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The anomalous origin of the left vertebral artery from the aorta is the second most common anatomical alteration of the aortic arch. We present a case of a patient with a descending thoracic aortic aneurysm and an anomalous origin of a dominant left vertebral artery. The artery was treated by transposition to the left common carotid artery via a cervical approach, followed by endovascular correction of the thoracic aortic aneurysm using a subclavian branched thoracic endoprosthesis. The patient had a good postoperative recovery with complete occlusion of the aneurysm and patency of the carotid-vertebral shunt and left subclavian artery.

Background: Mortality due to ischemic heart disease (IHD) is heterogeneously distributed globally, and identifying the sites most affected by it is essential in developing strategies to mitigate the impact of the disease, despite the complexity resulting from the great diversity of variables involved. Objective: To analyze the predictability of IHD mortality using machine learning (ML) techniques in combination with geospatial analysis in southern Brazil. Methods: Ecological study using secondary and retrospective data on mortality due to ischemic heart disease (IHD) obtained from the Mortality Information Systems (SIM-DATASUS) de 2018 a 2022, covering 1,191 municipalities in the states of Paraná (399), Santa Catarina (295), and Rio Grande do Sul (497). Ordinary Least Squares Regression (OLS), Geographically Weighted Regression (GWR), Random Forest (RF), and Geographically Weighted Random Forest (GWRF) analyses were performed to verify the model with the best performance capable of identifying the most affected sites by the disease based on a set of predictors composed by variables of procedures and access to health. Results: In the analyzed period, there were 59,093 deaths, 65% of which were men, 82.7% were white, and 72.8% occurred between 60 and 70 years of age. Ischemic heart disease presented the highest mortality rates in the northwest and north regions of the state of Paraná, and in the central-east, southwest and southeast regions of Rio Grande do Sul, the latter state accounting for 41% of total deaths. The GWRF presented the best performance with R2 = 0.983 and AICc = 2298.4, RMSE: 3.494 and the most important variables of the model in descending order were electrocardiograph rate, cardiac catheterization rate, access index to hemodynamics, access index of pre-hospital mobile units, cardiologists rate, myocardial scintigraphy rate, stress test rate, and stress echocardiogram rate. Conclusion: The GWRF identified spatial heterogeneity in the variation of geographic predictors, contrasting the limitation of linear regression models. The findings showed patterns of vulnerability in southern Brazil, suggesting the formulation of health policies to improve access to diagnostic and therapeutic resources, with the potential to reduce IHD mortality.

Objective: Despite significant advancements in understanding risk factors and treatment strategies, ischemic heart disease (IHD) remains the leading cause of mortality worldwide, particularly within specific regions in Brazil, where the disease is a burden. Therefore, the aim of this study was to estimate the risk of hospitalization and mortality from IHD in the state of Paraná (Brazil), using spatial analysis to identify areas with higher risk based on socioeconomic, demographic and health variables. Methods: This is an ecological study based on secondary and retrospective IHD hospitalization and mortality data obtained from the Brazilian Hospitalization and Mortality Information Systems during the 2010–2021 period. Data were analyzed for 399 municipalities and 22 health regions in the state of Paraná. To assess the spatial patterns of the disease and identify relative risk (RR) areas, we constructed a risk model by Bayesian inference using the R-INLA and SpatialEpi packages in R software. Results: A total of 333,229 hospitalizations and 73,221 deaths occurred in the analyzed period, and elevated RR of hospitalization (RR = 27.412, CI 21.801; 34.466) and mortality (RR = 15.673, CI 2.148; 114.319) from IHD occurred in small-sized municipalities. In addition, medium-sized municipalities also presented elevated RR of hospitalization (RR = 6.533, CI 1.748; 2.006) and mortality (RR = 6.092, CI 1.451; 2.163) from IHD. Hospitalization and mortality rates were higher in white men aged 40–59 years. A negative association was found between Municipal Performance Index (IPDM) and IHD hospitalization and mortality. Conclusion: Areas with increased risk of hospitalization and mortality from IHD were found in small and medium-sized municipalities in the state of Paraná, Brazil. These results suggest a deficit in health care attention for IHD cases in these areas, potentially due to a low distribution of health care resources.

Background:No other disease has killed more than ischemic heart disease (IHD) for the past few years globally. Despite the advances in cardiology, the response time for starting treatment still leads patients to death because of the lack of healthcare coverage and access to referral centers. Objectives:To analyze the spatial disparities related to IHD mortality in the Parana state, Brazil. Methods:An ecological study using secondary data from Brazilian Health Informatics Department between 2013–2017 was performed to verify the IHD mortality. An spatial analysis was performed using the Global Moran and Local Indicators of Spatial Association (LISA) to verify the spatial dependency of IHD mortality. Lastly, multivariate spatial regression models were also developed using Ordinary Least Squares and Geographically Weighted Regression (GWR) to identify socioeconomic indicators (aging, income, and illiteracy rates), exam coverage (catheterization, angioplasty, and revascularization rates), and access to health (access index to cardiologists and chemical reperfusion centers) significantly correlated with IHD mortality. The chosen model was based on p < 0.05, highest adjusted R² and lowest Akaike Information Criterion. Results:A total of 22,920 individuals died from IHD between 2013–2017. The spatial analysis confirmed a positive spatial autocorrelation global between IDH mortality rates (Moran’s I: 0.633, p < 0.01). The LISA analysis identified six high-high pattern clusters composed by 66 municipalities (16.5%). GWR presented the best model (Adjusted R²: 0.72) showing that accessibility to cardiologists and chemical reperfusion centers, and revascularization and angioplasty rates differentially affect the IHD mortality rates geographically. Aging and illiteracy rate presented positive correlation with IHD mortality rate, while income ratio presented negative correlation (p < 0.05). Conclusion:Regions of vulnerability were unveiled by the spatial analysis where sociodemographic, exam coverage and accessibility to health variables impacted differently the IHD mortality rates in Paraná state, Brazil. Highlights The increase in ischemic heart disease mortality rates is related to geographical disparities. The IHD mortality is differentially associated to socioeconomic factors, exam coverage, and access to health. Higher accessibility to chemical reperfusion centers did not necessarily improve patient outcomes in some regions of the state. Clusters of high mortality rate are placed in regions with low amount of cardiologists, income and schooling.

Objective The aim of this observational cross-sectional study was to analyse the spatial distribution of major lower limb amputation (MLLA) rates and associate them to socioeconomic, demographic and public healthcare access-related variables in the State of Paraná, Brazil, from 2012 to 2017.Method Data on MLLA, revascularisation surgeries, diagnostic exams and healthcare coverage were obtained from the Brazilian Public Hospital Information System. Socioeconomic data were obtained from the Brazilian Institute of Geography and Statistics. Spatial autocorrelation of the MLLA rates was tested using Moran’s I method. Multivariate spatial regression models using ordinary least squares regression (OLS) and geographically weighted regression (GWR) were used to identify the variables significantly correlated with MLLA.Results A total of 5270 MLLA were included in the analysis. Mean MLLA rates were 24.32 (±18.22)/100 000 inhabitants, showing a positive global spatial autocorrelation (Moran’s I=0.66; p