Regional Myocardial Remodeling Characteristics Correlates With Cardiac Events in Sarcoidosis.

BACKGROUND: The poor prognosis of cardiac sarcoidosis (CS) underscores the need for risk stratification. PURPOSE: To investigate the prognostic significance of ventricular/myocardial remodeling features in sarcoidosis. STUDY TYPE: Retrospective. POPULATION: In all, 132 biopsy-proven sarcoidosis patients imaged from 2008 to 2018. The primary endpoint was a composite of cardiac mortality, new onset arrhythmias, hospitalization for heart failure, and device implantation. FIELD STRENGTH/SEQUENCE: No field strength or sequence restrictions. ASSESSMENT: Global and regional ventricular/myocardial remodeling features were assessed by standard volumetric measurements and automated function imaging postprocessing analysis. STATISTICAL TESTS: Student's t-test or Mann-Whitney test (chi2 test or Fisher's exact test for categorical variables) were used for comparisons. Cox-proportional hazards regression model, univariate /multivariate analyses, and receiver operating characteristic were performed to relate clinical/lab data, imaging parameters to the endpoints. RESULTS: Over a median follow-up of 40.7 (interquartile range 18.8-60.5) months, 41 (31.1%) patients developed adverse cardiac events. Abnormal left ventricular (LV) geometric remodeling alterations (measured by LV mass index and relative wall thickness) occurred 3.66-fold more frequently in patients with endpoints than patients without. The ratio of patients with endpoints increased as ventricular remodeling phenotype progressed. In patients with endpoints, regional myocardial wall thickness (RMWT) was significantly (P = 0.022) increased in six clustered LV segments located in the middle interventricular septum and basal/middle anterolateral walls. In all of the abnormal ventricular remodeling stages, patients with endpoints constantly had higher mean RMWT than those without. Among clinical, electrocardiographic, and imaging parameters, LV mass index (hazard ratio [HR] 1.010 95% confidence interval [CI] 1.002-1.018, P = 0.017) and mean RMWT (HR 3.482 95% CI 1.679-7.223, P = 0.001) were independently associated with endpoints. Sarcoidosis patients without this RMWT distribution pattern were significantly (P < 0.001) more likely to be free of the occurrence of subsequent cardiac events. DATA CONCLUSION: Regional myocardial remodeling characteristics are associated with subsequent adverse cardiac events in sarcoidosis. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:499-509.

The NAFLD Decompensation Risk Score: External Validation and Comparison to Existing Models to Predict Hepatic Events in a Retrospective Cohort Study.

Background: The NAFLD decompensation risk score (the Iowa Model) was recently developed to identify patients with nonalcoholic fatty liver disease (NAFLD) at highest risk of developing hepatic events using three variables-age, platelet count, and diabetes. Aims: We performed an external validation of the Iowa Model and compared it to existing non-invasive models. Methods: We included 249 patients with NAFLD at Boston Medical Center, Boston, Massachusetts, in the external validation cohort and 949 patients in the combined internal/external validation cohort. The primary outcome was the development of hepatic events (ascites, hepatic encephalopathy, esophageal or gastric varices, or hepatocellular carcinoma). We used Cox proportional hazards to analyze the ability of the Iowa Model to predict hepatic events in the external validation (https://uihc.org/non-alcoholic-fatty-liver-disease-decompensation-risk-score-calculator). We compared the performance of the Iowa Model to the AST-to-platelet ratio index (APRI), NAFLD fibrosis score (NFS), and the FIB-4 index in the combined cohort. Results: The Iowa Model significantly predicted the development of hepatic events with hazard ratio of 2.5 [95% confidence interval (CI) 1.7-3.9, P < 0.001] and area under the receiver operating characteristic curve (AUROC) of 0.87 (CI 0.83-0.91). The AUROC of the Iowa Model (0.88, CI: 0.85-0.92) was comparable to the FIB-4 index (0.87, CI: 0.83-0.91) and higher than NFS (0.66, CI: 0.63-0.69) and APRI (0.76, CI: 0.73-0.79). Conclusions: In an urban, racially and ethnically diverse population, the Iowa Model performed well to identify NAFLD patients at higher risk for liver-related complications. The model provides the individual probability of developing hepatic events and identifies patients in need of early intervention.

Mortality Correlates in Patients With Takotsubo Syndrome During the COVID-19 Pandemic.

We completed a systematic review of Takotsubo syndrome (TTS) cases reported during the coronavirus disease 2019 (COVID-19) pandemic and performed clustering and feature importance analysis and statistical testing for independence on the demographic, clinical, and imaging parameters. Compared with the data before the COVID-19 pandemic, TTS was increasingly diagnosed in physical stress (mostly COVID-19 pneumonia)-triggered male patients without psychiatric/neurologic disorders, warranting further investigation to establish new reference criteria to improve diagnostic specificity. In clustering analysis, sex and inpatient mortality primarily contributed to the automated classification of the TTS. Both sex and inpatient mortality had essential correlations with COVID-19 infection/pneumonia. There is effect modification of sex on outcomes in patients with COVID-19 infection and TTS, with male patients having significantly worse inpatient mortality. Meanwhile, significantly more male patients with TTS were classified as high risk according to International Takotsubo Registry prognostic scores, suggesting that male COVID-19/TTS survivors will likely have worse long-term outcome.

Spatio-temporal hybrid neural networks reduce erroneous human "judgement calls" in the diagnosis of Takotsubo syndrome.

BACKGROUND: We investigate whether deep learning (DL) neural networks can reduce erroneous human "judgment calls" on bedside echocardiograms and help distinguish Takotsubo syndrome (TTS) from anterior wall ST segment elevation myocardial infarction (STEMI). METHODS: We developed a single-channel (DCNN[2D SCI]), a multi-channel (DCNN[2D MCI]), and a 3-dimensional (DCNN[2D+t]) deep convolution neural network, and a recurrent neural network (RNN) based on 17,280 still-frame images and 540 videos from 2-dimensional echocardiograms in 10 years (1 January 2008 to 1 January 2018) retrospective cohort in University of Iowa (UI) and eight other medical centers. Echocardiograms from 450 UI patients were randomly divided into training and testing sets for internal training, testing, and model construction. Echocardiograms of 90 patients from the other medical centers were used for external validation to evaluate the model generalizability. A total of 49 board-certified human readers performed human-side classification on the same echocardiography dataset to compare the diagnostic performance and help data visualization. FINDINGS: The DCNN (2D SCI), DCNN (2D MCI), DCNN(2D+t), and RNN models established based on UI dataset for TTS versus STEMI prediction showed mean diagnostic accuracy 73%, 75%, 80%, and 75% respectively, and mean diagnostic accuracy of 74%, 74%, 77%, and 73%, respectively, on the external validation. DCNN(2D+t) (area under the curve [AUC] 0·787 vs. 0·699, P = 0·015) and RNN models (AUC 0·774 vs. 0·699, P = 0·033) outperformed human readers in differentiating TTS and STEMI by reducing human erroneous judgement calls on TTS. INTERPRETATION: Spatio-temporal hybrid DL neural networks reduce erroneous human "judgement calls" in distinguishing TTS from anterior wall STEMI based on bedside echocardiographic videos. FUNDING: University of Iowa Obermann Center for Advanced Studies Interdisciplinary Research Grant, and Institute for Clinical and Translational Science Grant. National Institutes of Health Award (1R01EB025018-01).