RESUMO
Introduction: Breakthrough acute ischemic stroke (AIS) in patients with known, nonvalvular Atrial Fibrillation (AF), on Direct Oral Anticoagulants (DOAC), is an ongoing clinical conundrum. Switching anticoagulants was shown to be ineffective in preventing recurrent AIS. Systematic, patient-level chart review of so-called "DOAC failures" may offer insight into this phenomenon. Methods: We conducted an IRB-approved, 6-year, retrospective study of AIS admissions, already prescribed DOAC for known AF. We sought plausible, alternative reasons for the AIS using a novel classification schema, CLAMP: C for Compliance concerns, L for Lacunes (small-vessel disease), A for Arteriopathy (atherosclerosis, web, or vasculitis), M for Malignancy, and P for Patent Foramen Ovale (PFO). These categories were labeled as DOAC "Pseudo-failures." Conversely, absence of CLAMP variables were labeled as DOAC "Crypto-failures" conceivably from AF itself ("atriopathy") or pharmacokinetic/pharmacogenomic dysfunction (ie, altered DOAC absorption, clearance, metabolism, or genetic polymorphisms). Forward logistic regression analysis was performed on prespecified DOAC subgroups. Results: Of 4890 AIS admissions, 606 had AF, and 87 were previously prescribed DOAC (14.4% overall DOAC failure rate, 2.4% annualized over 6 years). Pseudo-failures comprised 77%: Compliance concerns (48.9%), Lacunes (5.7%), Arteriopathy (17.0%), Malignancy (26.1%), and PFO (2.3%). Crypto-failures comprised 23%, had lower CHADSVASc scores (AOR = .65, P = .013), and occurred more with rivaroxaban (41%) than apixaban (16%) or dabigatran (5.6%). Conclusion: In AIS patients with known AF, DOAC Pseudo-failures, with identified alternate etiologies, are 3 times more likely than DOAC Crypto-failures. The CLAMP schema represents a novel approach to diagnostic classification and therapeutic adjustments in patients already prescribed DOAC for AF.
RESUMO
Ambient fine particulate matter (PM2.5) data of similar continuously monitored species at two air monitoring sites with different characteristics within the City of Toronto were used to gauge the intra-city variations in the PM composition over a largely concurrent period spanning two years. One location was <8 m from the side of a major highway while the other was an urban background location. For the first time, multi-time resolution factor analysis was applied to dispersion-normalized concentrations to identify and quantify source contributions while reducing the influence of local meteorology. These factors were particulate sulphate (pSO4), particulate nitrate (pNO3), secondary organic aerosols (SOA), crustal matter (CrM) that were common to both sites, a hydrocarbon-like organic matter (HOM) exclusive to the urban background site, three black carbon related factors (BC, BC-HOM at the highway site, and a brown carbon rich factor (BC-BrC) at the urban background site), biomass burning organic matter (BBOM) and brake dust (BD) factors exclusive to the highway site. The PM2.5 composition was different between these two locations, over only a 10 km distance. The sum of SOA, pSO4 and pNO3 at the urban background site averaged 57% of the PM2.5 mass while the same species represented 43% of the average PM2.5 mass at the highway site. Local or site-specific factors may be of greater interest for control policy design. Thus, regression analyses with potential explanatory, site-specific variables were performed for results from the highway site. Three model approaches were explored: multiple linear regression (MLR), regression with a generalized reduced gradient (GRG) algorithm, and a generalized additive model (GAM). GAM gave the largest fraction of variance for the locally-found factors at the highway site. Heavy-duty vehicles were most important for explaining the black carbon (BC and BC-HOM) factors. Light-duty vehicles were dominant for the brake dust (BD) factor. The auxiliary modelling for the local factors showed that the traffic-related factors likely originated along the main roadways at their respective sites while the more regional factors, - pSO4, pNO3, SOA, - had sources that were both regional and local in origin and with contributions that varied seasonally. These results will be useful in understanding ambient particulate matter sources on a city scale that will support air quality management planning.
