Left ventricular ejection fraction (LVEF) is the most important parameter in the assessment of cardiac function. A machine-learning algorithm was trained to guide ultrasound-novices to acquire diagnostic echocardiography images. The artificial intelligence (AI) algorithm then estimates LVEF from the captured apical-4-chamber (AP4), apical-2-chamber (AP2), and parasternal-long-axis (PLAX) loops. We sought to test this algorithm by having first-year medical students without previous ultrasound knowledge scan real patients. Nineteen echo-naïve first-year medical students were trained in the basics of echocardiography by a 2.5 h online video tutorial. Each student then scanned three patients with the help of the AI. Image quality was graded according to the American College of Emergency Physicians scale. If rated as diagnostic quality, the AI calculated LVEF from the acquired loops (monoplane and also a “best-LVEF” considering all views acquired in the particular patient). These LVEF calculations were compared to images of the same patients captured and read by three experts (ground-truth LVEF [GT-EF]). The novices acquired diagnostic-quality images in 33/57 (58%), 49/57 (86%), and 39/57 (68%) patients in the PLAX, AP4, and AP2, respectively. At least one of the three views was obtained in 91% of the attempts. We found an excellent agreement between the machine’s LVEF calculations from images acquired by the novices with the GT-EF (bias of 3.5% ± 5.6 and r = 0.92, p < 0.001 in the “best-LVEF” algorithm). This pilot study shows first evidence that a machine-learning algorithm can guide ultrasound-novices to acquire diagnostic echo loops and provide an automated LVEF calculation that is in agreement with a human expert.

Aims Left heart diseases (LHDs) are the main driving forces for the development of functional tricuspid regurgitation (TR). Therefore, in most cases, the true prognostic value of TR remains concealed by concomitant LHD. This study aimed to analyse right heart remodelling in patients with TR without other valve disease and with normal systolic left ventricular function (sysLVF), and to stratify its prognostic value in the presence (dPH, maximal TR velocity signal (TRVmax) ≥ 3.5 m/s in echocardiography) or absence (nsPH, TRVmax &lt; 3.5m/s) of concomitant pulmonary hypertension (PH). Methods and results  We performed an observational analysis of all patients diagnosed with TR in the absence of other valve disease and reduced sysLVF at our institution between 1 January 2003 and 31 December 2013. Five-year mortality was chosen as endpoint. The final cohort entailed 29 979 consecutive patients (median age 60 years, interquartile range 46–70), 49.9% were male, mean follow-up was 95±49 months. Severe TR was present in 790 patients (2.6%). In dPH and in nsPH, severe TR was associated with an excess 5-year mortality that was even more pronounced in the dPH group (58.2% vs. 43.6%, P = 0.001). In nsPH, right ventricular dysfunction predicted mortality. In dPH, mortality was independent of presence or absence of right heart dilatation or dysfunction. Conclusion Severe TR without concomitant left heart valve disease or LV systolic dysfunction was a rare disease in this large-scale all-comer population and is associated with an unfavourable prognosis. The differentiation of patients with nsPH and dPH is essential as they present with different patterns of right heart remodelling and with different long-time outcomes.

Background: Presence of severe tricuspid regurgitation (TR) has a significant impact on assessment of right ventricular function (RVF) in transthoracic echocardiography (TTE). High trans-valvular pendulous volume leads to backward-unloading of the right ventricle. Consequently, established cut-offs for normal systolic performance may overestimate true systolic RVF. Methods: A retrospective analysis was performed entailing all patients who underwent TTE at our institution between 1 January 2013 and 31 December 2016. Only patients with normal left ventricular systolic function and with no other valvular lesion were included. All recorded loops were re-read by one experienced examiner. Patients without severe TR (defined as vena contracta width ≥7 mm) were excluded. All-cause 2-year mortality was chosen as the end-point. The prognostic value of several RVF parameters was tested. Results: The final cohort consisted of 220 patients, 88/220 (40%) were male. Median age was 69 years (IQR 52–79), all-cause two-year mortality was 29%, median TAPSE was 19 mm (15–22) and median FAC was 42% (30–52). In multivariate analysis, TAPSE with the cutoff 17 mm and FAC with the cutoff 35% revealed non-significant hazard ratios (HR) of 0.75 (95%CI 0.396–1.421, p = 0.38) and 0.845 (95%CI 0.383–1.867, p = 0.68), respectively. TAPSE with the cutoff 19 mm and visual eyeballing significantly predicted survival with HRs of 0.512 (95%CI 0.296–0.886, p = 0.017) and 1.631 (95%CI 1.101–2.416, p = 0.015), respectively. Conclusions: This large-scale all-comer study confirms that RVF is one of the main drivers of mortality in patients with severe isolated TR. However, the current cut-offs for established echocardiographic parameters did not predict survival. Further studies should investigate the prognostic value of higher thresholds for RVF parameters in these patients.