Automated estimation of echocardiogram image quality in hospitalized patients.

We developed a machine learning model for efficient analysis of echocardiographic image quality in hospitalized patients. This study applied a machine learning model for automated transthoracic echo (TTE) image quality scoring in three inpatient groups. Our objectives were: (1) Assess the feasibility of a machine learning model for echo image quality analysis, (2) Establish the comprehensiveness of real-world TTE reporting by clinical group, and (3) Determine the relationship between machine learning image quality and comprehensiveness of TTE reporting. A machine learning model was developed and applied to TTEs from three matched cohorts for image quality of nine standard views. Case TTEs were comprehensive studies in mechanically ventilated patients between 01/01/2010 and 12/31/2015. For each case TTE, there were two matched spontaneously breathing controls (Control 1: Inpatients scanned in the lab and Control 2: Portable studies). We report the overall mean maximum and view specific quality scores for each TTE. The comprehensiveness of an echo report was calculated as the documented proportion of 12 standard parameters. An inverse probability weighted regression model was fit to determine the relationship between machine learning quality score and the completeness of a TTE report. 175 mechanically ventilated TTEs were included with 350 non-intubated samples (175 Control 1: Lab and 175 Control 2: Portable). In total, the machine learning model analyzed 14,086 echo video clips for quality. The overall accuracy of the model with regard to the expert ground truth for the view classification was 87.0%. The overall mean maximum quality score was lower for mechanically ventilated TTEs (0.55 [95% CI 0.54, 0.56]) versus 0.61 (95% CI 0.59, 0.62) for Control 1: Lab and 0.64 (95% CI 0.63, 0.66) for Control 2: Portable; p = 0.002. Furthermore, mechanically ventilated TTE reports were the least comprehensive, with fewer reported parameters. The regression model demonstrated the correlation of echo image quality and completeness of TTE reporting regardless of the clinical group. Mechanically ventilated TTEs were of inferior quality and clinical utility compared to spontaneously breathing controls and machine learning derived image quality correlates with completeness of TTE reporting regardless of the clinical group.

Impact of the updated diastolic function guidelines in the real world.

BACKGROUND: Echocardiographic assessment of diastolic function is complex but can aid in the diagnosis of heart failure, particularly in patients with preserved ejection fraction. In 2016, the American Society of Echocardiography (ASE) and European Association of Cardiovascular Imaging (EACVI) published an updated algorithm for the evaluation of diastolic function. The objective of our study was to assess its impact on diastolic function assessment in a real-world cohort of echo studies. METHODS: We retrospectively identified 71,727 consecutive transthoracic echo studies performed at a tertiary care center between February 2010 and March 2016 in which diastolic function was reported based on the 2009 ASE Guidelines. We then programmed a software algorithm to assess diastolic function in these echo studies according to the 2016 ASE/EACVI Guidelines. RESULTS: When diastolic function assessment based on the 2009 guidelines was compared to that using the 2016 guidelines, there were significant differences in proportion of studies classified as normal (23% vs. 32%) or indeterminate (43% vs. 36%) function, and mild (23% vs. 23%), moderate (10% vs. 8%), or severe (1% vs. 2%) diastolic dysfunction, with poor agreement between the two methods (Kappa 0.323, 95% CI 0.318-0.328). Furthermore, within the subgroup of studies with preserved ejection fraction and no evidence of myocardial disease, there was significant reclassification from mild diastolic dysfunction to normal diastolic function. CONCLUSION: The updated guidelines result in significant differences in diastolic function interpretation in the real world. Our findings have important implications for the identification of patients with or at risk for heart failure.

Current Innovations of Metal Hexacyanoferrates-Based Nanocomposites toward Electrochemical Sensing: Materials Selection and Synthesis Methods.

Mixed valence transition metal hexacyanoferrates (MeHCF)-Prussian blue and its analogs receive enormous research interest in the electrochemical sensing field. In recent years, conducting materials such as conducting polymer, carbon nanomaterial, and noble metals have been used to form nanocomposites with MeHCF. The scope of this review offers the reasons behind the preparation of various MeHCF based nanocomposite toward electrochemical detection. We primarily focus on the current progress of the development of MEHCF-based nanocomposites. The synthesis methods for these nanocomposites are also reviewed and discussed.

Multimodality imaging of a pulmonary artery sarcoma.

Pulmonary artery sarcoma is a rare malignant neoplasm. Here, we describe a patient with a pulmonary artery sarcoma, which was only subtly visible and therefore not fully appreciated on initial transthoracic echocardiogram. Characterization of the tumor was aided by the use of multimodality imaging that included computed tomography, magnetic resonance imaging, and positron emission tomography. Familiarity with its appearance on multiple imaging modalities including echocardiography is important to ensure timely diagnosis, although the optimal treatment strategy is still unknown, and the prognosis remains poor.

Usefulness of the Atrial Emptying Fraction to Predict Maintenance of Sinus Rhythm After Direct Current Cardioversion for Atrial Fibrillation.

Atrial volumes indexed to body surface area (AVI) are robust predictors of nonvalvular atrial fibrillation (AF) recurrence after direct current cardioversion (DCCV). The incremental value of atrial emptying fraction (EmF) compared with atrial volumes as a predictor for recurrent AF after DCCV has not been evaluated. We sought to compare the predictive ability of baseline left atrial (LA) EmF, right atrial (RA) EmF, LAVI, and RAVI for post-DCCV AF recurrence at 6 months. The first 95 patients enrolled in the AF Clinic Registry with adequate echocardiogram imaging constituted the study cohort. Each patient underwent echocardiogram within 6 months before cardioversion. Maximal LAVI and RAVI, LA EmF, and RA EmF were performed offline using 4-chamber single-plane Simpson's method, averaged over 5 cycles. The mean age of the study cohort was 64 ± 12 years, and 67% were men. Only 28 patients (29%) who underwent DCCV remained in sinus rhythm at 6 months of follow-up. The remaining, 67 (71%) had reverted to AF or underwent ablation during the 6 months of follow-up. The overall performance for prediction of AF recurrence was greatest for RA EmF, area under the receiver operator characteristic curve (AUC): RA EmF 0.92, LA EmF 0.89, RAVI 0.76, and LAVI 0.63. RA and LA EmF AUCs were significantly higher than for LAVI or RAVI (max p = 0.02). In conclusion, although RAVI and LAVI are strong predictors of AF recurrence after DCCV, RA and LA EmF outperformed in this cohort.

Usefulness of tissue Doppler imaging in the diagnosis and prognosis of acute right ventricular infarction with inferior wall acute left ventricular infarction.

Tissue Doppler (TD) imaging is a novel echocardiographic technique that measures myocardial velocities. However, there are sparse data on TD imaging of the right ventricular (RV) free wall in the diagnosis and prognosis of RV myocardial infarction (MI) in inferior wall left ventricular MI. Fifty patients who had left ventricular MI underwent TD echocardiography and angiography within 48 hours of MI. For diagnosis, the ability of RV TD imaging to detect RV MI was assessed using coronary angiography as the reference standard. For prognosis, the ability of TD detection of RV dysfunction to predict cardiac death or rehospitalization at 1 year was determined. For diagnosis, the univariate predictors of RV MI included RV diastolic dimension (p = 0.001), TD imaging of tricuspid annular systolic velocity (p = 0.001), and early diastolic velocity (p = 0.002). On multivariate analysis, systolic annular velocity (p = 0.04) and RV dimension (p = 0.05) predicted RV MI. For prognosis, nonculprit coronary artery disease (p = 0.003), TD imaging of RV systolic annular velocity (p = 0.005), and early diastolic velocity (p = 0.01) were among the univariate predictors of cardiac death or rehospitalization. On multivariate analysis, nonculprit coronary artery disease (p = 0.02) and TD imaging of systolic annular velocity (p = 0.04) were independent predictors of outcome. Decreased RV systolic annular velocity on TD images detects RV MI in first left ventricular acute inferior MI and predicts cardiac death or rehospitalization at 1 year.

Left ventricular filling patterns and pulmonary artery pressures in patients aged 90 to 100 years with normal echocardiography results.

Left ventricular filling and pulmonary artery pressure (PAP) have not been well described in patients > or = 90 years old. We reviewed our database to obtain echocardiograms with normal findings in this age group. The peak early and late mitral filling velocities, the early and late velocity-time integral (VTI), the mitral deceleration time, and the atrial filling fraction (late VTI/total VTI) were measured. PAP was obtained from the peak tricuspid regurgitation velocity. Of 252 echocardiograms performed on patients > or = 90 years old, 47 (19%) were normal. The mean +/- SD values were: ratio of peak early and late mitral filling velocities, 0.69 +/- 0.16; deceleration time, 240 milliseconds +/- 31; ratio of early and late VTI, 1.0 +/- 0.3; atrial filling fraction, 47% +/- 7; and PAP, 31 mm Hg +/- 6. Left ventricular filling in patients aged 90 to 100 years demonstrates increasingly impaired left ventricular relaxation from patients in their eighth and ninth decades. PAP continues to increase with age and PAP < or = 40 mm Hg in the elderly could be considered normal.