Deep learning for automated left ventricular outflow tract diameter measurements in 2D echocardiography.

BACKGROUND: Measurement of the left ventricular outflow tract diameter (LVOTd) in echocardiography is a common source of error when used to calculate the stroke volume. The aim of this study is to assess whether a deep learning (DL) model, trained on a clinical echocardiographic dataset, can perform automatic LVOTd measurements on par with expert cardiologists. METHODS: Data consisted of 649 consecutive transthoracic echocardiographic examinations of patients with coronary artery disease admitted to a university hospital. 1304 LVOTd measurements in the parasternal long axis (PLAX) and zoomed parasternal long axis views (ZPLAX) were collected, with each patient having 1-6 measurements per examination. Data quality control was performed by an expert cardiologist, and spatial geometry data was preserved for each LVOTd measurement to convert DL predictions into metric units. A convolutional neural network based on the U-Net was used as the DL model. RESULTS: The mean absolute LVOTd error was 1.04 (95% confidence interval [CI] 0.90-1.19) mm for DL predictions on the test set. The mean relative LVOTd errors across all data subgroups ranged from 3.8 to 5.1% for the test set. Generally, the DL model had superior performance on the ZPLAX view compared to the PLAX view. DL model precision for patients with repeated LVOTd measurements had a mean coefficient of variation of 2.2 (95% CI 1.6-2.7) %, which was comparable to the clinicians for the test set. CONCLUSION: DL for automatic LVOTd measurements in PLAX and ZPLAX views is feasible when trained on a limited clinical dataset. While the DL predicted LVOTd measurements were within the expected range of clinical inter-observer variability, the robustness of the DL model requires validation on independent datasets. Future experiments using temporal information and anatomical constraints could improve valvular identification and reduce outliers, which are challenges that must be addressed before clinical utilization.

Impact of transcatheter aortic valve implantation on mechanical dispersion.

Objectives: The physiological determinants of left ventricular (LV) mechanical dispersion (MD) are not fully explored. We aimed to investigate the impact of afterload reduction and changes in ventricular conduction on LV MD after transcatheter aortic valve implantation (TAVI). Methods: Patients with severe aortic stenosis (AS) were examined in a prospective, repeated measures observational cohort study before and after an uncomplicated transfemoral TAVI in a single tertiary centre. LV MD was assessed by speckle tracking echocardiography. Valvulo-arterial impedance (ZVA) was used as a measure of global afterload. Results: We included 140 consecutive patients (83±8 years old, 49% women, logistic EuroSCORE 16±10) with severe AS (valve area 0.7±0.2 cm2, mean transvalvular gradient 54±18 mm Hg) and a relatively preserved LV ejection fraction (52%±11%). After TAVI, we observed favourable changes in transvalvular gradients and ZVA in all patients. Compared with baseline, postprocedural MD was significantly lower in 108 patients with unchanged ventricular conduction (55±17 ms vs 51±17 ms, p=0.02) and higher in 28 patients with TAVI-induced left bundle branch block (51±13 ms vs 62±19 ms, p≤0.001). During 22±9 months observation, 22 patients died. Postprocedural MD was associated with mortality in a univariate Cox regression model (HR=1.24 (1.01-1.52), p<0.04, per 10 ms increase). Conclusions: Isolated afterload reduction was associated with reduction of MD, while concomitant impairment of ventricular conduction resulted in a more pronounced MD after TAVI, indicating that loading conditions and conduction should be considered when evaluating MD. A pronounced postprocedural LV MD was associated with mortality.

Lamin A/C cardiomyopathy: young onset, high penetrance, and frequent need for heart transplantation.

Aims: Lamin A/C (LMNA) mutations cause familial dilated cardiomyopathy (DCM) with frequent conduction blocks and arrhythmias. We explored the prevalence, cardiac penetrance, and expressivity of LMNA mutations among familial DCM in Norway. Furthermore, we explored the risk factors and the outcomes in LMNA patients. Methods and results: During 2003-15, genetic testing was performed in patients referred for familial DCM. LMNA genotype-positive subjects were examined by electrocardiography, Holter monitoring, cardiac magnetic resonance imaging, and echocardiography. A positive cardiac phenotype was defined as the presence of atrioventricular (AV) block, atrial fibrillation/flutter (AF), ventricular tachycardia (VT), and/or echocardiographic DCM. Heart transplantation was recorded and compared with non-ischaemic DCM of other origin. Of 561 unrelated familial DCM probands, 35 (6.2%) had an LMNA mutation. Family screening diagnosed an additional 93 LMNA genotype-positive family members. We clinically followed up 79 LMNA genotype-positive [age 42 ± 16 years, ejection fraction (EF) 45 ± 13%], including 44 (56%) with VT. Asymptomatic LMNA genotype-positive family members (age 31 ± 15 years) had a 9% annual incidence of a newly documented cardiac phenotype and 61% (19/31) of cardiac penetrance during 4.4 ± 2.9 years of follow-up. Ten (32%) had AV block, 7 (23%) AF, and 12 (39%) non-sustained VT. Heart transplantation was performed in 15 of 79 (19%) LMNA patients during 7.8 ± 6.3 years of follow-up. Conclusion: LMNA mutation prevalence was 6.2% of familial DCM in Norway. Cardiac penetrance was high in young asymptomatic LMNA genotype-positive family members with frequent AV block and VT, highlighting the importance of early family screening and cardiological follow-up. Nearly 20% of the LMNA patients required heart transplantation.

Determinants of high sensitivity cardiac troponin T elevation in acute ischemic stroke.

BACKGROUND: A proportion of patients with acute ischemic stroke have elevated cardiac troponin levels and ECG changes suggestive of cardiac injury, but the etiology is unclear. The aims of this study were to assess the frequency of high sensitivity cardiac troponin T (hs-cTnT) elevation, to identify determinants and ECG changes associated with hs-cTnT elevation, to identify patients with myocardial ischemia and to assess the impact of hs-cTnT elevation on in-hospital mortality. METHODS: Patients discharged with a diagnosis of acute ischemic stroke during a 1-year period, were included. Patients diagnosed with acute myocardial infarction (MI) within the last 7 days before admission or during hospitalization were excluded. RESULTS: In all, 156 (54.4%) of 287 patients had elevated hs-cTnT. The factors independently associated with hs-cTnT elevation were age ≥ 76 years (OR 3.71 [95% CI 2.04-6.75]), previous coronary heart disease (CHD) (OR 2.61 [1.23-5.53]), congestive heart failure (OR 4.26 [1.15-15.82]), diabetes mellitus (OR 4.02 [1.50-10.76]) and lower eGFR (OR 0.97 [0.95-0.98]). Of the 182 patients who had two hs-cTnT measurements, 12 (6.6%) had both a rise or fall of hs-cTnT with at least one elevated value, and ECG manifestations of myocardial ischemia, e.g. meeting the criteria of acute MI. Both dynamic relative change (p = 0.026) and absolute change (p = 0.032) in hs-cTnT were significantly associated with higher in-hospital mortality. CONCLUSIONS: Established CHD and cardiovascular risk factors are associated with hs-cTnT elevation. Acute MI is likely underdiagnosed in acute ischemic stroke patients. Dynamic changes in troponin levels seem to be related to poor short-term prognosis.