StatementChest x-ray abnormalities in COVID-19 mirror those of CT, demonstrating bilateral peripheral consolidation. Chest x-ray findings have a lower sensitivity than initial RT-PCR testing (69% versus 91%, respectively). Key Results In a cohort of patients with COVID-19 infection and imaging follow-up, baseline chestx-ray had a sensitivity of 69%, compared to 91% for initial RT-PCR. Chest x-ray abnormalities preceded positive RT-PCR in 6/64 (9%) patients. Common chest x-ray findings mirror those previously described for CT: bilateral, peripheral, consolidation and/or ground glass opacities. I n P r e s sBackground Current COVID-19 radiological literature is dominated by CT and a detailed description of chest x-ray (CXR) appearances in relation to the disease time course is lacking. PurposeTo describe the time course and severity of the CXR findings of COVID-19 and correlate these with real time reverse transcription polymerase chain reaction (RT-PCR) testing for SARS-Cov-2 nucleic acid. Materials and MethodsRetrospective study of COVID-19 patients with RT-PCR confirmation and CXRs admitted across 4 hospitals evaluated between January and March 2020. Baseline and serial CXRs (total 255 CXRs) were reviewed along with RT-PCRs. Correlation with concurrent CTs (total 28 CTs) was made when available. Two radiologists scored each CXR in consensus for: consolidation, ground glass opacity (GGO), location and pleural fluid. A severity index was determined for each lung. The lung scores were summed to produce the final severity score. ResultsThere were 64 patients (26 men, mean age 5619 years). Of these, 58, 44 and 38 patients had positive initial RT-PCR (91%, [CI: 81-96%]), abnormal baseline CXR (69%, [CI: 56-80%]) and positive initial RT-PCR with abnormal baseline CXR (59 [CI:46-71%]) respectively. Six patients (9%) showed CXR abnormalities before eventually testing positive on RT-PCR. Sensitivity of initial RT-PCR (91% [95% CI: 83-97%]) was higher than baseline CXR (69% [95% CI: 56-80%]) (p = 0.009). Radiographic (mean 6  5 days) and virologic recovery (mean 8  6 days) were not significantly different (p= 0.33). Consolidation was the most common finding (30/64, 47%), followed by GGO (21/64, 33%). CXR abnormalities had a peripheral (26/64, 41%) and lower zone distribution (32/64, 50%) with bilateral involvement (32/64, 50%). Pleural effusion was uncommon (2/64, 3%). The severity of CXR findings peaked at 10-12 days from the date of symptom onset. ConclusionChest x-ray findings in COVID-19 patients frequently showed bilateral lower zone consolidation which peaked at 10-12 days from symptom onset. Abbreviations:RT-PCR -reverse transcriptase polymerase chain reaction, GGO-ground glass opacity

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.

Highlights Developed two simple-to use nomograms for identifying COVID-19 positive patients. Probabilities are provided to allow healthcare leaders to decide suitable cut-offs. Variables are age, white cell count, chest x-ray appearances and contact history. Model variables are easily available in the general hospital setting.

Introduction: Quantitative myocardial blood flow (MBF) analysis using stress cardiac magnetic resonance (CMR) has been shown to detect obstructive coronary artery disease (CAD) and coronary microvascular dysfunction (CMD) in several mostly small, single-center studies. The AQUA-MBF ( A ssessment of QUA ntitative MBF ) study is a multicenter initiative involving 16 centers. Hypothesis: The goal of this sub-study is to determine if MBF can differentiate CAD, CMD, and normal volunteers in this multicenter setting. Methods: We present data from 53 subjects (15 with CAD, 20 at risk for CMD and 18 controls) who underwent vasodilator stress CMR (Figure) using 1.5T and 3.0T MR scanners (General Electric). At risk for CMD was defined as having diabetes and 2 other risk factors in absence of ≥50% stenosis based on coronary CT. CAD was defined as the presence of stenosis ≥70% based on invasive coronary angiography. Stress perfusion images were acquired using the dual sequence technique. Stress MBF was measured in each of the 16 AHA segments using Fermi deconvolution (Circle Cvi42). In the CAD group, each segment was further classified as having late gadolinium enhancement (LGE), supplied by CAD, or a normal remote territory. The means of the 5 groups were compared using one-way analysis of variance. Results: The segmental stress MBF (ml/g/min) for the 5 groups are shown in figure. Compared to the normal group, segmental stress MBF in 4 disease groups were significantly lower (p<0.001). Segmental MBF in those at risk for CMD was lower than normal segments and greater than CAD segments (p<0.001). LGE and CAD segments had the lowest stress MBF but similar to each other (p=0.9). Conclusions: In this multicenter study, we show that quantification of MBF using the dual sequence stress perfusion CMR technique can differentiate diseased from healthy myocardium and also between obstructive CAD and those at risk for CMD.

Aims Heart failure with preserved ejection fraction (HFpEF) continues to be a diagnostic challenge. CMR atrial measurements, feature-tracking (CMR-FT), tagging have long been suggested to diagnose HFpEF and potentially complement echocardiography especially when echocardiography is indeterminate. Data supporting the use of atrial measurements, CMR-FT or tagging is 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 & Results 121 suspected HFpEF patients were prospectively recruited from four centres. Patients underwent echocardiography, CMR, NT-proBNP measurements within 24 hours 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) were determined by comparing HFpEF with non-HFpEF patients. 53 HFpEF (median age 78yrs, interquartile range 74-82yrs) and 38 non-HFpEF (median age 70yrs, interquartile range 64-76yrs). CMR left atrial (LA) reservoir strain (ResS), LA area indexed (LAAi) and LA volume indexed (LAVi) had the highest diagnostic accuracy (AUCs 0.803, 0.815 and 0.776 respectively). LA ResS, LAAi and LAVi had significantly better diagnostic accuracy than CMR-FT left ventricle (LV)/right ventricle (RV) parameters and tagging (p &lt; 0.01). Tagging circumferential and radial strain had poor diagnostic accuracy (AUC 0.644 and 0.541 respectively). Conclusion CMR LA ResS, LAAi and LAVi have the highest diagnostic accuracy to identify HFpEF patients from non-HFpEF patients amongst clinically suspected HFpEF patients. CMR-FT LV/RV parameters and tagging had low diagnostic accuracy to diagnose HFpEF.

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