ABSTRACT
BACKGROUND: Although sleep apnea and peripheral artery disease are prognostic factors for stroke, their added benefit in the acute stage to further prognosticate strokes has not been evaluated. OBJECTIVES: We tested the accuracy in the acute stroke stage of a novel score called the Non-Invasive Prognostic Stroke Scale (NIPSS). PATIENTS AND METHODS: Prospective cohort with imaging-confirmed ischemic stroke. Clinical data, sleep apnea risk score (STOPBANG) and blood pressure measures were collected at baseline. Primary outcome was the 90-day modified Rankin Scale (mRS), with poor outcome defined as mRS 3-6. Area under the ROC curve (AUC) was calculated for NIPSS and compared to six other stroke prognostic scores in our cohort: SPAN-100 index, S-SMART, SOAR, ASTRAL, THRIVE, and Dutch Stroke scores. RESULTS: We enrolled 386 participants. After 90 days, there were 56% with poor outcome, more frequently older, female predominant and with higher admission National Institute of Health Stroke Scale (NIHSS). Four variables remained significantly associated with primary endpoint in the multivariable model: age (OR 1.87), NIHSS (OR 7.08), STOPBANG category (OR 1.61), and ankle-braquial index (OR 2.11). NIPSS AUC was 0.86 (0.82-0.89); 0.83 (0.79-0.87) with bootstrapping. When compared to the other scores, NIPSS, ASTRAL, S-SMART and DUTCH scores had good abilities in predicting poor outcome, with AUC of 0.86, 0.86, 0.83 and 0.82, respectively. THRIVE, SOAR and SPAN-100 scores were fairly predictive. DISCUSSION AND CONCLUSIONS: Non-invasive and easily acquired emergency room data can predict clinical outcome after stroke. NIPSS performed equal to or better than other prognostic stroke scales.
Subject(s)
Brain Ischemia , Peripheral Arterial Disease , Sleep Apnea Syndromes , Stroke , Humans , Female , Prognosis , Brain Ischemia/diagnosis , Brain Ischemia/therapy , Prospective Studies , Triage , Stroke/diagnosis , Stroke/therapy , Peripheral Arterial Disease/diagnosis , Treatment OutcomeABSTRACT
Freezing of gait is a disabling feature of Parkinson's disease, and it has been shown that nonmotor symptoms, such as anxiety and cognitive impairment, may be involved in the pathophysiology of the phenomenon. However, the association between freezing of gait severity and nonmotor symptoms is yet to be determined. Therefore, the overall aim of this study was to determine factors that contribute to severity of freezing of gait in people with Parkinson's disease. Participants (N=78) were assessed by disease-specific and self-report measures, including the Hospital Anxiety and Depression Scale (HADS), the Montreal Cognitive Assessment, and the Freezing of Gait Questionnaire (FOG-Q). Participants were classified as "freezers" if they scored ≥1 on item 3 of the FOG-Q; the sum of items 3-6 was used to determine freezing of gait severity. Freezers (N=27) showed higher scores on the HADS anxiety (p=0.002) and HADS depression (p=0.006) subscales. A multivariate linear model showed that disease severity (as measured by using the modified Hoehn and Yahr scale) accounted for 31% of the variance in FOG-Q severity scores (p<0.001). The presence of HADS anxiety ≥8 points increased the explained variance to 38% (p=0.010), and the full model (reached by adding the levodopa equivalent dose) explained 42% of the variance in freezing of gait severity (p=0.026). The findings provide additional support for the contribution of anxiety to greater freezing of gait severity, taking into account not only the frequency but the duration of the episodes, and suggest that anxiety should be routinely evaluated in people with Parkinson's disease who present with freezing of gait.
