Background

Epidemiologic studies linking ambient air pollution to the onset of acute cardiovascular events often rely on date of hospital admission for exposure assessment.

Methods

We investigated the extent of exposure misclassification resulting from assigning exposure to particulate matter based on (1) date of hospital admission, or (2) time of hospital presentation compared with particulate matter exposure based on time of stroke symptom onset. We performed computer simulations to evaluate the impact of this source of exposure misclassification on estimates of air pollution health effects in the context of a time-stratified case-crossover study.

Results

Among 1101 patients admitted for a confirmed acute ischemic stroke to a Boston area hospital, symptom onset occurred a median of 1 calendar day before hospital admission (range = 0-30 days). The difference between ambient particulate matter exposure based on the calendar day of admission versus time of symptom onset ranged from -47 to 36 microg/m3 (-0.1 +/- 7.1 microg/m3; mean +/- SD). The simulation study indicated that for nonnull associations, exposure assessment based on hospitalization date led to estimates that were biased toward the null by 60%-66%, whereas assessment based on time of hospital presentation yielded estimates that were biased toward the null by 37%-42%.

Conclusions

Epidemiologic studies of air pollution-related risk of acute cardiovascular events that assess exposure based on date of hospitalization likely underestimate the strength of associations. Using data on time of hospital presentation would marginally attenuate, but not eliminate, this important source of bias.

Background and Purpose- Physiological effects of stroke are best assessed over entire brain networks rather than just focally at the site of structural damage. Resting-state functional magnetic resonance imaging can map functional-anatomic networks by analyzing spontaneously correlated low-frequency activity fluctuations across the brain, but its potential usefulness in predicting functional outcome after acute stroke remains unknown. We assessed the ability of resting-state functional magnetic resonance imaging to predict functional outcome after acute stroke. Methods- We scanned 37 consecutive reperfused stroke patients (age, 69±14 years; 14 females; 3-day National Institutes of Health Stroke Scale score, 6±5) on day 3 after symptom onset. After imaging preprocessing, we used a whole-brain mask to calculate the correlation coefficient matrices for every paired region using the Harvard-Oxford probabilistic atlas. To evaluate functional outcome, we applied the modified Rankin Scale at 90 days. We used region of interest analyses to explore the functional connectivity between regions and graph-computation analysis to detect differences in functional connectivity between patients with good functional outcome (modified Rankin Scale score ≤2) and those with poor outcome (modified Rankin Scale score >2). Results- Patients with good outcome had greater functional connectivity than patients with poor outcome. Although 3-day National Institutes of Health Stroke Scale score was the most accurate independent predictor of 90-day modified Rankin Scale (84.2%), adding functional connectivity increased accuracy to 94.7%. Preserved bilateral interhemispheric connectivity between the anterior inferior temporal gyrus and superior frontal gyrus and decreased connectivity between the caudate and anterior inferior temporal gyrus in the left hemisphere had the greatest impact in favoring good prognosis. Conclusions- These data suggest that information about functional connectivity from resting-state functional magnetic resonance imaging may help predict 90-day stroke outcome.