Structural MRI Ratios Fail to Distinguish Progressive Supranuclear Palsy From Parkinson Disease in Individual Patients.

Background and Objectives: Parkinson disease (PD) and progressive supranuclear palsy (PSP) are often difficult to differentiate in the clinic. The MR parkinsonism index (MRPI) has been recommended to assist in making this distinction. We aimed to assess the usefulness of this tool in our real-world practice of movement disorders. Methods: We prospectively obtained MRI scans on consecutive patients with movement disorders with a clinical indication for imaging and obtained measures of MRI regions of interest (ROIs) from our neuroradiologists. The authors reviewed all MRI scans and corrected any errors in the original ROI drawings for this analysis. We retrospectively assigned diagnoses using established consensus criteria from progress notes stored in our electronic medical record. We analyzed the data using multinomial logistic regression models and receiver operating curve analysis to determine the predictive accuracy of the MRI ratios. Results: MRI measures and consensus diagnoses were available on 130 patients with PD, 54 with PSP, and 77 diagnosed as other. The out-of-sample prediction error rate of our 5 regression models ranged from 45% to 59%. The average sensitivity and specificity of the 5 models in the testing sample were 53% and 80%, respectively. The positive predictive value of an MRPI ≥13.55 (the published cutoff) in our patients was 79%. Discussion: These results indicate that MRI measures of brain structures were not effective at predicting diagnosis in individual patients. We conclude that the search for a biomarker that can differentiate PSP from PD must continue.

Characterization of potentially avoidable neurological emergency department visits at a large urban public hospital.

This study characterized potentially avoidable neurological emergency department (ED) visits at a large urban public hospital. This was a retrospective review of Parkland Health (Dallas, TX) data from May 15, 2021, to July 15, 2021. The study population included encounters discharged home from the ED with any of the following: a primary neurological ED diagnosis, a neurological consultation in the ED, or a neurology clinic referral placed during the ED encounter. Neurovascular, strokelike, acute trauma, and nonneurological cases were excluded. The primary outcome was the number of ED visits by diagnosis category. A total of 965 ED discharge encounters met study criteria as potentially avoidable neurological ED visits, far higher than total neurology-related admissions over the same 2-month period. Headache (66%) and seizure/epilepsy (18%) syndromes were the most common. Thirty-five percent of all cases had neurology involvement in either the ED or the outpatient setting. This was lowest for headache (19%). The revisit rate within 3 months of the index ED visit was 29%, and it was highest for seizures/epilepsy (48%). Potentially avoidable nonvascular neurological ED visits occur frequently, especially for headache and seizure disorders. This study highlights the need for quality improvement and delivery innovation initiatives to optimize the site of care for patients with chronic neurological conditions.

APDM gait and balance measures fail to predict symptom progression rate in Parkinson's disease.

Parkinson's disease (PD) results in progressively worsening gait and balance dysfunction that can be measured using computerized devices. We utilized the longitudinal database of the Parkinson's Disease Biomarker Program to determine if baseline gait and balance measures predict future rates of symptom progression. We included 230, 222, 164, and 177 PD subjects with 6, 12, 18, and 24 months of follow-up, respectively, and we defined progression as worsening of the following clinical parameters: MDS-UPDRS total score, Montreal Cognitive Assessment, PDQ-39 mobility subscale, levodopa equivalent daily dose, Schwab and England score, and global composite outcome. We developed ridge regression models to independently estimate how each gait or balance measure, or combination of measures, predicted progression. The accuracy of each ridge regression model was calculated by cross-validation in which 90% of the data were used to estimate the ridge regression model which was then tested on the 10% of data left out. While the models modestly predicted change in outcomes at the 6-month follow-up visit (accuracy in the range of 66-71%) there was no change in the outcome variables during this short follow-up (median change in MDS-UPDRS total score = 0 and change in LEDD = 0). At follow-up periods of 12, 18, and 24 months, the models failed to predict change (accuracy in the held-out sets ranged from 42 to 60%). We conclude that this set of computerized gait and balance measures performed at baseline is unlikely to help predict future disease progression in PD. Research scientists must continue to search for progression predictors to enhance the performance of disease modifying clinical trials.