Prognostic Utility of Left Atrial Strain From MRI Feature Tracking in Ischemic and Nonischemic Dilated Cardiomyopathy: A Multicenter Study.

BACKGROUND. MRI-based prognostic evaluation in patients with dilated cardiomyopathy (DCM) has historically used markers of late gadolinium enhancement (LGE) and feature tracking (FT)-derived left ventricular global longitudinal strain (LVGLS). Early data indicate that FT-derived left atrial strain (LAS) parameters, including reservoir, conduit, and booster, may also have prognostic roles in such patients. OBJECTIVE. The purpose of our study was to evaluate the prognostic utility of LAS parameters, derived from MRI FT, in patients with ischemic or nonischemic DCM, including in comparison with the traditional parameters of LGE and LVGLS. METHODS. This retrospective study included 811 patients with ischemic or nonischemic DCM (median age, 60 years; 640 men, 171 women) who underwent cardiac MRI at any of five centers. FT-derived LAS parameters and LVGLS were measured using two- and four-chamber cine images. LGE percentage was quantified. Patients were assessed for a composite outcome of all-cause mortality or heart failure hospitalization. Multivariable Cox regression analyses including demographic characteristics, cardiovascular risk factors, medications used, and a wide range of cardiac MRI parameters were performed. Kaplan-Meier analyses with log-rank tests were also performed. RESULTS. A total of 419 patients experienced the composite outcome. Patients who did, versus those who did not, experience the composite outcome had larger LVGLS (-6.7% vs -8.3%, respectively; p < .001) as well as a smaller LAS reservoir (13.3% vs 19.3%, p < .001), LAS conduit (4.7% vs 8.0%, p < .001), and LAS booster (8.1% vs 10.3%, p < .001) but no significant difference in LGE (10.1% vs 11.3%, p = .51). In multivariable Cox regression analyses, significant independent predictors of the composite outcome included LAS reservoir (HR = 0.96, p < .001) and LAS conduit (HR = 0.91, p < .001). LAS booster and LGE were not significant independent predictors in the models. LVGLS was a significant independent predictor only in a model that initially included LAS booster but not the other LAS parameters. In Kaplan-Meier analysis, all three LAS parameters were significantly associated with the composite outcome (p < .001). CONCLUSION. In this multicenter study, LAS reservoir and LAS conduit were significant independent prognostic markers in patients with ischemic or nonischemic DCM, showing greater prognostic utility than the currently applied markers of LVGLS and LGE. CLINICAL IMPACT. FT-derived LAS analysis provides incremental prognostic information in patients with DCM.

Intraventricular 4D flow cardiovascular magnetic resonance for assessing patients with heart failure with preserved ejection fraction: a pilot study.

Diagnosing heart failure with preserved ejection fraction (HFpEF) remains challenging. Intraventricular four-dimensional flow (4D flow) phase-contrast cardiovascular magnetic resonance (CMR) can assess different components of left ventricular (LV) flow including direct flow, delayed ejection, retained inflow and residual volume. This could be utilised to identify HFpEF. This study investigated if intraventricular 4D flow CMR could differentiate HFpEF patients from non-HFpEF and asymptomatic controls. Suspected HFpEF patients and asymptomatic controls were recruited prospectively. HFpEF patients were confirmed using European Society of Cardiology (ESC) 2021 expert recommendations. Non-HFpEF patients were diagnosed if suspected HFpEF patients did not fulfil ESC 2021 criteria. LV direct flow, delayed ejection, retained inflow and residual volume were obtained from 4D flow CMR images. Receiver operating characteristic (ROC) curves were plotted. 63 subjects (25 HFpEF patients, 22 non-HFpEF patients and 16 asymptomatic controls) were included in this study. 46% were male, mean age 69.8 ± 9.1 years. CMR 4D flow derived LV direct flow and residual volume could differentiate HFpEF vs combined group of non-HFpEF and asymptomatic controls (p < 0.001 for both) as well as HFpEF vs non-HFpEF patients (p = 0.021 and p = 0.005, respectively). Among the 4 parameters, direct flow had the largest area under curve (AUC) of 0.781 when comparing HFpEF vs combined group of non-HFpEF and asymptomatic controls, while residual volume had the largest AUC of 0.740 when comparing HFpEF and non-HFpEF patients. CMR 4D flow derived LV direct flow and residual volume show promise in differentiating HFpEF patients from non-HFpEF patients.

Diagnostic accuracy of cardiovascular magnetic resonance strain analysis and atrial size to identify heart failure with preserved ejection fraction.

Aims: Heart failure with preserved ejection fraction (HFpEF) continues to be a diagnostic challenge. Cardiac magnetic resonance atrial measurement, feature tracking (CMR-FT), tagging has long been suggested to diagnose HFpEF and potentially complement echocardiography especially when echocardiography is indeterminate. Data supporting the use of CMR atrial measurements, CMR-FT or tagging, are absent. Our aim is to conduct a prospective case-control study assessing the diagnostic accuracy of CMR atrial volume/area, CMR-FT, and tagging to diagnose HFpEF amongst patients suspected of having HFpEF. Methods and results: One hundred and twenty-one suspected HFpEF patients were prospectively recruited from four centres. Patients underwent echocardiography, CMR, and N-terminal pro-B-type natriuretic peptide (NT-proBNP) measurements within 24 h to diagnose HFpEF. Patients without HFpEF diagnosis underwent catheter pressure measurements or stress echocardiography to confirm HFpEF or non-HFpEF. Area under the curve (AUC) was determined by comparing HFpEF with non-HFpEF patients. Fifty-three HFpEF (median age 78 years, interquartile range 74-82 years) and thirty-eight non-HFpEF (median age 70 years, interquartile range 64-76 years) were recruited. Cardiac magnetic resonance left atrial (LA) reservoir strain (ResS), LA area index (LAAi), and LA volume index (LAVi) had the highest diagnostic accuracy (AUCs 0.803, 0.815, and 0.776, respectively). Left atrial ResS, LAAi, and LAVi had significantly better diagnostic accuracy than CMR-FT left ventricle (LV)/right ventricle (RV) parameters and tagging (P < 0.01). Tagging circumferential and radial strain had poor diagnostic accuracy (AUC 0.644 and 0.541, respectively). Conclusion: Cardiac magnetic resonance LA ResS, LAAi, and LAVi have the highest diagnostic accuracy to identify HFpEF patients from non-HFpEF patients amongst clinically suspected HFpEF patients. Cardiac magnetic resonance feature tracking LV/RV parameters and tagging had low diagnostic accuracy to diagnose HFpEF.

Detection of COVID-19 Using Deep Learning Algorithms on Chest Radiographs.

PURPOSE: To evaluate the performance of a deep learning (DL) algorithm for the detection of COVID-19 on chest radiographs (CXR). MATERIALS AND METHODS: In this retrospective study, a DL model was trained on 112,120 CXR images with 14 labeled classifiers (ChestX-ray14) and fine-tuned using initial CXR on hospital admission of 509 patients, who had undergone COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR). The test set consisted of a CXR on presentation of 248 individuals suspected of COVID-19 pneumonia between February 16 and March 3, 2020 from 4 centers (72 RT-PCR positives and 176 RT-PCR negatives). The CXR were independently reviewed by 3 radiologists and using the DL algorithm. Diagnostic performance was compared with radiologists' performance and was assessed by area under the receiver operating characteristics (AUC). RESULTS: The median age of the subjects in the test set was 61 (interquartile range: 39 to 79) years (51% male). The DL algorithm achieved an AUC of 0.81, sensitivity of 0.85, and specificity of 0.72 in detecting COVID-19 using RT-PCR as the reference standard. On subgroup analyses, the model achieved an AUC of 0.79, sensitivity of 0.80, and specificity of 0.74 in detecting COVID-19 in patients presented with fever or respiratory systems and an AUC of 0.87, sensitivity of 0.85, and specificity of 0.81 in distinguishing COVID-19 from other forms of pneumonia. The algorithm significantly outperforms human readers (P<0.001 using DeLong test) with higher sensitivity (P=0.01 using McNemar test). CONCLUSIONS: A DL algorithm (COV19NET) for the detection of COVID-19 on chest radiographs can potentially be an effective tool in triaging patients, particularly in resource-stretched health-care systems.

Development and validation of risk prediction models for COVID-19 positivity in a hospital setting.

OBJECTIVES: To develop: (1) two validated risk prediction models for coronavirus disease-2019 (COVID-19) positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation. METHODS: Patients with and without COVID-19 were included from 4 Hong Kong hospitals. The database was randomly split into 2:1: for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer-Lemeshow (H-L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4 and 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). RESULTS: A total of 1330 patients (mean age 58.2 ± 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. The first prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880-0.941]). The second model developed has the same variables except contact history (AUC = 0.880 [CI = 0.844-0.916]). Both were externally validated on the H-L test (p = 0.781 and 0.155, respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV. CONCLUSION: Two simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation.

Frequency and Distribution of Chest Radiographic Findings in Patients Positive for COVID-19.

Background Current coronavirus disease 2019 (COVID-19) radiologic literature is dominated by CT, and a detailed description of chest radiography appearances in relation to the disease time course is lacking. Purpose To describe the time course and severity of findings of COVID-19 at chest radiography and correlate these with real-time reverse transcription polymerase chain reaction (RT-PCR) testing for severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2, nucleic acid. Materials and Methods This is a retrospective study of patients with COVID-19 confirmed by using RT-PCR and chest radiographic examinations who were admitted across four hospitals and evaluated between January and March 2020. Baseline and serial chest radiographs (n = 255) were reviewed with RT-PCR. Correlation with concurrent CT examinations (n = 28) was performed when available. Two radiologists scored each chest radiograph in consensus for consolidation, ground-glass opacity, location, and pleural fluid. A severity index was determined for each lung. The lung scores were summed to produce the final severity score. Results The study was composed of 64 patients (26 men; mean age, 56 years ± 19 [standard deviation]). Of these, 58 patients had initial positive findings with RT-PCR (91%; 95% confidence interval: 81%, 96%), 44 patients had abnormal findings at baseline chest radiography (69%; 95% confidence interval: 56%, 80%), and 38 patients had initial positive findings with RT-PCR testing and abnormal findings at baseline chest radiography (59%; 95% confidence interval: 46%, 71%). Six patients (9%) showed abnormalities at chest radiography before eventually testing positive for COVID-19 with RT-PCR. Sensitivity of initial RT-PCR (91%; 95% confidence interval: 83%, 97%) was higher than that of baseline chest radiography (69%; 95% confidence interval: 56%, 80%) (P = .009). Radiographic recovery (mean, 6 days ± 5) and virologic recovery (mean, 8 days ± 6) were not significantly different (P = .33). Consolidation was the most common finding (30 of 64; 47%) followed by ground-glass opacities (21 of 64; 33%). Abnormalities at chest radiography had a peripheral distribution (26 of 64; 41%) and lower zone distribution (32 of 64; 50%) with bilateral involvement (32 of 64; 50%). Pleural effusion was uncommon (two of 64; 3%). The severity of findings at chest radiography peaked at 10-12 days from the date of symptom onset. Conclusion Findings at chest radiography in patients with coronavirus disease 2019 frequently showed bilateral lower zone consolidation, which peaked at 10-12 days from symptom onset. © RSNA, 2020.