The primary endpoint was a composite of all-cause death, stroke or systemic embolism, hospitalization for heart failure, and bleeding at 1 year. The co-primary endpoint was procedural success. Results: The primary endpoint was similar between patients undergoing concomitant M-TEER+LAAC or isolated M-TEER (Kaplan Meier (KM) estimates 36.6% vs. 44.8%; plog-rank = 0.75). Procedural success was also similar . At 1- year, minor bleeds were lower in patients undergoing concomitant M-TEER and LAAC . Conclusion: In patients with concomitant MR and AF and eligible for M-TEER and LAAC treatment, a combined approach of M-TEER and LAAC was as safe as an M-TEER-alone strategy and associated with lower minor bleeding at 1 year.Background: Concomitant mitral transcatheter edge-to-edge repair (M-TEER) and left atrial appendage closure (LAAC) showed to be a feasible approach to optimize the treatment of patients eligible for both procedures, but mid-term outcomes are unclear. Methods: We retrospectively analyzed consecutive patients undergoing M-TEER and enrolled in the local prospective Getting Reduction of Mitral Insufficiency by Percutaneous Clip Implantation (GRASP) registry. We compared patients undergoing isolated M-TEER ( Atrial fibrillation (AF) is a frequent comorbidity in patients suffering from mitral regurgitation (MR). Subjects with MR are more likely to be affected by AF, largely imputable to atrial dilatation and electrophysiological remodeling frequently caused by regurgitant mitral flow. The presence of these arrhythmias adds different challenges in the management and treatment of patients with MR. Indeed, it often amplifies patients’ symptoms due to the paroxysm of the disturbance or the high ventricular response rate, and requires an anticoagulation therapy to avoid thromboembolic events in a population that often has a higher bleeding risk per se. As a corollary, managing these patients requires a comprehensive approach that addresses both MR and AF.In 1996, Blackshear et al. first highlighted that the left atrial appendage was the primary source of thromboembolism in non-valvular AF (91%), and afterwards different studies established the role of left atrial appendage closure (LAAC) in preventing thromboembolic strokes . Mitral transcatheter edge-to-edge repair (M-TEER) is currently the therapy of choice for patients affected by severe MR not eligible for surgery . In randomized clinical trials on M-TEER, AF is frequently observed in a significant proportion of patients, ranging from 33% to 57% of cases ,5,6.Most of these patients had a high bleeding risk or had contraindications for oral anticoagulation. In this setting, transcatheter LAAC has shown its safety and efficacy for both reducing hemorrhagic events and cardioembolic strokes . Over the past two decades, several transcatheter techniques and devices have been developed for the transcatheter treatment of mitral regurgitation and LAAC.LAAC can be performed as a staged procedure or concomitant with M-TEER.Combining M-TEER and LAAC seems an attractive strategy, avoiding repeat left atrial access in two different procedures. However, data about safety and effectiveness of this concomitant approach are scarce ,9. In this study, we aimed at reporting safety and mid-term outcomes of concomitant M-TEER and LAAC procedures, comparing them to those of patients undergoing isolated M-TEER throughout the same period.This study has the potential to give valuable insights into the potential benefits and risks associated with performing M-TEER and LAAC in the same setting, contributing to generate evidence that may guide clinicians towards the optimization of these patients’ managements.From January 2018 to December 2022 a total of 126 consecutive patients with severe MR affected by AF underwent isolated M-TEER or concomitant M-TEER+LAAC at our institution as part of the ongoing, local prospective Getting Reduction of Mitral Insufficiency by Percutaneous Clip Implantation (GRASP) registry. The study was conducted in accordance with the Declaration of Helsinki and details of the registry have been previously described . Forty-oIndications for isolated M-TEER or concomitant M-TEER+LAAC were decided by the local heart team according to current guidelines.Mitral regurgitation was graded according to the current guidelines of the European Society of Cardiology .M-TEER was performed using a 24 Fr MitraClip  or 22 Fr Pascal system  under general anesthesia with transesophageal echocardiographic (TOE) and fluoroscopic guidance. Patients received either third- (NTR and XTR) or fourth-  generation MitraClip or Pascal (P10 and Ace) devices.The right femoral vein was used as the access of choice for the procedures. Initially, a 7-F introducer was inserted, and then exchanged with an 8-F Mullins sheath  over a 0.32 guidewire. The transseptal puncture was performed using a Brockenbrough needle under TOE guidance.Invasive arterial pressure was monitored during the procedures through the left femoral artery, and a central venous catheter was placed in the left femoral vein for fluid and drug infusions.Patients without a coronary angiogram within 1 year of the procedure underwent repeat coronary angiography during the index TEER procedure.The LAAC procedure was performed systematically after M-TEER, through the same transseptal puncture site using the 14Fr TrueSeal Access System for the Watchman FLX device or the 14 Fr TorqVue delivery sheath for the Amulet device. The type and size of the device for LAAC procedure was decided intraoperatively according to TOE findings. After withdrawal of M-TEER system, the LAAC system was inserted and the ProGlide  or the figure of 8 stitch was partially tightened over it to ensure hemostasis for the rest of the procedure. At 45 days, TOE was systematically performed to exclude residual peri-device leak and device-related thrombosis. Clinical follow-up data were obtained by telephone interviews and review of medical records. The primary endpoint was the composite of all-cause death, stroke or systemic embolism, hospitalization for heart failure (HF), and bleedings at 1 year. The co-primary endpoint was procedural success. In the case of concomitant M-TEER and LAAC groups, procedural success was defined when both procedures were successful.Secondary endpoints were each single component of the primary endpoint, technical success, and vascular complications . All endpoints were defined according to the Mitral Valve Academic Research Consortium classification (MVARC), the Munich Consensus, and the Bleeding Academic Research Consortium classification (BARC) ,12,13. Continuous variables are presented as median and interquartile range (IQR); categorical variables are presented as number and percentage. The Kolmogorov–Smirnov test was used to test for a normal distribution of continuous variables. The two groups were compared using the Mann–Whitney U test for continuous variables and the chi-square test for categorical variables. Time-to-first-event rates for the study endpoints were estimated using the Kaplan–Meier method. p-value < 0.05 was considered the threshold for statistical significance. All statistical analyses were performed using SPSS version 27 (IBM software).Multivariable logistic regression analysis was performed to assess baseline factors associated with the primary composite endpoint. The results were reported as odds ratio (OR) with a 95% confidence interval (CI). All statistical tests were performed 2-tailed, and a Baseline characteristics of the study population are presented in the The median age was 79 years (IQR: 73–82) and 45% of patients were female. Patients had diabetes and hypertension in 32.3% and 83.8% of cases, respectively. p = 0.22 and p = 0.08 respectively). Chronic kidney disease (CKD) was more frequent in patients undergoing concomitant M-TEER+LAAC . The estimated mortality risks according to Society of Thoracic Surgeons (STS) score and EUROScore II were similar between patients undergoing concomitant M-TEER+LAAC or isolated M-TEER  and (median 3 vs. 2.5 p < 0.01), respectively. The median CHA2DS2-VASc score and HAS-BLED score were 4 (IQR: 3–5) and 3 (IQR: 2–4), respectively, being higher in patients undergoing concomitant M-TEER+LAAC . The etiology of mitral regurgitation was similar between the concomitant and isolated procedure groups. Considering the entire population, 23% had degenerative mitral regurgitation, 67% had functional mitral regurgitation, and 10% had mixed mitral regurgitation , followed by cauliflower and windsock (17% for both).Procedural characteristics are reported in p < 0.01). The concomitant M-TEER+LAAC group received a greater amount of contrast than the isolated M-TEER group (100 (IQR 67–160) vs. 30 [IQR 20–50] mL; p = 0.24).Procedural time was similar between the combined and the isolated groups (median 75 (IQR 63–81) vs. 71 (IQR 61–78) min; p = 0.64). Median fluoroscopy time was similar between the two groups (median 22 (IQR 14–28) vs. 21 (IQR 12–32) min, for patients undergoing M-TEER+LAAC and isolated TEER respectively; The MitraClip was the most used device for M-TEER (97.9%) and a single clip was mainly used for the procedure.The most used device for LAAC was the Watchman FLX (97.5%), and the most used access sheath was the double curve (85.4%).The isolated M-TEER group underwent TOE evaluation at 45 days. One device related thrombus was found. No device embolization, leaks, or pericardial effusion were detected.In-hospital outcomes are reported in No death, cardiac perforation, device embolization, acute kidney injury (AKI), or stroke occurred in either group. p = 0.41). It was due to pseudoaneurysm and successfully treated with surgical repair within the index hospitalization.One major vascular complication occurred in a patient undergoing M-TEER+LAAC . All arteriovenous fistulas were successfully treated with femoral balloon angioplasty during the index procedure. Seven minor vascular complications  occurred in patients undergoing the combined procedure, whereas 5  occurred in those undergoing isolated M-TEER   . A total of 6 patients did not achieve procedural success, 3 in each group. In the concomitant group, one case was due to an elevated mean gradient after leaflets grasping, one due to a left atrial appendage ostium exceeding the instruction for use of LAAC devices, and one due to a major vascular complication. In the isolated group, one was due to an elevated mean gradient after leaflets grasping, one due to clip detachment, and one due to the impossibility of guiding the procedure with bad TOE windows. p = 0.94)  . p = 0.16)  .p = 0.36).Median length of hospital stay was 5 days in both groups (log-rank = 0.75)  .log-rank = 0.45), hospitalization for HF , stroke, or systemic embolism  rates did not differ between patients undergoing concomitant M-TEER+LAAC or isolated M-TEER   . All bleMultivariable logistic regression analyses of baseline characteristics associated with the primary composite endpoint are reported in No predictors of the primary composite endpoint have been detected. The prevalence of atrial fibrillation in patients undergoing M-TEER is high, and a not negligible portion of patients are at high bleeding risk or have contraindications for oral anticoagulation therapy ,13,14,15We aimed at reporting the largest experience of performing concomitant M-TEER and LAAC procedures so far, comparing early and mid-term outcomes of consecutive patients included in our local prospective GRASP registry who underwent concomitant M-TEER+LAAC or isolated M-TEER procedures.The main findings of this analysis were the following. First, the procedural and technical successes were high and comparable in the two groups. In a single case, it was not feasible performing LAAC, due to the large dimension of left atrial appendage ostium. Remarkably, in this study, the LAAC procedure was not planned with TOE or computed tomography angiography before the index procedure. One could assume that the concomitant approach may result in a lower success rate of LAAC, in consideration of a non-ideal transseptal puncture site. We showed that effective LAAC can be achieved even with a superior transeptal puncture and no prolongation of operative times, as suggested by the similar procedural and fluoroscopy time between the two study groups. Moreover, the current study was the first to assess the effectiveness of the use of the Watchman FLX device (95.1% of the cases) in consecutive patients undergoing concomitant M-TEER and LAAC procedures. It might be argued that the improvements brought by this device iteration have been determinant to the high procedural success, even using a suboptimal transeptal puncture site for LAAC.The safety profile of the concomitant M-TEER+LAAC approach was supported by the absence of in-hospital adverse events , apart from a single major vascular accident.Of note, despite the concomitant group having more incidence of chronic kidney disease at baseline and receiving a higher volume of contrast medium, it did not result in higher AKI rates. log-rank = 0.008). Second, concomitant M-TEER+LAAC and isolated M-TEER had similar rates of all-cause death, HF rehospitalization, stroke, or systemic embolism at 1 year, but bleeding events were significantly lower in patients receiving LAAC (KM est. 18.9% vs. 0.0%, pThese results underline the effectiveness of adding LAAC during the index M-TEER procedure in eligible patients. Indeed, the possibility to withdraw oral anticoagulants after LAAC achieves the reduction of bleeding events in high bleeding risk patients without increasing the risk of ischemic events. In our study, only 9.7% of the patients who underwent LAAC continued with anticoagulation therapy at discharge, compared with 86.2% of subjects in the isolated M-TEER group.Staging the two procedures is typically better reimbursed in most countries, but combining the two treatments into a single procedure avoids patients undergoing repeat vascular access and transseptal puncture, thus lowering the procedural risk of a two-stage approach. Although the study should be considered hypothesis-generating due to the relatively small sample size and its retrospective nature, the results support the effectiveness of combining M-TEER and LAAC in anatomically unselected patients, with a benefit in terms of bleedings at mid-term. Larger, prospective, and adequately powered studies are awaited to confirm these findings.This was a retrospective, single-center study, and selection bias cannot be excluded. Nevertheless, baseline characteristics were homogenous between the study groups, with the experimental arm showing a higher, predicted bleeding risk. Finally, the relatively small sample size limits the generalization of the results.In patients affected by concomitant MR and AF deemed eligible for M-TEER and LAAC, combining M-TEER and LAAC in a single procedure was safe and effective with similar rates of all-cause death, HF rehospitalization, stroke or systemic embolism, and bleedings at 1 year compared to patients undergoing isolated M-TEER. Bleedings were significantly lower in patients receiving LAAC. Larger, prospective studies are required to confirm these findings.


Right ventricular–pulmonary artery coupling (RV-PA coupling) refers to the relationship between RV contractility and RV afterload. Normal RV-PA coupling is maintained only when RV function and pulmonary vascular resistance are appropriately matched. RV-PA uncoupling occurs when RV contractility cannot increase to match RV afterload, resulting in RV dysfunction and right heart failure. RV-PA coupling plays an important role in the pathophysiology and progression of cardiovascular diseases. Therefore, early and accurate evaluation of RV-PA coupling is of great significance for a patient’s condition assessment, clinical decision making, risk stratification, and prognosis judgment. RV-PA coupling can be assessed by using invasive or noninvasive approaches. The aim of this review was to summarize the pathological mechanism and evaluation methods of RV-PA coupling, the advantages and disadvantages of each method, and the application value of RV-PA coupling in various cardiovascular diseases. In recent years, there has been increasing appreciation for the importance of right ventricular (RV) coupling to the pulmonary arterial (PA) circulation, which is called right ventricular–pulmonary artery coupling (RV-PA coupling). RV-PA coupling refers to the relationship between RV contractility and RV afterload and is defined as the ratio of RV end-systolic elastance (contractility index) to PA elastance  ,2,3,4,5,RV-PA coupling is a comprehensive index that requires an overall understanding of RV function and RV afterload, and can be assessed by using integrated hemodynamic parameters related to RV function and afterload . RV-PA cPrevious studies have shown that multi-beat end-systolic elastance (Ees)/effective arterial elastance (Ea) from invasive pressure–volume loops by RHC has been regarded as the gold standard for assessment of RV-PA coupling ,16,17,18TAPSE is the most frequently used echocardiographic parameter of RV systolic function in clinical settings A. PulmonTricuspid annular systolic velocity (S’) measured by tissue Doppler imaging is another highly reproducible indicator of longitudinal RV contractility of the basal RV free wall B. S’/rigRight ventricular fractional area change (RVFAC) reflects RV longitudinal and transverse systolic function and is calculated as (RV end-diastolic area—RV end-systolic area)/RV end-diastolic area × 100% C 39]. T. T39]. TAs mentioned earlier, Ees is calculated as ESP/ESV and Ea is calculated as ESP/SV. Both have a common ESP, so the RV-PA coupling ratio can be further simplified as SV/ESV, which is an alternative volume-based method to RV-PA coupling assessment ,43. In aRecently, right ventricular longitudinal strain (RVLS) obtained by two-dimensional speckle tracking echocardiography has been demonstrated to be a more sensitive indicator for RV function than conventional echocardiographic parameters, and it can detect subclinical myocardial dysfunction earlier . The assRVEF is the most common used index of RV systolic function in everyday clinical practice and is strongly predictive of adverse outcomes in patients with a variety of cardiovascular diseases ,55. It hp = 0.02), and MB Ees/Ea predicted the time to clinical worsening in PAH. In addition, MB Ees/Ea was shown to be superior to RVEF and SV/ESV in its ability to predict CW in human PAH [p < 0.001). Additionally, PAH is one of the most important factors for the evaluation of patients before heart transplant, which is associated with prognosis after heart transplant [The level of clinical treatment continues to improve, but the quality of life and prognosis of PAH patients are still unsatisfying ,57. PAH uman PAH . RV-PA cuman PAH . In the uman PAH . In a stuman PAH . Their fansplant . In a stansplant . Thus, tHF is a clinical syndrome in which symptoms and/or signs are caused by structural and/or functional abnormalities of the heart, as evidenced by elevated levels of brain natriuretic peptide and/or objective evidence of pulmonary or systemic congestion ,68,69,70HfpEF accounts for about half of HF patients , and it The pathological process of HfrEF is similar to HfpEF, but it has a high incidence as well as poor prognosis. HfrEF is often accompanied by PAH, which can significantly affect disease progression and prognosis; thus, it is crucial to evaluate RV-PA coupling in patients with HfrEF. In a study cohort of 112 patients with HfrEF, Ees/Ea was closely associated with RV function and predicted the overall survival of HfrEF patients . FurtherCompared to the left heart, a few studies have investigated RV function in hypertensive patients. Actually, long-term overload of the left ventricle can result in increased pressure and injury, with intimal fibrosis and remodeling on the pulmonary vessels, leading to elevated PVR, PA pressure, and RV afterload, and ultimately right ventricular dysfunction came up . Patient‘/RVSP by echocardiography to investigate RV-PA coupling in patients with aortic stenosis who experienced transcatheter aortic valve replacement, and they found that S’ was increased in aortic stenosis patients after transcatheter aortic valve replacement, indicating enhanced RV-PA coupling [The tricuspid valve is closely related to RV and PA. A study investigating 1149 patients with secondary TR applied TAPSE/PASP to estimate whether RV-PA coupling could improve risk stratification and showed that 470 patients had a decreased TAPSE/PASP ratio; that is, decoupling of RV-PA, and patients with decoupled RV-PA had a higher incidence of symptoms and comorbidities, with more severe TR and RV remodeling . In addicoupling . This stcoupling . Thus, Rn = 84) and VPS with previous intervention (n = 45)), RV-PA coupling was lower in the TOF group FAC/RVSP (ratio 1.10 ± 0.29 vs. 1.48 ± 0.22 (p < 0.001)) and TAPSE/RVSP ratio (0.51 ± 0.15 vs. 0.78 ± 0.11 (p < 0.001)) compared to the healthy controls because of a higher RV afterload (RVSP 42 ± 3 mm Hg vs. 31 ± 3 mmHg (p = 0.012)) [Tetralogy of Fallot (TOF) is the most frequent cyanotic congenital heart disease. Patients with repaired TOF commonly develop RV dilatation, which can cause progressive RV failure. Previous studies primarily focused on the evaluation of RV dimensions and function, but RV is coupled to the low-pressure PA system with high compliance . Therefo 0.012)) . Similar 0.012)) . Additio 0.012)) . They fo 0.012)) . By eval 0.012)) . This st 0.012)) . In a re 0.012)) . Further 0.012)) . Thus, RDCM is characterized by the presence of systolic dysfunction and LV or biventricular dilatation and in the absence of coronary artery disease or abnormal loading conditions, which frequently has a genetic background . RecentlDecoupling of RV-PA is the terminal state of various cardiovascular diseases and poses a great harm to the health of patients. Therefore, early and accurate evaluation of RV-PA coupling in patients has important clinical value for the early identification of high-risk patients, delaying disease progression, guiding treatment, and improving prognosis. At present, various methods for RV-PA coupling assessment have been employed, including invasive and noninvasive approaches. The gold standard measurement of RV-PA coupling is Ees/Ea obtained by RHC, which is invasive, complex, and not widely available. It is expected to replace invasive techniques as the preferred method in the future with the continuous development of easy and noninvasive techniques such as Large sample, and prospective studies are needed to clarify the optimal application parameters of the noninvasive estimation of RV-PA coupling in order to help doctors to provide better management of patients.


Follow-up after acute myocarditis is important to detect persisting myocardial dysfunction. However, recovery of atrial function has not been evaluated after acute myocarditis so far. Thirty-five patients with strictly defined acute myocarditis underwent cardiovascular magnetic resonance  in the acute stage at baseline (BL) and at 3 months follow-up (FU). The study population included 13 patients with biopsy-proven “cardiomyopathy-like” myocarditis (CLM) and 22 patients with “infarct-like” (ILM) clinical presentation. CMR feature tracking (FT) was performed on conventional cine SSFP sequences. Median LA-GLS increased from 33.2  at BL to 37.0%  at FU in the entire study population. Median LA-GLS also increased from 36.7  at BL to 41.3%  at FU in the ILM subgroup and from 11.3  at BL to 21.4%  at FU in the CLM subgroup. Median RA-GLS significantly increased from BL with 30.8  to FU with 33.7%  in the entire study population. Median RA-GLS also significantly increased from 32.7  at BL to 35.8%  at FU in the ILM subgroup and from 22.8  at BL to 31.0%  at FU in the CLM subgroup. Our findings demonstrate recovery of LA and RA function by CMR-FT strain analyses in patients after acute myocarditis independent from clinical presentation. Monitoring of atrial strain could be an important tool for an individual assessment of healing after acute myocarditis. Follow up of patients after acute myocarditis is important in order to detect persisting or developing heart failure (HF) as well as enabling a safe return to physical activity . In ordeBesides ventricular function, the relevance of atrial function is increasingly recognized as an important factor in patients with HF: Left atrial (LA) dilatation represents a strong predictive marker in patients with dilatative cardiomyopathy (DCM) . A recenThe local ethics committee approved the study (PV3987) and written informed consent was obtained from all participants. This study is based on additional analyses in a subgroup of 35 consecutive patients of a recently published study population with strictly defined acute/active myocarditis, who underwent CMR at baseline (BL) and at 3 months follow-up (FU). We have published a detailed description of the entire study population before . BrieflyBiGLS) and volumes were calculated as the mean value of two and four-chamber measurements. In the following paragraphs, data referred to as “LA” represent biplane values, if not further specified. LV global myocardial strain was assessed as longitudinal (GLS), circumferential (GCS) as well as radial strain (GRS) employing endo- as well as epicardial contours. GLS for the thin walls of LA, RA and RV was obtained from endocardial contours, since epicardial contours are only available for LV strain by the Medis software   and therefore describe atrial reservoir function [The CMR protocol was conducted as previously described , 18. Brifunction .Fig. 1ExIn order to classify diastolic dysfunction for this sub-cohort-study, echocardiographic data, derived during the patients’ hospitalization, was utilized where available. The classification of diastolic dysfunction was performed as recommended by Nagueh et al. . UnfortuStatistical analyses were performed using MedCalc for Windows, version 13.3.3.0 . Continuous data are presented as median with first and third quartiles (Q1–Q3). BL and FU parameters were compared using Wilcoxon Test (paired samples) test. Diastolic dysfunction of ILM and CLM was compared by Fisher’s exact test. Twenty randomly selected data sets were analyzed independently by two experienced observers and inter-observer agreement was assessed by calculating intra-class-correlation coefficients (ICC). Two-sided P-values were calculated and P < 0.05 defined as statistically significant. We did not apply a correction for multiple testing. P < 0.05 was defined as statistically significant.SAXGCS 0.958  and LVLAXGLS 0.922 . Modest ICCs were found for LV-GRS with LVSAXGRS 0.575  and LVLAXGRS 0.686 .LA-GLS provided an excellent intraclass correlation coefficient (ICC) with 0.943 . RA-GLS also offered a very good ICC with 0.878 . ICCs were excellent as well for LV-strain analysis with LV2  at FU  significantly increased from BL to FU in the entire study population  vs. 59.5% ; P = 0.0019) as well as in the subgroups  and median right atrial volume index (RAVi) from BL to FU, neither in the entire study population, nor in the subgroup analyses Tables  and 2. HThere was a significant, inverse correlation of changes in heart rate between BL and FU with changes in LA-GLS  P = 0.0022) and RA-GLS  P = 0.0387).LAXGLS increased from BL to FU in CLM with − 10.4%  vs. − 13.0% , but also in ILM with − 17.6%  vs. − 20.8%   significantly improved from 48.0  at BL to 57.0%  at FU in CML, but not in ILM Table . Median S) as parameter for atrial reservoir function [This study evaluated atrial and ventricular function after acute myocarditis by CMR-FT. To the best of our knowledge, development atrial function by FT after acute myocarditis has not been investigated so far. Because of its superior reproducibility, we report total atrial strain , whereas patients presenting with symptoms of new-onset heart-failure can be categorized as having “cardiomyopathy-like myocarditis” (CLM) , 40, 41.with ILM . LVLAXGLm itself , 29. Upom itself . In thisFirst, our study population was relatively small. Furthermore, the study population is affected by an inherent selection bias, since patients with clinical worsening and subsequent ICD-implantation or heart transplantation were not able to participate at FU. However, our study population is representative for the majority of patients after acute myocarditis, who undergo regular follow-up visits in outpatient settings. Nevertheless, larger studies with long-term follow-up are warranted to better understand the relevance of functional recovery by atrial CMR-FT compared to myocardial tissue characterization, but also conventional biomarkers after acute myocarditis. We observed a significant correlation between changes in LA- and RA-strain with changes in heart rate from BL to FU, which could be explained by myocardial healing. However, changes in heart rate could constitute a potential technical confounder for the observed changes in LA-strain . In addiOur findings demonstrate recovery of LA and RA function by CMR-FT strain analyses in patients after acute myocarditis independent from clinical presentation. Monitoring atrial strain could be an important tool for an individual assessment of healing after acute myocarditis.


As yet, there is no agreed definition for the so-called “hypoplastic left heart syndrome”. Even its origin remains contentious. Noonan and Nadas, who as far as we can establish first grouped together patients as belonging to a “syndrome” in 1958, suggested that Lev had named the entity. Lev, however, when writing in 1952, had described “hypoplasia of the aortic outflow tract complex”. In his initial description, as with Noonan and Nadas, he included cases with ventricular septal defects. In a subsequent account, he suggested that only those with an intact ventricular septum be included within the syndrome. There is much to commend this later approach. When assessed on the basis of the integrity of the ventricular septum, the hearts to be included can be interpreted as showing an acquired disease of fetal life. Recognition of this fact is important to those seeking to establish the genetic background of left ventricular hypoplasia. Flow is also of importance, with septal integrity then influencing its effect on the structure of the hypoplastic ventricle. In our review, we summarise the evidence supporting the notion that an intact ventricular septum should now be part of the definition of the hypoplastic left heart syndrome. As yet, there is no agreement regarding the lesions that should be included within the so-called “hypoplastic left heart syndrome”. The definition by the International Nomenclature Society  specifieAccording to Noonan and Nadas, it was Lev who introduced the term “hypoplastic left heart syndrome” . This isIn their “guidelines” for treatment of infants born with the syndrome, the group convened by the European Associations for Paediatric Cardiology and Cardiothoracic Surgery had emphasised the phenotypic variability. They identified as subsets those with combined mitral and aortic atresia, those with mitral stenosis with aortic atresia, those with combined mitral and aortic stenosis, and those with the so-called hypoplastic left heart complex . They alAll of these discussions point to the need for a uniform definition for those individuals properly to be considered as belonging within the “syndrome”. Only when comparisons are made between comparable lesions will we finally unravel the genetic cues underscoring hypoplasia of the left ventricle, be it in the setting of an intact or deficient ventricular septum. We revisit here the developmental and morphological evidence supporting the notion that the lesions properly making up the syndrome, as opposed to the mere presence of left ventricular hypoplasia, are the consequence of an acquired disease of fetal life ,4. We doAfter formation and looping of the ventricular component of the developing heart, which occurred around 4 weeks after fertilisation in the human, and around the eleventh day of development in the mouse, the apical components of the developing right and left ventricles begun to balloon from the outer curvature of the ventricular loop A.At this initial stage, all the blood entering the developing left ventricle must pass through the interventricular communication to reach the developing right ventricle, which supports the entirety of the outflow tract. The first stage of ventricular remodeling is the expansion of the atrioventricular canal to provide the right ventricle with its own inlet B. The reWhen the partition is built at the ventricular base, sequestrating the cavity of the aortic root from the body of the right ventricle A, a commThe key feature of the circulation in the patients with left ventricular hypoplasia included in the original cohorts assembled by both Lev  and NoonIn the era prior to development of palliative surgical intervention , closureAnalysis of specimens collected together in the periods prior to development and widespread adoption of the Norwood sequence for surgical palliation  shows a The presence of a perforate but stenotic mitral valve, of course, permits flow to take place into the left ventricle. It is almost certainly this flow into a restricted left ventricle that induces the formation of the dense layer of fibroelastosis that is characteristic for the ventricle when the mitral valve is stenotic and the ventricular septum is intact. Such an elastotic lining is not seen when the mitral valve is atretic or imperforate. It is similarly not usually found when the left ventricle is hypoplastic with either an atrioventricular or a ventricular septal defect. The features of the atretic, as opposed to imperforate, valve are also of interest. As already discussed, when first formed, the atrioventricular canal is supported exclusively by the developing left ventricle A. PresenThe overall findings from the hearts collected together in archives created prior to the advent of surgical correction support the existence of the phenotypical subsets highlighted in the European guidelines . Thus, tThere is then an additional subset of lesions within the group of hearts having combined aortic and mitral stenosis. These hearts represent the best end of the spectrum of left ventricular hypoplasia found when the ventricular septum is intact. They have been nominated to represent a so-called “hypoplastic left heart complex” . The heaWe have shown the morphological findings that underscore the inclusion of an intact ventricular septum as part of the definition of the hypoplastic left heart syndrome. Such a definition does not deny the fact that the left ventricle can be grossly hypoplastic when there is hypoplasia of the aortic arch in the settings of either an atrioventricular or ventricular septal defect. In the latter arrangements, however, in which the left atrioventricular valve is itself patent but stenotic, it is rare to find the hypoplastic left ventricle lined by fibroelastosis unless the defect itself is grossly restrictive. This fact then points to the influence of abnormal flow as instituting the fibroelastotic changes ,14. The The morphological features of hearts found with hypoplasia of the left ventricle support dividing them into groups with and without an intact ventricular septum. The findings show that, when the ventricular septum is intact, the hearts can be interpreted as showing the features of an acquired disease of fetal life. For this reason, we recommend that the integrity of the ventricular septum should now be included as one of the defining features of the hypoplastic left heart syndrome.


Introduction: The 30–50% non-response rate to cardiac resynchronization therapy (CRT) calls for improved patient selection and optimized pacing lead placement. The study aimed to develop a novel technique using patient-specific cardiac models and machine learning (ML) to predict an optimal left ventricular (LV) pacing site (ML-PS) that maximizes the likelihood of LV ejection fraction (LVEF) improvement in a given CRT candidate. To validate the approach, we evaluated whether the distance DPS between the clinical LV pacing site (ref-PS) and ML-PS is associated with improved response rate and magnitude.Materials and methods: We reviewed retrospective data for 57 CRT recipients. A positive response was defined as a more than 10% LVEF improvement. Personalized models of ventricular activation and ECG were created from MRI and CT images. The characteristics of ventricular activation during intrinsic rhythm and biventricular (BiV) pacing with ref-PS were derived from the models and used in combination with clinical data to train supervised ML classifiers. The best logistic regression model classified CRT responders with a high accuracy of 0.77 (ROC AUC = 0.84). The LR classifier, model simulations and Bayesian optimization with Gaussian process regression were combined to identify an optimal ML-PS that maximizes the ML-score of CRT response over the LV surface in each patient.Results: The optimal ML-PS improved the ML-score by 17 ± 14% over the ref-PS. Twenty percent of the non-responders were reclassified as positive at ML-PS. Selection of positive patients with a max ML-score >0.5 demonstrated an improved clinical response rate. The distance DPS was shorter in the responders. The max ML-score and DPS were found to be strong predictors of CRT response (ROC AUC = 0.85). In the group with max ML-score > 0.5 and DPS< 30 mm, the response rate was 83% compared to 14% in the rest of the cohort. LVEF improvement in this group was higher than in the other patients (16 ± 8% vs. 7 ± 8%).Conclusion: A new technique combining clinical data, personalized heart modelling and supervised ML demonstrates the potential for use in clinical practice to assist in optimizing patient selection and predicting optimal LV pacing lead position in HF candidates for CRT. In addition to being at optimal medical treatment, cardiac resynchronization therapy (CRT) is an effective therapy for selected patients with chronic heart failure (HF). In the conventional configuration, CRT delivers biventricular (BiV) pacing to correct electromechanical dyssynchrony of the ventricles in order to increase cardiac output.Despite the well-documented CRT benefits for improving patient outcomes and reducing hospitalizations and mortality in CRT recipients, it still remains ineffective in 30–50% of cases . TherefoPacing lead configuration for CRT has been shown to be an essential determinant of patient improvement . SeveralThis highlights the need for further development and validation of useful multimodality imaging-guided strategies for CRT optimization.Predictive models have been developed using modern machine learning (ML) approaches to estimate CRT mortality or hospitalization risks from baseline clinical parameters , to straModelling studies have used detailed anatomical models of the heart to identify ventricular activation features predictive of CRT improvement . A recenIn all the studies mentioned, the question of pacing configuration is considered independently of and after the patient selection decision. In the current article, we develop a novel technique using a combination of clinical data, computational modelling and ML that may help to solve both problems simultaneously during preoperative patient evaluation: to assess the probability of CRT response for a given patient and to suggest an optimal pacing lead configuration to guide implantation if the patient is selected. We hypothesised that characteristics of ventricular activation during BiV pacing, derived from simulations in validated personalised cardiac models, can be used in combination with baseline clinical data to classify a patient as a potential responder/non-responder to pacing. In parallel, the technique identifies an optimal LV pacing site position for the individual patient that maximises the probability of a positive response.In our recent paper , we deveHere, we aimed to further develop a general concept of using a combination of clinical data, patient-specific model simulations and ML techniques to improve patient stratification and guide optimised clinical interventions. The concept is based on the idea of using personalised cardiac mechanistic models for preoperative testing of any clinically relevant conditions of cardiac activity  or interventions  to predict adverse events or assess the effects of therapy from the simulation results combined with ML predictive models. Comprehensive model testing can reveal a solution that predicts the consequences of interventions, which can aid decision making and/or assist in treatment planning and optimisation. Several exciting examples of the application of this concept were presented by N. Trayanova’s laboratory, which developed a model-based and ML technique for predicting arrhythmia inducibility in post-infarction patients , predictTo the best of our knowledge, we are the first to apply such a concept to develop an ML-based technique for CRT optimisation aimed at the preoperative identification of an optimal LV pacing site that predicts the maximum probability of CRT response during BiV pacing. A novel feature of our proposed methodology is the use of a multivariate ML classifier trained on the combination of clinical and model-derived data to predict LVEF improvement in chronic CRT recipients, to optimise the probability of CRT response (ML score generated by the ML prediction model) for a given patient by varying the available LV pacing site location across the LV surface, and to identify and visualize the optimal LV pacing site location. If the maximum ML score predicts a negative response, the patient should probably not be recommended for CRT. Conversely, a patient classified as positive could be proposed as a candidate for CRT implantation and the area of the LV surface with the maximum ML score could be considered as the target for lead implantation. The design of our methodology allows it to be further improved and extended using available and emerging clinical and simulation data, comparing different CRT response criteria and alternative pacing strategies to predict the best way to treat the patient.To validate our approach, we found a higher rate of the clinical CRT response in patients classified as positive by the maximum ML-score prediction. We also showed that a short distance from the site of the implanted LV pacing lead to the optimal position suggested by our algorithm was a strong predictor of the chronic response in our patient cohort.Thus, the contribution of our proof-of-concept study is to demonstrate the potential of an ML technique using simulations of personalised computational models to improve patient selection for CRT implantation and to suggest a pacing optimisation strategy in selected candidates.The pipeline for an ML technique using for BiV pacing optimization is as follows . A persoIn what follows, we first describe methods used to develop a ML classifier of CRT response. Next, we describe a technique we developed for LV pacing site optimization. The ML classifier is used as a key tool in the implementation of the CRT optimization technique.In the present study, we used our hybrid data approach to develop a supervised classifier of CRT response as previously described  Figure . The ML The study involved clinical data from 57 HF patients. The same cohort of patients has been involved previously and described in detail in our recent paper , so hereIn addition to the standard protocol of patient evaluation for CRT implantation, we also acquired data from 12-lead ECG and echocardiography recordings prior and after device implantation. Computer tomography (CT) was performed to visualize the torso and the heart. The series captured with a scanner  were imported into special Wave program version 2.14  to reconstruct the 3-dimensional geometry of the torso and heart. Finally, epi/endo ventricular surface models were manually built and active pacing sites for the RV and LV leads for BiV pacing were derived from the CT scans and used as reference for model simulations. RV electrodes were placed at a standard apical position in all the patients. Data from magnetic resonance imaging  with contrast (Gadovist or Magnevist) before CRT implantation were used to detect scar/fibrosis areas in the myocardium and to incorporate these data into personalized ventricular myocardial models. Patients were evaluated before CRT device implantation and during the follow-up period of 12 months after implantation. The clinical data in intrinsic sinus rhythm (baseline) and during BiV pacing in the patient cohort are presented in Responders and non-responders: Patient data were annotated into responder (n = 23/40%) and non-responder (n = 34/60%) groups according to LVEF improvement by more than 10%. Ventricular anatomy models: Based on the segmentation of CT imaging data, finite element models were constructed for the torso, lungs and RV-LV ventricles for each of the 57 patients (patients . MRI datpatients  for morepatients ), the scModels of myocardial electrical activation and ECG: Like in the previous work  for automatic QRS onset and offset detection.Consistency of the ventricular activation simulations with clinical data: The personalized ventricular models yielded average 12-lead QRSd values very close to the clinical data  as the difference between LV and RV total activation time characterizing inter-ventricular electrical uncoupling; intra-ventricular dyssynchrony index as the relative difference between the mean activation time of the LV free wall and the septum (ADSTLV = (mean ATLVlat—mean ATST)/TAT). Changes in the indices under BiV pacing in comparison with baseline (delta), in either absolute values or normalized to the baseline were also used for developing the ML classifier of CRT response.The second group of model-derived indices were derived from simulated activation maps and 12-lead ECG signals in baseline LBBB and during BiV pacing. The following ventricular activation characteristics were considered: maximum QRSd; total activation time of 95% myocardial tissue volume for the biventricular model (TAT95); RV-LV activation delay  at LBBB and during BiV pacing. Note, that two out of the four model-derived indices, Scar-LVPS distance and ADRVLV at BiV pacing, depend on the LV pacing site position, and may change with LV pacing site moving on the LV surface. These seven features were used to train the final LR classification model , body mass index , and LV end-diastolic diameter . The four model-derived features were: distance from LV pacing site to scarring area , total biventricular activation time , and RV-LV activation delay  and scarred areas (as ineffective) were excluded from consideration. A problem of ML-score optimization across the LV surface was solved. First, we varied the position of the LV pacing site between the centers of LV AHA segments on the epicardial surface , step 1.The small number of points with computed ML-scores did not allow us to accurately predict the optimal LV pacing site with maximum ML-score at the LV surface. Therefore, we used a Bayesian optimization method to predict the ML-score values over the entire LV surface accessible for pacing. This method involves building a regression model and its iterative refinement before converging at the optimal solution .Bayesian Optimization: The iterative process of the optimal ML-score prediction was performed using Bayesian Optimization with Gaussian process regression (GP regression) model  = μ + 2σ, where μ is an expected ML-score value predicted by GP regression and σ is the standard deviation of GP at this point   was found throughout the LV nodes. This LV pacing site was further used to calculate the electrophysiological model at BiV pacing. These model-derived features were fed to the LR classifier to compute the corresponding ML-score.n) model . We usedy value) , step 2.σ), and finding points where the value of the regression function is maximum (points with maximum μ).The Bayesian optimization method thus strikes a balance between finding points that allow one to refine the GP regression model , and use Bayesian optimisation to iteratively improve a position of the LV pacing site that provides the maximum ML-score across the surface using the GP apparatus and regression model uncertainty information. Note that each iteration step of the algorithm required an additional model calculation at the single point of the current extreme candidate, and in our case the method converged in about 5 iteration steps. Therefore, we consider this approach as reasonable because it is fast, does not require grid refinement, and not only identifies an LV location with maximum ML score, but also predicts all other areas on the LV surface with ML score > 0.5 (Ml-score map) that can be considered for LV pacing as positive for CRT response.Finally, we obtained an LV epicardial surface map of ML-score values , step 3,In addition to the ML-score based optimization of LV pacing lead position in the personalized models, we also used an alternative LAT area identified in LBBB for LV pacing site location, as suggested in several clinical studies . AnotherCardiac electrophysiology was simulated using an in-house software based on the FENICS library (for solving PDE problems)  and VTK t-test for normal distribution and the Wilcoxon test for non-normal distribution and McNemar’s test for qualitative data. Comparisons between dependent groups were made using non-parametric Friedman’s two-way ANOVA, followed by a pairwise comparison adjusted for multiple comparisons. Comparison between two independent groups (non-responders vs. responders) was carried out using Mann-Whitney test for quantitative data and Pearson’s chi-square test for qualitative data. Feature dependence was assessed using the Pearson correlation test for normal distribution and the Spearman rank correlation test for non-normal distribution. The critical level of statistical significance was taken equal to 0.05.Detailed analysis was performed using the IBM SPSS Statistics 23.0.0.0 software package (United States). For qualitative data, the frequency and percentage of total patients in the cohort were calculated. Quantitative data are median [25th–75th] quartiles or mean ± standard deviation if the criteria for a normal distribution are met. Comparisons between two dependent groups for quantitative data were made using the paired sample We used retrospective data for fifty seven  CRT recip< 0.001) with 23 (40%) of the patients classified as responders defined by more than 10% ΔLVEF during follow-up. In the responder group, LVEF increased from 23 ± 5% at baseline to 40 ± 6% at followup . In contrast, LVEF improved much less in the non-responder group from 29 ± 6% to 32 ± 7% . The only two clinical characteristics distinguished between responders and non-responders at baseline: lower body mass index  and lower LVEF  were found in responders compared to non-responders.Overall, LVEF increased from 26 ± 6% at baseline to 35 ± 8% (paired LVEF improvement (ΔLVEF) of 9 ± 8%, . 25  RV-LV pacing sites (ref-PS) were used to simulate BiV pacing in the personalized models. In two (9%) responders and in eight (24%) non-responders the LV segments with implanted electrodes were concordant with scar area. A larger distance from the LV pacing site to the scar/fibrosis zone was revealed in the responders compared with non-responders  or negative (ML-score < 0.5) for CRT response. Suggest selecting the patient as a CRT candidate according to the model prediction.7. In case the maximum ML-score is positive, visualize the LV surface map of the ML-score with the labeling of areas of pacing site location predictive of the positive response and indicating an optimal area for LV pacing site.Utilizing the LR classifier, a novel technique has been developed and implemented to identify an optimal LV pacing site position, which maximizes the ML-score of CRT response for a given patient. The technique combines the following steps :1. Compu#2) with a 12% LVEF improvement. In this patient, almost the entire ML-score map is red, predicting a positive CRT response with any available LV site located at the lateral wall. Here, the referent and optimal pacing sites are located in adjacent LV segments and the maximum ML-score of 0.95 at ML-PS is slightly above the referent value of 0.94. So, this patient is predicted as a positive for CRT response (ML-score > 0.5) at both the ref-PS and ML-PS. The LR prediction is in line with a considerable of LVEF improvement in this patient.The left panel demonstrates the ML-score map in a clinical responder (patient #1) with a 7% LVEF improvement. Here, the ML-scores at both the referent and optimal LV pacing sites are blue colored . Moreover, the overall map of ML-scores on the entire available LV surface is blue colored, predicting a low possible response to CRT in this patient. Correspondingly, this patient has a large postinfarction scar spreading over half of the LV segments (see the gray segments in LV AHA model).The central panel shows the ML-score map of a clinical non-responder (patient #7) with a 6% LVEF. The patient was classified by the LR predictive model as a true negative at the ref-PS . At the same time, our algorithm predicts a narrow red area at the basal inferior LV segments where the patient is predicted as positive for CRT response, particularly at the optimal ML-PS position with maximum ML-score = 0.77 (see ML-PS located in the red colour area on the ML-score map). Thus, our simulations suggest that this patient could possibly improve with the ML-based optimal pacing lead placement.The right panel shows the ML-score map of a clinical non-responder . Although it is difficult to identify specific factors contributing to the negative ML score value in the multifactorial LR model, we will discuss possible reasons for the false negative predictions in these patients below in the DiscussionAs defined above, the cut-off ML-score of 0.51 separates potential responders from non-responders. Comparing the ML-score at ML-PS and ref-PS, we found patients who were classified as negative  with ref-PS but reclassified as positive  with ML-PS. To explain the difference in the distributions of ML-scores depending on LV pacing site position, we compared the model-derived features characterising ventricular activation at ML-PS and ref-PS.p< 0.01, respectively). At the same time, no significant difference in average TAT and QRSd was found between ML-PS and ref-PS (In a majority of models (52 (91%) out of the 57 models), BiV pacing from ML-PS was associated with a considerable decrease in all simulated indices characterizing ventricular activation dyssynchrony as compared with the baseline LBBB activation. The median TAT  and QRSd  at ML-PS are seen to be significantly shorter than at baseline  in LBBB baseline have positive average values reflecting a significantly later activation of LV versus RV, and LV lateral wall versus septum  at ML-PS suggests later activation of the LV lateral wall as compared to the septum, while the negative index  at ref-PS reflects later activation of the septum.Both the inter- and intra-ventricular dyssynchrony indices  was found between the improvement in the ML-score and the extension of the Scar-LVPS distance when switching from ref-PS to ML-PS. Comparing the responder and non-responder groups, we observed that the Scar-LVPS distance was shorter for the non-responder group at ref-PS, while at ML-PS the distance increased significantly in the non-responder group, blurring the difference between the groups  identified by our optimised selection algorithm. Even with the use of an empirical approach to LV pacing lead implantation in patients from the selected positive group, the group contained 20 (65%) clinical responders and 11 (35%) non-responders paced from ref-PS. Thus, the clinical CRT response rate (65%) in the positive group selected according to our optimised approach was significantly higher than that of 40% in the entire cohort selected according to current guidelines.Our current study utilized retrospective clinical data, with no patients treated according to the optimization procedure for LV lead implantation that we have developed. However, we were able to perform a virtual “clinical trial” of our optimization approach. First, we selected a group of patients classified as positive for CRT response with a maximum ML-score > 0.5 , there were only 3 (12%) clinical responders and 23 (88%) non-responders. Therefore, the odds ratio of becoming a responder with a positive prognosis versus a negative prognosis was 13.9 CI in our cohort, suggesting a great potential of our approach to assist in the selection of CRT candidates.In contrast, in the group classified as negative for CRT response  reflecting the higher ratio of responders against the negative group. However, no difference in ΔLVEF was found between positive and negative responders, nor between positive and negative non-responders. This fact suggests that if LV pacing site optimisation is not applied to selected patients, no additional LVEF improvement compared to the guidelines could be expected in positive candidates.Not surprisingly, a higher ΔLVEF was found in the positive group  from the ref-PS to ML-PS as a measure of predicting CRT response. An average distance DPS in the responders from the entire cohort was shorter against the non-responders  versus 65 ± 30 mm . The distinction in DPS between responders and non-responders was even higher in the positive group: 40 ± 25 mm  versus 66 ± 28 mm , p = 0.005. Moreover, we found a moderate correlation  between the distance DPS and ΔLVEF .Furthermore, we selected the two features—the maximum ML-score > 0.5, and the distance DPS < 30 mm (n = 12 (21%) out of total 57), the response rate was 83% (10 responders) which is much higher than 29% (13 responders out of 45) in the rest of the cohort. Selection of CRT candidates based on the positive prediction with maximum ML-score > 0.5 and DPS < 30 mm has the odds ratio for CRT response of 12.3 CI . Moreover, an average ΔLVEF value of 16 ± 8  in this group is much higher than 7 ± 8 % (p< 0.01) in the rest of the population, demonstrating a great improvement in the selected patients with a maximum ML-score > 0.5 and LV pacing leads deployed in the proximity to the optimal LV pacing site.In the group of patients with a maximum ML-score > 0.5 (positive) and DThe above results evidence a high potential of our ML-score based optimal LV lead placement to assist in selecting CRT candidates and guiding lead implantation.In our recent study , we deveAs emphasised above, our approach to CRT optimisation can be applied during preoperative patient evaluation before data on RV-LV pacing sites and ECG recorded during BiV pacing are available for a given patient. The optimisation algorithm maximises an ML score of CRT response across the LV surface available for pacing using simulations of BiV pacing at different LV pacing site positions. Of note, a supervised ML classifier used to compute ML scores must be developed independently of and prior to the application of the optimisation procedure. At this stage, for the development of supervised LR classifiers, we need a training dataset of post-operative clinical data with determination of LV and RV pacing sites from CT images and determination of myocardial conductivity parameters based on clinically recorded QRSd at pacing. Since such a supervised LR classifier has been created (as a formulation with defined coefficients), one can use this classifier (with predefined and fixed coefficients) to compute ML scores for an arbitrary RV-LV lead setting using simulations of BiV pacing with that particular pacing site location. Of course, such an ML-classifier could be re-trained from time to time with an expanded dataset as new post-operative clinical data becomes available. However, since the ML classifier is set, no clinical pacing site location is required for our optimization algorithms.The patient assessment algorithm involves building a personalised ventricular model; performing simulations with LBBB activation pattern and under BiV pacing with multiple LV pacing site positions; calculating ML-scores from the LR classifier fed with selected pre-implant clinical data and model-derived features, building a map of ML-score values over the entire LV surface, and finding the optimal LV pacing site position with a maximum ML-score using Gaussian process regression and Bayesian optimisation . UltimatWe demonstrated a broad in-patient variation in the ML-score depending on the pacing site position . The ML-The strength of ML-based optimisation is clearly visualised in Several clinical trials have recommended avoiding apical and anterior regions for LV pacing, where possible . Supplemr = 0.18, p = 0.211).In our study, the extent of LV myocardial damage (both absolute and relative to the surviving myocardium volume) was not selected as a strong predictor of CRT response for the LR classifier. At the same time, the Scar-LVPS distance was selected as the third most important feature for CRT response prediction see . It was r = 0.419, p< 0.001). In addition, we revealed a strong positive correlation between the improvement in the ML-score at the optimal ML-PS and the increase in Scar-LVPS distance against the ref-PS . The higher Scar-LVPS distances were associated with the maximum ML-scores  and showed a higher ΔLVEF  compared to the entire cohort selected according to current guidelines. As expected, the ML-based positive group showed great advantages over the negative group with maximum ML-scores < 0.5, consisting of only 12% of responders and showing a low ΔLVEF of 3  (p< 0.01). These results demonstrate the high potential of our technology for patient selection for implantation.We analysed the benefits of our novel pacing site optimisation technology with respect to two clinical tasks: optimisation of patient selection and optimisation of procedure planning. The technology allows both steps to be performed preoperatively and simultaneously. Even without pacing site optimisation, a positive group of patients classified as favourable for CRT response by a maximum ML-score > 0.5 contained a higher proportion of clinically proven responders with LVEF improvement greater than 10%  and 3 false negative (max ML score < 0.5) out of 23 true clinical responders. The 3 false negative patients #4, #34, #44 were ischemic cardiomyopathy patients with a high relative scar size in the myocardial tissue volume  over the 75% quartile of the distribution 0.12  and a great majority of damaged segments according to the AHA LV model . Two of the three patients have BMI of 31, 32 over the average 30 in non-responders. One of the latter has a high baseline LVEF of 32 over the mean 29 in non-responders. For two of the three patients, a short Scar-LVPS distance from the clinical LV pacing site to the scar area (0 and 16 mm) was lower than the 25% quartile for the distribution 44  in the responders. The above factors favor the negative ML-score prediction of the response for the patients, and it is actually difficult to distinguish particular features that are responsible for the false prediction in the multifactorial LR classifier. Despite max ML-score in these patients was 2–3 times higher than ML-score at ref-PS, it was still lower than the cutoff of 0.5. Note that in patient #43 the max ML-score was of 0.43, close to the responder threshold. We think that such patients with great extent of myocardial damage need more accurate scar area segmentation to get more specific response prognosis (see below Limitation Sec.).PS from the clinical LV pacing position to the optimal site identified by our algorithm was an independent predictor of CRT response . The distance was shorter in the responders and correlated with LVEF improvement . Together with max ML-score > 0.5, a DPS of less than 30 mm is suggested as a cut-off for predicting more than 10% LVEF improvement. The group of patients with maximum ML-scores > 0.5 and DPS< 30 mm demonstrated an higher response rate ) and a better LVEF improvement  as compared with the rest of the cohort.Then, we demonstrated the validity of our optimisation approach as a procedure planning strategy. We showed that the distance Drovement . FinallyThe above results provide evidence of the high potential of our ML-score based LV pacing site optimisation for the selection of CRT candidates and for guiding lead implantation.LAT−LVPS from the ML-PS and ref-LV to LAT area. In addition, we estimated the interventricular RV-LV delay as a time interval between the activation of the LV and RV pacing sites in the LBBB pattern. This feature is often used instead of the Q-LV delay measured from the onset of QRS complex to the activation time of the LV electrode in baseline or during RV pacing. Several studies have showed favourable effects of using the RV-LV delay or DLAT−LVPS as a guide  from the simulated ventricular activation map at baseline LBBB in each personalised model. The LAT pacing site (LAT-PS) was tested as an alternative LV pacing site during BiV pacing in each model. Using LAT-PS, we also calculated the distance D a guide . By cont a guide .The other LV pacing alternative we tested involved LV pacing sites ensuring a minimum total biventricular activation time (TAT-PS). TAT is often used as a measure of ventricular dyssynchrony and its reduction via BiV pacing or at other pacing settings is considered as a target for stimulation design . In clinr = –0.14, p = 0.314). Similarly, there was no difference in the distance DLAT−LVPS between the responders and non-responders, and no correlation with LVEF improvement was found. Accordingly, in the univariate analysis these variables were not selected as independent predictors for CRT response; neither were they selected in the multivariate analysis. Therefore, none of these two features was used as an independent variable in our multivariable LR classifier of CRT response and in the algorithms of pacing site optimization.In our patient cohort, we found no difference in the average RV-LV delay at ref-PS between the responders and non-responders . NeitherAs we showed above, TAT95 simulated in baseline LBBB was selected as one of the most important features for the multivariable LR classifier of CRT response, reflecting the significance of a wide QRSd in guiding patient selection for CRT. However, in consistency with clinical and simulated data on QRSd, no correlation was found between LVEF improvement and simulated TAT at either LBBB or BiV pacing. No correlation was found between LVEF improvement and relative reduction in TAT at BiV pacing against baseline for any pacing site strategy we tested . The disp< 0.01), no improvement in the ML-scores over ref-PS was attained . In contrast, our ML-based LV pacing site optimization accounts for multiple significant features related to the CRT response. A major added benefit of our approach is the use of model-derived predictions of BiV pacing from any available LV pacing site to estimate the probability of CRT response and to select an optimal pacing site that provides the highest probability. In addition, an essential advantage of our approach is the assessment of CRT efficacy prior to implantation, which can improve the response rate through optimal candidate selection and, together with optimised pacing, improve the outcomes.In most studies, pacing site optimization is implemented either intra-procedurally with a parallel assessment of the acute response to BiV pacing, or post-implant in CRT device programming, particularly for quadripolar or multipace LV electrodes. Recently, the largest study using non-invasive 3D electrical activation mapping (ECGi) demonstrated the potential of using the ECGi technique in the pre-implantation planning strategy for CRT by enabling a non-invasive identification of the LAT area coupled to the identification of a suitable coronary vein . This apThere are several limitations in our study.Small dataset: In this study we had a limited dataset sampled from 57 patients. However, to the best of our knowledge, this is the largest model population used in simulation studies. Nevertheless, more data should be collected to split the datasets for training and testing the predictive models, in order to draw more robust conclusions. Still, more data are needed to validate the approach and confirm its usefulness for patient stratification and optimal lead guidance.Fibrosis/scar segmentation and simulation: We have shown a high importance of the distance DScar−LVPS from the LV pacing site to the myocardial damage area for CRT optimization. In this study, we have simulated LV scarring area based on the labeling of damaged LV segments performed by the expert that analyzed the MRI scans. A more accurate segmentation of the raw MRI data should be used to confirm model predictions. In addition, our functional model of an infarct injury region was also simplified as we simulated the scar region as an inexcitable obstacle. Such a simplified assumption has often been used in modelling studies addressing the effects of the scar region on excitation propagation (see a review in (65)). However, experimental data in experimental animals and humans using MRI and optical mapping techniques showed a much more complex local structure and behaviour in the infarct zone  and depends on many factors such as LV end-diastolic volume, the relative volume of excitable myocardial tissue to non-excitable tissue, the fraction of fibrotic tissue with reduced conductivity, the area of initial pacing and the location and proximity of the LV pacing lead to the infarct area, etc. These factors varied in the personalized models, which may explain a substantial variability in the global conductivity parameter  that we identified from the clinical QRSd. Global conductivity parameters tended to be higher in BiV settings than in LBBB settings. This observation can be seen as a penalty for using only one parameter to compensate for many factors that contribute to the difference in activation time with BiV pacing compared to the baseline LBBB pattern. In particular, the increase in the global conductivity parameter was greater in BiV models with greater QRS reduction and a higher proportion of viable myocardium. In this study, we were not able to account for the postoperative change in ventricular geometry and LV volume, which decreases in a majority of patients with chronic pacing , because we used postoperative CT scans for the ventricular geometry models used for both activation settings. It has recently been shown in a simulation study by Rodero and co-authors  that carModel consistency and further improvements: Nevertheless, even with all the aforementioned assumptions and simplifications, we constructed and fitted our personalised models, which demonstrated the ability to capture clinical characteristics of ventricular activation at baseline and during BiV pacing and to reproduce clinically seen synchronisation of ventricular activation during BiV pacing (cond) defined from the postoperative QRSd at BiV pacing or the conductivity (LBBBcond) defined to match the preoperative clinical QRSd at baseline. The latter global conductivity parameter was used to compute simulated characteristics at both LBBB setting and BiV pacing with different tested LV pacing site locations. Corresponding ML scores were then computed using the predefined ML classifier we developed and used to predict an optimal LV lead position across the LV surface. We found that in 55 out of 57 (96%) patient models, the optimal lead position at the LBBBcond parameter was predicted in the same or neighboring segments of the LV AHA model as predicted from model simulations with the BiVcond parameter. The median distance between optimal LV pacing site locations was 3.9 mm, which is approximately the size of the stimulation area with the point source. In 75% of the models the distance was less than 10 mm and in 49 (86%) of the 57 models the distance was less than 20 mm. The larger distance was observed in a few models with a large infarct size, where the optimal LV pacing location was predicted at the same distance from the infarct zone, but at the opposite position of the infarct area. Notably, ML-scores predicted from BiV simulations with different global conductivity parameters were not significantly different and predicted the same positive or negative CRT response for a patient. The only exception was observed in a non-responder model (patient #50), whose optimal ML-scores at different conductivities for BiV settings were close to each other (0.48 versus 0.52) and to the ML-score threshold of 0.5, which did not allow one to guarantee the positive response at the optimal position.To ensure that the results of our study were robust to the global conductivity parameter used in the BiV settings, we compared the predictions of the optimal LV pacing site using BiV simulations with either the global conductivity parameter (BiVThese observations allow us to suggest that, despite the limitations of the model, it is clearly able to predict the optimal position for the LV pacing site based on the only preoperative clinical data used for simulation at the BiV settings.However, further improvements of personalised models could be performed in future to assess effects of more specific simulation of the Purkinje network morphology and LBBB level; scar/fibrosis area location, shape and texture; and tailoring personalised electrophysiological parameters based on the morphology of the QRS complex or the entire time-dependent ECG signal. Several approaches to the latter problem, assuming spatial heterogeneity of conductivity within the myocardial tissue and taking into account partially excitable areas of damaged myocardium, have recently been developed by several groups, including our team , and couCoronary sinus data merge: In this study, we identified a sort of ideal optimal LV pacing site on the entire LV surface. Here, we did not consider realistic anatomical constraints for epicardial LV lead access, which are limited by the available coronary sinus veins traditionally used for LV lead implantation. We plan to circumvent this limitation and validate our ML-based optimisation technique using ventricular models merged with coronary sinus visualisations. This will allow us to test the LV pacing sites within the coronary sinus approach and verify if our ML-based approach is able to improve CRT candidate selection and procedure outcome with more realistically optimised BiV pacing.Multi-modal clinical data: We have not addressed a number of strategies for patient selection and pacing site optimization that are becoming available with further development in ECGI and other imaging techniques. Recent studies have paid much attention to utilyzing vector ECG characteristics for CRT response prediction (ediction . The morediction  and to fediction . Moreoveediction  and beinAlternative strategies for ventricular pacing: In this proof-of-concept study, we used our ML-based approach to optimize BiV pacing. Currently, there are data emerging on different pacing modality enhancing effects in selected patients depending on the ischemic or non-ischemic origin of HF, LBBB or non-LBBB activation, the levels of conduction system block, etc. New techniques for His-Purkinje and/or conduction system pacing, selective LV, RV or BiV, or multipole pacing, epicardial versus endocardial pacing and their possible combinations together with AV and VV delay optimisation for optimal ventricular fusion create a challenge for making the best choice (t choice . Only coOur approach enables one to account for more data available from various imaging modalities in the analysis and training of multivariable ML classifiers. Thus, the dataset for analysis can be expanded, CRT response criteria can be improved, and our general ML-based simulation approach to finding an optimal pacing configuration that maximises the probability of response can be further improved to accommodate data expansion. Furthermore, there is a general conclusion that can be drawn from the studies suggesting a strategy for LV pacing site optimisation to improve CRT response, that is, any such strategy needs to be evaluated prospectively in an intention-to-treat trial, in which data from different modalities can be included and different strategies can be compared.We have developed algorithms to identify an optimal LV pacing position across the accessible LV surface that maximizes the ML-score generated by the LR classifier of CRT response.In the group of selected patients with maximum ML values > 0.5 predictive of a positive CRT response, the response rate improved compared to the negative group and the overall cohort.The proximity of implanted LV leads to the optimal pacing site, which can be determined non-invasively and preoperatively using our optimisation technique, was shown to be a strong independent predictor of CRT response.Our non-invasive approach to identifying a maximum ML score of CRT response can help improve patient selection for CRT. At the same time, the best pacing site location predicted by our algorithms can be used to guide lead placement. Overall, our approach may help improve the efficacy of CRT.


The autonomic nervous system is related to involuntary body functions, including gastrointestinal mechanisms, blood pressure, skin muscle activity and heart rate Hall, . The litAbdalbari et al., which applied Granger Causality to capture brain-heart mechanisms during wakefulness and sleep. Granger Causality is a statistical test that can also be used as a measure for bidirectional connectivity in EEG   and its association with VVS. The CGRP is a peptide localized to C and Aδ sensory fibers with important role in sensory and efferent function, which presents a broad innervation throughout the body with large perivascular location , also known as neurocardiogenic syncope or reflex syncope, is an impairment investigated worldwide , root mean square of successive differences (RMSSD), low-frequency power (LF), high-frequency power (HF), and LF/HF. As the main data, heart rate variability was increased by taxane and carboplatin treatment.Another study related to heart rate variability was published by Arai et al. tried to establish a new animal model related to stress cardiomyopathy. The study investigated epilepsy-induced stress cardiomyopathy and the involvement of neuropeptide Y in cardiac dysfunction. The report evidenced that activation of the sympathetic nervous system leads to upregulation of neuropeptide Y in cardiac sympathetic nerves and stellate ganglion.Finally, Takotsubo cardiomyopathy is another pathology involved with the relationship between autonomic injury and cardiovascular risk increase (Amin et al., Briefly, the articles published in this Research Topic provide additional information regarding brain-heart interaction, vagus nerve activity, heart rate variability and stress cardiomyopathy. Therefore, the results obtained in this Research Topic supply compelling evidence that adds valid data to better comprehend the association between cardiovascular risk and autonomic nervous system.VV and LV drafted the editorial and gave final approval.