Bhopalwala, Huzefa

The analysis and interpretation of cardiac magnetic resonance (CMR) images are often time-consuming. The automated segmentation of cardiac structures can reduce the time required for image analysis. Spatial similarities between different CMR image types were leveraged to jointly segment multiple sequences using a segmentation model termed a multi-image type UNet (MI-UNet). This model was developed from 72 exams (46% female, mean age 63 ± 11 years) performed on patients with hypertrophic cardiomyopathy. The MI-UNet for steady-state free precession (SSFP) images achieved a superior Dice similarity coefficient (DSC) of 0.92 ± 0.06 compared to 0.87 ± 0.08 for a single-image type UNet (p < 0.001). The MI-UNet for late gadolinium enhancement (LGE) images also had a superior DSC of 0.86 ± 0.11 compared to 0.78 ± 0.11 for a single-image type UNet (p = 0.001). The difference across image types was most evident for the left ventricular myocardium in SSFP images and for both the left ventricular cavity and the left ventricular myocardium in LGE images. For the right ventricle, there were no differences in DCS when comparing the MI-UNet with single-image type UNets. The joint segmentation of multiple image types increases segmentation accuracy for CMR images of the left ventricle compared to single-image models. In clinical practice, the MI-UNet model may expedite the analysis and interpretation of CMR images of multiple types.

ABSTRACT Background: There are limited data on the temporal trends, incidence, and outcomes of ST-segment-elevation myocardial infarction-cardiogenic shock (STEMI-CS). Methods: Adult (>18 years) STEMI-CS admissions were identified using the National Inpatient Sample (2000–2017) and classified by tertiles of admission year (2000–2005, 2006–2011, 2012–2017). Outcomes of interest included temporal trends, acute organ failure, cardiac procedures, in-hospital mortality, hospitalization costs, and length of stay. Results: In ∼4.3 million STEMI admissions, CS was noted in 368,820 (8.5%). STEMI-CS incidence increased from 5.8% in 2000 to 13.0% in 2017 (patient and hospital characteristics adjusted odds ratio [aOR] 2.45 [95% confidence interval {CI} 2.40–2.49]; P < 0.001). Multiorgan failure increased from 55.5% (2000–2005) to 74.3% (2012–2017). Between 2000 and 2017, coronary angiography and percutaneous coronary intervention use increased from 58.8% to 80.1% and 38.6% to 70.6%, whereas coronary artery bypass grafting decreased from 14.9% to 10.4% (all P < 0.001). Over the study period, the use of intra-aortic balloon pump (40.6%–37.6%) decreased, and both percutaneous left ventricular assist devices (0%–12.9%) and extra-corporeal membrane oxygenation (0%–2.8%) increased (all P < 0.001). In hospital mortality decreased from 49.6% in 2000 to 32.7% in 2017 (aOR 0.29 [95% CI 0.28–0.31]; P < 0.001). During the 18-year period, hospital lengths of stay decreased, hospitalization costs increased and use of durable left ventricular assist device /cardiac transplantation remained stable (P > 0.05). Conclusions: In the United States, incidence of CS in STEMI has increased 2.5-fold between 2000 and 2017, while in-hospital mortality has decreased during the study period. Use of coronary angiography and PCI increased during the study period.

AbstractBackgroundA subset of patients with hypertrophic cardiomyopathy (HCM) is at high risk of sudden cardiac death (SCD). Practice guidelines endorse use of a risk calculator, which requires entry of left atrial (LA) diameter. However, American Society of Echocardiography (ASE) guidelines recommend the use of LA volume index (LAVI) for routine quantification of LA size. The aims of this study were to (a) develop a model to estimate LA diameter from LAVI and (b) evaluate whether substitution of measured LA diameter by estimated LA diameter derived from LAVI reclassifies HCM‐SCD risk.MethodsThe study cohort was comprised of 500 randomly selected HCM patients who underwent transthoracic echocardiography (TTE). LA diameter and LAVI were measured offline using digital clips from TTE. Linear regression models were developed to estimate LA diameter from LAVI. A European Society of Cardiology endorsed equation estimated SCD risk, which was measured using LA diameter and estimated LA diameter derived from LAVI.ResultsThe mean LAVI was 48.5 ± 18.8 mL/m2. The derived LA diameter was 45.1 mm (SD: 5.5 mm), similar to the measured LA diameter (45.1 mm, SD: 7.1 mm). Median SCD risk at 5 years estimated by measured LA diameter was 2.22% (interquartile range (IQR): 1.39, 3.56), while median risk calculated by estimated LA diameter was 2.18% (IQR: 1.44, 3.52). 476/500 (95%) patients maintained the same risk classification regardless of whether the measured or estimated LA diameter was used.ConclusionsSubstitution of measured LA diameter by estimated LA diameter in the HCM‐SCD calculator did not reclassify risk.

Abstract Objective Determine if information regarding hypertrophic cardiomyopathy (HCM) can be accurately retrieved from cardiac magnetic resonance (CMR) reports using natural language processing (NLP). Background CMR imaging is used for diagnosis and risk stratification of HCM. Manual annotation of information from CMR is time-consuming. NLP is an artificial intelligence method for automating extraction of information from narrative text. Methods We identified 200 HCM patients who had CMR reports from 1998 to 2018. These patients were randomly allocated into training (100 patients with 185 CMR reports) and testing sets (100 patients with 206 reports). An NLP system with 2 tiers was developed; the first extracted information regarding HCM diagnosis while second extracted categorical or numeric concepts for HCM classification. NLP performance was compared with gold-standard manual annotation. Results NLP algorithms achieved very high performance across all concepts with mean positive predictive value (PPV) = 0.96. An outlier was the performance for abstracting the presence of an apical pouch from CMR reports, which had noticeably lower PPV= 0.78 which be attributed to the low number of cases with this finding. Conclusions The algorithms developed can be translated to clinical decision support systems to increase efficiency and contribute to improved quality of care. Funding Acknowledgement Type of funding source: Other. Main funding source(s): Study supported by the National Heart, Lung and Blood Institute of National Institutes of Health (K01HL124045), the Mayo Clinic Center for Clinical and Translational Science (CCaTS), and the Mayo Clinic K2R award. Content is solely the responsibility of authors and does not necessarily represent official views of the National Institutes of Health.