Risk Factors for In-hospital Nonhemorrhagic Stroke in Patients With Acute Myocardial Infarction Treated With Thrombolysis: Results from GUSTO-I

BACKGROUND: Nonhemorrhagic stroke occurs in 0.1% to 1.3% of patients with acute myocardial infarction who are treated with thrombolysis, with substantial associated mortality and morbidity. Little is known about the risk factors for its occurrence. METHODS AND RESULTS: We studied the 247 patients with nonhemorrhagic stroke who were randomly assigned to one of four thrombolytic regimens within 6 hours of symptom onset in the GUSTO-I trial. We assessed the univariable and multivariable baseline risk factors for nonhemorrhagic stroke and created a scoring nomogram from the baseline multivariable modeling. We used time-dependent Cox modeling to determine multivariable in-hospital predictors of nonhemorrhagic stroke. Baseline and in-hospital predictors were then combined to determine the overall predictors of nonhemorrhagic stroke. Of the 247 patients, 42 (17%) died and another 98 (40%) were disabled by 30-day follow-up. Older age was the most important baseline clinical predictor of nonhemorrhagic stroke, followed by higher heart rate, history of stroke or transient ischemic attack, diabetes, previous angina, and history of hypertension. These factors remained statistically significant predictors in the combined model, along with worse Killip class, coronary angiography, bypass surgery, and atrial fibrillation/flutter. CONCLUSIONS: Nonhemorrhagic stroke is a serious event in patients with acute myocardial infarction who are treated with thrombolytic, antithrombin, and antiplatelet therapy. We developed a simple nomogram that can predict the risk of nonhemorrhagic stroke on the basis of baseline clinical characteristics. Prophylactic anticoagulation may be an important treatment strategy for patients with high probability for nonhemorrhagic stroke, but further study is needed

Stroke in Patients With Acute Coronary Syndromes: Incidence and Outcomes in the Platelet Glycoprotein IIb/IIIa in Unstable Angina: Receptor Suppression Using Integrilin Therapy (PURSUIT) Trial

BACKGROUND: The incidence of stroke in patients with acute coronary syndromes has not been clearly defined because few trials in this patient population have been large enough to provide stable estimates of stroke rates. METHODS AND RESULTS: We studied the 10 948 patients with acute coronary syndromes without persistent ST-segment elevation who were randomly assigned to placebo or the platelet glycoprotein IIb/IIIa receptor inhibitor eptifibatide in the Platelet Glycoprotein IIb/IIIa in Unstable Angina: Receptor Suppression Using Integrilin Therapy (PURSUIT) trial to determine stroke rates, stroke types, clinical outcomes in patients with stroke, and independent baseline clinical predictors for nonhemorrhagic stroke. Stroke occurred in 79 (0.7%) patients, with 66 (0.6%) nonhemorrhagic, 6 intracranial hemorrhages, 3 cerebral infarctions with hemorrhagic conversion, and 4 of uncertain cause. There were no differences in stroke rates between patients who received placebo and those assigned high-dose eptifibatide (odds ratios and 95% confidence intervals 0.82 [0.59, 1.14] and 0.70 [0.49, 0.99], respectively). Of the 79 patients with stroke, 17 (22%) died within 30 days, and another 26 (32%) were disabled by hospital discharge or 30 days, whichever came first. Higher heart rate was the most important baseline clinical predictor of nonhemorrhagic stroke, followed by older age, prior anterior myocardial infarction, prior stroke or transient ischemic attack, and diabetes mellitus. These factors were used to develop a simple scoring nomogram that can predict the risk of nonhemorrhagic stroke. CONCLUSIONS: Stro

A review of spatial causal inference methods for environmental and epidemiological applications

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines, but have only recently begun to take hold in spatial applications. Spatial casual inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality, and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies, and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype