Developing a Clinical Prediction Rule for Gait Independence at Discharge in Patients with Stroke: A Decision-Tree Algorithm Analysis

Effects of Eye Movement Training and Changes of Gaze During Walking in a Patient With Oculomotor Disorder After Brainstem Hemorrhage

We report a case of a patient who developed pontine hemorrhage and presented with eye movement disorders but was able to regain conjugate eye movement through eye movement training, resulting in improved walking ability.  The patient was a 39-year-old man who presented with cerebral hemorrhage. He was admitted to the hospital due to a pontine hemorrhage extending from the midbrain to the medulla oblongata and perforation of the fourth ventricle. Symptoms included right hemiplegia, right upper and lower limb paresthesias, ataxia of the trunk, left abducens nerve palsy, left facial nerve palsy, right oculomotor nerve palsy, right trochlear nerve palsy, right Horner's syndrome, and longitudinal nystagmus. From the 121st day, eye movement training was performed for five days per week for 10 weeks to treat oculomotor dysfunction. For eye movement evaluation, left and right eye movements during pursuit eye movement, which involved following the contours of a figure, and during walking were measured with an eye movement measuring device (eye camera) (TalkEye Light; Takei Kiki Co. Ltd., Japan). In addition, motor function assessment included ataxia, lower limb muscle strength, physical balance function, and walking ability. Measurements were taken before the start of the eye movement training, two weeks after walking ability improved, and then at 10 weeks. After 10 weeks of eye movement training, the range of motion of the eyeballs during pursuit eye movement was expanded, and both eyes moved in the same direction and by the same amount. The eyes moved similarly to those of a healthy subject during walking two weeks and 10 weeks after the start of eye movement training, when walking ability improved, the left and right gazes overlapped, and both eyes were focused on the center of the forward visual field. Motor function improved in all categories. The eye movement training improved eye movements, and strabismus and diplopia were no longer observed. We suggest that eye movement training, in addition to conventional motor training, may be a means to improve walking ability in stroke patients in order to obtain a stable gait.

Logistic regression analysis and machine learning for predicting post-stroke gait independence: a retrospective study

This study investigated whether machine learning (ML) has better predictive accuracy than logistic regression analysis (LR) for gait independence at discharge in subacute stroke patients (n = 843) who could not walk independently at admission. We developed prediction models using LR and five ML algorithms-specifically, the decision tree (DT), support vector machine, artificial neural network, ensemble learning, and k-nearest neighbor methods. Functional Independence Measure sub-items were used to evaluate the ability to walk independently. Model predictive accuracies were evaluated using areas under receiver operating characteristic curves (AUCs) as well as accuracy, precision, recall, F1 score, and specificity. The AUC for DT (0.812) was significantly lower than those for the other algorithms (p < 0.01); however, the AUC for LR (0.895) did not differ significantly from those for the other models (0.893-0.903). Other performance metrics showed no substantial differences between LR and ML algorithms. In conclusion, the DT algorithm had significantly low predictive accuracy, and LR showed no significant difference in predictive accuracy compared with the other ML algorithms. As its predictive accuracy is similar to that of ML, LR can continue to be used for predicting the prognosis of gait independence, with additional advantages of being easily understandable and manually computable.

Comparison of stroke process measures and clinical outcomes between English and Non-English preferring patients

BACKGROUND

In the United States, limited English proficiency may reduce the quality of care and worsen outcomes after stroke. The aim was to compare stroke process measures and clinical outcomes between English preferring and non-English preferring stroke patients.

METHODS/MATERIALS

This single-center retrospective cohort study evaluated patients from one United States hospital with acute ischemic stroke between July 2013 and June 2022. The primary outcomes were defect-free care, a composite of 7 stroke process measures, and independent ambulation at hospital discharge. Multivariate logistic regression models quantified the association between language preference and outcomes. Secondary outcomes included individual components of defect-free care, discharge modified Rankin scale, and discharge disposition.

RESULTS

There were 4,030 patients with acute ischemic stroke identified, of which 2,965 were matched with language data from the electronic medical record. There were 373 non-English preferring patients, among which 76.9% preferred Spanish and 23.1% were non-English, non-Spanish preferring. In the multivariable model, there was no significant association between non-English preference and defect-free care (OR=0.64, 95% CI=0.26-1.59) or independent ambulation at discharge (OR=0.89, 95% CI=0.67-1.17). When compared to Spanish preferring patients, non-English, non-Spanish preferring patients had more severe strokes (P<0.001) but there was no difference in defect-free care or independent ambulation after adjustment.

CONCLUSION

Our results suggest that process and clinical outcomes are similar regardless of language preference; although, our data are limited by small numbers of non-English, non-Spanish preferring patients. Additional research is needed among this population.

Development of a Clinical Prediction Rule for Treatment Success with Transcranial Direct Current Stimulation for Knee Osteoarthritis Pain: A Secondary Analysis of a Double-Blind Randomized Controlled Trial

The study’s objective was to develop a clinical prediction rule that predicts a clinically significant analgesic effect on chronic knee osteoarthritis pain after transcranial direct current stimulation treatment. This is a secondary analysis from a double-blind randomized controlled trial. Data from 51 individuals with chronic knee osteoarthritis pain and an impaired descending pain inhibitory system were used. The intervention comprised a 15-session protocol of anodal primary motor cortex transcranial direct current stimulation. Treatment success was defined by the Western Ontario and McMaster Universities’ Osteoarthritis Index pain subscale. Accuracy statistics were calculated for each potential predictor and for the final model. The final logistic regression model was statistically significant (p < 0.01) and comprised five physical and psychosocial predictor variables that together yielded a positive likelihood ratio of 14.40 (95% CI: 3.66−56.69) and an 85% (95%CI: 60−96%) post-test probability of success. This is the first clinical prediction rule proposed for transcranial direct current stimulation in patients with chronic pain. The model underscores the importance of both physical and psychosocial factors as predictors of the analgesic response to transcranial direct current stimulation treatment. Validation of the proposed clinical prediction rule should be performed in other datasets.

Decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury

The study aims to measure the effectiveness of an AI-based traumatic intracranial hemorrhage prediction model in the decisions of emergency physicians regarding ordering head computed tomography (CT) scans. We developed a deep-learning model for predicting traumatic intracranial hemorrhages (DEEPTICH) using a national trauma registry with 1.8 million cases. For simulation, 24 cases were selected from previous emergency department cases. For each case, physicians made decisions on ordering a head CT twice: initially without the DEEPTICH assistance, and subsequently with the DEEPTICH assistance. Of the 528 responses from 22 participants, 201 initial decisions were different from the DEEPTICH recommendations. Of these 201 initial decisions, 94 were changed after DEEPTICH assistance (46.8%). For the cases in which CT was initially not ordered, 71.4% of the decisions were changed (p < 0.001), and for the cases in which CT was initially ordered, 37.2% (p < 0.001) of the decisions were changed after DEEPTICH assistance. When using DEEPTICH, 46 (11.6%) unnecessary CTs were avoided (p < 0.001) and 10 (11.4%) traumatic intracranial hemorrhages (ICHs) that would have been otherwise missed were found (p = 0.039). We found that emergency physicians were likely to accept AI based on how they perceived its safety.