Feature tracking cardiac magnetic resonance imaging to assess cardiac manifestations of systemic diseases.

Feature-tracking cardiac magnetic resonance (FT-CMR), with the ability to quantify myocardial deformation, has a unique role in the evaluation of subclinical myocardial abnormalities. This review aimed to evaluate the clinical use of cardiac FT-CMR-based myocardial strain in patients with various systemic diseases with cardiac involvement, such as hypertension, diabetes, cancer-therapy-related toxicities, amyloidosis, systemic scleroderma, myopathies, rheumatoid arthritis, thalassemia major, and coronavirus disease 2019 (COVID-19). We concluded that FT-CMR-derived strain can improve the accuracy of risk stratification and predict cardiac outcomes in patients with systemic diseases prior to symptomatic cardiac dysfunction. Furthermore, FT-CMR is particularly useful for patients with diseases or conditions which are associated with subtle myocardial dysfunction that may not be accurately detected with traditional methods. Compared to patients with cardiovascular diseases, patients with systemic diseases are less likely to undergo regular cardiovascular imaging to detect cardiac defects, whereas cardiac involvement in these patients can lead to major adverse outcomes; hence, the importance of cardiac imaging modalities might be underestimated in this group of patients. In this review, we gathered currently available data on the newly introduced role of FT-CMR in the diagnosis and prognosis of various systemic conditions. Further research is needed to define reference values and establish the role of this sensitive imaging modality, as a robust marker in predicting outcomes across a wide spectrum of patients.

Myocardial Function Prediction After Coronary Artery Bypass Grafting Using MRI Radiomic Features and Machine Learning Algorithms.

The main aim of the present study was to predict myocardial function improvement in cardiac MR (LGE-CMR) images in patients after coronary artery bypass grafting (CABG) using radiomics and machine learning algorithms. Altogether, 43 patients who had visible scars on short-axis LGE-CMR images and were candidates for CABG surgery were selected and enrolled in this study. MR imaging was performed preoperatively using a 1.5-T MRI scanner. All images were segmented by two expert radiologists (in consensus). Prior to extraction of radiomics features, all MR images were resampled to an isotropic voxel size of 1.8 × 1.8 × 1.8 mm3. Subsequently, intensities were quantized to 64 discretized gray levels and a total of 93 features were extracted. The applied algorithms included a smoothly clipped absolute deviation (SCAD)-penalized support vector machine (SVM) and the recursive partitioning (RP) algorithm as a robust classifier for binary classification in this high-dimensional and non-sparse data. All models were validated with repeated fivefold cross-validation and 10,000 bootstrapping resamples. Ten and seven features were selected with SCAD-penalized SVM and RP algorithm, respectively, for CABG responder/non-responder classification. Considering univariate analysis, the GLSZM gray-level non-uniformity-normalized feature achieved the best performance (AUC: 0.62, 95% CI: 0.53-0.76) with SCAD-penalized SVM. Regarding multivariable modeling, SCAD-penalized SVM obtained an AUC of 0.784 (95% CI: 0.64-0.92), whereas the RP algorithm achieved an AUC of 0.654 (95% CI: 0.50-0.82). In conclusion, different radiomics texture features alone or combined in multivariate analysis using machine learning algorithms provide prognostic information regarding myocardial function in patients after CABG.

Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network.

OBJECTIVES: The current study aimed to design an ultra-low-dose CT examination protocol using a deep learning approach suitable for clinical diagnosis of COVID-19 patients. METHODS: In this study, 800, 170, and 171 pairs of ultra-low-dose and full-dose CT images were used as input/output as training, test, and external validation set, respectively, to implement the full-dose prediction technique. A residual convolutional neural network was applied to generate full-dose from ultra-low-dose CT images. The quality of predicted CT images was assessed using root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Scores ranging from 1 to 5 were assigned reflecting subjective assessment of image quality and related COVID-19 features, including ground glass opacities (GGO), crazy paving (CP), consolidation (CS), nodular infiltrates (NI), bronchovascular thickening (BVT), and pleural effusion (PE). RESULTS: The radiation dose in terms of CT dose index (CTDIvol) was reduced by up to 89%. The RMSE decreased from 0.16 ± 0.05 to 0.09 ± 0.02 and from 0.16 ± 0.06 to 0.08 ± 0.02 for the predicted compared with ultra-low-dose CT images in the test and external validation set, respectively. The overall scoring assigned by radiologists showed an acceptance rate of 4.72 ± 0.57 out of 5 for reference full-dose CT images, while ultra-low-dose CT images rated 2.78 ± 0.9. The predicted CT images using the deep learning algorithm achieved a score of 4.42 ± 0.8. CONCLUSIONS: The results demonstrated that the deep learning algorithm is capable of predicting standard full-dose CT images with acceptable quality for the clinical diagnosis of COVID-19 positive patients with substantial radiation dose reduction. KEY POINTS: • Ultra-low-dose CT imaging of COVID-19 patients would result in the loss of critical information about lesion types, which could potentially affect clinical diagnosis. • Deep learning-based prediction of full-dose from ultra-low-dose CT images for the diagnosis of COVID-19 could reduce the radiation dose by up to 89%. • Deep learning algorithms failed to recover the correct lesion structure/density for a number of patients considered outliers, and as such, further research and development is warranted to address these limitations.

Irreversible coronary aneurysm presenting as acute coronary syndrome in a child with hypereosinophilic syndrome: a case report.

Hypereosinophilic syndrome is defined as persistent eosinophilia in the blood for more than 6 months, without any identifiable cause and with end-organ involvement evidence. Cardiac manifestations of HES include heart failure due to restrictive cardiomyopathy, arrhythmia, intraventricular thrombosis, and coronary artery involvement occurs frequently. In rare instances, coronary ectasia, aneurysms, or dissection can occur and cause morbidity and mortality in these patients.A coronary aneurysm occurs rarely in adult patients with HES but to our knowledge, this is the first report of this association in a 14-year-old boy who was presented to us as coronary aneurysm due to hypereosinophilic syndrome.

Left ventricular intra-myocardial dissection after myocardial infarction.

Left ventricular (LV) intra-myocardial dissection or dissecting hematoma is a rare complication of myocardial infarction that could occur in the acute phase, during remodeling process and even after coronary revascularization. LV intra-myocardial dissection has a high mortality, and the best management strategy remains controversial. Here, we present a case of dissection of left ventricle late after anterior myocardial infarction diagnosed by multimodality imaging.

Hepatic Hemangioma in a Cluster of Iranian Population.

INTRODUCTION: Hemangioma is the most common benign lesion of the liver. It is mostly asymptomatic and may be found incidentally during cross-sectional liver or abdominal imaging. This study aimed to investigate the prevalence and clinical characteristics of hepatic hemangioma in an Iranian population. MATERIALS AND METHODS: This retrospective study was conducted between July and November 2017 in Firoozgar Hospital affiliated to Iran University of Medical Sciences, Tehran, Iran. The study population consisted of adult patients (>18 years) referred for transabdominal ultrasonography to ultrasound unit of the hospital. RESULTS: Totally, 1985 patients were included in the study. There were 1282 women (64.5%) and 703 men (35.4%). A total of 47 hemangiomas were found in 41 patients. The prevalence of hemangioma was 2.04% in our study population. Four patients had more than one hemangioma; all of them were women. Twelve men (1.70%) versus 29 women (2.26%) found to have hemangioma. The mean age of patients with hemangioma was 47.65 ± 14.84 years. Thirty-four patients (82.9%) had hemangioma in their right lobe of the liver whereas seven patients (17.1%) had hemangioma in the left lobe of the liver. The mean diameter of hemangioma was 16.70 ± 8.42 mm. The mean diameter of hemangioma in women was 17.2 ± 9.33 mm and in men was 15.25 ± 4.91 mm (P = 0.495). CONCLUSION: Hepatic hemangioma is prevalent in the Iranian population. It is more prevalent among women and in the VII segment of the liver.

Differential privacy preserved federated learning for prognostic modeling in COVID-19 patients using large multi-institutional chest CT dataset.

BACKGROUND: Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID-19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitation, we set out to predict the prognosis of a large multi-institutional cohort of patients with COVID-19 using a DL-based model. PURPOSE: This study aimed to evaluate the performance of deep privacy-preserving federated learning (DPFL) in predicting COVID-19 outcomes using chest CT images. METHODS: After applying inclusion and exclusion criteria, 3055 patients from 19 centers, including 1599 alive and 1456 deceased, were enrolled in this study. Data from all centers were split (randomly with stratification respective to each center and class) into a training/validation set (70%/10%) and a hold-out test set (20%). For the DL model, feature extraction was performed on 2D slices, and averaging was performed at the final layer to construct a 3D model for each scan. The DensNet model was used for feature extraction. The model was developed using centralized and FL approaches. For FL, we employed DPFL approaches. Membership inference attack was also evaluated in the FL strategy. For model evaluation, different metrics were reported in the hold-out test sets. In addition, models trained in two scenarios, centralized and FL, were compared using the DeLong test for statistical differences. RESULTS: The centralized model achieved an accuracy of 0.76, while the DPFL model had an accuracy of 0.75. Both the centralized and DPFL models achieved a specificity of 0.77. The centralized model achieved a sensitivity of 0.74, while the DPFL model had a sensitivity of 0.73. A mean AUC of 0.82 and 0.81 with 95% confidence intervals of (95% CI: 0.79-0.85) and (95% CI: 0.77-0.84) were achieved by the centralized model and the DPFL model, respectively. The DeLong test did not prove statistically significant differences between the two models (p-value = 0.98). The AUC values for the inference attacks fluctuate between 0.49 and 0.51, with an average of 0.50 ± 0.003 and 95% CI for the mean AUC of 0.500 to 0.501. CONCLUSION: The performance of the proposed model was comparable to centralized models while operating on large and heterogeneous multi-institutional datasets. In addition, the model was resistant to inference attacks, ensuring the privacy of shared data during the training process.

Insights into pelvic venous disorders.

Pelvic venous disorders (PeVD), sometimes referred to as pelvic congestion syndrome (PCS), widely impact affected patients-mainly young women's quality of life, causing puzzling, uncomfortable symptoms sometimes requiring months or years to get an explanation, while simply remaining undiagnosed in other cases. Because pelvic pain is a non-specific symptom, an appropriate diagnosis requires a careful patient workup, including a correlation between history and non-invasive imaging. Invasive imaging is frequently required to confirm the diagnosis and plan treatment. Current therapeutic approaches principally rely on minimally invasive techniques delivered through endovascular access. However, while comprehensive descriptive classifications such as the symptoms-varices-pathophysiology (SVP) classification exist, universally accepted guidelines regarding therapy to apply for each SVP category are still lacking. This review strongly focuses on PeVD imaging and discusses available therapeutic approaches with regard to pathophysiological mechanisms. It proposes a new classification scheme assisting clinical decision-making about endovascular management to help standardize the link between imaging findings and treatment.

Cardiac calcified amorphous tumor in a patient with colon cancer.

Although one of the most important differential diagnoses of cardiac masses in cancer patients is metastasis from the underlying tumor, it may also be caused by benign etiologies. In this article, we describe cardiac calcified amorphous tumor, which is one of the benign causes of cardiac masses, in a patient with colon cancer.

Computed Tomography-Based Coronary Artery Calcium Score Calculation at a Reduced Tube Voltage Utilizing Iterative Reconstruction and Threshold Modification Techniques: A Feasibility Study.

BACKGROUND: The coronary artery calcium score (CACS) indicates cardiovascular health. A concern in this regard is the ionizing radiation from computed tomography (CT). Recent studies have tried to introduce low-dose CT techniques to assess CACS. We aimed to investigate the accuracy of iterative reconstruction (IR) and threshold modification while applying low tube voltage in coronary artery calcium imaging. METHODS: The study population consisted of 107 patients. Each subject underwent an electrocardiogram-gated CT twice, once with a standard voltage of 120 kVp and then a reduced voltage of 80 kVp. The standard filtered back projection (FBP) reconstruction was applied in both voltages. Considering Hounsfield unit (HU) thresholds other than 130 (150, 170, and 190), CACS was calculated using the FBP-reconstructed 80 kVp images. Moreover, the 80 kVp images were reconstructed utilizing IR at different strength levels. CACS was measured in each set of images. The intraclass correlation coefficient (ICC) was used to compare the CACSs. RESULTS: A 64% reduction in the effective dose was observed in the 80 kVp protocol compared to the 120 kVp protocol. Excellent agreement existed between CACS at high-level (strength level = 5) IR in low-kVp images and the standard CACS protocol in scores ≥ 11 (ICC > 0.9 and p < 0.05). Increasing the threshold density to 190 HU in FBP-reconstructed low-kVp images yielded excellent agreement with the standard protocol in scores ≥ 11 (ICC > 0.9 and p < 0.05) and good agreement in score zero (ICC = 0.84 and p = 0.02). CONCLUSIONS: The modification of the density threshold and IR provides an accurate calculation of CACS in low-voltage CT with the potential to decrease patient radiation exposure.

Post-Pulmonary Embolism Impairment Six Months after Acute Pulmonary Embolism: A Prospective Registry.

BACKGROUND: Little evidence is available on post-pulmonary embolism impairment (PPEI), a recently defined complication of pulmonary embolism (PE) encompassing dysfunctional clinical and imaging parameters. In the present study, we sought to evaluate its frequency with a focus on the main components. METHODS: In this prospective registry, we included patients with a confirmed diagnosis of acute PE and focused on those with initial right ventricular (RV) dysfunction. Their baseline, pre-discharge, and 6 month follow-up clinical and imaging characteristics were recorded. The main study outcomes were incomplete RV functional recovery, exercise capacity limitations (based on the 6 minute walk test), and their combination, which defines PPEI, within six months of acute PE. RESULTS: Of 170 consecutive patients with a confirmed diagnosis of acute PE, 123 accepted to participate in the follow-up study, of whom 87 had initial RV dysfunction. The 6 month rates of incomplete RV functional recovery, signs of an intermediate-to-high echocardiographic probability of PH, and exercise limitations were observed in 58.6, 32.1, and 45.9%, respectively. A total of 22 (25.2%; 95% CI 15.5-34.4%) patients had PPEI. The RV/LV ratio and the fractional area change on discharge after acute PE were more often impaired among patients with incomplete RV recovery, exercise limitations, and a high probability of PH at 6 months. In contrast, an initial impaired RV diastolic function indices appeared to characterize patients with a limited exercise capacity at 6 months. DISCUSSION: PPEI affects one fourth of patients surviving acute PE with half of them presenting with RV dysfunction or exercise limitations.

Vena Cava Filters: Toward Optimal Strategies for Filter Retrieval and Patients' Follow-Up.

Mortality rates associated with venous thromboembolism (VTE) are high. Inferior vena cava filters (IVCFs) have been frequently placed for these patients as part of their treatment, albeit the paucity of data showing their ultimate efficacy and potential risk of complications. Issues regarding long-term filter dwell time are accounted for in society guidelines. This topic has led to an FDA mandate for filter retrieved as soon as protection from pulmonary embolism is no longer needed. However, even though most are retrievable, some were inadvertently left as permanent, which carries an incremental lifetime risk to the patient. In the past decade, attempts have aimed to determine the optimal time interval during which filter needs to be removed. In addition, distinct strategies have been implemented to boost retrieval rates. This review discusses current conflicts in indications, the not uncommon complications, the rationale and need for timely retrieval, and different quality improvement strategies to fulfill this aim.

A rare case of left inferior vena cava presenting with May-Thurner syndrome.

BACKGROUND: May-Thurner anomaly is characterized as external venous compression by the arterial framework against hard bony structures. This chronic anatomical lesion infrequently leads to deep vein thrombosis in the lower extremity, and it may lead to leg swelling as a long-term post-thrombotic complication. Left iliac vein compression may not be as uncommon as was previously thought, and it typically occurs in women more than men. Congenital anomalies of venous tree are not rare; they exist in 8.7% of the general population. CASE-PRESENTATION: We herein present the first case of right-sided May Thurner Syndrome in a patient with IVC anomalies. In our patient, both common iliac veins formed the left-sided IVC, which extended to the hemiazygos vein and the superior vena cava. Additionally, there was a right-sided suprarenal IVC, which extended to the right atrium. CONCLUSION: Understanding the proper anatomy in May-Thurner syndrome helps in better decision making for management of disease pathophysiology.

Left main coronary artery atresia in a 2-year-old toddler with de novo heart failure: Case report and review of the literature.

Congenital coronary anomalies are among the rare disorders of the otherwise normal heart. A 2-year-old toddler was evaluated for de novo heart failure after a flu-like event 2 months before being suspicious of post-Covid-19 dilated cardiomyopathy. The cardiac magnetic resonance (CMR) technique displayed the basal to mid subendocardial to transmural scar, suggestive of an ischemic etiology. Further assessment with CT and invasive angiography confirmed the very uncommon left main coronary artery atresia (LMCAA) as the main cause of the patient's heart failure. This is not only the first reported LMCAA case that had undergone a CMR study but was also initially suspected with characteristic CMR findings.

Pulmonary Blood Volume Measured by Dual-Energy Computed Tomography Can Help Distinguish Patients With Pulmonary Hypertension.

Purpose: To evaluate the correlation between whole lung enhancement (WLE) and pulmonary blood volume (PBV) obtained through dual energy computed tomography pulmonary angiography (DECTPA) and echocardiography-derived systolic pulmonary arterial pressure (SPAP). Methods: Sixty-eight patients who underwent DECTPA were enrolled in the study after giving informed consent. A transthoracic echocardiography was performed for all the subjects within 48 h of their DECTPA study to measure SPAP. The correlation of the two DECTPA-derived parameters, WLE and PBV, with SPAP was assessed. In addition, the predictive strength of these parameters was compared with that of traditional computed tomography (CT) signs of pulmonary hypertension (PH). Results: The SPAP value showed a moderate correlation with main pulmonary artery (MPA) diameter (r = 0.48, P < 0.001), while having a weak correlation with WLE (r = -0.33, P = 0.007), PBV (r = -0.31, P = 0.01) and MPA/ascending aorta (MPA/AA) ratio (r = 0.26, P = 0.03). On regression analysis, MPA diameter (B ± SE: 1.8 ± 0.6, P = 0.004) and WLE (B ± SE: -0.5 ± 0.3, P = 0.042) had significant association with SPAP. In addition, SPAP ≥30 mmHg was related to the right to left ventricular diameter (RV/LV) ratio [OR (CI 95%): 24.39 (1.3-573.2), P = 0.04] and reversely associated with PBV [OR (CI 95%): 0.96 (0.93-0.98), P = 0.005]. Acquired cutoff value of 83% for PBV showed sensitivity and specificity of 73% to identify SPAP ≥30 mmHg [AUC (CI 95%):0.727 (0.588-0.866), P = 0.008]. Conclusions: Automated postprocessing calculation of iodine distribution analysis by DECTPA could be considered as an adjunctive tool to investigate for PH.

COLI-Net: Deep learning-assisted fully automated COVID-19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images.

We present a deep learning (DL)-based automated whole lung and COVID-19 pneumonia infectious lesions (COLI-Net) detection and segmentation from chest computed tomography (CT) images. This multicenter/multiscanner study involved 2368 (347'259 2D slices) and 190 (17 341 2D slices) volumetric CT exams along with their corresponding manual segmentation of lungs and lesions, respectively. All images were cropped, resized, and the intensity values clipped and normalized. A residual network with non-square Dice loss function built upon TensorFlow was employed. The accuracy of lung and COVID-19 lesions segmentation was evaluated on an external reverse transcription-polymerase chain reaction positive COVID-19 dataset (7'333 2D slices) collected at five different centers. To evaluate the segmentation performance, we calculated different quantitative metrics, including radiomic features. The mean Dice coefficients were 0.98 ± 0.011 (95% CI, 0.98-0.99) and 0.91 ± 0.038 (95% CI, 0.90-0.91) for lung and lesions segmentation, respectively. The mean relative Hounsfield unit differences were 0.03 ± 0.84% (95% CI, -0.12 to 0.18) and -0.18 ± 3.4% (95% CI, -0.8 to 0.44) for the lung and lesions, respectively. The relative volume difference for lung and lesions were 0.38 ± 1.2% (95% CI, 0.16-0.59) and 0.81 ± 6.6% (95% CI, -0.39 to 2), respectively. Most radiomic features had a mean relative error less than 5% with the highest mean relative error achieved for the lung for the range first-order feature (-6.95%) and least axis length shape feature (8.68%) for lesions. We developed an automated DL-guided three-dimensional whole lung and infected regions segmentation in COVID-19 patients to provide fast, consistent, robust, and human error immune framework for lung and pneumonia lesion detection and quantification.

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients.

BACKGROUND: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. METHODS: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. RESULTS: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. CONCLUSION: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.

Pulmonary angioplasty: A step further in the continuously changing landscape of chronic thromboembolic pulmonary hypertension management.

Chronic thromboembolic pulmonary hypertension (CTEPH) is a potentially fatal and frequently undiagnosed form of pulmonary hypertension (PH), classified within group 4 by the World Health Organization (WHO). It is a type of precapillary PH, which uncommonly develops as a peculiar sequel of acute pulmonary embolism due to the partial resolution of the mechanically obstructing thrombus with a coexisting inflammatory response from pulmonary vessels. CTEPH is one of the potentially treatable forms of PH whose current standard of care is surgical pulmonary endarterectomy. Medical therapy with few drugs in non-operable disease is approved and has shown improvement in patients' hemodynamic condition and functional ability. Recently, balloon pulmonary angioplasty (BPA) has shown promising results as a treatment option for technically inoperable patients, those with unacceptable risk-to-benefit ratio and in a case of residual PH after endarterectomy. Lack of meticulous CTEPH screening programs in post-pulmonary embolism patients leading to underdiagnosis of this condition, complex operability assessment, and diversity in BPA techniques among different institutions are still the issues that need to be addressed. In this paper, we review the recent achievements in the management of non-operable CTEPH, their outcome and safety, based on available data.

Myocardial strain by cardiac magnetic resonance: A valuable predictor of outcome after infarct revascularization.

PURPOSE: To evaluate the prognostic value of left ventricular strains by cardiac magnetic resonance feature tracking (CMR-FT) in patients with re-perfused myocardial infarction (MI). METHODS: The study enrolled 58 patients with re-vascularized MI who underwent CMR within a week from acute MI. An 18-month follow-up was carried out for the composite endpoint of major adverse cardiovascular events (MACE). A 3 to 6-month post-MI ejection fraction (EF) was also measured. The predictive value of global longitudinal, circumferential, and radial strains (GLS, GCS, and GRS, respectively) for MACE and the follow-up EF was evaluated. RESULTS: All the global strains showed significant impairment in MACE positive cases (P < 0.05 for all). On univariate regression, MACE was reversely associated with early post-MI EF (OR: 0.90, 95% CI: 0.83-0.98, P: 0.01), and directly associated with GLS (OR: 1.32, 95% CI: 1.03-1.69, P: 0.02), GCS (OR: 1.23, 95% CI: 1.00-1.50, P: 0.04) and EDVI (OR:1.02, 95 %CI: 1.00-1.04, P: 0.01). On multivariate regression model, only the interaction between EF and GLS showed a significant association with MACE (OR[CI95%]: 1.1 [1.06-1.21]). EF < 30% and GLS > -8.9% had the highest sensitivity (78.9% and 89.5%, respectively) and specificity (45.2% and 54.8%, respectively) to predict MACE. The combination of EF < 30% and GLS > -8.9% increased the sensitivity to 94.7%. In addition, the cutoff values of 35.1% for early post-MI EF and -10% for GLS could identify patients with impaired follow-up EF with more than 80% sensitivity and specificity [AUC (CI95%): 0.893(0.76-1.00) for EF and AUC (CI95%):0.836(0.67-1,00) for GLS, P < 0.05 for both)]. CONCLUSIONS: GLS by CMR-FT is a powerful prognosticator of MACE and functional recovery in MI survivors, with incremental value added to early post-MI EF alone.

Structured team-oriented program to follow patients after vena cava filter placement: a step forward in improving quality for filter retrieval.

To reduce inferior vena cava filter (IVCF) related complications, retrieval is recommended whenever possible. Nevertheless, IVCF retrieval rates remain lower than expected, likely due to insufficient follow-up after placement. We evaluated the value of a structured program designed to follow patients by the interventional radiology team up to 5 months after IVCF placement. We prospectively enrolled 366 consecutive patients (mean age 64 ± 17 years; 201 men and 165 women) who benefited from IVCF between March 2015 and February 2020. The program consisted of advising the patient and clinicians to consider IVCF retrieval as soon as possible (standard workflow) and systematically planning an additional follow-up visit at 5-month. Clinical and technical eligibility, as well as technical success for retrieval (TSR) were evaluated. At 5-months, 38 (10.4%) patients were lost to follow-up, and 47 (12.8%) had died. Among survivors, the overall retrieval rate was 58%. The retrieval rates were 83% and 97% for the clinically eligible and technically eligible patients for retrieval, respectively. The 5-month visit enabled 89 additional retrievals (47.8%) compared to the standard workflow. No significant difference was seen in TSR before and after 5 months (p = 0.95). Improved patient tracking with a dedicated IVCF program results in an effective process to identify suitable patients for retrieval and drastically improves retrieval rates in eligible patients. Involving interventionalists in the process improved IVCF patient management.