Currently, there is no therapy targeting septic cardiomyopathy (SC), a key contributor to organ dysfunction in sepsis. In this study, we used a machine learning (ML) pipeline to explore transcriptomic, proteomic, and metabolomic data from patients with septic shock, and prospectively collected measurements of high-sensitive cardiac troponin and echocardiography. The purposes of the study were to suggest an exploratory methodology to identify and characterise the multiOMICs profile of (i) myocardial injury in patients with septic shock, and of (ii) cardiac dysfunction in patients with myocardial injury. The study included 27 adult patients admitted for septic shock. Peripheral blood samples for OMICS analysis and measurements of high-sensitive cardiac troponin T (hscTnT) were collected at two time points during the ICU stay. A ML-based study was designed and implemented to untangle the relations among the OMICS domains and the aforesaid biomarkers. The resulting ML pipeline consisted of two main experimental phases: recursive feature selection (FS) assessing the stability of biomarkers, and classification to characterise the multiOMICS profile of the target biomarkers. The application of a ML pipeline to circulate OMICS data in patients with septic shock has the potential to predict the risk of myocardial injury and the risk of cardiac dysfunction. Sepsis is a life-threatening organ dysfunction caused by a dysregulated immune response to infection  and septAt present, there is no therapy specially targeting SC. While positive inotropes, mainly dobutamine, are used clinically to ameliorate cardiac functions and improve both cardiac output and systemic oxygen delivery, excessive β-adrenergic stimulation can be associated with harm . InitialThe characterisation of patients at the molecular level is a promising approach to identify pathophysiological mechanisms and specific targets for new therapeutic interventions in critically-ill patients with a particular condition . For exaIn this work, we used a machine learning (ML) pipeline to investigate the prospects of its application to analyse transcriptomic, proteomic and metabolomic data gathered at two time points during Intensive Care Unit (ICU) stay in patients with septic shock and prospectively collected measurements of high-sensitive cardiac troponin and echocardiography. Our primary aim was to identify and characterise the multiOMICs profile of myocardial injury in patients with septic shock. Second, we aimed at identifying if a distinct profile exists when cardiac dysfunction in patients is associated with myocardial injury.This manuscript follows the STROBE guidelines for reporting observational studies  11]..11].This study is part of the multicentre prospective observational trial “ShockOmics”  . PatientAs detailed elsewhere , we inclWe excluded patients expected to die within 24 h after ICU admission and with terminal illness; those receiving more than four units of red blood cells or >1 fresh frozen plasma transfused; with active haematological malignancy, metastatic cancer, chronic immunodepression, pre-existing end-stage renal disease requiring renal replacement therapy, recent cardiac surgery, and Child-Pugh C cirrhosis. The main reason for our exclusion criteria was to avoid confounding factors that would make it difficult to distinguish at transcriptomic, proteomic, and metabolomic levels what is due to septic shock and what to the comorbidities. We also excluded patients who did not have data in the three OMICS domains. Furthermore, we prospectively planned at least two time points in our study using expensive technologies, hence the two exclusion criteria of “terminal illness” and high risk of death within 24 h of ICU admission. Demographic and clinical characteristics in patients with and without myocardial injury (Table 1 and Table 2) were compared using Fisher’s exact test or Mann–Whitney U test, as appropriate.For transcriptomics: after adding 400 µL of 2X Denaturing solution  to an equal volume of blood, samples were stored at −20 °C until analysis;For proteomics, metabolomics and hscTnT quantification: after adding 900 µL of a protease inhibitor solution  to 6 mL of blood, 0.5 mL plasma aliquots were obtained by two-step centrifugation and stored at −80 °C until analysis.Blood samples were collected in EDTA tubes and treated as follows:All subsequent analytic steps were performed in batches.As detailed elsewhere , total RDESeq2 package (Bioconductor)  built-in® column . Eluted proteins were reduced, alkylated, and double trypsin digested. Seven TMT-10plex experiments were performed. After peptide labelling, samples were subjected to high pH fractionation with a high pH reversed phase peptide fractionation kit . A total of eight fractions from each TMT-10plex batches were analysed using an Orbitrap Fusion Lumos™ Tribrid mass spectrometer . The mass spectrometer was operated in a data-dependent acquisition (DDA) mode. MS2-MS3 analysis was conducted with a top speed approach. Thermo Proteome Discover (v.2.1) Thermo Scientific, Waltham, MA, USA) was used to search with Sequest HT search engine against the Swiss-Prot human public database. For each TMT batch, eight raw files corresponding to the eight fractions injections from the MS analyses were used to perform a single search against this database. The quantification of proteins was conducted by summing, within each TMT™ 10plex experiment, the reporter ion intensities of unique peptides. Libra channel normalization was performed for each TMT™ 10plex experiment.The proteomics analysis was performed using Tandem Mass Tag, TMT-10plex (Thermo Scientific) technique. Firstly, immunoaffinity depletion of highly abundant proteins from plasma samples was performed using IgY14 SepproWe performed a targeted quantitative approach using a combined direct flow injection and liquid chromatography (LC) tandem mass spectrometry (MS/MS) assay , as detailed elsewhere . This meMyocardial injury was defined as a circulating hscTnT level >14 ng/L, the 99th percentile upper reference limit of the assay, according to the Fourth universal definition of myocardial infarction . CardiacMetabolomics and transcriptomics data have been previously published. Proteomics data has not. No analyses regarding the phenotypes of myocardial injury and cardiac dysfunction or the construction of multiOMICS models have been previously published for this cohort.p-value and a reduced q-value [The ML pipeline presented in this paper is divided into two main experimental phases: FS and classification. The FS experiments started with the execution of tests to compare distributions where the data from our analysis pipeline has been further divided into six groups. Each group corresponds to its own dataset: transcriptomics at T1, transcriptomics at T2, proteomics at T1, proteomics at T2, metabolomics at T1, and metabolomics at T2. The FS phase started with a one-way ANOVA and a Kruskal–Wallis test to select the variables that yield a significant  q-value , as apprThe set of markers obtained in our FS phase was further analysed in an enrichment and pathway analysis. Particularly, the transcripts were analysed with Enrichr , proteinp-value obtained in the McNemar’s test, and the sensitivity and specificity are shown together with their binomial confidence interval.Since this is a knowledge discovery study, our main assumption here is that the biomarker sets yielding the best performances to assess myocardial injury and cardiac dysfunction (based on troponin and ejection fraction or an inotrope requirement) will also shed light on the pathophysiological processes involved in SC. For this reason, the set of biomarkers that result from the FS phase was used to create a set of classifiers to predict cardiac injury and dysfunction. In our case, we used logistic regression, classification trees (CART), and a support vector classifier (SVC) . All of stats, caret, randomForest, rpart, and e1071.All of the analyses were performed using R and the main packages employed were: Between October 2014 and December 2015, we screened 529 patients for the whole ShockOmics study, of which 27 patients with septic shock were included in the present analysis . Twenty p = 0.143). At 100 days after admission, there were 7 (35%) deaths in the injury group and none (0%) death in the no-injury group (p = 0.168).Five (25%) patients with myocardial injury died in the ICU, while no patients died in the no-injury group . The same analysis for proteins and cardiac injury has shown a significant gene overlap with the complement and coagulation cascades. In particular, the coagulation factor XIII A chain (P00488), the complement C8 gamma chain (P07360), and the coagulation factor XIII B chain (F13B) are significant with p-value ≪ 0.005 and q-value 0.014. An enrichment analysis with Enrichr has shown that the genes considered significant in relation to myocardial injury are related to the pathways listed in All elements considered significant in the multiscale analysis were used as input for the enrichment analysis. The enrichment analysis with impala for transcripts and myocardial injury has yielded an overlap with the inactivation of the anaphase-promoting complex (APC/C) via direct inhibition of the APC/C complex pathway . The same analysis for the significant proteins yielded a significant overlap with the clotting cascade  and fibrin clot formation  pathways. Finally, the enrichment analysis with MetaboAnalyst has found L-carnitine to be relevant in the beta oxidation of long chain fatty acids .The enrichment analysis for cardiac dysfunction in patients with myocardial injury with Impala has shown that catenin alpha 3 is related to arrhythmogenic right ventricular cardiomyopathy .The sets of biomarkers obtained in our FS phase were used to predict the risk of myocardial injury assessed with circulating levels of troponin and the risk of cardiac dysfunction in patients with myocardial injury, assessed with the ejection fraction or an inotrope requirement. LOOCV was applied on the cohort of patients selected to study. In this manner, as many models as patients for each target variable per time point were created to obtain a reliable and unbiased estimate of model performance. In our study we used a logistic regression classifier as the baseline for comparison. CART models were executed with a maximum tree depth of 5 and 10 trees were explored at a time. With respect to the SVC, C = 1. The results of this classification are reported in The present prospective clinical investigation demonstrates that the application of a ML pipeline to circulating transcriptomics, proteomics, and metabolomics data in patients with septic shock has huge potential to predict both the risk of myocardial injury (assessed with circulating levels of troponin) and the risk of cardiac dysfunction (assessed with echocardiography-derived left ventricle ejection fraction or an inotrope requirement). To our knowledge, the present results are the first data linking serial cardiac and hemodynamic measurements using a ML pipeline of OMICS data. The advantage of such a pipeline lies in the fact that it can untangle relevant relations between different markers and related them to a particular outcome  through a data-driven approach. Thus, the application of ML techniques can improve the standard methods used in the classical clinical practice to assess these relations. Even though ML approaches require large amounts of data from big cohorts of patients to draw conclusions, this paper shows that it is also possible to obtain sensible results even with scarce data.These results are of great interest as they throw light on the hypothesis that the root causes of cardiomyocyte injury and cardiac dysfunction in patients with septic shock may be approached using a ML pipeline tailored for OMICS analysis.We offer an explanation to reinforce that changes in complement and coagulation systems were associated with myocardial injury in patients with septic shock. These observations are coherent with data showing that the coagulation system and microthrombosis are the main causes of ischemic heart affection and myocardial injury . MicrothIschemia-reperfusion injury has been shown to play a role in sepsis-associated organ dysfunction . In thisChanges in the coagulation system have also been associated with myocardial dysfunctions in patients with ischemic myocardial injury. Indeed, there is now ample evidence supporting the concept of cardiac injury causing local inflammation and increased activation of pro-coagulant processes in patients with STEMI . MoreoveThe underlying cause of SC could be, also, a disorder in communication between the intracellular contractile apparatus and extracellular matrix, resulting in attenuation of the myocardial contraction. In this regard, the fact that selected biomarkers could predict myocardial injury with good accuracy may contribute to underline the main causes of this transitory contraction interruption observed for the heart during sepsis.We could also observe an association of alpha-1-antichymotrypsin and serum paraoxonase/arylesterase with myocardial injury. A recent proteomic study showed that circulating alpha-1-antichymotrypsin level was higher in patients with myocardial injury compared with stable angina or healthy controls . Alpha-1In defiance of data scarcity, this study casts light on the relation between different biomarkers that play a role in patients with septic shock and strengthens some of the hypothesis posed by other aforementioned works. This is an exploratory methodology that may be further exploited over larger cohorts to elucidate the association between OMICS data and the biomarkers of interest.In summary, this study showed that ML methods, applied to circulating OMICS data, can give an accurate estimation of myocardial injury and cardiac dysfunction in septic shock patients. This approach was also useful to investigate septic cardiomyopathy at molecular level and to identify a role of complement, coagulation, and inflammation pathways in the pathophysiology of myocardial injury. Our results, obtained in a small sized cohort of septic shock patients, show that the analysis of circulating OMICS data with a ML pipeline is a valuable tool to conduct research in critically ill patients.Circulating troponin can be influenced by age and comorbidities  besides the burden of acute illness caused by septic shock. Hence using a fixed threshold of troponin at ICU admission to classify patients as having myocardial injury may lead to an overestimation of cases. However, in the current analysis, we focused on identifying biomarkers acquired at ICU simultaneously to troponin measurements. In addition, there is no validated method to choose a different cut-off in patients with impaired renal clearance. Furthermore, elevated troponin has been shown to be associated with increased mortality independently of renal failure and elevated creatinine . It is pThe present findings indicate that the application of a ML pipeline to circulating OMICS data in patients with septic shock has the potential to predict the risk of myocardial injury (assessed with circulating levels of troponin) and the risk of cardiac dysfunction (assessed with echocardiography-derived left ventricle ejection fraction). This study is part of the multicentre prospective observational trial “ShockOmics”  .


Firefighting-related environmental and physiological factors associated with cardiovascular strain may promote arrhythmias and myocardial ischemia, which induce sudden cardiac events (SCE) in susceptible individuals. The present study evaluated electrocardiographic (ECG) changes that may reflect increased SCE risk following simulated live-firefighting.Using a repeated measures design, ECG tracings from 32 firefighters were recorded 12-hours post-firefighting in a residential structure and compared with a 12-hour control period.Ventricular Ventricular arrhythmias were present in 20%, and ST segment changes indicative of myocardial ischemia in 16%, of firefighters 12-hours post-firefighting that were not detected in the control period.Live-firefighting induces significant ECG changes that include ventricular arrhythmias and ST segment changes, which may reflect myocardial ischemia. The implications of such ECG changes explaining increased cardiovascular risk in firefighters warrants further research. These studies have provided conflicting results, with some studies reporting ECG abnormalities indicative of myocardial ischemia and others demonstrating no significant ECG changes. Moreover, conclusions from these studies have been confounded by methodological weaknesses, including absence of a non-firefighting control group, variations in ECG criteria for indicating myocardial ischemia, and reliance on computer ECG analysis. It is uncertain, as well, whether ECG changes reported during or after firefighting reflect risk for cardiac death or are instead presumably-benign manifestations of sustained heart work .12The validity of the assumption that the stresses of firefighting are responsible for provoking cardiac events would be supported by evidence that electrocardiographic (ECG) abnormalities indicative of ventricular tachyarrhythmias and myocardial ischemia are triggered during firefighting. The technical challenge of obtaining adequate ECG tracings during actual firefighting, however, has precluded substantial, direct insights. As feasible alternatives, ECG responses to a fire alarm,The purpose of this study was to examine electrocardiographic tracings of firefighters for a 12-hour period following a simulated bout of live-fire active firefighting in comparison to findings in the same individuals during a control period in which individuals engaged in daily activities but did not undertake firefighting or other strenuous activities. Specifically, the goal was to test the hypothesis that firefighting activities create increased cardiac risk via arrhythmias and myocardial ischemia as manifest by electrocardiographic changes.This study was conducted at the University of Illinois at Urbana Champaign, in collaboration with the First Responder Health and Safety Laboratory at Skidmore College, the National Institute of Occupational Safety and Health (NIOSH) and Underwriters Laboratories (UL) Firefighter Safety Research Institute (FSRI). IRB approval was acquired from the University of Illinois at Urbana-Champaign and NIOSH. Participants were recruited through a nationwide multimedia effort among individuals who teach and train at the Illinois Fire Service Institute’s (IFSI) Champaign campus. Inclusion criteria included being a fire fighter between the ages of 18 and 55 years, not being a smoker or other tobacco user, no known cardiovascular diseases or history of any neurological disorders, no history of abnormal digestive system complications including abnormalities with swallowing, esophageal or bowel strictures, gastrointestinal obstruction, or fistulas and do not use a pacemaker. Female participants were included after confirmation that they were neither pregnant nor lactating. All participants were required to complete a medical evaluation consistent with National Fire Protection Association (NFPA) 1582 within 12 months of study participation. Only experienced firefighters whose training was current and who were familiar with live-fire policies and procedures took part in the study. Standardized personal protective equipment (PPE) that was compliant with applicable NFPA standards was provided to study participants. Throughout the protocol, firefighters wore full PPE and self-contained breathing apparatus (SCBA).2 with an average of 13.4±7.3 years of experience in the fire service.Forty-one firefighters (37 men) from departments in Illinois, Georgia, Indiana, Ohio, South Dakota, and Wisconsin participated in this study. Due to issues with ECG data quality and one firefighter withdrawing prior to his first data collection session, the final sample for analysis included 32 firefighters. On average, these firefighters were 36.7±8.1 years old, 1.80±0.08m tall, weighed 88.3±15.4 kg, and had an average body mass index (BMI) of 27.1±3.4 kg/m2 purpose built residential structure.13 The scenario was designed to more closely replicate typical fireground conditions found at a residential fire than is commonly replicated in training fire exercises that are often performed in a concrete building using only wood and straw as a fuel. Thus, firefighting was performed in a wood-framed residential structure that was finished with gypsum board. The structure also contained furnishings that are typically found in a residence to create a realistic fuel load and fire conditions that replicate typical fire scenarios. Briefly, common residential furniture was placed in the dining room and living room, which was not ignited by the fire, but which provided common obstacles to reaching the fires in the bedrooms. The bedrooms were furnished with beds, dressers, and a chair, and finished with bedding, curtains and wall hangings, and also contained a television and lamp. The exact same make and manufacture of furniture and furnishings were used in each replicate. Details of the thermal strain and chemical exposures experienced by the firefighter participants have been published elsewhere.14As described fully elsewhere, participants completed a realistic firefighting response that involved a multiple-room fire  in a 111mWorking in teams of two, firefighters performed typical firefighting operations such as incident command/pump operations, fire suppression, search and rescue, ventilation, and overhaul activities. The fire response scenarios lasted approximately 30minutes, including performing overhaul activities after the fire was suppressed. Once firefighters completed their job assignments, they were released to a “rehab” area, approximately 40m from the structure, to doff their gear. Firefighters remained in the rehab area for approximately 20 minutes where they cooled down and rehydrated. Once released from rehab, the firefighters showered and changed in to clean clothing, and ECG leads were attached .Descriptive measures, including height and weight were obtained prior to testing using a digital scale . ECG data were collected continuously for 12-hour periods following firefighting activity and during a control day using a portable ECG monitor . Electrodes were placed by trained personnel after showering. During the post fire suppression period, firefighters were released to a classroom space where they were free to engage in activities as they would at a fire station . Meals were provided at lunch and dinner time. Firefighters were instructed to perform typical activities on their control day and not to engage in firefighter, fire training, or strenuous activity. An off-duty day was chosen for the 12-hour control period. Electrodes were placed on the firefighters in the morning and they were asked to wear the monitor for at least 12 hours.ECG recordings were downloaded and processed using the Global Instruments Holter System Client  using the arrhythmia and ST-episode detection program algorithms. A board-certified cardiologist (T.R.) reviewed and either confirmed or rejected the system-identified arrhythmias and ST-segment deviations. For the ST-segment review, 12-lead strips, obtained following firefighting and during the control day of all ST-segment elevations and depressions identified by the program as abnormal, were printed. ST segment changes considered consistent with myocardial ischemia were defined as a segment depression of more than or equal to 2mm which did not return to the baseline for more than or equal to 0.08seconds. Tracings in which similar configuration of the ST segment occurred with a depression of 1.0 to 2.0mm were considered to reflect possible myocardial ischemia. A depression of the J-point (junction of QRS complex and ST segment) with upward sloping of the ST segment that reached the baseline in less than 0.08seconds was considered physiologic.A 12-lead strip, which was artifact free (baseline), was selected from each 12-hour control file and printed for analysis of the baseline electrocardiogram by the cardiologist. Analysis included heart rate, rhythm, intervals , axes , ST-T wave changes, and signs of left ventricular hypertrophy.P<0.05.Data were analyzed using IBM SPSS Statistics for Windows . Data are reported as mean±standard deviation in tables and mean±standard error of the mean in figures. Ventricular and supraventricular ectopy were considered as dichotomous variables (“yes” or “no”) in statistical analyses. The McNemar test was used to determine whether the proportion of firefighters with detected ventricular or supraventricular ectopy differed between the 12-hour control and 12-hour post-firefighting activity periods. Tests were two-sided and statistical significance was taken at Baseline ECG characteristics are reported in In the 12-hour period following firefighting activity, arrhythmias were recorded in 28 participants. These included 25 firefighters with supraventricular beats and 18 with ventricular ectopy. Ventricular couplets, triplets, or runs of ventricular tachycardia occurred in three firefighters.Two firefighters displayed 11 or more episodes of ventricular ectopy during both the control and post-firefighting periods, with more episodes displayed during control than post-firefighting for one firefighter (54 vs 14) but not the other (16 vs 68). Three firefighters each had 55 or more episodes of ventricular ectopy, which consisted of isolated ventricular beats in all but one episode, detected following firefighting activity. One firefighter with 56 episodes of ventricular ectopy following firefighting also demonstrated 132 episodes of isolated supraventricular beats during the same period; during control, this same firefighter demonstrated only three episodes of isolated ventricular ectopy and no supraventricular beats.Eleven or more supraventricular beats were detected in seven firefighters during control and three firefighters following firefighting activity, but only one firefighter who had 20 and 27 supraventricular beats during control and post-firefighting, respectively, was included in both groups. The number of supraventricular episodes detected for the more than and equal to 11 category  ranged fP=0.34) or supraventricular (P=1.00) disturbances of normal cardiac rhythm between the control and post-firefighting periods.As shown in n=4, definite n=1) following firefighting.During the control period, the ECG of one of the firefighters demonstrated episodes of possible (1–2mm depression) and definite (>2mm depression) myocardial ischemia. Following firefighting, markers of ischemia were observed to reflect possible ischemia in four firefighters and definite ischemia in two, with one of these also exhibiting several episodes of possible ischemia. The one firefighter with ischemic ST changes in the control period continued to demonstrate both possible and definite ischemia following simulated firefighting. Therefore, five participants who had normal ECGs in the control period developed indicators of myocardial ischemia  demonstrated the onset of ventricular ectopy while five firefighters (16%) developed new ECG findings of possible or definite myocardial ischemia. This study supports previous research that has found that firefighting may lead to significant ECG changes but extends that work by comparing results from the 12-hour period after firefighting to a control period.11 It is possible that such ECG changes reflected occult underlying coronary artery disease in the ostensibly healthy firefighters in this study. However, if true, this finding is especially troubling as all participants were medically evaluated and cleared for duty within the previous 12 months. Further, none of the firefighters revealed any signs or symptoms of cardiac stress during the firefighting or the control period. Still, that such ECG responses to firefighting might serve as harbingers of sudden cardiac death is consistent with a theoretical model that poses that cardiac strain associated with the combined stressors of firefighting can cause ischemic or arrhythmogenic changes that can precipitate sudden cardiac events in fire fighters, and this is more far more likely in individuals with underlying disease.6Although the findings of this study are consistent with some previously published research and provides a potential mechanistic link with the increased risk of sudden cardiac events in the hours after firefighting, interpretation is challenging given the complexity of the findings and the difficulties in directly linking ECG changes with sudden cardiac events. Some have considered ECG changes during and immediately following firefighting as reflecting an etiologic link between ECG markers of ischemia and electrical instability with the augmented risk of sudden death during firefighting activities.11 for instance, described ECG changes during and for 2 hours following a live fire-fighting exercise in 42 firefighters with a mean age of 31±9 years. Eleven participants demonstrated “pathological” ST segments during firefighting (defined as greater than 0.5mm of ST depression in leads V2 to V3 or greater than 1mm in 2 or more contiguous leads for at least 5 minutes), and such changes were observed in two additional firefighters during recovery. Half of the subjects were described as having a prolonged QT interval during firefighting. However, as noted by the authors, this finding can be questioned because of the difficulty of making such a measurement during the tachycardia of exercise. Moreover, all ECG analyses were performed by a computer rather than expert interpretation. The authors concluded that “This study demonstrates that fire suppression...induces tachycardia and is associated with transient ECG changes suggestive of myocardial ischemia in firefighters at low risk for cardiovascular disease... These findings provide potential pathophysiological explanations for the high proportion of cardiovascular deaths among firefighters.”11The ECG findings in this study are not dissimilar to those of previous reports. Al-Zaiti et al,9 described electrocardiographic changes in 19 healthy male firefighters (mean age 41±7 years) before and after simulated firefighting. Compared with the control period, post-firefighting ECGs demonstrated the expected tachycardia (mean 162bpm) and a significant increase in “ST depression,” which was defined in terms of extent of depression of the J point plus 50ms on the ECG tracing. This result becomes difficult to interpret, since J point depression during tachycardia is highly common in normal healthy individuals and is considered physiologic when accompanied by a rapid upsloping ST segment.12 The authors concluded that “exposure to extreme heat and physical exertion during simulated fire suppression increases thrombogenicity, impairs vascular function, and causes myocardial injury in healthy firefighters” .9Hunter et al15 performed near-maximal treadmill exercise tests on 90 random men ages 40 to 59 years from the City of Los Angeles Fire Department. Ischemic changes, defined as “at least 1mm of horizontal or down slanting depression of the ST segment observed either during exercise or in the 5 minute post exercise recovery period,” were found in nine participants (10%). A similar exercise study of insurance underwriters in the same exercise testing laboratory revealed an incidence of 8%.16It should be noted that electrocardiographic patterns associated with myocardial ischemia are not infrequently observed during exercise testing in non-selective studies of both firefighters and the general adult population. Barnard et al17 The conclusion here is that ST changes during or after heavy work by firefighters does not necessarily imply either the presence of occult cardiovascular disease or risk for sudden death.In these studies, too, the extent that such numbers reflect occult coronary artery disease is uncertain. In fact, it is recognized that “up to 10% of normal men and an even higher percentage of normal women may have false-positive tests (ST depressions without obstructive coronary disease).”18 reported that 9.3% of the runners in a Berlin marathon race demonstrated evidence of non-sustained ventricular tachycardia and 7.5% showed transient ST-T wave changes during the event.There is growing awareness that conditions of extended cardiac work by itself can be accompanied by appearance of biomarkers of heart stress. A rise in cardiac enzymes, left ventricular diastolic dysfunction by echocardiography, an increase in serum troponin level, and abnormal electrocardiographic findings—all markers of myocardial stress—have been reported in over half of the participants in ultra-endurance running events. For example, Herm et al19 It is generally accepted, though, that such responses to prolonged heart work do not reflect clinically-important myocardial dysfunction nor disposition to risk for sudden cardiac death given: (a) the magnitude of change of such biomarkers is small and does not mimic that of true myocardial dysfunction , (b) these changes are transient and rapidly reverse to normal , and (c) the observation of the natural “experiment” that if such changes truly reflected myocardial damage, it should have become apparent among the hundreds of thousands of participants in such endurance athletic events who, to the contrary, maintain not only their well-being but superior cardiac function.The mechanistic explanation and clinical implications of these changes are not altogether clear.9 measured plasma levels of cardiac troponin  in their study of healthy firefighters before and after a bout of simulated firefighting. Mean value doubled from 1.5 to 3.0 ng/L post firefighting simulation. The latter value was still within the normal reference range and was less than that observed in clinical setting of myocardial injury. Similar findings have been described in recent reviews of troponin changes with marathon and triathlon participation in which over half of the participants demonstrated a rise in post-race troponin concentration.21Could the ECG changes reported in a minority of firefighters with the strenuous work of fire suppression activities fit into this explanatory category? There is, in fact, some evidence to support this conclusion. Hunter et al12 Fernhall et al22 demonstrated similar echocardiographic findings of depressed lateral ventricular wall relaxation velocity immediately following a 3-hour period of live firefighting exercise.Echocardiographic assessment of athletes immediately after ultra-endurance events of over 1-hour duration have demonstrated a “nearly ubiquitous” evidence of transient left ventricular diastolic dysfunction.4 On the other hand, alternative explanations are possible. Such ECG changes could, for instance, reflect an expression of strenuous or prolonged cardiac work that, like those of endurance athletes, are transient and benign. Future research efforts to assess the frequency and “meaning” of ECG changes during fire suppression are justified to better assure the safety and health of firefighters. Additionally, research into better strategies to detect potential underlying cardiovascular disease in firefighters who are exposed to high levels of cardiac strain is also needed.In conclusion, the findings in this study support the limited research literature indicating that a minority of firefighters will demonstrate electrocardiographic findings of myocardial ischemia as well as isolated ventricular ectopy in response to a period of active fire suppression. It is not illogical to suppose that such findings reflect true myocardial stress that could impose a risk for sudden death. In fact, this logic is consistent with epidemiological evidence indicating that strenuous activity can trigger sudden cardiac events, and data documenting much higher risk of sudden cardiac events following firefighting activity compared with station duties.


Duchenne muscular dystrophy (DMD) leads to progressive cardiomyopathy. Detection of myocardial fibrosis with late gadolinium enhancement (LGE) by cardiovascular magnetic resonance (CMR) is critical for clinical management. Due to concerns of brain deposition of gadolinium, non-contrast methods for detecting and monitoring myocardial fibrosis would be beneficial.cc) and longitudinal (εls) strain can detect myocardial fibrosis.We hypothesized that native T1 mapping and/or circumferential . LGE was graded as: (1) presence/absence by segment, slice, and globally; (2) global severity from 0 (no LGE) to 4 (severe); (3) percent LGE using full width half maximum (FWHM). εls and εcc measured using feature tracking. Regression models to predict LGE included native T1 and either εcc-tag or εls and εcc measured at each segment, slice, and globally.156 CMRs with gadolinium were performed in 66 DMD boys and included: (1) left ventricular ejection fraction (LVEF), (2) LGE, (3) native T1 mapping and myocardial tagging  while global native T1 did not. Global εcc, εls, and native T1 predicted global severity score . εls correlated with change in LGE by severity score  and εcc-tag correlated with change in percent LGE by FWHM .Mean age and LVEF at first CMR were 14 years and 54%, respectively. Global εls and εcc being more predictive in most models, but there was not an observable advantage over using LVEF as a predictor. Change in LGE was predicted by εls  and εcc-tag (FWHM). While statistically significant, our results suggest these sequences are currently not a replacement for LGE and may only have utility in a very limited subset of DMD patients.Pre-contrast sequences predict presence and severity of LGE, with εThe online version contains supplementary material available at 10.1186/s12968-021-00736-1. Duchenne muscular dystrophy (DMD) leads to progressive skeletal and cardiac myopathy and affects up to 1 in 4700 live male births . There iCardiovascular imaging is necessary to detect both early onset and rapid progression of cardiomyopathy. While transthoracic echocardiography has been the mainstay of cardiovascular evaluation in DMD, the field has increasingly shifted to cardiovascular magnetic resonance (CMR) . This iscc) and longitudinal strain (εls) can be used as surrogates for LGE in DMD subjects, allowing for less frequent gadolinium contrast administration.Recently, mapping of the longitudinal time constant, T1, has gained popularity for myocardial tissue characterization. Native T1 maps can be performed without contrast administration, while extracellular volume (ECV) maps require a combination of native T1 and post-contrast T1 maps as well as a recent hematocrit –16. NatiSubjects were drawn from prospective observational studies, all of which were approved by the Vanderbilt Institutional Review Board. All patients signed approved consents or assents. Inclusion criteria were the following: (1) DMD diagnosed phenotypically and confirmed with either genetic testing or muscle biopsy, (2) at least one CMR performed. Exclusion criteria were: (1) other genetic diagnosis in addition to DMD, (2) renal disease or other diagnosis precluding CMR with contrast, (3) no LGE imaging performed on CMR, (4) inadequate T1 map quality in all three imaging slices on all available CMRs. As the incidence of renal disease is low in the general pediatric and DMD populations, creatinine is not usually assessed unless there is a clinical concern. However, screening blood chemistries including creatinine are performed clinically, particularly for patients on angiotensin converting enzyme inhibitors or aldosterone inhibitors. Of the patients enrolled in our studies, there were a total of 8 CMRs where a patient did not receive contrast and so were removed from our analysis. Of those, one was because of anxiety in the scanner so the patient was removed early, 7 were in patients in whom no intravenous access could be obtained or the intravenous catheter infiltrated.®, Bayer Healthcare Berlin, Germany or gadobutrol, Gadovist®, Bayer Healthcare) was administered through a peripheral intravenous line at a dose of 0.2 mmol/kg. LGE imaging was performed using: (1) single shot (bSSFP) and segmented (turboFLASH) inversion recovery with optimized inversion recovery to null the signal from the myocardium, as well as phase sensitive inversion recovery (PSIR) bSSFP with an inversion time of 300 ms.CMR was performed on a 1.5T CMR system . Functional imaging was performed as previously described using balanced steady state free-precession (bSSFP) images in a short axis stack . Intrave2, matrix size 256 × 144, slice thickness 8 mm, voxel size 1.3 × 1.9 × 8.0 mm3, TR/TE 2.6 ms/1.1 ms, parallel imaging factor of 2. The matrix size was decreased to 192 × 128 for heart rates > 90 . The pre-contrast MOLLI acquired 5 images after the first inversion with the equivalent of a 3 s pause followed by 3 images after the second inversion, or 5(3s)3  [Myocardial tagging was performed in the LV short axis at the base, papillary muscles, and apex using a segmented k-space fast gradient echo sequence with electrocardiographic (ECG) triggering. Grid tagging was performed with a spacing of 8 mm and 9–13 phases. Typical imaging parameters included: slice thickness 6–8 mm, field-of-view 340 mm × 340 mm, matrix size 256 × 192, and minimum echo time and repetition time. The sequences were breath-holds and parallel imaging with generalized autocalibrating partially parallel acquisition (GRAPPA) with an acceleration factor of two. Breath-held modified Look-Locker inversion recovery (MOLLI) sequences (investigative) were performed prior to contrast administration in the LV short axis at the base, mid-LV, and apex at the same slice locations as the tagging . MOLLI s of 120) . A goodncc-tag values for each segment (16 segment model) and slice . Our prior work has demonstrated excellent reproducibility for harmonic phase magnetic resonance (HARP) analysis [LV volume, mass, and function were calculated as previously described . The precc and εls, respectively). For εcc, cine images in the LV short axis at the base, mid-LV, and apex were chosen and for εls, cine images in the long axis 4-chamber, 3-chamber, and 2-chamber views were chosen. Endocardial borders were traced on each image at end diastole and end systole. Images were inspected closely to confirm adequate tracking and segments that did not track were removed from analysis; more than one segment in each slice with poor tracking resulted in the removal of that CMR from analysis. Global and segmental circumferential and longitudinal strain were calculated automatically by the software.Qstrain  was used to calculate feature tracking circumferential and longitudinal strain , epicardial and endocardial borders were manually drawn on native T1 maps within the LV mesocardium. The superior RV insertion was identified and automatically divided into segments using the American Heart Association standard 16 segment model . RegionsIn patients with adequate maps at the base and mid-LV slices, global myocardial T1 was calculated. Apical slices were not used to minimize errors from partial volume averaging. Imaging artifact was not contoured. For native T1 maps, segments were not included in the analysis if the bounds of the myocardium could not be distinguished from surrounding tissue and blood pool or if image registration was inadequate in those segments. The total number of excluded segments was 112 out of 2018, with 27/750 from the base, 23/864 from the mid-LV, and 62/404 from the apex. The mean myocardial wall thickness of the baseline CMRs was 6.7 ± 1.3 mm at the septum and 6.0 ± 0.9 mm at the free wall. Figure ls, εcc, and T1 mapping pre-specified as predictors and adjusting for age (and baseline measurements in the change model) as covariates.Continuous data are presented as a mean and standard deviation. A missing data summary analysis was performed ; any worsening of LGE global severity score ; any worsening of FWHM (linear regression). It should be noted that inclusion of LVEF in these models was considered; however, the decision was made to exclude LVEF and indexed LV end-systolic volume (LVESV) due to significant multicollinearity with strain parameters. We have included examples of univariate models for LVEF and indexed LVESV in Additional file A total of 156 CMRs were analyzed from 66 enrolled patients . The average total scan time was 40 min. At the time of the CMR, there were 33 patients on corticosteroids and 45 patients on cardiac medications. Seventeen patients were ambulatory. Forty-four patients (70%) were positive for LGE, with 12 patients having global severity score (GSS) = 1, 12 with GSS = 2, 13 with GSS = 3, and 7 with GSS = 4  and GSS  while native T1 was predictive of GSS but not presence/absence of LGE , and global εls (C) are shown in Fig. For tagged analysis, global ε2 = 0.057. Figure cc, εls, and native T1 for DMD patients with LVEF ≥ 55% (n = 95).A subset analysis was performed to evaluate presence of LGE in subjects with a normal LVEF. Given the smaller number of CMRs , the model performed poorly, with a C = 0.64 and rls, εcc, and εcc-tag were predictive of presence or absence of LGE, global severity score, and to a lesser extent FWHM  as predicted by strain and T1 at each slice were also evaluated. Basal and mid-LV εcc and/or εls predicted presence/absence of LGE in almost all segments assessed except for the basal anterior, mid inferolateral, and mid anterolateral segments  and globally , mid , and globally  , mid , and globally .Fifty-five patients had more than one CMR and were included in the longitudinal analysis. Mean changes in follow-up time, LVEF, and CMR parameters between studies are shown in Table cc, εls, and native T1 mapping had moderate predictive power for segmental LGE changes by severity score but was less accurate for FWHM  modeling using pre-contrast sequences modestly predicts presence and severity of LGE for individual CMRs; (2) strain appears more predictive than native T1 in most models; (3) strain, but not native T1 or initial global LGE, predicts change in severity of LGE estimated by GSS (for feature tracking) and FWHM ; (4) Non-contrast sequences are not a substitute for LGE imaging in DMD. While strain was statistically predictive of most outcome variables, the performance of strain was only modest, and native T1 was not predictive in most models. The performance of most non-contrast models evaluated did not meet thresholds for routine clinical use, emphasizing the importance of LGE in the DMD population.This study presents the largest cohort of CMRs in DMD patients evaluating εNative T1 mapping has shown promise as a tool for detecting myocardial fibrosis in dilated cardiomyopathy as assessed by LGE and histologically . Given tcc and myocardial fibrosis has been previously demonstrated based on histopathology and CMR assessment with LGE and ECV in adult patients with aortic stenosis [By contrast, strain is a more functional assessment and areas demonstrating abnormal strain patterns secondary to fibrotic changes would be expected to progress as the pathological process progresses. Good correlation with εstenosis  and hypestenosis . Our datstenosis . This succ-tag, however, this likely is of less clinical importance given that the apex in DMD is relatively spared until late in the disease. It is still unclear what outcome measures are best used for DMD cardiomyopathy. LVEF and LGE have been the standard because clinicians have the most experience with their use and interpretation [It is important to note the segmental distribution of the predictive models using native T1 and strain. Unlike many other forms of cardiomyopathy, the fibrosis seen in DMD dilated cardiomyopathy tends to be patchy and found in the subepicardial region, often in one or more segments of the lateral free wall. Segmental differences in native T1 and strain are more likely to be predictive in areas where LGE is usually found in DMD patients. Thus, there may be some benefit to monitoring for changes in segmental parameters even in patients with normal LVEF and no previous LGE. Strain calculated using either feature tracking or myocardial tagging was predictive of LGE presence and extent with similar efficacy. It should be noted that HARP analyzes mid-myocardial strain while feature tracking analyzes endocardial strain. There is debate about which one is more relevant clinically, but we suspect that the difference is a result of the underlying software differences. Apical LGE was better predicted by εretation . It is pls and Ɛcc, have potential to help in further reducing contrast administration in this patient population.Emphasizing the importance of limiting contrast exposure, a recent study demonstrated no difference in medical management seen with traditional versus conservative contrast administration in DMD patients . Of noteGiven the substantial mobility challenges and other morbidities, such as contractures and severe scoliosis, seen in DMD patients, reduction in scanner time is an important consideration for patient comfort as well as patient compliance for breath holding in order to obtain the best quality studies. Based on our institutional data, the average total scan time was 39.6 min, with 10 min attributed to post-contrast imaging from sequences that would not have been used if contrast was not administered. Thus, eliminating contrast administration would allow for sub-30 min studies in most cases, which is an important consideration for this patient population in particular, but may also be important in centers were scanner access is limited.This study is limited by the fact that only 33% (18 of 55 patients) had a change in GSS ≥ 1; a larger change may have improved model performance. Additionally, 70% of patients had LGE severity score of at least 1 on their initial scan. This limited our ability to do subset analysis with sufficient power comparing the severity score 0 group alone with those ≥ 1. As discussed above, native T1 may not be completely linearly related to LGE extent and this may bias the data toward non-significance when there actually is a detectable difference for larger changes in LGE severity. Additionally, the variation in native T1 values is inversely related to flip angle, with lower flip angles having higher standard deviations between measurements . We chosMost of the patients assessed were on steroids or cardiovascular medications. While medications may temper the progression of LGE, which could certainly increase the number of patients with no change in LGE on longitudinal assessment, therapies are unlikely to substantially change the predictive potential of these sequences. Despite these therapies, there does appear to be a relationship with mortality. It is important to assess prediction in a clinically relevant setting, and all of these medications represent standard therapies at most institutions.Finally, another limitation of this analysis is that the models will require validation secondary to the large number of multiple comparisons.cc-tag was superior for identification of change in severity by FWHM and εls was superior for change in GSS. Although these models were predictive, they were only modestly so, suggesting that non-contrast studies cannot currently replace LGE imaging. Given the critical clinical data provided by LGE, we suggest that DMD subjects should continue to receive gadolinium contrast using a conservative protocol for clinical management. Non-contrast imaging should only be used to estimate LGE when contrast cannot be administered  or if total time must be decreased due to physical limitations.In this cohort of DMD boys, models using pre-contrast sequences are predictive of presence and severity of LGE for a particular CMR using both feature tracking and myocardial tagging. For longitudinal assessment for individual patients, εAdditional file 1:Figure S1. Univariate Prediction Models for LVEF, LVESVin, and LVEDVin. Models demonstrate modest areas under the curve (AUC) for prediction of presence/absence of LGE for LVEF (A), LVESVin (B) and poor performance for LVEDVin (C). LVEF does well for predicting high grade (GSS ≥ 3) vs low grade (GSS ≤ 2) LGE (D), with less robust performance by LVESVin (E) and LVEDVin (F).Additional file 2:Figure S2. Analysis of missing data points in longitudinal cohort. The variable with the highest proportion of missing values was change in basal ɛls (A). Combination analysis was performed with blue squares indicating observed data points and red squares indicating missing data (B). This demonstrated the majority of CMRs had all data points (indicated by the bar graph to the right of the matrix), with the next most common being missing only change in basal ɛls.Additional file 3: Table S1. Models for prediction of presence and severity of LGE .Additional file 4: Table S2. Models for prediction of presence/absence LGE and FWHM by segment .Additional file 5:Table S3. Models for prediction of FWHM and change in FWHM by segment (feature tracking).Additional file 6: Table S4. Models for prediction of change in LGE severity by segment .


Brugada syndrome is an autosomal dominant arrhythmogenic disease associated with an increased risk of ventricular fibrillation and sudden cardiac death. The mainstay of treatment in high-risk patients is an implantable cardioverter-defibrillator (ICD), however radiofrequency ablation has been proposed over the past decade as an additional therapy in patients with recurrent ICD firing. We report a case of Brugada syndrome with an electrical storm which was successfully managed by radiofrequency ablation. Loss of function in the SCN5A gene, which controls voltage gated sodium channels, is found in approximately 30% of affected individuals  is an inherited condition characterized by ST-segment elevation in the right precordial leads, associated with ventricular arrhythmias (VAs) and sudden cardiac death (SCD), especially in young malesividuals 2.The standard therapy for the prevention of SCD in BrS is the use of an implantable cardioverter-defibrillator (ICD) especially in patients who have experienced a prior cardiac arrest or sustained VAs. However, ICDs do not prevent the occurrence of VAs but react to terminate/defibrillate the arrhythmic episode, thereby preventing SCD.3. In the last decade, substrate ablation of the right ventricular outflow tract (RVOT)/right ventricle (RV) has emerged as a promising therapeutic option for BrS patients with recurrent VAs.Quinidine has been reported to reduce the ventricular arrhythmic burden in BrS, however, quinidine is not widely available and has been associated with intolerable adverse effectsWe report the case of a 28-year-old male patient who was discovered to have Brugada pattern on his surface ECG, after resuscitation from sudden cardiac arrest . He fullAfter informed consent, the procedure was performed under general anesthesia. Two right femoral venous accesses were obtained, one for positioning of CS catheter, the other for endocardial mapping of RV. A Chiba spinal needle was used for the epicardial access. To avoid epicardial coronary artery injury, a coronary angiography was performed to visualize the course of the left anterior descending artery (LAD). High-density substrate mapping was performed on RV endocardium and epicardium using an Advisor HD grid mapping catheter and Ensite Precision mapping system .In sinus rhythm we looked for abnormal electrograms (EGMs) which included fractionated potentials, split EGMs, isolated late potentials and low voltage EGMs . These aThe patient recovered well after the procedure and was discharged home the next day. After one year of close follow up, there have been no recurrences of ventricular arrhythmias upon frequent interrogations of his ICD .et al in 2003, where they targeted premature ventricular complexes (PVCs) originating from RVOT that were thought to trigger VF. Given the fact that PVCs in patients with BrS could be quite scarce, wide clinical application of this idea was impractical4.Catheter ablation for BrS patients with recurrent VF was first introduced by Haïssaguerre 5. Over the following years, there was growing evidence that epicardial substrate modification is beneficial in abolishing the recurrence of VF. Most importantly, in 2011, Nademanee et al reported normalization of the ECG pattern and abolition of recurrent VF episodes after epicardial RVOT substrate ablation in a cohort of 9 patients with Brs and recurrent ICD shocks6.In 2009, an experimental study on mongrel dogs with induced Brugada pattern concluded that radiofrequency catheter ablation applied to the epicardium of the RVOT may be more effective than endocardium in elimination of VAs6. Recently, Talib et al, in a series of 21 patients, introduced an endocardial approach as a first step in ablation of patients with BrS with an overall success rate of 67% 8.In 2017 multicenter trial, Pappone, Brugada, Santinelli and colleagues published the results of epicardial ablation in 135 consecutive patients. In this large series, the study group determined the arrhythmogenic electrophysiological substrate before and after ajmaline provocation using three-dimensional electroanatomical mapping. Elimination of this substrate led to subsequent ECG normalization and VT/VF non-inducibility in all ablated patients. More importantly, the group reported no complications related to the pictureAs our case was a redo case, it was reasonable to map both endocardial and epicardial surfaces of the RV using high-density mapping technology to ensure full coverage of all possible electrical substrates. However, all significant signals were present only on the epicardial surface. Thorough high-density mapping, after sodium channel blocker infusion, clearly identified areas of abnormal EGMS that were possibly missed at the first attempt, or most likely, not sufficiently ablated.7.It is important to highlight that despite the invasive nature of epicardial mapping and ablation, studies on this approach for patients with BrS and other conditions, such as arrhythmogenic right ventricular cardiomyopathy and dilated cardiomyopathy, consistently showed low periprocedural complication rates. Careful handling of the puncture of the epicardial space with adequate experience and knowledge of fluoroscopic anatomy of RV, renders this approach mostly safe •Catheter ablation is a valid option in patients with BrS who experienced recurrent shocks. •The use of sodium channel blockers helps to unmask the specific electric substrate in BrS. •The appropriate use of up-to-date technologies, such as high-density mapping, can significantly improve the outcomes in complex ablation procedures.


Measurement of mitral and tricuspid annular plane systolic excursion  by echocardiography provides additional information for biventricular functional assessment with high reproducibility. To date, cardiovascular magnetic resonance (CMR) equivalents of MAPSE and TAPSE for paediatric patients are not available. This study aimed to provide a set methodology for the evaluation of longitudinal function and provide the imaging community with normal reference values of CMR-derived MAPSE and TAPSE in healthy paediatric cohorts.Siemens Medical Solutions, Germany) and were analysed by a single observer using CMR post-processing software .The study population consisted of all children and adolescents retrospectively selected from who underwent a CMR scan between 2016 and 2020, resulting in normal cardiac findings. Images were obtained with 1.5 T scanners  and TAPSE were measured from 4-chamber bSSFP cines. L-MAPSE and S-MAPSE were measured as the distance between the cutting edge of mitral annulus with left ventricular (LV) lateral wall or septum, respectively, captured at end-diastole and at end-systole. TAPSE was measured as the distance between the cutting edge of the tricuspid annulus with right ventricular (RV) free wall captured at end-diastole and at end-systole Fig. .Fig. 1MASimple linear regression was used to establish the association between MAPSE or TAPSE with age, gender, body surface area (BSA), heart rate, ventricular end-diastolic volume index  and ejection fraction . Multiple linear regression was than performed including variables that were significant in the univariate analysis. Intra-observer and inter-observer variability was determined in 25 patients measured by two independent observers, using intraclass correlation coefficient (ICC). All tests were two tailed and significance was set at 0.05.2 (0.3) [0.7–2.2]. We established three age groups , looking for normal distributions.Fulfilling the inclusion criteria were 138 paediatric patients, of which 88 males, with a mean (SD) [range] age of 13.3 years (2.9) [4.0–17.0]. The mean body surface area (BSA) was 1.6 mTable For L-/S-MAPSEs, simple linear regression showed a weak but significant correlation with age^2 , BSA  and LVEDVi  but no significant correlation with LVEF. When tested in multivariable model, only correlation with age^2 for both MAPSEs and with LVEDVi for L-MAPSE was maintained.For TAPSE, simple linear regression showed a weak but significant correlation with age^2 , BSA  and RVEDVi  but no significant correlation with RVEF or LVEF. In multivariable model, only correlation with RVEDVi was maintained.The methods for measuring MAPSEs and TAPSE demonstrated excellent intra-observer agreement  and high inter-observer agreement . The mean time to repeat intra-observer measurements was 21 days [13–38].Ejection fraction (EF) is a widely recognised index of global systolic function in guidelines and clinical practice. However, EF alone has low sensitivity in detecting early contractile impairment with limitations in certain conditions .It is widely considered that the more longitudinally oriented subendocardial fibres provide a significant contribution to normal cardiac function. Strain imaging enables quantification of longitudinal contractile function and has been proven to improve the identification of subclinical LV systolic dysfunction in patients with heart failure with preserved EF (HFpEF). However, compared to echocardiography, CMR-derived longitudinal strain analysis is time consuming and limited by a lack of validation and general availability.Also MAPSE and TAPSE have been shown to be more sensitive than EF in certain settings including HFpEF. TAPSE showed high negative predictive power in detecting RV dysfunction and has been shown to be an independent predictor of cardiovascular death in the general population, especially in individuals with normal EF . As with


Right ventricular (RV) three-dimensional (3D) strains can be measured using novel 3D RV analytical software (ReVISION). Our objective was to investigate the prognostic value of RV 3D strains.We retrospectively selected patients who underwent both 3D echocardiography (3DE) and cardiac magnetic resonance from January 2014 to October 2020. 3DE datasets were analyzed with 3D speckle tracking software and the ReVISION software. The primary end point was a composite of cardiac events, including cardiac death, heart failure hospitalization, or ventricular tachyarrhythmia.p < 0.05). Multivariate analysis revealed that 3D RVEF, three 3D strain values were significantly associated with cardiac events after adjusting for age, chronic kidney disease, and left ventricular systolic/diastolic parameters. Kaplan-Meier survival curves showed that 3D RVEF of 45% and median values of 3D RVGCS, 3D RVGLS, and 3D RVGAS stratified a higher risk for survival rates. Classification and regression tree analysis, including 22 clinical and echocardiographic parameters, selected 3D RVEF  first, followed by diastolic blood pressure  and 3D RVGAS  for stratifying two high-risk group, one intermediate-risk group, and one low-risk group.341 patients were included in this analysis. During a median of 20 months of follow-up, 49 patients reached a composite of cardiac events. In univariate analysis, 3D RV ejection fraction (RVEF) and three 3D strain values  were significantly associated with cardiac death, ventricular tachyarrhythmia, or heart failure hospitalization (Hazard ratio: 0.88 to 0.93, RV 3D strain had an equivalent prognostic value compared with 3D RVEF. Combining these parameters with 3D RVEF may allow more detailed stratification of patient's prognosis in a wide array of cardiac diseases. For the last decade, the right ventricle has gained increasing attention for morphological and functional assessment. Although tricuspid annular plane systolic excursion (TAPSE), right ventricular (RV) fractional area change, systolic tricuspid annular velocity (RVs'), and longitudinal strains are commonly used for assessment of RV function with two-dimensional 2D) and tissue Doppler echocardiography, these measurements do not cover all aspects of RV function and 2D echocardiographic assessment of RV function has several limitations due to the complex morphology of the right ventricle. Three-dimensional echocardiography 3DE) provides accurate and reproducible values of RV volumes and ejection fraction (RVEF) , 2 and cD and tisDE providThe ReVISION  software was recently introduced, enabling comprehensive 3D echocardiographic analysis of the right ventricle . With thWe hypothesized that 3D RV strains would provide incremental prognostic information over 3D RVEF. Accordingly, we sought to investigate the prognostic value of RV 3D strains in a diverse group of subjects.This was a single-center, retrospective, observational study. Using a cardiac magnetic resonance (CMR) database, we retrospectively selected patients who underwent both CMR and transthoracic echocardiography on the same day at the University of Occupational and Environmental Health Hospital from January 2014 to October 2020. Exclusion criteria were repeat examinations, age <20 years, patients with <30 days of follow-up, patients without echocardiographic datasets, and patients with extremely poor image quality. Clinical characteristics such as anthropometric data, risk factors, and medication information on the day of the echocardiography were collected. This study protocol was approved by the local Ethics Committee of the University of Occupational and Environmental Health (IRB No: UOEHCRB20-181). The requirement for written informed consent was waived because of the retrospective nature of the study.Comprehensive transthoracic two-dimensional and Doppler echocardiographic examination were performed using a commercially available ultrasound system .3DE was performed according to guidelines of the American Society of Echocardiography (ASE) using an iE33, Epic7G, or Vivid E95 equipped with a 3DE transducer  . Traditional transthoracic 2D and Doppler echocardiographic parameters were measured according to current guidelines of the ASE and the European Association of Cardiovascular Imaging .Image quality of 3DE datasets was subjectively evaluated by visibility of the endocardium in 2D echocardiography images extracted from 3DE datasets . Each view was divided into six segments, and each segment was scored as 0 , 0.5 , or 1 . These scores were summed to calculate the total image score (range: 0–18). Results were categorized as good , fair (13 ≤ score < 16), poor (10 ≤ score < 13), or extremely poor (score < 10) , 3DE datasets that focused on the left heart chamber were analyzed using vendor-independent 3D speckle tracking software . The software automatically extracted apical four-chamber, two-chamber, long-axis, and short-axis views from 3DE datasets and detected endocardial surface at LV end-diastole. Endocardial borders at LV end-diastole and end-systole were manually corrected as required. After endocardial boundaries were determined, the software conducted 3D speckle tracking analysis over the entire cardiac cycle and automatically measured LV end-diastolic volume (LVEDV), LV end-systolic volume (LVESV), LV ejection fraction (LVEF), and LV global longitudinal strain (LVGLS).www.revisionmethod.com). First, 3D models of the right ventricle were created with 4D RV function 3. After selecting a 3DE dataset that focused on the right heart chamber, the software automatically extracted two orthogonal views of the left and right ventricles from the 3DE datasets. After aligning each view and determining several anatomic landmarks , RV endocardial boundaries were automatically drawn by the software. RV endocardial borders at end-diastole were manually corrected as required. The software performed speckle tracking analysis resulting in calculation of RV volumes and RVEF. 3D RV models were exported throughout the cardiac cycle as a 3D object file series and transferred to ReVISION software. The detailed analytical method of ReVISION software has been described elsewhere (end−systole-Cend−diastole/Cend−diastole) of the 15 latitudes by 100. To evaluate 3D RVGLS, 45 longitudinally oriented contours were generated by connecting the RV apex and predefined points of the RV base and were tracked throughout the cardiac cycle. The length of the longitude (L) can be calculated as the sum of the apex-middle section of the RV and middle section of the RV-RV base geodesic distances. RVGLS can be calculated by multiplying the sum of (Lend−systole-Lend−diastole/Lend−diastole) of 45 longitudes by 100. RVGAS was calculated from the relative change of the endocardial surface between end-diastole and end-systole. The surface is divided into a triangular mesh and its surface area (A) is calculated. The sum of the surface areas at the end of systole (Aend−systole) and at the end of diastole (Aend−diastole) is calculated, and the RVGAS is calculated by multiplying (Aend−systole-Aend−diastole/Aend−diastole) by 100  and ReVISION software  by 100 . RV voluFollow-up information was obtained by two researchers , who were not involved in the echocardiographic analysis. Patients were followed up regularly in the outpatient clinic. For patients attending our hospital, prognostic information, such as whether and when a cardiac event occurred, was obtained from the attending physician or the electronic medical record. For patients undergoing treatment at other hospitals, we called the patient at home. If consent was obtained, we asked the patient or family about their current health status and whether and when a cardiac event had occurred. The day of echocardiography was defined as day 0, and final follow-up data were obtained in February 2021. The primary endpoint was a composite of cardiac events, including cardiac death, sustained ventricular tachyarrhythmia, or heart failure (HF) hospitalization. If patient developed multiple events, we selected hardest one as following order . The secondary endpoint was HF hospitalization.Intra-observer variability of 3D RV volumes and 3D RVEF by 4D RV function 3 was evaluated by repeating measurements taken by the examiner on 35 randomly selected patients at interval of at least one-month, inter-observer variability was evaluated by a second examiner taking these measurements on the same 35 patients.t-tests or Mann-Whitney U tests for continuous variables, and Fisher's exact test or the chi-square test for categorical variables. A correlation analysis was performed with the r value of the Spearman rank correlation coefficient. Numbers needed to treat (NNTs) were calculated as indicators of effect size . Continuous variables were represented as medians and interquartile ranges (IQR). Categorical variables were expressed as frequencies or percentages. Comparisons between the two groups were analyzed using ect size . Survivaect size .Of 432 patients enrolled in the CMR database from January 2014 to October 2020, 341 patients were included as a final study population . Feasibi2 (IQR: 71–124 mL/m2), 52 mL/m2 (36–85 mL/m2), 41% (28–50%), and 12.2% (7.8–15.5 %), respectively. For 3DE RV parameters, the median values of RVEDV index, RVESV index, RVEF by TomTec software were 61 mL/m2 (51–76 mL/m2), 32 mL/m2 (25–42 mL/m2), 48% (40–54%), respectively. The median values of RVEDV index, RVESV index, RVEF, RVGCS, RVGLS, and RVGAS by ReVISION software were 61 mL/m2 (51–76 mL/m2), 33 mL/m2 (25–42 mL/m2), 47% (39–54%), 19.5% (15.7–23.3%), 15.2% (12.1–18.4%), and 30.0% (24.2–35.4%), respectively. 3D LVEF had a significant correlation with 3D LVGLS (r = 0.92) and 3D RVEF (r = 0.64). 3D RVEF also had a significant strong correlation with 3D RVGCS (r = 0.90), 3D RVGLS (r = 0.87), and 3D RVGAS (r = 0.93). Echocardiographic parameters are presented in For 3DE LV parameters, median values of LVEDV index, LVESV index, LVEF, and LVGLS were 90 mL/m2), resulting in classification into two high-risk groups, one intermediate-risk group, and one low-risk group (During a median of 19.8 IQR: 9.0–38.5) months of follow-up, 49 patients reached a composite of cardiac events, 14 of whom suffered cardiac death. Thirty patients were HF hospitalization, and 5 patients developed sustained ventricular tachyarrhythmia. Of the 14 patients with cardiac death, worsened heart failure was the cause of death in eight patients, ventricular tachyarrhythmia was the cause of death in three, sudden cardiac death was the cause in two, and myocardial infarction was the cause of death in one patient. 8.5 monthsk group .During a median of 19.7 IQR: 8.7–37.7) months of follow-up, 37 patients reached HF hospitalization. 7.7 monthTo evaluate the reliability of the ReVISION method, 3D RVEF values by the ReVISION method were compared to corresponding values obtained from TomTec software or CMR. The prognostic value was also evaluated among three RVEFs. A correlation coefficient of RVEF between the ReVISION method and TomTec software was 0.99 and that between the ReVISION method and CMR was 0.67 . The proThe intra- and inter-observer variability of 3D RVEDV, 3D RVESV and 3D RVEF was 4.5–5.4% [intraclass correlation coefficients (ICC): 0.92–0.97] and 7.4–9.2% (ICC: 0.86–0.93), respectively .To the best of our knowledge, this is the first report of the prognostic utility of RV 3D strains in patients with diverse cardiac diseases. The major findings of our study can be summarized as follows:, (i) Univariate analysis showed that 3D RVEF and RV 3D strains were associated with future outcomes, (ii) Multivariate analysis revealed that 3D RVEF, 3D RVGCS and 3D RVGAS are independently associated with cardiac events after adjusting for age, CKD, 3D LVEF, and average mitral E/e'. 3D RVEF and all 3D RV global strains are independently associated with cardiac events after adjusting for age, CKD, 3D LVEF, and 3D maximum LAVI, (iii) CART selected 3D RVEF first, followed by DBP and 3D RVGAS, which divided the patients into four groups stratified for risk of cardiac events of different degrees.Echocardiographic cardiac function analysis has focused mainly on left cardiac chambers, including LVEF, LVGLS, and left atrial volumes and function. The right ventricle, on the other hand, has been regarded for many years as the neglected or forgotten chamber of the heart, with less relevance to RV disease as a primary cardiac disease . HoweverRV pump function consists of three main mechanisms: (i) shortening of the longitudinal axis with traction of the tricuspid annulus toward the apex; (ii) inward  movement of the RV free wall; (iii) bulging of the interventricular septum into the RV during LV contraction and stretching of the free wall over the septum , 16. ImpUnivariate and multivariate Cox proportional hazard analysis revealed that not only 3D RVEF but also 3D RV global strains were significantly associated with a composite of cardiac events as well as HF hospitalization. However, because of the collinearity of 3D RVEF and RV 3D strains, multivariate analyses including both 3D RVEF and RV 3D strains were not conducted in this study.In the CART analysis, 3D RVEF was selected first, followed by DBP, and finally 3D RVGAS, when we included clinical and echocardiography parameters. Selection of 3D RVGAS makes sense because of its smaller NNT next to that of 3D RVEF and because its value reflects strain values in both longitudinal and circumferential directions. Previous publications have reported that lower DBPs are associated with worse prognoses –22. In aIn the present study, RV 3D strain had prognostic value equivalent to that of 3D RVEF, but not better than 3D RVEF. This may be due to the inclusion of left-sided heart disease of various etiologies and the inclusion of patients with preserved or impaired LV function, which may have resulted in different mechanisms of RV dysfunction among patients. Left-sided heart disease was also thought to diffusely impair the right ventricle. As a result, 3D RV strain, which is an indicator of one or two directional motion components, was equivalent but not superior to 3D RVEF, which is a global indicator. This was also consistent with the fact that for RV 3D strain values, 3D RVGAS, a multidimensional index, was a better parameter for prognosis than 3D RVGCS or 3D RVGLS. Combining these parameters with RVEF may allow more detailed stratification of patient prognosis, especially in a specific type of cardiovascular diseases.The cut-off values of 3D RVGCS, 3D RVGLS, and 3D RVGAS found for outcome analysis in this study were 19.5, 15.2, and 30.0%, respectively. These values were correctly below the lower limit of normality (LLN) of 3D RVGCS (LLN: 21.3%), 3D RVGLS (LLN: 24.7%), and 3D RVGAS LLN: 34.8%) reported by Lakatos et al. % reporte–27, sincSome limitations must be acknowledged. This study was single-center, retrospective, observational study that included patients selected from the CMR database. Selection bias should be recognized because only patients undergoing CMR were included, which might be biased toward certain left-sided cardiac conditions. Some echocardiographic parameters and information on the severity of valvular heart disease were not used for the analysis. ReVISION software relies on 3D RV endocardial meshes generated using vendor-independent, commercially available 3DE speckle tracking software. The relatively small number of events in this study did not allow for extensive subgroup analysis. Further studies are needed to investigate the potential usefulness of RV 3D strains in specific cardiac diseases. The decision tree obtained from the CART analysis has been optimized for our population and needs to be externally validated in future studies.RV 3D strains provided equivalesnt prognostic usefulness compared with 3D RVEF in patients with diverse cardiac diseases. Combining these parameters with 3D RVEF may allow for a more detailed stratification of patient prognoses.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee at the University of Occupational and Environmental Health. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.TK: conceptualization, data curation, data analysis, investigation, methodology, and writing-original draft. AK: investigation, methodology, and writing-original draft. YN: data curation. MT, AF, and BL: investigation and methodology. MT: conceptualization, data analysis, investigation, methodology, writing-original draft, and supervision. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.