120 Superiority of CT scan over transthoracic echocardiography in predicting aortic regurgitation after TAVI

BackgroundParavalvular aortic regurgitation (AR) occurs in up to 86% of patients undergoing Transcatheter Aortic Valve Implantation (TAVI). Its prevalence remains unchanged after one year follow-up but its determinants are unclear. We sought to evaluate the impact of annulus measurement by transthoracic echocardiography (TTE) and by CT scan on the occurrence of AR.MethodsThe study included 43 symptomatic patients (83±8 years, 72% in NYHA≥III) with severe aortic stenosis [0.76±0.19cm2, mean gradient 42±14mmHg] who underwent TAVI using CoreValve® LLC Percutaneous Aortic Valve Implantation System, Medtronic, Minneapolis USA. Left ventricular outflow tract (LVOT) area was computed from LVOT diameter (21±2mm) by TTE using a spherical model and from CT using an ellipsoidal model according to the larger (25±3mm) and the smaller outflow tract diameters (22±3mm). These data were compared to the prosthesis area and the occurrence of AR after TAVI.ResultsIn patients with AR greater or equal to 2/4 (32%), LVOT area measured by CT was significantly greater as compared to patients with no or mild AR (478±65mm 2 vs. 411±85mm2, p=0.009). Furthermore, the difference between actual prosthesis area and LVOT area measured by CT scan was significantly smaller (113±55 vs. 171±67, p=0.009) in patients with significant AR (≥2/4) after TAVI. In contrast, LVOT area from TTE did not correlate with AR severity.ConclusionCT scan is more accurate than TTE for calculating LVOT area for prosthesis sizing before TAVI in order to avoid post-implantation AR

(Radio)Biological Optimization of External-Beam Radiotherapy

“Biological optimization” (BIOP) means planning treatments using (radio)biological criteria and models, that is, tumour control probability and normal-tissue complication probability. Four different levels of BIOP are identified: Level I is “isotoxic” individualization of prescription dose at fixed fraction number. is varied to keep the NTCP of the organ at risk constant. Significant improvements in local control are expected for non-small-cell lung tumours. Level II involves the determination of an individualized isotoxic combination of and fractionation scheme. This approach is appropriate for “parallel” OARs (lung, parotids). Examples are given using our BioSuite software. Hypofractionated SABR for early-stage NSCLC is effectively Level-II BIOP. Level-III BIOP uses radiobiological functions as part of the inverse planning of IMRT, for example, maximizing TCP whilst not exceeding a given NTCP. This results in non-uniform target doses. The NTCP model parameters (reflecting tissue “architecture”) drive the optimizer to emphasize different regions of the DVH, for example, penalising high doses for quasi-serial OARs such as rectum. Level-IV BIOP adds functional imaging information, for example, hypoxia or clonogen location, to Level III; examples are given of our prostate “dose painting” protocol, BioProp. The limitations of and uncertainties inherent in the radiobiological models are emphasized

Prognostic significance and normal values of 2D strain to assess right ventricular systolic function in chronic heart failure.

International audienceBACKGROUND: Normal values and the prognostic significance of right ventricle (RV)-2D strain in chronic heart failure (CHF) patients are unknown. METHODS AND RESULTS: Between 2005 and 2010, we prospectively enrolled 43 controls and 118 stable CHF patients. Standard echocardiographic variables, tricuspid annular plane systolic excursion, peak systolic velocity of tricuspid annular motion using tissue Doppler imaging, and RV and left ventricle (LV) 2D-strain were measured. The primary outcome was death or emergency transplantation or emergency ventricular assist device implantation or acute heart failure. RV-2D strain was measurable in 39 controls (58±17 years, 50% men), whose median value was 30% (95% confidence interval [95%CI], 39%; 20%); and in 104 CHF patients (80% men, mean age 57±11 years, and mean LV ejection fraction 29%±8%), whose median value was 19% (95%CI, 34%; 9%). During the mean follow-up of 37±14 months, 44 experienced the primary outcome. By Cox proportional hazards multivariate analysis, only RV-2D strain and log B-type natriuretic peptide independently predicted experiencing the primary outcome within the first year. The best RV-2D strain cut-off by receiver-operating characteristics analysis was 21%, and patients with values >21% were at greatest risk (χ(2)-log-rank test=14.1, P<0.0001). CONCLUSIONS: RV-2D strain is a strong independent predictor of severe adverse events in patients with CHF and may be superior to other systolic RV or LV echocardiographic variables

Atrial Fibrillation Detection With an Analog Smartwatch: Prospective Clinical Study and Algorithm Validation

BackgroundAtrial fibrillation affects approximately 4% of the world’s population and is one of the major causes of stroke, heart failure, sudden death, and cardiovascular morbidity. It can be difficult to diagnose when asymptomatic or in the paroxysmal stage, and its natural history is not well understood. New wearables and connected devices offer an opportunity to improve on this situation. ObjectiveWe aimed to validate an algorithm for the automatic detection of atrial fibrillation from a single-lead electrocardiogram taken with a smartwatch. MethodsEligible patients were recruited from 4 sites in Paris, France. Electrocardiograms (12-lead reference and single lead) were captured simultaneously. The electrocardiograms were reviewed by independent, blinded board-certified cardiologists. The sensitivity and specificity of the algorithm to detect atrial fibrillation and normal sinus rhythm were calculated. The quality of single-lead electrocardiograms (visibility and polarity of waves, interval durations, heart rate) was assessed in comparison with the gold standard (12-lead electrocardiogram). ResultsA total of 262 patients (atrial fibrillation: n=100, age: mean 74.3 years, SD 12.3; normal sinus rhythm: n=113, age: 61.8 years, SD 14.3; other arrhythmia: n=45, 66.9 years, SD 15.2; unreadable electrocardiograms: n=4) were included in the final analysis; 6.9% (18/262) were classified as Noise by the algorithm. Excluding other arrhythmias and Noise, the sensitivity for atrial fibrillation detection was 0.963 (95% CI lower bound 0.894), and the specificity was 1.000 (95% CI lower bound 0.967). Visibility and polarity accuracies were similar (1-lead electrocardiogram: P waves: 96.9%, QRS complexes: 99.2%, T waves: 91.2%; 12-lead electrocardiogram: P waves: 100%, QRS complexes: 98.8%, T waves: 99.5%). P-wave visibility accuracy was 99% (99/100) for patients with atrial fibrillation and 95.7% (155/162) for patients with normal sinus rhythm, other arrhythmias, and unreadable electrocardiograms. The absolute values of the mean differences in PR duration and QRS width were <3 ms, and more than 97% were <40 ms. The mean difference between the heart rates from the 1-lead electrocardiogram calculated by the algorithm and those calculated by cardiologists was 0.55 bpm. ConclusionsThe algorithm demonstrated great diagnostic performance for atrial fibrillation detection. The smartwatch’s single-lead electrocardiogram also demonstrated good quality for physician use in daily routine care. Trial RegistrationClinicalTrials.gov NCT04351386; http://clinicaltrials.gov/ct2/show/NCT0435138