Radiomics is a new emerging field that includes extraction of metrics and quantification of so-called radiomic features from medical images. The growing importance of radiomics applied to oncology in improving diagnosis, cancer staging and grading, and improved personalized treatment, has been well established; yet, this new analysis technique has still few applications in cardiovascular imaging. Several studies have shown promising results describing how radiomics principles could improve the diagnostic accuracy of coronary computed tomography angiography (CCTA) and magnetic resonance imaging (MRI) in diagnosis, risk stratification, and follow-up of patients with coronary heart disease (CAD), ischemic heart disease (IHD), hypertrophic cardiomyopathy (HCM), hypertensive heart disease (HHD), and many other cardiovascular diseases. Such quantitative approach could be useful to overcome the main limitations of CCTA and MRI in the evaluation of cardiovascular diseases, such as readers’ subjectiveness and lack of repeatability. Moreover, this new discipline could potentially overcome some technical problems, namely the need of contrast administration or invasive examinations. Despite such advantages, radiomics is still not applied in clinical routine, due to lack of standardized parameters acquisition, inconsistent radiomic methods, lack of external validation, and different knowledge and experience among the readers. The purpose of this manuscript is to provide a recent update on the status of radiomics clinical applications in cardiovascular imaging.The online version contains supplementary material available at 10.1007/s11547-023-01658-x. Over the past two decades, the utility of coronary computed tomography angiography (CCTA) and cardiac magnetic resonance (CMR) as diagnostic technique for improving patient care in cardiovascular medicine has been well established , 2. In cCurrently, advances in the field of cardiac imaging have made possible to define and validate cardiac damage markers, such as late gadolinium or iodine enhancement. However, the reader’s experience plays a determinant role in visual evaluation of CMR and CCTA, and therefore the ability to discern subtle differences or to define new imaging markers. New image analysis techniques, based on quantitative evaluation of data extracted from radiological images, can improve diagnostic and prognostic accuracy, decreasing readers’ visual biases and subjectivity .In the new era of “personalized therapy” and “precision medicine,” many emerging translational fields of research, such as genomics, proteomics, or metabolomics, raised up to support evidence-based clinical decision making , 7. In aRadiomics is a promising field of medical research consisting in the extraction and analysis of quantitative metrics from medical images, resulting in a conversion of images into mineable data , 9. The The aim of this review is to provide an overview of radiomic technical principles and to describe the clinical applications in cardiovascular imaging and the prominent results focusing on CCTA and CMR.Radiomics workflow is divided into subsequent and interdependent steps: images acquisition, raw data reconstruction, images pre-processing, image segmentation, features extraction, features selection, model construction, and validation Fig.  12].Fig.Fig12].FImaging acquisition is probably the most challenging step, since the lack of standardized acquisition protocols affects analysis reproducibility. Therefore, the implementation of standardized data acquisition protocols and data analysis techniques is of paramount importance to provide a solid framework for radiomic analysis , 14.Image segmentation is the second step of radiomic workflow, in which one or more ROIs are drawn to encompass the anatomical region to be analyzed . DiffereIn field of radiomic workflow automation, the application of artificial intelligence is proving to have a potential role in overcoming some limitations, such as the long lead times and the slow process of manual analysis , 17.shape features, describing the shape and geometry of the ROIs : first-order, second-order, and higher-order statistics [Once images have been segmented, quantitative parameters are extracted from previously outlined ROIs; the obtained features are divided into atistics .First-order statistics are also referred to as intensity-based and assess voxel distributions without considering their spatial relationship, including kurtosis, skewness, first-order entropy, mean of all pixel, mean of positive pixels, and standard deviation .Second-order statistics takes into account spatial relationships among adjacent voxels , includiOnce features are extracted, the next steps of the radiomic workflow consist of designing statistical models in which the most performing image features are combined with clinical data to provide a tool capable of supporting clinical decision making. Several statistical methods are usually applied to build such models, such linear regression, logistic regression, Cox proportional hazards regression, least absolute shrinkage, and selection operator (LASSO) . Once raTexture-based metrics have been widely used in oncology to improve diagnostic accuracy, staging, grading, and response to treatment. Nevertheless, such novel technique has been scarcely applied in cardiac imaging, probably due to the complexity of cardiovascular imaging techniques .This new discipline has the potential to increase our medical knowledge and ensure greater efficacy of clinical decisions.CCTA should be considered in selected patients, stratified by clinical likelihood, as the first-line diagnostic test in chronic coronary syndromes, as well as other noninvasive techniques and invasive coronary angiography (ICA), according to ESC 2019 guidelines , due to Radiomics applied to CCTA consists in building big-data repositories in which each lesion is characterized by hundreds of different parameters impossible to detect by mere visual assessment, thereby allowing the improvement of post-processing analysis and interpretation of radiological images . The aimPlaque rupture with subsequent intraluminal thrombosis is the most common cause of acute myocardial infarction (AMI) . TherefoFour distinct plaque characteristics derived from CCTA have been linked to major adverse cardiovascular events: positive remodeling, low attenuation, spotty calcification, and napkin-ring sign (NRS) , 22. TheIn a retrospective analysis, Kolossváry et al.  revealedThese results demonstrated that radiomics analysis on coronary plaques is possible and effective, reaching greater diagnostic performance than conventional evaluation. Results also revealed that voxels’ spatial distribution parameters  had a better predictive value as imaging markers of NRS detection than first-order statistics.CCTA data should be integrated with data from other imaging modalities in order to fully understand the evolution of CAD and to prevent an adverse event. Invasive coronary angiography is characterized by great spatial resolution and detailed vessel lumen depiction, yet fails to visualize the vessel wall . In cont18-PET) identifies inflammation and microcalcifications in coronary atherosclerotic plaques, widely accepted hallmarks of plaque metabolic activity and vulnerability [On the other side, combined PET/CT merge anatomical and functional information , 28: in rability , 29; theSince each modality has one or more limitations and cannot assess the entire spectrum of plaque characteristics, a noninvasive imaging modality able to identify alternative biomarkers of plaque vulnerability would be highly desirable; again, radiomics may be a suitable and effective candidate.18-PET, IVUS, and OCT, aiming at assessing the reliability of CCTA radiomics features in finding new markers of plaque vulnerability. Among the extracted radiomic features, fractal box counting dimension of high attenuation voxels showed good diagnostic accuracy to identify IVUS-attenuated plaques (AUC 0.72) and excellent diagnostic accuracy to identify OCT thin-cap fibroatheromas. (AUC 0.80). Fractal dimensions are related to the spatial complexity of structures: it quantifies the changes of object details in relation to variations of scale [fractal box counting dimensions of high attenuation voxels. On the other hand, stable plaques have scattered higher attenuation voxels, resulting in higher values of that radiomic feature.A recent investigation  analyzedof scale . High-ri18-PET positive plaques, characterized by inflammation and microcalcifications. The surface of high attenuation voxels parameter reached the best diagnostic accuracy (AUC 0.87) in identifying plaques with increased radionuclide uptake.Although CCTA is an anatomical and non-functional imaging modality, this research also showed that radiomics can efficiently identify NaFIn conclusion, these results suggested that radiomics principles applied to CCTA may represent a noninvasive alternative to IVUS, OCT, and PET/CT in identifying with good-to-excellent diagnostic accuracy markers of plaque vulnerability, outperforming conventional CT metrics.Advanced atherosclerotic lesions, like early and late fibroatheromas and thin-cap fibroatheromas, are frequently associated with a higher risk of myocardial infarction ;Moreoverplaque attenuation pattern scheme, a model able to differentiate atherosclerotic coronary plaques into homogeneous, heterogeneous, and with NRS. Both classification systems are visual and have several limitations, above all the lack of reproducibility, even among experienced readers [histogram-based method, was developed aiming at overcoming visual analysis limitations, demonstrating that density values < 60 HU accurately identify lipid-core plaques [Conventional CCTA classification, which divides plaques in calcified, partially calcified, and noncalcified, is outperformed by the  readers . A quant plaques ; however plaques .least angles regression model provided the best discriminatory power. Thus, radiomics-based ML technique improves the diagnostic power of CCTA and outperforms expert conventional visual assessment and histogram-based methods in the identification of advanced atherosclerotic lesions.A recent investigation  demonstrThe pericoronary adipose tissue (PCAT) influences the local vascular biology through a bidirectional interaction with the coronary wall: adipocytes release the adipokines, active molecules that exert paracrine effects on the vascular wall, contributing to the local inflammation reaction, which plays a key role in atherogenesis. Therefore, quantitative and qualitative evaluations of PCAT are currently considered critical in classifying individual cardiometabolic risk profile .Radiomics could be a useful tool for an objective analysis of PCAT inflammation-induced permanent changes and, indirectly, of chronic coronary inflammation. In this regard, in a recent study of Oikonomou et al.  three diwavelet-transformed mean attenuation, a first-order feature, was the best performing metric for PCAT inflammation detection, as confirmed by the relative expression of TNFA. On the other side, higher-order statistics features showed better performances in detection of adipose tissue fibrosis and vascularity than the simple mean attenuation, as confirmed by the relative expression of COL1A1  and the endothelial marker CD31 .The first part of the study evaluated the correlation between fat biology and radiomic features, including 167 patients who underwent CCTA and PCAT sampling during cardiac surgery. Results revealed that In part two, a pool of 5487 CCTA included in the CRISP-CT  & SCOTHEFinally, the third part of the study included 88 patients (44 patients with AMI and 44 stable CAD controls) and assessed the ability of FRP in detection of unstable coronary plaques and changes over 6 months post-AMI. Results showed that the FRP was significantly higher in AMI group, without any modification changes over 6 months post-AMI, suggesting one more time its potential role in detection of irreversible changes, such as vascular alterations and fibrosis of adipose tissue. In contrast, the perivascular FAI (Fat Attenuation Index) measured around the right coronary artery (RCA) changed dynamically at 6 months, demonstrating that it could be considered a specific biomarker of the overall reversible and dynamic changes due to coronary inflammation.Surprising results of TA applied to PCAT were also reached by Lin et al.  that comAdditionally, very recent studies highlighted the properties of radiomics-based model in combination with other already known parameters (such as FAI) or in combination with different tool of imaging analysis  in predicting hemodynamically significant coronary stenosis: results showed that combining PCAT radiomics model with FAI or CT-FFR offers higher diagnostic performances in the identification of coronary stenosis , 44.In conclusion, radiomics FRP significantly improves individual cardiac risk profile identification beyond the current state-of-the-art and discriminates patients with AMI from those with stable disease; moreover, it could be a useful diagnostic tool in detection of PCAT persistent structural changes, providing additional risk stratification in both primary and secondary prevention.CMR is the optimal imaging modality for noninvasively analyzing myocardial structure and function, playing an essential role in clinical practice: structural or functional heart and vascular abnormalities, alterations of blood hemodynamics and valvular diseases, perfusion, and coronary microvascular changes can be assessed with this imaging modality. Moreover, CMR offers a unique capability in myocardial tissue characterization among alternative imaging modalities .However, sometimes qualitative descriptors and basic geometric quantifiers, based on intrinsic features of different tissues, are often not sufficient to detect the gap between disease and healthy tissue or to distinguish between morphologically similar but clinically different diseases . ConsequCardiomyopathies are a group of primary pathologies due to a congenital or acquired structural alteration of the myocardium. According to the specific defect, four main forms are distinguished, hypertrophic cardiomyopathy (HCM), DCM, restrictive cardiomyopathy, and arrhythmogenic right ventricular dysplasia (ARVD) .HCM is the most common form, with an incidence of 1/500, is characterized by generalized cardiac hypertrophy, mostly of the septum, myofibrillar disarray, and it is often cause of SCD (sudden cardiac death) in young people. HCM patients usually show typical ventricular alterations distinguishing it from other forms of cardiac hypertrophy, such as hypertensive heart disease: asymmetric distribution of hypertrophy, the presence of systolic anterior motion, and patchy LGE. However, sometimes, especially in the diffuse variants or in early stage of HCM, differentiating these two forms of ventricular myocardial hypertrophy may not be easy , 52.With regard to HCM, several studies underline how radiomics represents a promising radiological tool, providing a helpful support in differential diagnosis with healthy patients and hypertensive heart disease .GLevNonU (gray-level non-uniformity), WavEnLL (energy of wavelet coefficients in low-frequency sub-band), Fraction, and Sum Average. In particular, GLevNonU with a cutoff value of 46 achieved the highest diagnostic performance, with 94% sensitivity and 90% specificity. GLevNonU showed also significant differences between LGE—patients and healthy controls, with 100% sensitivity and 90% specificity. Thus, TA on T1-weighted non-contrast images could allow the detection of myocardial tissue alterations in the HCM setting with excellent accuracy, providing new parameters for a non-contrast assessment of myocardial structure alterations, as an alternative to LGE [Among these, Baeßler et al.  investige to LGE , 57, 58.Another useful CMR imaging tool is the parametric mapping, such as T1, T2, T2*, and ECV mapping. It provides a quantitative evaluation of heart relaxation times, high spatial information, and good detection of diffuse disease, usually hidden on conventional CMR .Differential diagnosis between hypertensive heart disease (HHD) and HCM is not always straightforward, since both diseases are characterized by left ventricular wall thickening and diffuse fibrosis, mostly localized in the septum in HCM patients; mean native-T1 values are conventionally considered as a surrogate for fibrosis. Radiomics analysis of native T1 images has been proven effective in discriminating between HHD and HCM patients, providing incremental value over global native T1 mapping through the detection of fibrosis spatial distribution differences .Despite therapeutic advances, myocardial infarction still has an acute phase mortality about 10%, with a subsequent mortality rate of 10% in the first year of follow-up .A peculiar characteristic of myocardial infarction is the segmental alteration of ventricular mechanics: the replacement of muscle tissue with non-contractile fibrous tissue is a chronic reparative response to acute myocardial infarction, involved in the process of cardiac remodeling after an acute event, and it determines regional changes in the ventricular function ; the sizCMR is the reference standard technique for evaluating myocardial structural changes in IHD; the two most common sequences include cine-MRI sequences, essential for the evaluation of contractile function, ventricular volumes and ejection fraction, and LGE sequences, for quantifying any residual scar area , 64.The quantification of the infarct area with the LGE technique is mandatory to establish the myocardial viability that represents the possibility of myocardial function restore after revascularization , 66; in Patients with CAD have a high prevalence of coexisting chronic kidney disease , requiriIn that scenario, Larroza et al.  aimed atBest results were obtained with 2D + t LBP  features, which showed high diagnostic performance in identification of non-viable, viable, and remote segments compared with LGE evaluation.These results confirmed what had already emerged in the study by Shriki et al. : chemicaLarroza et al.  proposedThe idea behind the study was that the presence of edema and fibrosis characterized the most important corresponding pathological changes of heart structure in AMI and CMI, respectively; these alterations affect the gray levels of myocardium but are imperceptible to visual evaluation.This retrospective study was performed on 44 patients, equally distributed between AMI and CMI, demonstrating that the radiomics model, based on a subset of specific features , had the potential to differentiate AMI from CMI in both LGE and cine-MRI analysis, revealing that TA can be useful also on standard cine sequences, in which the infarction detection is challenging in most cases.Perc.01, Variance), 1 from second-order (SSumEntrp), and 2 higher-order features . The most effective statistical model was obtained combining Perc.01 and Teta1 features: in both large scar and small scar subgroups, it was able to differentiate between normal myocardium and fibrosis with excellent accuracy, demonstrating that TA could be a valid alternative to LGE imaging.Baessler et al.  further Cardiac arrhythmia designates a group of conditions from para-physiological state to pathological one, all characterized by irregular beats. These disorders can be clinically asymptomatic or, sometimes, may be manifested as palpitations or can cause shortness of breath and syncope .Arrhythmias are particularly common in the acute phase of IMA (70–90% of cases), so much that are widely considered part of the natural history of the IHD . PathophPatients with previous AMI can be divided into high-arrhythmic risk and low-arrhythmic risk; the differential diagnosis between them is crucial for an optimal prevention of SCD since only the high-risk group has been shown to benefit from the insertion of the implantable cardioverter defibrillator (ICD) . CurrentKotu et al.  tested rResults showed that the radiomic-based model was comparable to the conventional model, suggesting that it could be included in future arrhythmic risk stratification or management.Arrhythmias can be also the consequence of structural pathologies of the myocardium altering the normal cardiac electrical conduction, as frequently occur in HCM patients.entropy LL) reached promising results in distinguishing patients with and without a history of ventricular tachyarrhythmias (AUC 0.72), even if its performances were still lower than the visual evaluation of LGE extension (AUC 0.96).Amano et al.  studied Finally, arrhythmia can also be the result of myocarditis, an inflammatory process affecting the myocardium typically associated with viral or bacterial infections; although in some cases it is possible a complete recovery from the disease, permanent structural alterations can follow the phase of maximum acuity and compromised cardiac function and electrical conduction.An early diagnosis remains a crucial point for an optimal therapeutic management, and CMR has now taken the place of biopsy, thanks to its less invasiveness .A recent analysis by Baessler et al.  conducteRadiomics is an innovative quantitative image analysis technique with the potential to improve diagnostic and predictive capabilities of both CT and MRI examinations, providing unique information about tissue pathophysiology and with the potential to overcome the limit of subjective imaging evaluation.Although there are still many unresolved technical challenges, the current combination of big-data availability and high computational power allows us to consider these challenges as achievable.On the other side, radiomics tools and applications are fields in constant development, not free from critical issues that should be faced and resolved. To date, the major limitation of existing studies is the absence of external validation and lack of standardization of the proposed models. Therefore, it is necessary to build models on large, high-quality training datasets, and further multicentric studies are necessary to keep testing and validating the radiomics approach, in order to implement radiomics in clinical use . In thisIn the specific field of cardiovascular imaging, radiomics could be considered as a complementary tool for the clinicians, allowing them to combine clinical and quantitative data and to take another step forward toward personalized medicine.Video 1S: Myocardial Volumetric Segmentation. Example of volumetric segmentation of left ventricle using a free open-source software (3D Slicer), applied to a 3D whole heart sequence: left ventricle wall was semi-automatically selected through threshold modulation of voxel intensity values and the 3D-volume was extracted, after automatic exclusion of extra-cardiac tissues (MP4 5982 KB)Below is the link to the electronic supplementary material.


Cardiac infiltration is the major predictor of poor prognosis in patients with systemic amyloidosis, thus it becomes of great importance to evaluate cardiac involvement.We aimed to evaluate left ventricular myocardial deformation alteration in patients with cardiac amyloidosis (CA) using layer-specific tissue tracking MR.Thirty-nine patients with CA were enrolled. Thirty-nine normal controls were also recruited. Layer-specific tissue tracking analysis was done based on cine MR images.P < 0.01). In addition, GRS and GLS, as well as subendocardial and subepicardial GLS, GRS, and GCS, were all diminished in patients with CA and reduced LVEF, when compared to those with preserved or mid-range LVEF . GCS showed the largest AUC  with a sensitivity of 93.1% and specificity of 90% to predict reduced LVEF (<40%). Moreover, GCS was the only independent predictor of LV systolic dysfunction .Compared with the control group, a significant reduction in LV whole layer strain values  and layer-specific strain values was found in patients with CA (all Layer-specific tissue tracking MR could be a useful method to assess left ventricular myocardial deformation in patients with CA. Systemic amyloidosis can lead to a progressive accumulation of insoluble amyloid protein fibrils in multiple organs, thus destroying the normal tissue structure and function –5. CardiWith the unique capacity for non-invasive analysis of the heart structure and function, echocardiography has become the conventional diagnostic method for many kinds of cardiovascular diseases, including CA. Speckle-tracking echocardiography-derived global longitudinal strain (GLS) provides an alternate measure for systolic dysfunction, which has been shown to be superior to left ventricular (LV) ejection fraction (EF) in identifying various cardiovascular diseases .Recently, tissue tracking MR based on cine-imaging was introduced as a novel method to evaluate LV deformation by tracking the whole myocardial tissue voxel motion on routine MR cine images , 10, 11.Accordingly, the purposes of our study were to: (1) assess the alteration of global strains at different layers of LV myocardia in patients with CA using layer-specific tissue tracking MR; (2) evaluate the power of different layer-specific LV deformation parameters to discriminate CA patients with reduced systolic function (LVEF < 40%) from those with mid-range (40% ≤ LVEF < 50%) or preserved LVEF (≥50%).2)  , diseaseCMR imaging was performed on a clinical 1.5 Tesla whole-body scanner , using a spine and 18-channel body phased-array coil during a breath hold. End expiratory cine images were obtained including consecutive short-axis covering the whole LV and standard long axis  , 15. ParCardiovascular MR image analysis was performed by two experienced cardiovascular radiologists who were independent and blind to the subjects’ clinical information.Body surface area indexed LV end-diastolic volume (LVEDVi), LV end-systolic volume (LVESVi), LV stroke volume (SVi), and LVEF were assessed using dedicated software . Papillary muscles and trabeculations were excluded from myocardial mass and recorded in LV volume.Endo- and epicardial borders of LV were manually drawn at the end-diastolic phase of all cine images using off-line commercial software  by the two cardiovascular radiologists mentioned above. The insertion of the right ventricle and LV at the end-diastolic phase of short-axis images were defined as short-axis reference points, and the rest phases of all cine images were automatically traced and segmented according to the 16 American Heart Association segmentation of a bull’s-eye plot. LV global whole layer strain values were automatically calculated in longitudinal, circumferential, and radial directions, then expressed as GLS, global circumferential strain (GCS), and global radial strain (GRS), as described in the study of Xu et al. . MoreoveA sub-analysis was performed to evaluate the differences in layer-specific parameters stratified by LVEF. Patients with CA who had preserved LVEF (≥50%) or mid-range LVEF (40% ≤ LVEF < 50%) were compared to those with a reduced LVEF (<40%).Intra- and interobserver variabilities for strain values were assessed in 15 randomly selected patients with CA using intraclass correlation coefficient (ICC) analysis. Interobserver variability was assessed on the same image set analyzed by two independent cardiovascular radiologists. Intraobserver variability was assessed on the same image set by one cardiovascular radiologist 2 weeks later.2 or Fisher exact tests (two-sided). Correlations were assessed by Pearson correlation or Spearman coefficient. The diagnostic accuracy of LV deformation parameters was evaluated by means of area under the curve (AUC) of receiver operating characteristic (ROC) and logistic regression; odds ratio(OR) and 95% CIs were calculated. A two-sided P-value < 0.05 was considered statistically significant.All statistical analyses were performed using IBM SPSS Statistics version 25  and GraphPad Prism version 8.0.0 . Continuous variables were tested for normality using the Kolmogorov-Smirnov normality test and were presented as mean ± standard deviation, while categorical variables were expressed as counts with percentages. Differences in continuous data were compared using a student t-test. Categorical variables were compared by χP > 0.05, Baseline clinical, echocardiography, and CMR characteristics are summarized in P < 0.05, Of all patients with CA, 10  had preserved (≥50%) or mid-range LVEF (40% ≤ LVEF < 50%) and 29  did not (LVEF < 40%). There were significantly increased LVESVi and decreased SVi in patients in the CArEF group . ROC curve analyses demonstrated that GCS showed the largest AUC  with a sensitivity of 93.1% and specificity of 90% to predict reduced LVEF (When reduced LVEF (<40%) was used as a dependent variable, the logistic regression model showed that GCS was an independent predictor of LVEF reduction .As shown in In this study, we assessed LV deformation in patients with CA using layer-specific tissue tracking MR, and our study yielded several important findings. First, whole-layer and layer-specific LV deformation parameters, including GLS, GCS, and GRS, were all significantly diminished in patients with CA. Second, when compared to the patients with preserved or mid-range LVEF, GRS and GLS, as well as subendocardial and subepicardial GLS, GRS, and GCS, were all incrementally diminished in patients with reduced LVEF. Last, GCS was the only independent predictor of LVEF reduction to below 40% in patients with CA.Heart involvement is a strong predictor of poor outcomes in patients with systemic amyloidosis , 17; theCardiovascular MR has emerged as an effective non-invasive diagnostic technique for patients with CA. The characteristic manifestation and extent of left ventricular (LV) diffused transmural late gadolinium enhancement (LGE) is associated with the burden of interstitial fibril protein infiltration and myocardial viability –31. But Layer-specific strain measurements provide better detection of subtle myocardial pathology . To the Myocardial infiltration of amyloid fibrils typically leads to restrictive cardiomyopathy, then progressive congestive heart failure (HF), and even sudden death. Survival time after diagnosis could shorten significantly if patients presented with congestive HF . ConsequInterestingly, the discrepancy of GLS, GCS, and GRS and their layer-specific components to predict LV systolic dysfunction was found in the present study. In the study of Xu et al. , they foThere were several limitations that warrant comments on our study. First, we used combined criteria with LV wall thickness, LV transmural enhancement on LGE, and biopsy from any site as the definition of cardiac amyloidosis, thus myocardial biopsy was not done in the present study. However, this combined criterion was reported to be sensitive and specific for CA. Second, the sample size was relatively small; we recruited CA patients with transmural LGE, but the LGE pattern may be atypical and patchy in patients with CA. Thus, some patients may be excluded from our study. Third, other diseases causing LV wall thickening were not included in this research, such as hypertensive heart disease and hypertrophic cardiomyopathy. Finally, this was a cross-sectional study without long-term follow-up. For these reasons, further investigations with larger numbers of different types of patients are required to confirm the predictive value of LV deformation parameters.In conclusion, layer-specific tissue tracking MR imaging provides a useful non-invasive method to evaluate LV systolic dysfunction in patients with CA.


In species with vocal learning, acquiring species-typical vocalizations relies on early social orienting. In songbirds, for example, learning song requires dynamic social interactions with a “tutor” during an early sensitive period. Here, we hypothesized that the attentional and motivational processes that support song learning recruit the oxytocin system, which is well-understood to play a role in social orienting in other species. Juvenile male zebra finches naïve to song were each tutored by two unfamiliar adult males. Before exposure to one tutor, juveniles were injected subcutaneously with oxytocin receptor antagonist  and before exposure to the other, saline (control). Treatment with OTA reduced behaviors associated with approach and attention during tutoring sessions. Using a novel operant paradigm to measure preference while balancing exposure to the two tutor songs, we showed that the juveniles preferred to hear the song of the control tutor. Their adult songs more closely resembled the control tutor’s song, and the magnitude of this difference was predicted by early preference for control over OTA song. Overall, oxytocin antagonism during exposure to a tutor seemed to bias juveniles against that tutor and his song. Our results suggest that oxytocin receptors are important for socially-guided vocal learning. Social stimuli are particularly relevant for many species and preferential orienting to social cues is critical for early social development. In human infants, for example, a “socio-motivational gate” is thought to bias attention towards a targeted social cue, such as a caregiver’s face or voice, resulting in the experience of social reward and facilitated development of socially acquired skills3. The acquisition of language in particular is thought to rely on social gating of incoming sensory information during dynamic, rewarding social interactions5.Young animals face a multitude of choices about which objects in the world to pay attention to. During development, some stimuli acquire more relevance than others; these stimuli become “special”, or even rewarding6. In zebra finches (Taeniopygia guttata), a species in which only the males sing, juveniles are most likely to choose their father as their tutor. This choice is clearly based on social, not genetic, factors, as young birds will copy the song of a foster father even when the biological father can be heard in the same room7. Even before they begin to sing, juvenile male zebra finches form strong preferences for the song of their adult male caregiver9. Young birds will key-press more to hear the song of their chosen tutor than to hear another familiar male, and the magnitude of this preference predicts how well that song will be learned8. These studies represent only a small fraction of a large literature suggesting that the mechanisms underlying song learning may share features in common with human vocal development—namely that song learning relies on social reward.In songbirds, learning to sing is similarly social. Decades of experimental work demonstrate that juveniles learn song most effectively from an adult “tutor” with which they can interact11. Although oxytocin is sometimes assumed to facilitate early orienting to a caregiver, including in humans, few studies have actually tested this hypothesis14. In juvenile mice, administration of an oxytocin antagonist early in life reduced motivation to return to caregivers after a period of separation15. Similarly, in newborn lambs, oxytocin antagonist reduced exploration of the mother’s body and impaired preference for her16. In visually naïve chicks , administration of the avian homolog of oxytocin increased motivation to approach hens17. Thus, the role of oxytocin in attraction to social cues may be conserved across species and developmental stages. Accordingly, we and others have hypothesized that, in songbirds, oxytocin receptors (OTR) mediate attraction and attention to tutor song, thus facilitating song learning21.One of the key neuromodulators underlying social reward is oxytocin23; we will use the name “OTR” for that homolog, in keeping with established nomenclature25. OTR is expressed in many of the same brain regions in mammals and birds, including the lateral septum and auditory cortex26; in songbirds it is also expressed in song control areas26. We recently mapped OTR binding and mRNA in zebra finches throughout development, from day post-hatch (dph) five to adulthood, and found that this receptor is expressed in all of these regions throughout the entire period of song learning18. In the song system, OTR is expressed at higher levels in males than in females18. Together, these findings have formed the basis for our hypothesis that OTR facilitates song learning by directing attention to tutors, promoting the formation of social preferences, and guiding the development of selective encoding of tutor song19.Birds have a clear homolog of OTR26. Because zebra finches can produce accurate copies of songs after hearing as little as a few seconds per day over a few days28, we were able to limit exposure to live tutors precisely to times of pharmacological manipulation of OTR29. We hypothesized that pairing a tutor with OTR antagonism would reduce the reward value of that tutor’s song as well as the extent to which that song was ultimately learned. Thus, we predicted that the juveniles would prefer to hear the song of the control tutor (no OTR antagonism) over that of the tutor paired with OTR antagonism, and that they would more accurately copy the song of the control tutor.To show that OTR activity contributes to the formation of song preferences during development, and ultimately to song learning, we blocked OTR in juvenile, song-naive male zebra finches during early exposure to one of two song tutors. We used an antagonist known to effectively displace the endogenous ligands in brain tissue in this species30 and inhibit pair bond formation31 in adult zebra finches. In our study, each treatment was always associated with the same tutor; in other words each juvenile was exposed to an “OTA tutor” and a “control tutor”  in a within-subjects design. Here, we will use the terms “control song” and “OTA song” to refer to the songs heard by the pupil during each treatment condition.Song-naïve juvenile male “pupils” (n = 9) were tutored just prior to nutritional independence (27–33 dph) by two unfamiliar adult males in separate sessions Fig. . Each pu37. First, we scored approach to the tutor, operationalized in two ways. Time spent close to the tutor was defined as being within 12 cm of the wall facing the tutor’s cage . Pupils tended to remain in the tutor zone regardless of treatment . We also detected no relationships between the number of live songs heard by pupils and subsequent learning of that song (see below).OTA treatment of the pupils affected song rate in the tutors; the number of songs sung per minute by tutors when presented with OTA-treated pupils was significantly lower than when presented with control-treated pupils. We detected this effect two ways, first within-pupil  and were also excluded, leaving six pupils that completed the key-pressing portion of the study.After two tutoring sessions with each of the two tutors , pupils were moved (at 35 dph) into a cage containing two keys that triggered playbacks of tutor song when pressed Fig. ”, exposup < 0.001; Fig. 8. In the present study, many individual trajectories reflected this pattern in that they showed a peak in the proportion of presses for control song before 51 dph, followed by an increase in the proportion for OTA song after 51 dph , the effect size was large dav = 1.15; Fig. For the six pupils that completed the key-pressing assay, we recorded their crystallized songs at 101–103 dph for comparison with both the control song and OTA song. Similarity scores, calculated with Sound Analysis Pro 2011preferred control song over OTA song predicted the degree to which they learned control song more effectively than OTA song. We found a highly significant, positive correlation between the average preference for control song and the learning difference score, calculated as the similarity . The correlation persisted also within condition; preference for control song was positively correlated with similarity between the pupil’s song and control song .We next tested whether the degree to which the pupils ity see “” of the X2 = 1.189; p = 0.276). Further, there was a negative relationship between the proportion of live songs heard from the control tutor (vs. the OTA tutor) and the preference for control song, both over the entire trajectory of vocal development  and early during development . Thus, we have no evidence that the preference for control song was caused by hearing more live song from control tutors. Similarly, the number of live songs heard from the control tutor did not significantly predict learning of control song . Notably, the song of the pupil that heard only recorded songs from its OTA tutor was more similar to the OTA song than to the control song, and vice versa for the pupil that heard only recorded songs from its saline tutor. Thus, we have no evidence that the number of live songs heard, vs. recorded ones, affected learning in this study. We emphasize that all pupils self-tutored with 30 recordings of each song per day from 35dph to adulthood, so they were all exposed to the same number of recordings after live tutoring ended.Because the pupils’ OTA status affected the song rate of the tutor, requiring more recorded songs to be played during the OTA condition on average, we next checked whether the reduced tutor song rate in the OTA condition Fig. F could e5.Here, we have shown that OTR antagonism during song tutoring both reduced preferences for that tutor’s song and inhibited learning of that song. This result is consistent with our hypothesis that OTR stimulation increases attraction to and attention to tutors during an early critical period, facilitating song learning. These findings add to a small but growing literature suggesting that OTR may promote early social bonds with caregivers, thus creating conditions conducive to enhanced social learning. Such a mechanism may be particularly relevant in the context of human language learning, which requires attention to a targeted social object such as a caregiver’s face or voice. In this way, vocal development may be ‘gated’ by dynamic social interactions37. OTR activation may promote self-grooming, particularly in social contexts. Other researchers have hypothesized that in mice, the oxytocin-dependent neural circuits that organize self-grooming are activated by social stimuli44. Preening as well as approach may be particularly relevant when these behaviors are contingent with singing by the tutor; Houx et al.37 reported that both these behaviors, when they occurred during playback of tutor song, correlated positively with the degree to which that tutor’s song was copied. Thus, although we did not analyze our current data with respect to contingencies, the timing of pupil behavior relative to tutor singing may be an important factor to consider in future studies.In addition to its effects on preferences and learning, OTA also measurably altered behavior during tutoring itself. We found that when treated with OTA, the pupils were less likely to engage in behaviors that may indicate interest in the tutor, namely approaching the tutor to peck on the cage wall. We also found that OTA-treated birds spent less time preening, a behavior done while sitting quietly. Both preening and quiet stillness were previously shown to predict the accuracy of song copying in zebra finches, perhaps because these behaviors facilitate listeningpupils affected the behavior of tutors. When presented with OTA-treated pupils, tutors sang at lower rates than when presented with control-treated pupils. This finding suggests that tutors may have been attending specifically to oxytocin-dependent behaviors of their pupils. Other researchers have hypothesized that tutor song rate may be affected by pupil behavior45; however, there is little information on what those behaviors might be. Carouso-Peck et al.46 found that song learning was enhanced when tutor song followed pupil song. In our study, the tutoring sessions took place before the pupils began to practice singing, so we could not test for relationships between the song rates of tutors and pupils. In an exhaustive study of contingent tutor-pupil behaviors, Houx & ten Cate45 found no evidence that tutor song was predicted by any overt behavior from the pupil. Because of our small sample size and because we scored only a limited number of behaviors, we could not isolate a particular behavior in pupils that predicted tutor song rate. We also do not know the extent to which changes in tutor behavior, caused either by the pupils or by hearing their own recorded song through a speaker, might have affected song preferences and learning in the pupils46. Although we can say with confidence that preference and learning were not positively affected by the song rates of the tutors, we cannot rule out the possibility that OTA treatment of the pupils affected other tutor behaviors that, in turn, influenced the pupils’ tutor choices. Such influences are not easily teased apart in behavioral studies with live dyads. Nonetheless, our data implicate oxytocin receptors in the subtle dynamics of these dyadic interactions.The importance of tutor-pupil behavioral contingencies suggests that the tutoring context is best viewed as a dynamic environment in which both tutor and pupil play active roles. One of the most interesting results of the present study was that treatment of the 8. In that study, pupils were naturally reared by their parents, both mother and father. At 35 dph, those pupils were transferred into an operant chamber with two keys, one of which was associated with the father’s song and the other with the song of a familiar “neighbor” that they had been able to hear but not see in their home cage during rearing. In that study, we found that the degree to which the pupils preferred the song they ultimately sang  strongly predicted the quality of learning of that song. That finding was replicated here; the degree to which each pupil preferred the control song over the OTA song significantly predicted the degree to which the pupil learned the control song better than the OTA song  song in juvenile male zebra finchesong Fig. C. Even t8 in several other ways, including the shape of the developmental trajectory of song preference. In that study, the pupils demonstrated a significant preference for the father’s song during the auditory phase of learning. Then, at around 55 dph, their preferences shifted to the neighbor’s song, reaching statistical significance at 57–65 dph. Then, these males showed no preference for either song until crystallization, when there was another significant peak in preference for the neighbor’s song. Thus, the trajectory of preference for the father’s song, which is what all of the pupils ultimately sang in that study, was nearly identical to the trajectory of preference for control song in the current study. In other words, the pupils in the current study appeared to treat the control song as if it were the song of their father, and the OTA song as if it were the song of a familiar male with whom they had no social bond.Our current findings recapitulate the findings of Rodriguez-Saltos et al.47. The young male finch is unlikely to memorize a new song that he hears after this time, preferring to practice the song that he has already chosen to learn32. His song rate increases dramatically34 and the syllables in his song begin to resemble those in tutor song35. At the same time, he begins social exploration, spending more time away from the parents and instead interacting with unrelated finches48. Thus, we interpret the shift in preference as a sign of social exploration, or wanting to hear songs other than the one the pupil has himself chosen to learn.The timing of the shift in preference from the father’s song to the neighbor’s song, or in this study, from the control song to the OTA song, is important. In both studies, the preference for the song that was ultimately learned crossed the 0.5 mark, in other words shifted to the other song, precisely at the time that juveniles leave the auditory learning phase and begin singing plastic song Fig. . This ti19, we quantified the expression of OTR mRNA in regions relevant to sociality or song learning, such as the lateral septum, the auditory forebrain, HVC (a song control nucleus), and the dorsal arcopallium, which makes up part of the ‘shelf’ region around another song nucleus, the robust nucleus of the arcopallium. We found that in each of these regions, OTR mRNA expression declined precipitously around 55 dph. Expression then rebounded to previous levels by 65 or 75 dph. We hypothesize that this decrease, which was widespread in the brain yet limited to a relatively short time window, plays a role in the co-occurring shift in social preferences and behaviors.Post-hatch day 55 is significant also because at this time, OTR expression in the brain undergoes interesting changes. In two previous studies26 and these receptors have been implicated in early sociality and song learning. Baran et al.50 treated zebra finch hatchlings with daily intracranial injections of Manning Compound, a V1aR antagonist, and tracked their behavior until maturity. Treated birds exhibited atypical social development and showed no clear preference to affiliate with their parents versus unfamiliar conspecifics. Treated males were also impaired in their song acquisition, and the degree of impairment was correlated with measures of atypical social behaviors1. Therefore, like OTR, V1aR may play a role in early attachment and song learning.This is not the first study to show effects of nonapeptides on song learning. In addition to OTR, songbirds also express receptors homologous to the vasopressin receptor V1aR in the brain52 and could affect heart rate and other processes related to stress53 The antagonist we used has been shown by others to affect social behavior in zebra finches when administered peripherally31; it will be necessary, however, to conduct central infusions, targeting particular receptor populations in the brain, to better understand the role of OTR in social reward during song learning. The binding pattern of the antagonist used in this study, ornithine vasotocin, resembles the distribution of OTR mRNA, not V1aR mRNA, regardless of age22. We therefore believe that if the antagonist used in this study is crossing into the brain, it is binding primarily to OTR rather than V1aR or another type of nonapeptide receptor. We cannot, however, make strong predictions about the relative displacement of the two endogenous ligands, oxytocin and vasopressin, by this antagonist. In birds, OTR is thought to serve as a common receptor for both nonapeptides52, which are predicted to bind equally well to this receptor22. Brain regions associated with sociality, such as the lateral septum and bed nucleus of the stria terminalis, contain dense immunoreactivity for both nonapeptides; regions associated with auditory processing and song learning, however, likely receive denser oxytocinergic than vasopressinergic input54.A limitation of this study was that we administered OTA subcutaneously rather than directly into the brain, so it could have acted peripherally to alter behavior and learning. In other species, including chickens, OTR can be found in a variety of peripheral tissues including the heart, vasculature, and adrenal glands55 or even stronger9. OTR expression, namely in song control regions, is higher in males than females during the auditory learning phase18, suggesting that some OTR populations could be involved in song learning specifically. Future experiments should explore the possibility that social reward, particularly attraction to song, could rely on different OTR populations in female vs. male songbirds.Another potential limitation is that, because we were primarily interested in the effects of oxytocin antagonism on tutor choice, we included only males in this study. Others have shown that females also show preferences for their father’s song in choice tests. In adulthood, these preferences are just as strong as in males56. Sex was later confirmed by plumage.All procedures involving handling and experimentation with animals were approved by the Institutional Animal Care and Use Committee at Emory University under protocol 2003144-052615N. All methods were performed in accordance with the relevant regulations and guidelines . Juveniles used in the study (n = 9), or “pupils”, were produced in our breeding colony at Emory University. All birds were housed under a 12-h-light/12-h dark cycle. Cages (36 × 36 × 40 cm) were kept individually in sound-attenuating chambers  to prevent exposure to adult songs. To discourage nestlings from developing a side bias that could affect our ability to measure song preference in the operant assay, all food cups and water bottles were matched by color and positioned symmetrically in the cage. The father of the nestlings was removed at four dph, counting from the hatch date of the oldest nestling, leaving the female to provide all parental care. After the father’s removal, therefore, hatchlings did not hear song until tutoring began . Moreover, for each pupil, the two tutors shared no more than two syllables with each other . The shared syllables were primarily stereotyped, call-like sounds that typically vary little among individuals33.We selected adult male tutors with high song rates. Song rate was determined by placing the cage of an adult male next to the cage of either an adult female or a juvenile male for one hour and noting the rate of singing. Even though each juvenile’s father was removed from the breeding cage by 4 dph, for each juvenile we nonetheless chose tutors with songs dissimilar to the father’s in order to limit potential bias towards learning one of the two tutor songs. This dissimilarity was assessed by visually inspecting spectrographs created in Audacity. We looked for shared “syllables”, or short, stereotyped sounds that are separated by a brief period of silence57. Thirty minutes before each of four tutoring trials, pupils were treated with either sterile 0.9% saline (control) or 5 µg oxytocin receptor antagonist  -Vasotocin trifluoroacetate salt (Bachem) dissolved in saline. This dose of this antagonist was previously shown to inhibit pair-bond formation31 when administered peripherally in zebra finches. The same antagonist had similar effects on social approach, also in zebra finches, whether administered peripherally at this dose or centrally30. In this study, both OTA and saline were administered via a 0.05 mL subcutaneous injection into the shoulder area between the feather tracts. Trials were separated by ~ 48 h and the treatments, OTA or saline, were alternated. The order in which the pupils received each treatment was counterbalanced, such that some pupils received OTA first and some received saline first.Tutoring sessions took place at 27, 29, 31, and 33 dph, which corresponds to a period of high receptivity to tutoring29. Immediately after receiving the subcutaneous injection of OTA or saline, a pupil was placed into a testing cage, brought into a booth, and given 30 min alone to acclimate to this environment before the tutoring session commenced. The tutor was given about 15 min to habituate in a separate, identical booth before being moved, in its cage, to the booth with the pupil. The tutor and pupil remained in separate cages during the tutoring sessions but could interact visually and vocally. We defined the start of the tutoring session as the moment when the experimenter left the room after placing the tutor in the booth with the pupil. Audio and video recordings were made of all tutoring sessions using a camera placed inside the booth.For each pupil, each of the two treatments was always associated with the same tutor; in other words, there was an “OTA tutor” and a “control tutor” for each pupil. Tutor pairs were each used for two pupils, with the identity of the control tutor and OTA tutor reversed. Pupils spent two tutoring sessions with each of their tutors, for a total of four sessions per pupil. Each tutoring session took place inside a walk-in sound-attenuating booth and lasted about an hourSelection of tutors, above), we estimated the approximate number of songs that we expected each pupil to hear during each of the four sessions. If this number was not reached by 45 min into the session, recordings of that tutor’s song were played from a speaker at 75 dB during the last ~ 15 min of the tutoring session. When the tutor sang at a rate higher than anticipated, tutoring sessions were cut a few minutes short in order to control the number of songs heard by the pupil. The average session duration was 58.6 min ± 5.0 min. Recordings were played in a large majority of sessions (86%). For ten of the sessions (six control and four OTA), the tutor did not sing and all songs heard by the pupil during that session were playbacks. As a result, there were three pupils that did not hear live songs from one of their tutors; in two cases, that tutor was the control tutor and in the other case it was the OTA tutor. After each session, the pupil and the tutor were brought back to their respective home cages.We took steps to balance the number of times each pupil heard each tutor’s song during tutoring sessions, such that each pupil heard roughly the same number of songs from each of the two tutors. On the basis of our calculations of each tutor’s singing rate, which was made when selecting the tutors . We focused on behaviors that others have previously found to be predictive of song learning, such as approaching the tutor36. To quantify flying, we scored a behavior that occurred frequently in the most active pupils: two-foot wall contacts, in other words when the pupil flew and landed with both of its feet on any side wall of the cage. Because the cages were 36 × 36 × 40 cm, there was little opportunity to remain airborne for extended periods of time or fly longer distances; thus, counting the number of times a bird alit on the sides of the cage served as a proxy for flying bouts. In addition, we counted all vocalizations made by the pupils. These vocalizations did not include song, as the tutoring sessions took place largely before juveniles begin producing even immature song7. As a final measure of attention, we also scored preening, a grooming behavior that we defined as when the pupil used its beak to clean its feathers or another part of its body. Preening during tutoring has been found to be positively associated with learning, possibly because the bird sits quietly while doing it37. In addition to the pupil’s behavior, we also scored singing by the tutor.Attention during tutoring, or more specifically, lack thereof, has been operationalized by others as the level of intense activity, such as flying, and number of vocalizations by the pupil, which both negatively predict learninglmer function in the lme4 package58. To test for a significant effect of Treatment on each variable of interest, we performed a likelihood ratio test using the anova function to compare the full model to a reduced model with Treatment removed. As a measure of effect size, we calculated Cohen’s dav as described by Cumming59 and Lakens60 which takes into account the within-subjects design.To test for effects of OTA treatment on behavior during tutoring, we used linear mixed models (LMMs) in R (v4.04). Separate tests were conducted for each measure. In all models, pupil ID was included as a random effect to accommodate the within-subjects design. For all variables, we used the following model: ~ Trial + Treatment + (1|Pupil ID), which allowed us to control for the effects of Trial (i.e. the first or second tutoring session for each treatment). To perform the LMM analyses, we used the 33. To create the playbacks used during tutoring sessions  and in the preference assay , two consecutive motifs from each tutor were isolated in audio files. Male zebra finches typically produce a series of call-like introductory notes immediately before beginning to sing; these introductory notes were excluded. The files were then cleaned using Audacity as described by Rodriguez-Saltos et al.8.A unit of song contains two consecutive repeats of a highly stereotyped motif33. The operant set-up was as described by Rodriguez-Saltos et al.8. Two keys, accessible from a perch, were placed on opposing walls. . Mgcv produced a 95% confidence interval of the trajectory by multiplying the standard error of the trajectory by two, subtracting this result from the trajectory to find the lower bound, and summing it to the trajectory for upper bound63. At any given age the pupils significantly preferred the control song over OTA song if the trajectory was above 0.5 and the confidence interval excluded that value.After the data were cleaned as described above, we calculated daily “preference scores” for each pupil as a proportion of presses on one of the keys, e.g.: Key A presses/(Key A + Key B presses). Preference scores were calculated as the preference for the key associated with the song of the control tutor . To reconstruct the average trajectory of preference for control song, we fitted a generalized additive model65. All songs used for this analysis were cleaned to remove introductory notes, etc. as described above. Song recordings were imported as .WAV files into Sound Analysis Pro 2011 (SAP). To calculate similarity between tutor and pupil songs, we segmented songs into syllables on the basis of an amplitude threshold66. Thresholds were adjusted for every song because each one was sung at a different distance from the microphone; thus, song-specific amplitude thresholds allowed for each song to be properly segmented. Similarity scores were calculated using SAP’s “Song Similarity” tool, which compares the acoustic features of one song’s syllables to another and quantifies similarity66. Similarity scores were calculated for each pupil-tutor pair using three to six song exemplars per pupil. Because we were primarily interested in the highest degree of match a pupil was capable of, rather than the pupil’s propensity to sing one song vs. the other, we used the maximum similarity score for each pupil-tutor pair in our analyses69. The average similarity scores are also presented and the results of those analyses are largely the same  using the following model: ~ Treatment + (1|Pupil ID), where Pupil ID was included as a random effect. To test for relationships among factors such as song preferences, song learning, and the number of live tutor songs heard, we used Spearman correlation tests (SPSS v.28). Power analyses were done using G*power (v. 3.9.6).To test the effect of OTA on song imitation, we compared similarity scores across condition for each exemplar using LMMs as described above (Supplementary Information 1.Supplementary Information 2.


DSP) gene, the gene coding for the desmoplakin protein, a structural protein found in the cardiac desmosome. Pathogenic variants in the DSP gene are associated with dilated and arrhythmogenic cardiomyopathy. DSP cardiomyopathies may cause recurring myocardial injury mimicking an acute coronary syndrome or myocarditis. Cardiac magnetic resonance imaging is key in its diagnosis due to its specifying imaging features. Genetic testing is essential for the evaluation and confirmation of the diagnosis.We present two female patients with recurrent episodes of myocardial injury, consisting of acute chest pain and elevated cardiac markers without coronary artery disease. Cardiovascular magnetic resonance imaging identified extensive late gadolinium enhancement suggestive of an inherited cardiomyopathy. Genetic testing showed heterozygous pathogenic variants in the desmoplakin (The online version of this article contains 2 videos. The article and the videos are online available (10.1007/s12471-022-01735-2). The videos can be found in the article back matter as “Electronic Supplementary Material”. Inherited cardiomyopathies are diagnosed more frequently due to improved screening and diagnostic methodologies. The prevalence of inherited or idiopathic dilated cardiomyopathies (DCM), based on adult screening studies, is estimated to be approximately 1 in 250  and high-sensitivity cardiac troponin T [hs-cTnT] 5510 ng/l [ULN 14]). Her medical and family history was unremarkable. The electrocardiogram (ECG) showed low-voltage QRS complexes and subtle inferolateral ST-segment elevations  with potential RNA splicing effect was found. Variants were classified according to the American College of Medical Genetics and Genomics (ACMG) guidelines [During follow-up, symptomatic polymorphic premature ventricular contractions (2% burden during Holter monitoring) were present, successfully treated with atenolol. Approximately 20 months later, patient presented again with acute chest pain and elevated cardiac markers (CK 499 U/l and hs-cTnT 1300 ng/l) after intense physical exertion. Repeated CMR showed similar results. An Patient B is a 28-year-old woman with a history of type 1 diabetes mellitus and a family history of DCM with an affected father. She presented with acute chest pain and elevated hs-cTnT (831 ng/l) with normal CK values. Her ECG at presentation showed low-voltage QRS complexes and non-specific repolarisation abnormalities, particularly of the inferolateral leads, with no acute findings  implantation. Cascade testing revealed the same DSP variant in her father known with DCM.A few days after the CMR she presented again with acute chest pain and hs-cTnT increase (585 ng/l). ECG showed no new findings. Differential diagnostic considerations were myocarditis (despite low infectious parameters), coronary artery spasm  or an inherited cardiomyopathy. Her medication was adjusted, with the cessation of all prior medication, and the initiation of a beta blocker and an angiotensin-converting enzyme inhibitor. An ILR was implanted, after which the patient was discharged. Multi-gene panel testing revealed a pathogenic variant (class 5) c.1904-2A > G in the DSP is a structural intracellular protein, which is critical in the assembly of the anchoring desmosomes within myocardial and epidermal skin cells [DSP gene are associated with ACM and DCM. The recurrent episodes of acute myocardial injury , as described in both patients, occurs in about one fifth of DSP cardiomyopathy patients and is associated with left ventricular LGE, even in patients with normal LVEF [DSP cardiomyopathy may be misdiagnosed as an infectious myocarditis, or diagnosed as a MINOCA. Non-infectious myocarditis is often described in DSP-related ACM [in cells . These dmal LVEF –6. This ated ACM , 7, 8. Sated ACM . The exaated ACM .DSP cardiomyopathy, which may present recurrently. Besides the differential diagnosis of infectious myocarditis, MINOCA may also be considered in these patients. Indeed, the SMINC‑2 trial found that CMR diagnosed a small subset of MINOCA patients with primary DCM [DSP cardiomyopathy can show left and/or right ventricular involvement, and specific attention should be paid to fibrofatty replacement; its presence makes T1 mapping challenging [DSP cardiomyopathies and can be extensive, particularly in the sub-epicardium with patterns similar as those seen in myocarditis. Ring-like patterns of LGE, as found in both patients, are often observed in ACM and are associated with an increased risk of ventricular arrhythmias [DSP cardiomyopathy.Acute myocardial injury is therefore a common presentation of patients with mary DCM . In addimary DCM . CMR shomary DCM . Cine imllenging , 14. T2 hythmias , 16. Pathythmias . HoweverDSP cardiomyopathy presents as a predominantly left ventricular cardiomyopathy, whereas right ventricular involvement was only found in 14% of patients [DSP variants, namely woolly hair and palmoplantar keratoderma. Rare examples of DSP-related syndromes are the autosomal-recessive Naxos disease with those extra-cardiac findings and right ventricular involvement; and Carvajal syndrome with the same findings and left ventricular involvement [DSP cardiomyopathy, females are overrepresented, particularly in patients presenting with acute myocardial injury. In the case series of Smith et al., 69% of 107 DSP cardiomyopathy patients were female, but disease progression and burden did not significantly differ between sexes [DSP variant presenting with a myocarditis-like phenotype [The review by Smith et al. showed that the clinical phenotype of patients . Additioolvement , 19. Thiolvement . In repoen sexes . In addihenotype . NeverthDSP cardiomyopathy and is associated with acute myocardial injury and proband status [DSP variants correlate with ventricular arrhythmias and that left ventricular dysfunction (LVEF < 55%) is strongly associated with sustained ventricular arrhythmias in DSP cardiomyopathies [DSP cardiomyopathy experience sustained ventricular arrhythmia or sudden cardiac death, even in the absence of overt left ventricular dysfunction, which makes risk stratification for primary ICD implantation challenging in these patients [In general, decreased LVEF is correlated with a higher risk for all-cause and cardiac mortality as well as heart failure-related adverse outcomes in patients , 22. Thed status . Furthern LVEF < 5% is stropathies . During patients , 24. HowWhilst diagnosis by imaging remains challenging, DSP cardiomyopathies present a suggestive LGE pattern which may aid distinguishing it from infectious myocarditis when presented in combination with acute myocardial injury. , 26. UltDSP cardiomyopathy mainly affects the left ventricle and can be associated with acute myo-inflammatory episodes of chest pain with elevated cardiac enzymes in the absence of coronary artery disease. It should be a differential diagnostic consideration in any patient with unexplained myocardial injury mimicking myocarditis, particularly in the case of recurrent episodes. CMR and genetic testing are key in its evaluation and diagnosis, which may not be missed due to the increased risk of ventricular arrhythmias.Supplementary Video S1 Video of patient A of 4‑chamber cine images.Supplementary Video S2 Video of patient B of 4‑chamber cine images.


Cardiac remodeling and dysfunction can be caused by atrial fibrillation (AF). The aim of this research is to investigate the relationship between the systemic inflammatory response index (SIRI) and left ventricular (LV) remodeling and systolic function in individuals with AF.416 patients with AF who were admitted to the Second Department of Cardiology in the East Ward of the Qingdao Municipal Hospital between January 2020 and May 2022 were included in the present retrospective research. The relationship between SIRI and various cardiac parameters was analyzed. The patients’ left atrial (LA) enlargement and left ventricular (LV) hypertrophy and systolic dysfunction were evaluated. SIRI was calculated by the formula: neutrophil × monocyte/lymphocyte.p = 0.025), LVEF (ln-transformed) (p = 0.005), and LAD (ln-transformed) (p = 0.007). In multivariate logistic regression, the highest quartile of SIRI (SIRI > 1.62) was significantly associated with LV hypertrophy (p = 0.026), impaired LV systolic function (p = 0.002), and LA enlargement (p = 0.025).SIRI significantly correlated with LV end-diastolic diameter (LVDd), LV posterior wall thickness at end-diastole (LVPWTd), interventricular septal thickness at end-diastole (IVSTd), LV mass index (LVMI), LV ejection fraction (LVEF), LA diameter (LAD), C-reactive protein (CRP), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) in patients with AF. In multivariate linear regression analyses, SIRI was discovered to be significantly related to LVMI (ln-transformed) (SIRI was significantly associated with LV remodeling and systolic function impairment in patients with AF. SIRI may serve as a reliable and convenient inflammatory biomarker for detecting impaired cardiac structure and systolic function in patients with AF. Atrial fibrillation (AF) has emerged as the predominant arrhythmia with a steady rise in incidence and prevalence over the past two decades, affecting a substantial proportion of the global population, estimated at approximately 37.5 billion as of 2017  + 0.6; BSA = 0.0061 × height (cm) + 0.0128 × weight (kg) – 0.1529; Relative wall thickness(RWT) = (2 × LVPWTd)/(LVDd)[The baseline characteristics of patients were obtained from the inpatient record system, which included gender, age, height, weight, history of type 2 diabetes mellitus, history of hypertension, history of coronary heart disease and medication history. Laboratory data was collected on the second day after admission from fasting venous blood samples, which included C-reactive protein (CRP), cardiac troponin I (cTN-I), N terminal pro B type natriuretic peptide (NT-proBNP), the contents of triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C). The echocardiographic parameters recorded were left atrial diameter (LAD), LV ejection fraction (LVEF), LV diastolic diameter (LVDd), the diastolic interventricular septal wall thickness (IVSTd), and the diastolic LV posterior wall thickness (LVPWTd). LA enlargement was defined as LAD > 40 mm. LV hypertrophy (LVH) was determined using LV mass index (LVMI) > 95 g/m² for female patients and LVMI > 115 g/m² for male patients, calculated as LVMI = left ventricular mass (LVM) / body surface area (BSA); LVM = 0.8 × 1.04 [(LVDd + LVPWTd + IVSTd)Using SPSS version 27.0, data for this investigation were analyzed. GraphPad Prism version 9.5.1 was utilized for graphic plotting. Extreme values  that exceeded three standard deviations from the mean were excluded before SIRI values were grouped. SIRI levels were categorized into quartiles, with Q1 representing the first quartile, Q2 representing the second quartile, Q3 representing the third quartile, and Q4 representing the fourth quartile. The Q-Q diagram was used to determine whether or not the continuous data distribution was normal. To compare continuous variables with normal distributions that were provided as mean ± standard deviation (SD), one-way analysis of variance was used. By showing variables that did not follow a normal distribution as median and interquartile range, the Kruskal-Wallis test was used to analyze variables. The Pearson χ2 test was used to compare the baseline characteristics of categorical variables, which were given as numbers and percentages. To examine the relationship between SIRI and LAD, LVEF, LVDd, LVPWTd, IVSTd, LVMI, RWT, CRP and NT-proBNP, Spearman’s rank correlation was used. SIRI, LAD, LVEF, LVDd, LVPWTd, IVSTd, LVMI, and RWT were found to have a nonnormal distribution and were converted to a natural log transformation (ln-transformed) to make it more close to a normal distribution, referred as ln-transformed SIRI, ln-transformed LAD, ln-transformed LVEF, ln-transformed LVDd, ln-transformed LVPWTd, ln-transformed IVSTd, ln-transformed LVMI, and ln-transformed RWT. Linear regression analysis was investigated the connection between SIRI and the following echocardiographic measurements: LAD (ln-transformed), LVEF (ln-transformed), LVDd (ln-transformed), LVPWTd (ln-transformed), IVSTd (ln-transformed), LVMI (ln-transformed), and RWT (ln-transformed). The SIRI was set as an independent variable, and echocardiographic parameters were set as dependent variables, and their correlation was analyzed using univariate linear regression (Model 1). Two multivariate linear regression models (Model 2 and Model 3) were used after adjusting for confounding factors. Age, sex, BMI, smoking history, and alcohol use were all taken into account in Model 2, while Type 2 diabetes mellitus, Hypertension, Coronary heart disease, and type of AF were all taken into account additionally in Model 3. Univariate and multivariate logistic regression analyses were used to explore the relationship between SIRI levels and the prevalence of LVH, LV impaired systolic function, and LA enlargement, using the above three models.p < 0.05, Table p < 0.05, Table The study included 416 patients, with a mean age of 68.71 ± 12.70 and a gender split of 194 males and 222 females. Figure p < 0.01; Q4 vs. Q1, adjusted p < 0.0001), IVSTd  and LVMI  increased significantly as SIRI quartiles increased, while LVEF decreased significantly . There was no significant difference in LVDd, LVPWd, and RWT between SIRI quartiles. Additionally, laboratory data revealed that CRP and NT-proBNP increased as SIRI quartile level increased  , LVEF , LVDd , LVPWTd , IVSTd , LVMI , CRP , and NT-proBNP  in patients with AF , LVPWTd (ln-transformed) (p = 0.025), LVMI (ln-transformed) (p = 0.001), LVEF (ln-transformed) (p < 0.001) and LAD (ln-transformed) (p < 0.001) (Table p = 0.017), LVEF (ln-transformed) (p = 0.001) and LAD (ln-transformed) (p < 0.001) (Table p = 0.025), LVEF (ln-transformed) (p = 0.005) and LAD (ln-transformed) (p = 0.007) (Table Univariate linear regression (Model 1) analysis performed that SIRI was positively related to IVSTd (ln-transformed) p = 0.00, LVPWTdp = 0.004) and LA enlargement  , impaired LV systolic function , p < 0.001 for Model 2; OR = 4.78, 95%CI = 1.79–12.78, p = 0.002 for Model 3) and LA enlargement  , 41 (9.9%) and 241 (57.9%). In univariate logistic regression (Model 1), the highest SIRI quartile (SIRI > 1.62) was significantly associated with impaired LV systolic function  and impaired left ventricular systolic function as well as LA enlargement. Additionally, SIRI levels displayed positive correlations with IVSTd (ln-transformed), LVPWTd (ln-transformed), LVMI (ln-transformed), LVEF (ln-transformed), and LAD (ln-transformed). In models 2 and 3, a significant association was found only between the fourth quantile SIRI (SIRI > 1.62) and LVH, impaired left ventricular systolic function, and LA enlargement when compared with the first quantile. Additionally, SIRI levels displayed positive correlations with LVMI (ln-transformed), LVEF (ln-transformed), and LAD (ln-transformed). Previously, only patients with a left ventricular ejection fraction of 40% or less were considered to have heart failure , but subsequent evidence has shown that patients with a left ventricular ejection fraction of 41 to 49%  have similar characteristics to HFrEF , 39. Amohe AF LV .This study’s limitations encompass the enrollment of patients receiving inpatient care and the single-center, small-scale design. Additionally, the employment of SIRI as a dynamic biomarker for computing leukocyte counts is susceptible to variations stemming from inaccuracies in blood sample collection, preservation, and measurement. Furthermore, this research is of a retrospective, cross-sectional case-control nature; future prospective studies are necessary to corroborate the findings.In summary, our investigation had evidenced a substantial correlation between elevated SIRI and LV remodeling and systolic functional impairment in patients with AF. The observed association primarily manifested as LVH, LV systolic dysfunction, and left atrial enlargement. Our findings suggested that SIRI may serve as a dependable and convenient inflammatory biomarker for detecting cardiac structural damage and systolic functional impairment in AF patients. The incorporation of SIRI as an inflammatory marker in the evaluation and care of individuals with AF holds promise for risk stratification, treatment decision-making, and patient surveillance. This approach enables the implementation of a more individualized management approach for patients with AF.