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Background: A index of non-invasive myocardial work (MWI) can account for pressure during the assessment of cardiac function, potentially separating the influence of loading conditions from the influence of the underlying tissue remodelling. The aim is to assess LV function accounted for loading and explore hypertensive MWI distribution by comparing healthy individuals to hypertensive patients without and with localized basal septal hypertrophy (BSH).Methods and results: An echocardiogram was performed in 170 hypertensive patients and 20 healthy individuals.BSH was defined by a basal-to-mid septal wall thickness ratio ≥ 1.4. LV speckle-tracking was performed, and the MWI calculated globally and regionally for the apical, mid and basal regions. An apex-to-base gradient, seen in regional strain values, was preserved in the distribution of myocardial work, with the apical region compensating for the impairment of the basal segments. This functional redistribution was further pronounced in patients with localized BSH. In these patients, segmental MWI analysis revealed underlying impairment of regional work unrelated to acute loading conditions.Conclusions: Non-invasive MWI analysis offers the possibility to compare LV function regardless of blood pressure at the time of observation. Changes in MWI distribution can be seen in hypertension unrelated to the load-dependency of strain. Accentuated functional changes affirm the role of BSH as an echocardiographic marker in hypertension.

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Funding Acknowledgements Horizon 2020 European Commission Project MSCA-ITN-2016 (764738), Grant from Fundacio La Marató de TV3 (040310). Background and aim Localized basal septal hypertrophy (BSH) is a known marker of increased afterload and localized deformation impairment, and can be seen in one-fifth of patients with arterial hypertension. Although there is variability in the classification, BSH is mainly defined from ratios between several wall thickness measurements. We hypothesize that the curvature of the septum is reflective of localized hypertrophy and will be significantly increased in patients with BSH. Speckle tracking endocardial delineations of the left ventricle (LV) can be used to quantify curvature, with the potential to create a novel, semi-automatized parameter for recognition of patients with an increased impact of afterload on cardiac structure and function. Methods An echocardiogram was performed on a total of 149 patients with a diagnosis of long-standing hypertension, treated with at least one antihypertensive drug and on 19 healthy age and sex-matched controls. The interventricular septum thickness was measured at basal and mid-level in the parasternal long axis (PLAX) and 4-chamber (4C) views. BSH was identified from a two-part criterion: both a positive visual assessment of an abrupt change in septal thickness seen in the 4C or PLAX views and a basal to mid-septal ratio ≥ 1.4. A dedicated software for speckle tracking was used to trace the endocardial border of the LV in 4C and 3C view. In post-analysis, we quantified the maximal curvature of the antero- and inferoseptal segments from the exported myocardial contour. Curvature, measured in m-1, was defined as the reciprocal value of the radius of the circle fitted into the curve defined by three subsequent neighboring points in the myocardial contour. Curvature was considered negative if the curve was convex with respect to the LV long-axis. Results Using septal wall thickness measurements, 19% (n = 28) of hypertensive patients were classified as having BSH, whereas all healthy controls had normal geometry. Basal antero- and inferoseptal wall thickness was significantly increased in the BSH group, which was coupled with regional deformation impairment (basal inferoseptum, controls vs. non-BSH vs. BSH: 16.1 ± 2.33 vs. 15.14 ± 2.8 vs. 13.02 ± 2.98 %, p < 0.001). The curvature of the basal inferoseptum was significantly higher in the BSH group (controls vs. non-BSH vs BSH: -23.4 (-27.2, -10.9) vs. -28.3 (-40.2, -19.3) vs. -50.5 (-66.8, -33.9) m-1, p < 0.001) (Figure 1), with the same trend seen in the basal anteroseptum. The inferoseptal curvature showed a moderately strong correlation with the inferoseptal basal-to-mid wall thickness ratio (R = 0.527, p <0.001). Conclusion Increased septal curvature is an easily quantifiable, single-value, semi-automated parameter reflective of localized thickening that could easily be incorporated into the output of the LV speckle tracking workflow, possibly aiding in the recognition of hypertensive patients in need of a closer clinical follow-up. Abstract P735 Figure 1

Funding Acknowledgements Horizon 2020 European Commission Project H2020-MSCA-ITN-2016 (764738), Grant from Fundacio La Marató de TV3 (040310) Background and aim Contemporary echocardiography provides complex data on cardiac function contained in both blood-pool and tissue deformation traces. Their current interpretation relies on clinical experience and selected peak or averaged velocity values, which might not capture the complexity throughout the cardiac cycle. The aim is to investigate if machine learning could recognize relevant patient profiles in arterial hypertension by integrating all echocardiographic data to better define potential pathophysiological changes in left ventricle (LV) remodeling. Methods An echocardiogram was performed in 100 patients with established arterial hypertension (> 3 years). Myocardial deformation traces of the LV and the left atrium (LA), assessed by 2D speckle tracking, the aortic outflow Doppler trace, the lateral and septal mitral annular Doppler velocity traces, and the mitral inflow Doppler trace were assessed as measures of cardiac function. An unsupervised machine learning algorithm (multiple kernel learning) was used to reduce the dimensionality of these data, and to position the patients based on the similarities of echocardiographic data. The main patterns of variability present in the data were interpreted through non-linear regression analysis. Classic echocardiographic parameters, measured by a clinician, were then compared between the intermediate and extreme patient profiles across the variability spectrum. Results Figure 1 shows differences in velocity and deformation traces between the three representative patient profiles. While at one end of the spectrum, all echocardiographic traces were normal (red and green), the data of the other extreme patient profile (blue) describes a characteristic and consistent LV pressure overload remodeling pattern, with slightly reduced and delayed aortic outflow velocities, fused E and A waves with the ratio < 1, lower e’ mitral annulus velocities, decreased basal septal strain with post-systolic motion, prolonged relaxation in early diastole as seen by the deformation traces, and a change in atrial deformation dynamic with augmentation of LA contractile strain. The clinical measurements concurred with the remodeling profile describing smaller end-diastolic LV diameter and end-systolic and end-diastolic LV volumes, a reduced E/A ratio and e’ medial annular velocity, reduced TAPSE and increased LA contractile strain. Conclusion Machine learning based assessment of complex echocardiographic data has the potential to recognize an integrated and comprehensive patient profile related to LV remodeling within the hypertensive cohort without relying on classical clinical measurements and parameters, but by learning from subtle differences globally present in velocity and deformation echocardiographic data. Abstract 421 Figure 1