Introduction
Endovascular thrombectomy was recently established as a new standard of care in acute ischemic stroke patients with large artery occlusions. Using small area health statistics, we sought ...to assess dissemination of endovascular thrombectomy in Catalonia throughout the period 2011–2015.
Patients and methods
We used registry data to identify all endovascular thrombectomies for acute ischemic stroke performed in Catalonia within the study period. The SONIIA registry is a government-mandated, population-based and externally audited data base that includes all reperfusion therapies for acute ischemic stroke. We linked endovascular thrombectomy cases identified in the registry with the Central Registry of the Catalan Public Health Insurance to obtain the primary care service area of residence for each treated patient, age and sex. We calculated age-sex standardized endovascular thrombectomy rates over time according to different territorial segmentation patterns (metropolitan/provincial rings and primary care service areas).
Results
Region-wide age-sex standardized endovascular thrombectomy rates increased significantly from 3.9 × 100,000 (95% confidence interval: 3.4–4.4) in 2011 to 6.8 × 100,000 (95% confidence interval: 6.2–7.6) in 2015. Such increase occurred in inner and outer metropolitan rings as well as provinces although highest endovascular thrombectomy rates were persistently seen in the inner metropolitan area. Changes in endovascular thrombectomy access across primary care service areas over time were more subtle, but there was a rather generalized increase of standardized endovascular thrombectomy rates.
Discussion
This study demonstrates temporal and territorial dissemination of access to endovascular thrombectomy in Catalonia over a 5-year period although variation remains at the completion of the study.
Conclusion
Mapping of endovascular thrombectomy is essential to assess equity and propose actions for access dissemination.
Three-dimensional protein structures are invaluable sources of information for the functional annotation of protein molecules. These structures are best determined by experimental methods such as ...X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. However, the experimental methods cannot always be applied. In such cases, prediction of the protein structure by computational methods can frequently result in a useful model. Protein structures can be modeled either ab initio from sequence alone or by comparative methods that rely on a database of known protein structures (1,2). Ab initio methods are largely based on the laws of physics, while comparative methods, including comparative (or homology) modeling and threading, are based primarily on statistical learning. Although there have been significant improvements in the ab initio (3) and threading methods (4), comparative modeling gives the most accurate results if a known protein structure that is sufficiently similar to the modeled sequence is available (1).
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