Effects of a Province-wide Triaging System for TIA: The ASPIRE Intervention.

BACKGROUND AND OBJECTIVES: Urgent transient ischemic attack (TIA) management to reduce stroke recurrence is challenging, particularly in rural and remote areas. In Alberta, Canada, despite an organized stroke system, data from 1999 to 2000 suggested that stroke recurrence after TIA was as high as 9.5% at 90 days. Our objective was to determine whether a multifaceted population-based intervention resulted in a reduction in recurrent stroke after TIA. METHODS: In this quasi-experimental health services research intervention study, we implemented a TIA management algorithm across the entire province, centered around a 24-hour physician's TIA hotline and public and health provider education on TIA. From administrative databases, we linked emergency department discharge abstracts to hospital discharge abstracts to identify incident TIAs and recurrent strokes at 90 days across a single payer system with validation of recurrent stroke events. The primary outcome was recurrent stroke; with a secondary composite outcome of recurrent stroke, acute coronary syndrome, and all-cause death. We used an interrupted time series regression analysis of age-adjusted and sex-adjusted stroke recurrence rates after TIA, incorporating a 2-year preimplementation period (2007-2009), a 15-month implementation period, and a 2-year postimplementation period (2010-2012). Logistic regression was used to examine outcomes that did not fit the time series model. RESULTS: We assessed 6,715 patients preimplementation and 6,956 patients postimplementation. The 90-day stroke recurrence rate in the pre-Alberta Stroke Prevention in TIA and mild Strokes (ASPIRE) period was 4.5% compared with 5.3% during the post-ASPIRE period. There was neither a step change (estimate 0.38; p = 0.65) nor slope change (parameter estimate 0.30; p = 0.12) in recurrent stroke rates associated with the ASPIRE intervention implementation period. Adjusted all-cause mortality (odds ratio 0.71, 95% CI 0.56-0.89) was significantly lower after the ASPIRE intervention. DISCUSSION: The ASPIRE TIA triaging and management interventions did not further reduce stroke recurrence in the context of an organized stroke system. The apparent lower mortality postintervention may be related to improved surveillance after events identified as TIAs, but secular trends cannot be excluded. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that a standardized population-wide algorithmic triage system for patients with TIA did not reduce recurrent stroke rate.

Breaking bad news in amyotrophic lateral sclerosis: the need for medical education.

The manner in which physicians deliver difficult diagnoses is an area of discontent for patients with amyotrophic lateral sclerosis (ALS). The American Academy of Neurology's Practice Parameter for care of the ALS Patient recommended teaching and evaluating strategies for disclosing the diagnosis (10). Our objective was to examine residents' ability in and perceptions of communicating the diagnosis of ALS. Twenty-two resident physicians were videotaped and rated by two ALS neurologists as they delivered an ALS diagnosis to a standardized patient (SP) during an objective structured clinical examination (OSCE). Residents self-rated immediately after the OSCE, again after viewing their videotape, and completed a survey regarding the OSCE and delivering difficult diagnoses. OSCE performance was suboptimal, particularly for communication skills and empathy. The two examiners' scores correlated except for the empathy subscore. Residents' self-assessments did not align with the examiners' scores either before or after watching their videotape. The survey uncovered residents' apprehension and dissatisfaction with their training in diagnosis delivery. The results highlight a need for resident education in delivering an ALS diagnosis. The lack of correlation between residents' and examiners' scoring requires further study. Evaluation of empathy is particularly challenging. Residents agreed that OSCE participation was worthwhile.

Prediction and uncertainty source analysis of the spatial and temporal disturbance from off-road vehicular traffic in a complex ecosystem.

Vehicle use during military training activities results in soil disturbance and vegetation loss. The capacity of lands to sustain training is a function of the sensitivity of lands to vehicle use and the pattern of land use. The sensitivity of land to vehicle use has been extensively studied. Less well understood are the spatial patterns of vehicle disturbance. Since disturbance from off-road vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) was used to predict the pattern of vehicle disturbance across a training facility. An uncertainty analysis of the model predictions assessed the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty. For the most part, this analysis showed that mapping and modeling process errors contributed more than 95% of the total uncertainty of predicted disturbance, while satellite imagery error contributed less than 5% of the uncertainty. When the total uncertainty was larger than a threshold, modeling error contributed 60% to 90% of the prediction uncertainty. Otherwise, mapping error contributed about 10% to 50% of the total uncertainty. These uncertainty sources were further partitioned spatially based on other sources of uncertainties associated with vehicle moment, landscape characterization, satellite imagery, etc.

The effect of model structure and data in modeling land conditions in disturbed complex ecosystems.

Off-road vehicles increase soil erosion by reducing vegetation cover and other types of ground cover, and by changing the structure of soil. The investigation of the relationship between disturbance from off-road vehicles and the intensity of the activities that involve use of vehicles is essential for water and soil conservation and facility management. Models have been developed in a previous study to predict disturbance caused by off-road vehicles. However, the effect of data on model quality and model performance, and the appropriate structure of models have not been previously investigated. In order to improve the quality and performance of disturbance models, this study was designed to investigate the effects of model structure and data. The experiment considered and tested: (1) two measures of disturbance based on the Vegetation Cover Factor (C Factor) of the Revised Universal Soil Loss Equation (RUSLE) and Disturbance Intensity; (2) model structure using two modeling approaches; and (3) three subsets of data. The adjusted R-square and residuals from validation data are used to represent model quality and performance, respectively. Analysis of variance (ANOVA) is used to identify factors which have significant effects on model quality and performance. The results of the ANOVA show that subsets of data have significant effects on both model quality and performance for both measures of disturbance. The ANOVA also detected that the C Factor models have higher quality and performance than the Disturbance models. Although modeling approaches are not a significant factor based on the ANOVA tests, models containing interaction terms can increase the adjusted R-squares for nearly all tested conditions and the maximum improvement can reach 31%.

Uncertainty analysis of predicted disturbance from off-road vehicular traffic in complex landscapes at fort hood.

The US Army Engineering Research Development Center (ERDC) uses a modified form of the Revised Universal Soil Loss Equation (RUSLE) to estimate spatially explicit rates of soil erosion by water across military training facilities. One modification involves the RUSLE support practice factor (P factor), which is used to account for the effect of disturbance by human activities on erosion rates. Since disturbance from off-road military vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) is used to predict the distribution of P factor values across a training facility. This research analyzes the uncertainty in this model's disturbance predictions for the Fort Hood training facility in order to determine both the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty. This analysis shows that a three-category vegetation map used by the disturbance model was the greatest source of prediction uncertainty, especially for the map categories shrub and tree. In areas mapped as grass, modeling error (uncertainty associated with the model parameter estimates) was the largest uncertainty source. These results indicate that the use of a high-quality vegetation map that is periodically updated to reflect current vegetation distributions, would produce the greatest reductions in disturbance prediction uncertainty.