Anatomic Approach to Transseptal Puncture for Structural Heart Interventions

The use of transseptal puncture continues to rise given the increase in left atrial cardiac interventions. The authors review an anatomic approach to transseptal puncture incorporating multimodality imaging both pre- and intraprocedurally with stepwise escalation algorithms to ensure safe and efficacious large-bore transseptal puncture.     

Eighteen‐ to 30‐year‐olds more likely to link to hepatitis C virus care: an opportunity to decrease transmission

Hepatitis C virus (HCV) infection incidence among 18‐ to 30‐year‐olds is increasing and guidelines recommend treatment of active injection drug users to limit transmission. We aimed to : 1 measure linkage to HCV care among 18‐ to 30‐year‐olds and identify factors associated with linkage; 2 compare linkage among 18‐ to 30‐year‐olds to that of patients >30 years. We used the electronic medical record at an urban safety net hospital to create a retrospective cohort with reactive HCV antibody between 2005 and 2010. We report seroprevalence and demographics of seropositive patients, and used multivariable logistic regression to identify factors associated with linkage to HCV care. We defined linkage as having evidence of HCV RNA testing after reactive antibody. Thirty two thousand four hundred and eighteen individuals were tested, including 8873 between 18 and 30 years. The seropositivity rate among those ages 18–30 was 10%. In multivariate analysis, among those 18–30, diagnosis location (Outpatient vs Inpatient/ED) (OR 1.78, 95% CI 1.28–2.49) and number of visits after diagnosis (OR 5.30, 95% CI 3.91–7.19) were associated with higher odds of linking to care. When we compared linkage in patients ages 18–30 to that among those older than 30, patients in the 18–30 years age group were more likely to link to HCV care than those in the older cohort even when controlling for gender, ethnicity, socioeconomic status, birthplace, diagnosis location and duration of follow‐up. Eighteen‐ to 30‐year‐olds are more likely to link to HCV care than their older counterparts. During the interferon‐free treatment era, there is an opportunity to prevent further HCV transmission in this population.     

Why and How to Measure Aortic Valve Calcification in Patients With Aortic Stenosis

The first-line evaluation of aortic stenosis severity is Doppler echocardiography. However, in up to 40% of patients, resting echocardiographic assessment of aortic stenosis severity is discordant, leading to clinical uncertainty. Interest has therefore grown in aortic valve calcium scoring by multidetector computed tomography (CT-AVC) as an alternative load independent assessment of aortic stenosis severity. This paper will briefly review the pathophysiology of aortic stenosis and the crucial role that calcification plays in driving progressive obstruction of the valve. Subsequently, it will describe published reports that have investigated CT-AVC, validating this parameter against histology, and establishing its diagnostic accuracy versus echocardiography as well as its powerful independent prognostic capability. Finally, this review seeks to provide a practical guide about how best to acquire and interpret CT-AVC with a close focus on potential pitfalls and how these might be best avoided as this technique becomes more widely adopted in to clinical practice.     

Machine Learning to Optimize the Echocardiographic Follow-Up of Aortic Stenosis

BackgroundDisease progression in patients with mild-to-moderate aortic stenosis is heterogenous and requires periodic echocardiographic examinations to evaluate severity.ObjectivesThis study sought to explore the use of machine learning to optimize aortic stenosis echocardiographic surveillance automatically.MethodsThe study investigators trained, validated, and externally applied a machine learning model to predict whether a patient with mild-to-moderate aortic stenosis will develop severe valvular disease at 1, 2, or 3 years. Demographic and echocardiographic patient data to develop the model were obtained from a tertiary hospital consisting of 4,633 echocardiograms from 1,638 consecutive patients. The external cohort was obtained from an independent tertiary hospital, consisting of 4,531 echocardiograms from 1,533 patients. Echocardiographic surveillance timing results were compared with the European and American guidelines echocardiographic follow-up recommendations.ResultsIn internal validation, the model discriminated severe from nonsevere aortic stenosis development with an area under the receiver-operating characteristic curve (AUC-ROC) of 0.90, 0.92, and 0.92 for the 1-, 2-, or 3-year interval, respectively. In external application, the model showed an AUC-ROC of 0.85, 0.85, and 0.85, for the 1-, 2-, or 3-year interval. A simulated application of the model in the external validation cohort resulted in savings of 49% and 13% of unnecessary echocardiographic examinations per year compared with European and American guideline recommendations, respectively.ConclusionsMachine learning provides real-time, automated, personalized timing of next echocardiographic follow-up examination for patients with mild-to-moderate aortic stenosis. Compared with European and American guidelines, the model reduces the number of patient examinations.     

Lot to give,got to live - the restless minds of the “Liver on Tour” project

The Liver on Tour was a special project devoted to increase the public awareness on Liver Health and Liver Diseases that the Portuguese Association for the Study of Liver Diseases launched throughout the country in 2010.