living.