The clinical spectrum of atrial fibrillation means that a patient-individualized approach is required to ensure optimal treatment. Cardiac computed tomography can accurately delineate atrial structure and function and could contribute to a personalized care pathway for atrial fibrillation patients. The imaging modality offers excellent spatial resolution and has been utilised in pre-, peri- and post-procedural care for patients with atrial fibrillation. Advances in temporal resolution, acquisition times and analysis techniques suggest potential expanding roles for cardiac computed tomography in the future management of patients with atrial fibrillation. The aim of the current review is to discuss the use of cardiac computed tomography in atrial fibrillation in pre-, peri- and post-procedural settings. Potential future applications of cardiac computed tomography including atrial wall thickness assessment and epicardial fat volume quantification are discussed together with emerging analysis techniques including computational modelling and machine learning with attention paid to how these developments may contribute to a personalized approach to atrial fibrillation management. Atrial fibrillation is conventionally classified according to a single domain - the temporal pattern of the arrhythmia. Whilst the use of classification systems has increased our understanding of atrial fibrillation to some extent, such an approach can overlook the importance of the interplay of factors related to the patient, their symptom severity, risk factors and the atrial electrophysiological substrate underpinning atrial fibrillation. Given these interactions, the management of atrial fibrillation necessitates the delivery of a coordinated patient-individualized care pathway to ensure optimal treatment. Whilst more research to explore the patient-specific pathophysiological basis of atrial fibrillation is necessary, the principles of patient-individualized care have been encapsulated in the 4S-AF scheme which aims to provide a structured, pathophysiology-based characterisation of a patient's atrial fibrillation comprising four domains - stroke risk, symptom severity, severity of atrial fibrillation burden and substrate severity.Within this framework, computed tomography may provide useful information to guide atrial fibrillation management by enabling assessment of cardiac structure and function. Advances in computed tomography temporal and spatial resolution have strengthened its utility in the field of electrophysiology.This review will discuss the current state-of-the-art for computed tomography in atrial fibrillation management together with potential future applications that form part of ongoing research .Fig. 1Th22.1In atrial fibrillation, cardiac computed tomography has been used prior to direct current cardioversion, radiofrequency catheter ablation and left atrial appendage closure to investigate for contraindications, delineate anatomy and guide procedures.In the context of atrial fibrillation ablation procedures, pre-procedural atrial imaging can define patient-specific atrial anatomy.Prior to percutaneous left atrial appendage occlusion, pre-procedural imaging is utilised to guide left atrial appendage occluder device selection and sizing, and to facilitate procedure planning.2.2In patients with sub-therapeutic anticoagulation (missed anticoagulant doses or short duration of therapy), investigation for left atrial appendage thrombi prior to direct current cardioversion or atrial instrumentation is required.,Computed tomography can be used to differentiate between left atrial appendage thrombus and slow blood flow by comparing early arterial and delayed phase images.2.32) score. In a recent simulation study by Qureshi et al., it has been demonstrated that left atrial appendage morphology may affect coagulation dynamics in blood flow during sinus rhythm and atrial fibrillation, and subsequent risk of thrombus formation.Cardiac computed tomography can be used to accurately characterize left atrial appendage morphology . Left at2.4Prior to radiofrequency catheter ablation of the pulmonary veins in atrial fibrillation, an awareness of a patient's left atrial and pulmonary vein anatomy can enable accurate targeting and planning through the measurement of ostial diameter, characterization of variants in pulmonary vein anatomy such as accessory pulmonary veins or common pulmonary vein ostia, and the identification of anomalies such as anomalous pulmonary venous return, pulmonary vein occlusion or the presence of a persistent left superior vena cava.Variations in pulmonary vein anatomy are common and can be easily assessed with computed tomography.2.5Pulmonary vein stenosis is a recognized complication of atrial fibrillation ablation close to or within the pulmonary vein ostia. Symptoms typically take weeks to months to manifest and include shortness of breath, dry cough and, in some instances, chest pain.2.6Injury to extra-cardiac structures is a recognized, but fortunately rare, complication of atrial fibrillation ablation. The esophagus lies adjacent to the posterior wall of the left atrium leaving it vulnerable to injury during radiofrequency catheter ablation.Methods to reduce risk of atrio-esophageal fistula formation such as active esophageal coolingThe proximity of the right and left phrenic nerves with the right superior pulmonary vein and left atrial appendage roof respectively also leaves these structures vulnerable to injury during ablation.3Advances in computed tomography image acquisition and analysis provide the opportunity for its role in atrial fibrillation assessment to expand. Techniques including computational modelling, artificial intelligence-based imaging, atrial wall thickness assessment and epicardial adipose tissue characterisation may enable an individualized precision-cardiology approach to atrial fibrillation management. This section summarizes these approaches and suggests areas where future research developments using computed tomography may address knowledge gaps in the management of atrial fibrillation.3.1The ability of cardiac computed tomography to accurately delineate atrial structure can enable the acquisition of complex three-dimensional human atrial geometry which can form the basis of computational organ-level atrial models .45 TheseComputational models could be used to characterize an individual's substrate severity and lead to personalized atrial fibrillation ablation therapy. Hwang et al. used cardiac computed tomography to create atrial models which could be used to compare different ablation strategies for atrial fibrillation.ClinicalTrials.gov, #NCT05057507) study will aim to use computational modelling to define patient-specific mechanisms of atrial fibrillation.Computational modelling for personalized atrial fibrillation procedure planning is an area of active research, offering the potential to better understand arrhythmia pathophysiology and improve clinical arrythmia prevention, risk stratification and treatment.Future research developments using computational models will require three-dimensional anatomical geometry and cardiac computed tomography is therefore likely to play a role in the generation of these models. A key advantage of cardiac computed tomography in this setting is the high spatial resolution of image acquisition which enables accurate anatomical assessment of wall thickness and left atrial appendage morphology. Previous studies using magnetic resonance imaging to evaluate the role of computational modelling in atrial fibrillation therapy have been unable to include variations in left atrial wall thickness or appendage morphology.3.2Machine learning is likely to be beneficial for personalized atrial fibrillation care in the future.Improved image quality with machine learning-based reconstruction algorithms may improve the ability to assess thin structures such as the atrial wall.The ability of cardiac computed tomography to characterise atrial structure may prove to be of prognostic importance as structural parameters such as left atrial volume3.3Changes in the structure and electrical behavior of the left atrium are associated with conditions which predispose to atrial fibrillation, although it is also recognized that these changes may occur in response to atrial fibrillation itself.Furthermore, we have developed reference values for left atrial wall thickness measurements on computed tomography, measured left atrial wall thickness at two locations (roof and floor) using computed tomography in 100 persistent atrial fibrillation patients., measured thickness around the pulmonary vein antra in patients undergoing repeat ablation procedures and found that antral wall thickness was higher in areas of pulmonary vein reconnection., found no association between left atrial wall thickness measurements and pulmonary vein isolation success rates.Studies have also been performed to examine for an association between left atrial wall thickness measurements and atrial fibrillation ablation success rates. Zuo et al.The use of computed tomography to assess atrial wall thickness and provide non-invasive substrate determination may enable us to identify areas where the risk of recurrence is potentially higher and personalize therapy to improve outcomes. The utility of computed tomography-derived left atrial wall thickness in guiding catheter ablation approach has been demonstrated by Teres et al., who found that personalized ablation approaches adapted to left atrial wall thickness allowed pulmonary vein isolation with lower radiofrequency delivery, fluoroscopy, and procedure times.3.4Cardiac computed tomography enables a three-dimensional assessment of the volume, thickness, and attenuation of the epicardial adipose tissue C.71 Some,Several studies have shown that patients with atrial fibrillation have increased epicardial adipose tissue thickness, mass and volume compared with patients without atrial fibrillation . A meta-,,,Recently, assessment of the pericoronary adipose tissue attenuation has gained attention due to its association with subsequent cardiac events.Notably these studies have considered only single-plane analysis or peri-atrial fat attenuation limited to the posterior left atrium. Furthermore, cut-off values to accurately define abnormal epicardial adipose tissue attenuation have not been defined and vary between research studies. Further mechanistic studies are therefore needed to study the role of global peri-atrial fat and fat attenuation in atrial fibrillation pathogenesis.In a study by Zhang et al., it has been shown that the radiomic characteristics of epicardial adipose tissue may improve the identification of patients who have atrial fibrillation compared to quantification of epicardial adipose tissue volume.At present it is unknown whether changes in epicardial adipose tissue are a cause or consequence of atrial fibrillation. Epicardial adipose tissue, which contains ganglionated plexi, actively secretes cytokines and adipokines which may lead to inflammation and atrial remodelling. The secretion of pro-fibrotic factors by epicardial adipose tissue may contribute to atrial myocardial fibrosis and this may act as a substrate for atrial fibrillation.4Cardiac computed tomography can provide important anatomical information for patients with atrial fibrillation and has been used in the pre-, peri- and post-procedural stages of management. New techniques such as computational modelling and machine learning are improving our understanding of the development of atrial fibrillation and responses to therapy. In the future information such as atrial structure, atrial wall thickness, and epicardial adipose tissue from computed tomography may be used to provide a personalized approach for the management or prevention of atrial fibrillation.MCW has given talks sponsored by Canon Medical Systems, Siemens Healthineers and Novartis. SN receives research funding from Siemens for computed tomography image analysis. SEW has consulting agreements with EPD/Phillips, GSK and Biosense Webster.


Heart failure is a leading cause of mortality and morbidity worldwide, and Atrial fibrillation (AF) is among many modifiable risk factors for heart failure. No estimates are available on the magnitude of the burden of heart failure associated with AF, and this study estimated the global, regional, and national burdens associated with AF.We used the comparative risk assessment method to estimate the disease burden in terms of prevalence and years lived with disability (YLD). The population-attributable fraction for heart failure and AF was calculated from prevalence estimates of AF and the recalculated relative risks of heart failure associated with AF from a systematic review summarising the longitudinal association between AF and outcomes. The burden of heart failure was retrieved from the Global Burden of Disease database.Globally, 2.6%  of the burden of heart failure is associated with AF. This was 1.5 (95% UI 0.6 to 3.2) million people in 2019, a 49.8% increase from 1990. The highest prevalence was from South-East Asia, East Asia and Oceania. The highest YLD was estimated for Central Europe, Eastern Europe and Central Asia. High-income countries showed a sharp decline in the age standardised prevalence and YLD rates from 1990 to 2019.The burden of heart failure associated with AF has increased substantially over the past two decades despite the advances in AF management. However, falling prevalence and YLD rates of heart failure associated with AF in high-income countries over time indicate that reducing this burden is possible.The online version contains supplementary material available at 10.1186/s12872-023-03375-9. Heart failure and atrial fibrillation (AF) emerged as two epidemics of cardiovascular disease during the last two decades . In hearAtrial fibrillation (AF), a supraventricular tachyarrhythmia with uncoordinated atrial electrical activation and, consequently, ineffective atrial contraction , is a coAtrial fibrillation negatively influences the prognosis with established heart failure  where soThe burden of heart failure associated with AF has not been quantified in the Global Burden of Disease (GBD) or other national burdens of disease studies. This knowledge gap prevents prioritised planning and resource allocation for heart failure associated with AF at global, regional, and national levels.The first aim of our study was to summarise the longitudinal evidence of an association between AF and the later development of heart failure by redistributing study weights according to the quality of studies included in previous systematic reviews and meta-analyses . The secWe used the comparative risk assessment method previously used to estimate ischemic heart disease and stroke burdens attributable to exposure to long working hours . Atrial The prevalence of AF was required as an input parameter in estimating the population-attributable fraction. We retrieved prevalence estimates from the Global Burden of Disease 2019 study . The GBDn = 1) and prospective (n = 5) cohort studies that examined the association between AF and heart failure. Studies reported effect sizes and 95% uncertainty intervals (UIs) comparing those with and without AF. The following details were extracted for each study: study design, country, sample size, gender, method of AF ascertainment, median follow-up period, and measurement of heart failure, as well as effect sizes and 95% UI, which are presented in s1 in the Relative risk (RR) estimates were required as input parameters to calculate the population-attributable fraction. Based on an existing systematic review and meta-analysis , we reca2 statistics to evaluate whether the pooled studies represent a homogeneous distribution of effect sizes.A quality effects meta-analytic model was used to pool the RR estimates for heart failure. This model is a modified version of the fixed-effects inverse variance method, which gives greater weight to studies of higher quality and lower weight to studies of lower quality. This is achieved by calculating quality scores for each study , 22. HetThe quality of studies was assessed using the Joanna Briggs Institute critical appraisal checklist for cohort studies . This toThe following formula was used to calculate PAFs .\documenWhere “P” is the prevalence of AF and “RR” is the pooled relative risk of heart failure associated with AF from meta-analyses, adjusted for demographics, lifestyle risk factors and baseline cardiovascular problems or outcome measures at baseline. Population-attributable fractions for heart failure associated with AF at global, regional, and national levels for 1990 and 2019 were calculated using the age-standardised AF prevalence for the respective levels and year. Thus, calculated population-attributable fractions were used to estimate the burden of heart failure associated with AF at corresponding levels and years.Population attributable fractions calculated as described above were then applied to estimate the burden of heart failure associated with AF at respective global, regional, and national levels, measured in terms of prevalence and years lived with disability (YLD). All estimates are reported at the global, regional, and national level, by all ages, for both sexes and the year 2019.We estimated the global burden for heart failure associated with AF for all ages and both sexes for 1990 and 2019, as well as their percentage change for this period. These findings are presented as absolute counts with 95% uncertainty intervals for prevalence and YLDs and further stratified 2019 global burden for heart failure associated with AF by age groups and sex. These results are presented in age-sex pyramids for absolute counts and age-standardised rates for YLDs.In assessing the regional burden, we calculated the age-standardised prevalence and YLD rates for the burden of heart failure associated with AF, for both sexes, for 2019 and presented in a bar graph for the GBD super regions. In addition, we also analysed the time trends for prevalence and YLDs between 1990 and 2019 for each region, and these are presented in line graphs. The national burden was calculated using the country-level population attributable fraction to the burden of heart failure in terms of absolute counts and age-standardised YLD rated for each individual country and are presented in a world map.th and 97.5th values of this distribution to reflect the lower and upper limits for the 95% uncertainty interval, respectively. This interval reflects the main sources of sampling uncertainty prevalence of AF and relative risks, in the calculations used. All input data and burden are provided in the Macro simulation-modelling techniques using MS Excel software were used to calculate uncertainty ranges around point estimates. In this technique, we produced 10,000 iterations of AF prevalence and relative risk for AF and heart failure. This pair-wise distribution yielded a distribution of 10,000 iterations of respective burden values. We used the 2.5The result of the meta-analysis for RR estimates for AF and heart failure is presented in Table For individuals with AF, the calculated PAF for heart failure for both sexes of all ages were 2.6% (95% UI 1.3 to 4.7%). Overall, the burden of heart failure associated with AF accounted for 0.015% of all-cause YLD (95% UI 0.007 to 0.031%) for both sexes and all ages globally in 2019.Despite the reductions in age-standardised rates for prevalence and YLDs, the absolute numbers for heart failure associated with AF have increased from 1990 to 2019  and YLD  during this period, while all other regions show either an increase or a marginal reduction for both prevalence and YLD for heart failure associated with AF Fig.  and S7.FWe calculated the national burden for heart failure associated with AF in terms of age standardised YLD rates per 100,000 population Fig. . In 2019The GBD study estimated that approximately 5 million YLDs for both sexes and all ages were globally attributable to heart failure in 2019 . We estiThe present study’s findings also indicate that the YLD due to heart failure associated with AF has increased by 49.8% from 1990 to 2019. The increase in these estimates may be attributable to genuine societal health changes, namely, more people living with heart failure and AF. This increase in estimates may also be attributable, in part, to better identification and diagnosis of concomitant AF and heart failure. These findings highlight the potential for reducing the overall disease burden of people living with heart failure through better management of AF. Age-standardised rates were higher among males than females, and rates for both sexes progress with age, consistent with prior studies in the field .Regional estimates for the burden of heart failure associated with AF indicated substantial variation in terms of its prevalence and YLD. Southeast Asia, East Asia and Oceania have the highest prevalence of heart failure associated with AF, even though the burden of AF in this region is much lower than in high-income countries or Central Europe, Eastern Europe and Central Asia . The mecOnly high-income countries have been able to reduce the prevalence of heart failure associated with AF between 1990 and 2019. Greater awareness, early detection, improved access to specialist medical care and treatment, and increased investment are likely key factors that have contributed to the reduction in heart failure prevalence, despite the higher prevalence of AF in high-income countries. Gaining a better understanding of the underlying factors behind this reduction in AF-associated heart failure would provide valuable insights and inform the potential for similar improvements to be achieved in other regions.Regarding the burden of heart failure associated with AF in terms of YLD, high-income countries and Central Europe, Eastern Asia and Central Asia region have the highest estimates. This is consistent with the higher AF prevalence in these regions, but also may reflect the longevity of patients with AF and heart failure in these regions. On the other hand, a reduction in YLD rates from 1990 to 2019 was seen only in high-income countries. This reduction, considered with their similar reduction of prevalence for heart failure associated with AF, is most likely to be due to lowering the occurrence of heart failure in patients with AF, rather than a rise in their mortality rates. However, the burden of fatal cases for heart failure associated with AF could not be estimated as the independent parameter for years of life lost, which reflects fatal outcomes of a disease, are not available for heart failure in the GBD database. The increase in the YLD for other regions could also be due to higher rate of AF diagnosis.As AF is episodic and often asymptomatic, undiagnosed episodes of AF are common and can remain undiagnosed until or even after the development of complications such as stroke, ischaemic heart disease and heart failure. It is estimated that approximately 10-40% of AF patients remained asymptomatic and undiagnosed even in developed countries such as Japan and the US , 36. AsyThere are several strengths of this study. The quality effects model allows quantifying studies according to sample size and by study quality, giving greater weight to studies of high quality. Furthermore, this study controlled for pre-existing cardiovascular disease problems using pooled RRs adjusted for baseline cardiovascular outcomes to quantify PAFs. This protected against the risk of overestimating the disease burden as the continuation of pre-existing cardiovascular disease was adjusted for in the estimates. Finally, PAF estimates provide an opportunity to quantify the burden of heart failure that could be avoided by reducing AF prevalence through effective interventions designed to prevent and treat AF.The current study also has limitations, which should be considered when interpreting the findings. Firstly, the pathophysiological relationship between AF and heart failure has been only partially elucidated , and no Being an episodic disease, a substantial proportion of AF remains undiagnosed , 36. ThiAtrial fibrillation poses a significant burden to patients with heart failure and healthcare systems globally. By highlighting the extent of the disease burden of heart failure associated with AF, the present study illustrates the potential for health benefits that could arise from implementing effective programmes to prevent complications of AF. In this study we examined disease burden using YLD; however, healthcare resource use and costs are another important element of societal burden that was beyond the scope of the present study but remains a priority for future research.Additional file 1: Table S1. Data extracted from the included studies in SR and MAto estimate the pooled RR for AF and HF. Table S2. Quality assessment tool: JBI critical appraisal checklist for cohort studies. Table S3. Quality assessment using the JBI critical appraisal checklist for cohort studies. Table S4. Absolute numbers and age standardised YLD rates for heart failure associated with atrial fibrillation by age group by sex, for 2019. Table S5. Age-standardised prevalence and YLDs for heart failure associated with atrial fibrillation by GBD super regions, for 2019. Table S6. Time trends for age-standardised rates for YLD for heart failure associated with AF per 100,000 population from 1990 to 2019 for GBD super regions. Table S7. Time trends for age-standardised rates for YLD for heart failure associated with AF per 100,000 population from 1990 to 2019 for GBD super regions. Table S8. Age standardised YLD rate for heart failure associated with atrial fibrillation per 100,000 population by country, for 2019. Figure S1. Forest plot for the meta-analysis for pooled RR.


Pulmonary vasculature anatomy is variable between patients and the pulmonary artery size, angulation of vessels and depth of sensor location from the chest wall in heart failure patients who may be candidates for pressure sensors remains largely unexamined. The present study analyses the size, angulation, and depth of the pulmonary artery at the position of implantation of two pulmonary artery pressure sensors: the CardioMEMS sensor typically implanted in the left pulmonary artery and the Cordella sensor implanted in the right pulmonary artery. Thirty-four computed tomography pulmonary angiograms from patients with heart failure were analysed using the MIMICS software. Distance from the bifurcation of the pulmonary artery to the implant site was shorter for the right pulmonary artery (4.55 ± 0.64 cm vs. 7.4 ± 1.3 cm) and vessel diameter at the implant site was larger (17.15 ± 2.87 mm vs. 11.83 ± 2.30 mm). Link distance (length of the communication path between sensor and reader) was shorter for the left pulmonary artery (9.40 ± 1.43 mm vs. 12.54 ± 1.37 mm). Therefore, the detailed analysis of pulmonary arterial anatomy using computed tomography pulmonary angiograms may alter the choice of implant location to reduce the risk of sensor migration and improve readability by minimizing sensor-to-reader link distance.In patients with heart failure, guideline directed medical therapy improves outcomes and requires close patient monitoring. Pulmonary artery pressure monitors permit remote assessment of cardiopulmonary haemodynamics and facilitate early intervention that has been shown to decrease heart failure hospitalization. Pressure sensors implanted in the pulmonary vasculature are stabilized through passive or active interaction with the anatomy and communicate with an external reader to relay invasively measured pressure by radiofrequency. A body mass index  > 35 kg/m Prognosis remains poor, despite the development of effective pharmacological and non‐pharmacological interventions8. Increased left ventricular filling pressures precede symptoms of decompensation, making accurate measurements a priority in clinical management11. Historically, repeated measurements of cardiopulmonary haemodynamics via invasive right heart catheterization (RHC) have been used to guide therapeutic escalation, transition to advanced therapies and to determine eligibility, and inform the timing of, heart transplantation12. However, repeated measurements are limited due to the requirement for an invasive, hospital-based procedure. The development of implanted pressure monitoring devices offers the unique opportunity to monitor clinical relavant physiology in patients with CHF, allowing for more timely interventions, and gauging the effect of the interventions in real-time while the patient is in the community. Pulmonary artery pressure monitors are implanted by passing a Swan-Ganz catheter into the pulmonary artery from percutaneous access of the venous system in the jugular or femoral location. Following fluoroscopic angiography of the target area, a guidewire was placed through the access catheter and the delivery catheter advanced to the implantation site in either the left (CardioMEMS) or right (Cordella) pulmonary artery  is a chronic condition associated with significant symptoms and frequent episodes of decompensation that lead to frequent healthcare interactions, hospitalization, and premature death13. Pulmonary vasculature anatomy is variable between patients and the size, angulation of vessels, and depth of sensor location from the chest wall is largely unexamined. Sensor implant stability is crucial for accurate readings. However, sensor migration has been observed both at the time of, and after, implantation. Possible mechanisms include placement in a larger than recommended pulmonary artery (PA), a more transverse artery course , or implantation in the proximal or distal to the target area within the vessel15.The accuracy and reliability of the pulmonary artery pressure (PAP) readings depend critically on the position of the sensor in the pulmonary vasculature and its relationship to the interrogating device on the anterior chest wall or back. Pressure sensors implanted in the pulmonary vasculature are stabilized through passive or active interaction with the anatomy and communicate with an external reader to wirelessly transmit the invasively measured hemodynamic data using radiofrequencyThe present study describes the anatomy and morphology of the PA in a population of patients with heart failure eligible for pressure monitor implantation to highlight key differences in anatomy relevant to PAP monitor implantation, orientation, link distance and clinical use.A retrospective study of thirty-eight computed tomography pulmonary angiogram (CTPA) scans undertaken at Sheffield Teaching Hospitals NHS Foundation Trust between 2018 and 2019 were analysed to evaluate the dimensions and metrics of the PA. The experimental setup was approved and conducted in accordance with the ASPIRE  code of ethics and with the institutional review board/ethics committee, REC:16/YH/0352 and the public access to the database is closed. All the methods were carried out in accordance with the relevant guidelines and regulations. The eligible patients were men or women over 18 years old with a diagnosis of New York Heart Association (NYHA) class I-IV heart failure (HF) ≥ 6 months with reduced or preserved ejection fraction at the time of screening. Patients were receiving appropriate medical management for HF for 3 months prior to screening and were clinically stable for at least 1 month prior to study entry. Patients with unrepaired significant congenital heart disease, pulmonary arterial hypertension, pulmonary embolism, deep vein thrombosis, myocardial infarction, stroke, glomerular filtration rate (GFR) < 25 ml/min, renal dialysis, planned for heart transplant or left ventricular assist device or with mechanical right heart valves were excluded from the study. From this, 34 computed tomography (CT) scans were chosen for PA characterization analysis based on the quality of images and measurability at distal segments  scanners and reported by a consultant radiologist prior to reconstruction. The parameters of the imaging included 100 mA with automated dose reduction, 120 kV, pitch 1, with a rotation time of 0.5 s and 0.625 collimation. The field of view used was 400 mm × 400 mm with an acquisition matrix of 512 × 512. For the imaging, 100 ml of Ultravist and Bayer IV contrast agents were used and administered at 5 mL/s. High-resolution CT scans (HRCTs) were reconstructed using contrast-enhanced acquisitions with 1.25 mm collimation from the apex of the lung to the diaphragmA three-dimensional (3D) model of the PA was constructed through MIMICS medical imaging software using imported patient DICOM images and volume-based 3-D reconstruction was undertaken by a qualified clinician with specific training in the process. A “mask” was created in MIMICS to highlight the area of interest in the images. The “mask” was then split to help differentiate PA from extraneous highlighted segments.The PA measurements were in made using the MIMICS software. A 3-D model of the PA was constructed and an automated centreline of the vessels identifed. MIMICS is programmed to determine the diameter at any point on the centreline and the segment length and angle at between selected points. By utilizing this program, all measurements were calculated automatically by MIMICS at our preferred points of interest.The PA metrics evaluated in this study were vessel diameter for both RPA and left pulmonary artery (LPA), the distance of the segment length from the zone where the implantable sensor would be placed in the vessels to the main PA bifurcation, the distance between the sensor and the sensor reader, called the link distance, the angle of the RPA downturn and the chest circumference of each patient. As the 3-D reconstruction of the PA is key for anatomical characterization prior to PAP monitor implantation, a detailed stepwise methodology and quality checks are further described in the online Supplementary Document “”.The LPA and RPA were divided into 3 zones based on the anatomical landmarks and the diameter of each zone was determined for analysis. Zone 1  was defined as the section on the RPA and LPA between the main PA bifurcation and the first branch of the RPA and LPA and proximal to the sensor deployment zone. Zone 2 (Sensor) was defined as the section distal to Zone 1 i.e., Bifurcation of PA and then the Truncus arteriosus (TA) branch of RPA where the Cordella sensor (RPA) and CardioMEMS (LPA) are deployed. Zone 3  was defined to be 2 cm distal to Zone 2 Fig. A. The ch2) between the link distance and chest circumference was calculated to assess whether there was a correlation between changes in both variables. The closer the R2 value was to 1, the stronger the correlation between the two variables was determined to be. The coefficient of variation (CV) was also calculated to help compare metrics of different nature with different units as it is a statistical measure of the relative dispersion of data points in a data series around a mean.Statistical analysis was performed using Microsoft Excel  to determine the normal distribution of the data. The distribution of data was described using the mean and standard deviation. The coefficient of determination  or the front (Cordella) in the seated position. The signal is affected by the composition of body tissue between the reader and the pressure sensor and by the distance that separates the 2 components (link distance). The mean link distance for the Cordella sensor in the right pulmonary artery to the reader on the anterior chest was 12.54 ± 1.37 mm. The average link distance for the CardioMEMS sensor in the left pulmonary artery to the reader on the posterior chest was 9.40 ± 1.43 mm  interventions are possible in real-time, bridging the gap between clinic visits, and allowing for proactive remote monitoring to reduce HF-related readmissions and mortality17.Heart failure remains a cause of significant morbidity and mortality and places a large burden on healthcare expenditure18. The vessel diameter at the site of the sensor deployment was 17.15 ± 2.87 mm in this study, well within the range of the design specifications. The distal anchor has a parent shape-set configuration approximately orthogonal to the sensor body. When deployed, the distal anchor deflects distally and, due to the shape memory effect of nitinol, exerts a bending force in the direction of its parent shape-set, towards the vessel wall, securing the sensor in place. The angle of the downturn of the RPA was 135.5 ± 8.2 degrees in this study indicating that there is adequate strain put on the anchor across this patient population to exert a sufficient bending force to secure the anchor in place and control rotation. Fixing the sensor at a known landmark, the downturn of the RPA has potential advantages including orientation and stabilization of the sensor and standardization of implanting15. Both anchors of the CardioMEMS sensor use the principle of outward radial force to interact and fix to the vessel wall. The pre-selected target vessel is within the lower lobe of either lung with the vessel directed towards the feet and back, the vessel diameter is ≥ 7 mm and has < 30-degree angulation where the body of the sensor will be placed, and the vessel diameter is 5–8 mm where the distal anchor will be placed19. The vessel diameter at the site of sensor body deployment was 11.83 ± 2.30 mm, well within the range of the implant recommendations. The RPA sensor location provides a sensor-to-anterior chest link distance of 12.54 ± 1.37 mm which permits signal transmission detection from the anterior chest with a hand-held reader in contrast to supine, posterior readings with the LPA implant sensors. Furthermore, the route of implantation is feasible from the groin or internal jugular vein (IJV) with either device. Jugular implantation of CardioMEMS has been demonstrated to be safe and feasible20. Additionally, the opportunity to take standing and ambulatory readings with the hand-held reader opens the possibility to assess patient haemodynamics while ambulating, in the clinic or the home environment.Both the CardioMEMS and Cordella devices use micro-electromechanical systems (MEMS) and radiofrequency technology to accurately transduce pressure and communicate measurements to their respective readers. MEMS can act as sensors, receiving information from their environment and providing an electrical output signal for an external reader. The strength of the communication signal is altered by the alignment and length of the path between the sensor and the reader. Key design features influence implant location with implications for device use and accurate, accessible home readings. The CardioMEMS sensor resides in the lumen of the LPA and PAP is captured by the patient while lying supine on the reader pillow. The Cordella sensor resides against the anterior wall of the RPA and PAP is captured using a hand-held reader from either the seated or supine position. The proximal anchor of the Cordella sensor uses the principle of outward radial force to interact with and fix to the vessel wall. The pre-selected target implant location in the RPA should measure 12–26 mm to allow for proper proximal anchor engagementIn this paper CT images of the population with sensor implants and their relationship between vessel measurements were examined. The left and right pulmonary arteries were characterized as the locations for the CardioMEMS and Cordella PA sensors. The CardioMEMS sensor has a “diameter-based” design and is typically implanted in the LPA where anatomy permits. The Cordella sensor has a “landmark-based” design meant to be deployed in the RPA, distal to the apical bifurcation, where the interlobar artery typically turns downward and posterior. Across the study population, chest circumference correlated with sensor-reader link distance with the Cordella sensor having a smaller link distance with respect to the anterior chest allowing for front-sided readings with a hand-held reader. The CardioMEMS sensor has a smaller link distance to the patient’s back, consistent with the design of the reader pillow. Analysis of pulmonary vascular anatomy with respect to PA diameter, angulation of vessels, and depth of sensor location prior to PAP sensor implantation may contribute to device choice and inform implant strategy.Supplementary Information.


BACKGROUND: The B-type natriuretic peptide (BNP) and N-terminal pro-brain natriuretic peptide (pBNP) are predictors of cardiovascular morbidity and mortality. Since the artificial intelligence (AI)-enabled electrocardiogram (ECG) system is widely used in the management of many cardiovascular diseases (CVDs), patients requiring intensive monitoring may benefit from an AI-ECG with BNP/pBNP predictions. This study aimed to develop an AI-ECG to predict BNP/pBNP and compare their values for future mortality. METHODS: The development, tuning, internal validation, and external validation sets included 47,709, 16,249, 4001, and 6042 ECGs, respectively. Deep learning models (DLMs) were trained using a development set for estimating ECG-based BNP/pBNP (ECG-BNP/ECG-pBNP), and the tuning set was used to guide the training process. The ECGs in internal and external validation sets belonging to nonrepeating patients were used to validate the DLMs. We also followed-up all-cause mortality to explore the prognostic value. RESULTS: The DLMs accurately distinguished mild (≥500 pg/mL) and severe (≥1000 pg/mL) an abnormal BNP/pBNP with AUCs of ≥0.85 in the internal and external validation sets, which provided sensitivities of 68.0–85.0% and specificities of 77.9–86.2%. In continuous predictions, the Pearson correlation coefficient between ECG-BNP and ECG-pBNP was 0.93, and they were both associated with similar ECG features, such as the T wave axis and correct QT interval. ECG-pBNP provided a higher all-cause mortality predictive value than ECG-BNP. CONCLUSIONS: The AI-ECG can accurately estimate BNP/pBNP and may be useful for monitoring the risk of CVDs. Moreover, ECG-pBNP may be a better indicator to manage the risk of future mortality. Globally, more than 26 million individuals are afflicted by heart failure (HF) and cardiac dysfunction, constituting significant challenges to public health on a global scale. Given their elevated morbidity and mortality rates, diagnosing HF and cardiac dysfunction holds paramount importance in both clinical and forensic medicine ,2. The iEchocardiography serves as the benchmark for heart failure (HF) diagnosis, being the most valuable method and a cornerstone for guiding diagnostic and therapeutic decisions. Nevertheless, echocardiography’s drawbacks include its high costs and limited effectiveness in patients with obesity or concurrent chronic lung diseases accompanied by respiratory distress. As a result, biochemical markers have evolved into essential clinical tools, simplifying and enhancing the accuracy of HF diagnosis and prognosis by delineating its occurrence and stage. The B-type natriuretic peptide (BNP) and N-terminal pro-BNP (pBNP) have become routine laboratory tests, extensively studied for their clinical utility. They have demonstrated effectiveness in distinguishing severe acute respiratory distress syndrome and in diagnosing and assessing HF ,7. HowevIt was discovered in 1988 that a peptide from the porcine brain exhibited a natriuretic effect, which was named BNP . SubsequThe resting 12-lead electrocardiogram (ECG) is another potential tool to aid in the diagnosis of HF ,24. SimiThis retrospective study was ethically approved by the institutional review board of Tri-Service General Hospital, Taipei, Taiwan (IRB NO. C202105049). We reviewed the patients who visited our hospital during January 2010 and September 2021 from the electronic medical records (EMRs). Patients who simultaneously received ECG examinations and BNP/pBNP tests within 24 h were included in this study. The BNP and pBNP tests were not simultaneously conducted in our hospital, and physicians made the decision to use only one of the tests for assessing the cardiac risk of patients. Therefore, the records with a BNP test did not have pBNP information in this study, and vice versa.All ECGs in this study were measured by the standard 12-lead ECG Philips machine (PH080A) with a 500 Hz sampling frequency for 10 s and stored in a digital format. Due to the “black box” property of DLMs, 8 quantitative ECG measures and 31 diagnostic pattern classes were also collected from the structured findings statements based on the key phrases that are standard within the Philips system. These features were used to train an extreme gradient boosting (XGB) model, and the DLM was trained via raw ECG traces. Pattern classes included the abnormal T wave, atrial fibrillation, atrial flutter, atrial premature complex, complete AV block, complete left bundle branch block, complete right bundle branch block, first degree AV block, incomplete left bundle branch block, incomplete right bundle branch block, ischemia/infarction, junctional rhythm, left anterior fascicular block, left atrial enlargement, left axis deviation, left posterior fascicular block, left ventricular hypertrophy, low QRS voltage, pacemaker rhythm, prolonged QT interval, right atrial enlargement, right ventricular hypertrophy, second degree AV block, sinus bradycardia, sinus pause, sinus rhythm, sinus tachycardia, supraventricular tachycardia, ventricular premature complex, ventricular tachycardia, and Wolff–Parkinson–White syndrome. The detailed items have been described in previous works ,28. BlooWe additionally collected demographic information, including sex, age, and body mass index, and disease histories, including diabetes mellitus (DM), hypertension (HTN), hyperlipidemia (HLP), chronic kidney disease (CKD), acute myocardial infarction (AMI), stroke (STK), coronary artery disease (CAD), heart failure (HF), atrial fibrillation (Afib), and chronic obstructive pulmonary disease (COPD), based on the corresponding International Classification of Diseases, Ninth Revision and Tenth Revision  from EMRs. The detailed look-up tables for ICD codes and diseases have been described previously ,31,32,33All-cause mortality events were also collected in this study. The survival time was calculated with reference to the date of the ECG test. Patient status  was captured through the EMRs. Data for alive visits were censored at the patient’s last known hospital alive encounter to limit bias from incomplete records. The end of follow-up in this study was 31 September 2021. The median follow-up periods were 25.2 months (interquartile range [IQR]: 6.3–46.5 months) and 15.3 months (IQR: 2.0–38.2 months) in the internal and external validation sets, respectively, with maximum follow-up times of 123.8 months and 120.7 months. The number of mortality events was 463 (3.93 per 1000 person-years) and 666 (4.59 per 1000 person-years) in the internal and external validation sets, respectively.We directly trained DLMs using 12-lead ECG trace signals to estimate BNP and pBNP. We used the architecture of ECG12Net, which has been previously described in detail . Supplemk. This module included the following components: (1) a 1 × 1 convolutional layer with k/4 filters for dimension reduction, (2) a batch normalization layer for normalization, (3) a rectified linear unit (ReLU) layer for introducing non-linearity, (4) a 3 × 1 convolutional layer with k/4 filters to extract features, (5) a batch normalization layer for normalization, (6) a ReLU layer for non-linearity, (7) a 3 × 1 convolutional layer with 4K filters to further extract features, (8) a 1 × 1 convolutional layer with k filters to restore feature shape, (9) a batch normalization layer for normalization, (10) a ReLU layer for non-linearity, and (11) a squeeze-and-excitation (SE) module for feature weighting. The SE module comprised: (1) an average global pooling layer, (2) a fully-connected layer with k/r neurons, and (3) another fully-connected layer with k neurons. The constant r remained fixed at 8 across all experiments. The residual module concluded with a shortcut connection, creating direct connections between each layer and all subsequent layers.We defined a “residual module” as a neural combination with a constant factor When there is a change in the size of feature maps, concatenating the residual module becomes infeasible. As a solution, our architecture employs a “pool module” to facilitate the concatenation of each residual module during down-sampling. This module consists of similar concatenated layers as the residual modules, but the stride of the 3 × 1 convolution layer is altered to 2 × 1. Down-sampling is achieved through an average pooling layer with a 2 × 1 kernel size and stride. These components are integrated using the concatenation function.The input data first pass through a batch normalization layer, followed by an 11 × 1 convolution layer with a 2 × 1 stride and 16 filters, another batch normalization layer, a ReLU layer, and a pool module. Subsequently, the data traverse a series of residual modules and pool modules, yielding a 32 × 12 × 1024 array. This is followed by a global pooling layer and the last residual module. The array is then split into 12 lead-specific feature maps, each containing 1024 features. These feature maps undergo processing via a fully-connected layer with one neuron to generate lead-specific predictions.To enhance the interpretive capacity of the DLM, an attention mechanism based on a hierarchical attention network is employed to combine these blocks. The attention module involves a fully connected layer with 8 neurons, followed by a batch normalization layer, a ReLU layer, and another fully-connected layer with one neuron that generates weights for each lead. Attention scores are computed for each ECG lead, and then integrated and standardized through the final linear output layer. These standardized attention scores are used to weight the outputs of the 12 ECG leads via simple multiplication. The weighted outputs are summed and processed through a prediction module to yield the final prediction value.−4. We trained two types of DLMs, binary output and continuous output, to conduct the corresponding tasks. In continuous predictions, the categorical encoding technology and the training details used were according to previous studies [During training, these DLMs use a batch size of 32 and an initial learning rate of 0.001, with an Adam optimizer employing standard parameters (β1 = 0.9 and β2 = 0.999). The learning rate undergoes decay by a factor of 10 whenever the validation cohort’s loss plateaus after an epoch. To curb overfitting, we employ early stopping by saving the network after each epoch and selecting the DLMs with the lowest loss on the validation cohort. In this study, the sole regularization method for preventing overfitting is L2 regularization with a coefficient of 10 studies ,35,36, aThe presentation of patient characteristics included means and standard deviations for continuous variables, along with numbers and percentages for categorical variables. The statistical analysis was conducted using R version 3.4.4, and the implementation of our Deep Learning Models (DLMs) was done using the MXNet package version 1.3.0. To evaluate the performance of the DLMs in identifying mild and severe abnormal BNP/pBNP, we employed receiver operating characteristic (ROC) curves and calculated areas under the curve (AUCs). The sensitivity, specificity, positive predictive value, and negative predictive value were determined based on the maximum Youden’s index from the tuning set. The selected operating point was consistently used for the analysis in both internal and external validation sets. For the continuous analysis, scatter plots and Pearson correlation coefficients (r) were used to compare the predictions from the electrocardiogram (ECG) with actual BNP/pBNP values. The importance analysis of ECG features was initiated with an eXtreme gradient boosting (XGB) model for predicting ECG-BNP/ECG-pBNP, resulting in the presentation of feature importance rankings. The relationship between ECG features and ECG-BNP/ECG-pBNP was visually represented using a bar chart with quartiles. In the context of the mortality analysis, a Kaplan–Meier (KM) curve was generated to assess the prognostic contribution of ECG-BNP/ECG-pBNP. Additionally, Cox proportional hazard models were fitted to calculate grouping hazard ratios (HRs), along with corresponding 95% confidence intervals (95% CI). The C-index was employed as a global indicator to compare the predictive performance between ECG-BNP and ECG-pBNP.Patient internal and external validation sets were distinguished as BNP and pBNP subsets. There were 8.8%/10.2% and 10.2%/11.8% patients with mild and severe abnormal BNP in the internal/external validation set, respectively, and there were 9.0%/26.8% and 10.1%/27.7% patients with mild and severe abnormal pBNP in the internal/external validation set, respectively. The mean age of these datasets ranged from 68.9 to 74.0 years, and the proportion of males ranged from 49.6% to 53.0%. The other detailed distributions are shown in DLMs show the promising ability to capture BNP/pBNP information from raw ECG signals, and a strong and continuous relationship was shown. This study simultaneously used DLMs to estimate ECG-BNP and ECG-pBNP, and the association analysis showed similar concepts between them. However, there remained a small difference between ECG-BNP and ECG-pBNP. The latter captured more unknown ECG features than ECG-BNP, which might contribute a high prognostic value to mortality prediction in further follow-up analyses. In summary, the AI-enabled ECG system could accurately estimate ECG-pBNP and provide more information to manage future cardiovascular diseases.To help diagnose acute HF, the ESC guidelines recommend that all patients with suspected acute HF have their plasma natriuretic peptide levels (BNP and pBNP) measured. BNP has a normal upper limit of 35 pg/mL in the nonacute setting, while pBNP has a normal upper limit of 300 pg/mL in the nonacute setting and 125 pg/mL in the acute setting . In clinThere has been a widespread use of BNP and pBNP as clinical indicators of heart failure and cardiac dysfunction ,41,42. PThe concentration of BNP is closely related to the incidence and severity of heart failure; Indeed, its magnitude escalates as the severity rises, a classification established by the New York Heart Association's functional assessment. Therefore, it can be used to predict a patient’s status and develop a treatment plan . In the Certain limitations should be mentioned. First, this was a retrospective study. A prospective study is needed to validate its efficacy in the community. Second, we did not evaluate echocardiography in these patients. The cause of elevated BNP/pBNP could be cardiac or noncardiac. Echocardiography could help to identify patients with systolic left ventricular dysfunction. Thirdly, it's important to note that ECG traits can exhibit variability across various racial backgrounds . A comprIn conclusion, the assessment of BNP/pBNP using the AI-enabled ECG is a rapid and inexpensive method. This method can allow us to identify patients with high BNP/pBNP and may be considered an alternative option for plasma BNP/pBNP. ECG-pBNP provides a prognostic value in long-term mortality.


After euthanasia, myocardial tissue was sampled for pathobiological evaluation. Seven pigs were used as controls. Results: Calcineurin inhibition prevented increases in pulmonary vascular resistance and RV-arterial decoupling induced by BD. BD was associated with an increased RV pro-apoptotic Bax-to-Bcl2 ratio and RV and LV apoptotic rates, which were prevented by tacrolimus. BD induced increased expression of the pro-inflammatory IL-6-to-IL-10 ratio, their related receptors, and vascular cell adhesion molecule-1 in both the RV and LV. These changes were prevented by tacrolimus. RV and LV neutrophil infiltration induced by BD was partly prevented by tacrolimus. BD was associated with decreased RV expression of the β-1 adrenergic receptor and sarcomere (myosin heavy chain [MYH]7-to-MYH6 ratio) components, while β-3 adrenergic receptor, nitric oxide-synthase 3, and glucose transporter 1 expression increased. These changes were prevented by tacrolimus. Conclusions: Brain death was associated with isolated RV dysfunction. Tacrolimus prevented RV dysfunction induced by BD through the inhibition of apoptosis and inflammation activation.Background: Right ventricular (RV) dysfunction remains a major problem after heart transplantation and may be associated with brain death (BD) in a donor. A calcineurin inhibitor tacrolimus was recently found to have beneficial effects on heart function. Here, we examined whether tacrolimus might prevent BD-induced RV dysfunction and the associated pathobiological changes. Methods: After randomized tacrolimus ( Right ventricular (RV) dysfunction after cardiac transplantation remains a major cause of postoperative mortality . In reciHeart transplantation faces an increasing imbalance between high-quality graft donor supply and demand. Maximizing donor heart performance is thus a crucial issue. Significant brain injury of any etiology will cause a systemic response , creatinPulmonary hypertension and right heart dysfunction are characteristic elements of hemodynamic disturbance associated with brain death . The calHowever, whether tacrolimus has a direct effect on BD-associated RV dysfunction remains unknown because major studies were performed in recipients after a heart transplant.There is a consensus that a better characterization and quantification of a single “primum movens” of brain-death-induced right heart injury would be of therapeutic relevance, with the goal of improving early and long-term graft function after transplantation. In this way, animal models can fill gaps in the knowledge, which are unattainable in clinical settings . Our modIn this study, we explored the effects of tacrolimus as a tool to inhibit the calcineurin pathway in an experimental model of BD induced by subdural autologous blood infusion ,28,29, wThis work is the first to simultaneously record the ventriculo-arterial coupling in the right and left ventricles in brain death and show isolated right heart decoupling with preserved left ventricular function.This study is also the first to show the positive contribution of calcineurin inhibition by tacrolimus in preserving right ventricular function during brain death. Tacrolimus could therefore increase the proportion of usable hearts in BD donors by reducing right ventricular dysfunction in the recipient.At baseline, hemodynamic parameters were similar between the three groups . One pig−1.min−1 at CR + 7 h. BD increased HR and PAP 1 h after the CR, while SAP progressively decreased. Indexed CO increased 1 h after the CR (CR + 1 h) to decrease progressively afterward and reach the basal value at CR + 7 h. Pulmonary vascular resistance (PVR) progressively increased with an approximately tripled value at CR + 7 h compared to baseline. LAP and RAP also increased, with occluded PAP remaining below 12 mmHg throughout the protocol. Systemic vascular resistance (SVR) decreased from CR + 1 h, with increasing need for NA up to 0.13 µg.kgTacrolimus completely prevented changes observed in HR, CO, PVR, SAP, SVR, and RAP at CR + 7 h and limited the increase in PAP without affecting LAP. The need for NA administration was also significantly less important after tacrolimus.BD was associated with a progressive decrease in RV Ees/Ea, suggesting progressive RV-pulmonary arterial uncoupling after the CR. At CR + 1 h, RV systolic function remained adapted to its afterload, as indicated by proportional increases in Ees and Ea, but at CR + 7 h, BD resulted in an insufficient increase in RV systolic function before a marked increase in RV afterload, which resulted in a profound decrease in Ees/Ea, suggesting RV-pulmonary arterial uncoupling. In contrast, the LV Ees-to-Ea ratio remained unchanged throughout the experiment.Tacrolimus completely prevented RV-pulmonary arterial uncoupling observed after BD. Indeed, RV afterload remained unchanged after tacrolimus pretreatment, with adapted RV systolic function throughout the protocol. LV-arterial coupling was not altered by tacrolimus.Serum levels of C-reactive protein, troponin-I, and creatinine remained stable throughout the protocol. BD increased serum urea levels. Nevertheless, all these plasma levels were already within the normal range for pigs in the three groups. After tacrolimus, there was no change in these circulating marker levels compared to the BD group.BD was associated with Bax-to-Bcl-2 and Bax-to-Bcl-XL gene ratios in the RV and LV. To confirm the activation of apoptotic processes, TUNEL staining was performed to evaluate the myocardial apoptotic rate. BD was associated with three-fold increases in RV and LV apoptotic rates at CR + 7 h.Tacrolimus prevented BD-induced Bax−to−Bcl−2 and Bax−to−Bcl−XL ratios overexpression, as well as the increase in the apoptotic rate in the RV  and LV (partly).The RV Ees/Ea ratio was inversely correlated with the relative gene expression of Bax−to−Bcl−2, Bax−to−Bcl−XL ratios, and the apoptotic rate in the RV.p = 0.06), while the expression of ICAM-1 and ICAM-2 remained unchanged. In the LV, the IL-6R mRNA content, the IL-6R-to-IL-10R ratio and Gp130 (p = 0.07), HO-1, and VCAM-1 (p = 0.06) increased after BD, with no changes in IL-1β and its antagonist receptor IL-1RN, IL-6, and IL-10, Gp130, TNF-α, iNOS, ICAM-1, and ICAM-2.As illustrated in 2 in the RV and LV , PLB (p = 0.084), and RyR2, all implicated in calcium translocation from the cytosol into the sarcoplasmic reticulum, with decreased expression of CACNA1C, a voltage-dependent calcium channel mediating calcium influx into the cell. BD-induced RV dysfunction was also associated with decreased expression of calcium/calmodulin-dependent protein kinase CAMKIId (p = 0.0001), while the RV expression of the SERCA2-to-PLB ratio and Na+-calcium exchanger NCX1 implicated in cardiac relaxation did not change. In the LV, the gene expression of RyR2, CACNA1C, and CAMKIId (p = 0.0001) decreased, with no changes in SERCA2A, PLB, and NCX1 gene expression after BD  in the RV.The RV Ees/Ea ratio was positively correlated with the gene expression of CAMKIId  decreased in pigs with BD-induced RV dysfunction, while the RV expression of NPPB (encoding BNP) increased. No changes were noticed in the RV MYH6 (encoding a-MHC) expression and in the MYH7-to-MYH6 ratio. In the LV, BD induced decreased gene expression of MYH6 (p = 0.01) and MYH7 (p = 0.0004) and increased NPPB gene expression, while ACTA1 and NPPA did not change.We assessed the gene expression of recognized hypertrophic markers and found that the RV gene expression of NPPA (encoding ANP), and MYH7 (encoding β-myosin heavy chain (MHC); p = 0.04). The MYH7-to-MYH6 ratio was also increased in the RV after tacrolimus. Tacrolimus did not impact altered RV ad LV gene expression of cardiac hypertrophic markers compared to those observed in non-treated animals, except for RV MYH6 gene expression, which further decreased after tacrolimus pretreatment  and thereby increases BMP downstream in pulmonary arterial endothelial cells [We previously experimentally studied the hemodynamic response to a comparable acute increase in RV afterload and showed that RV dysfunction was mainly related to RV activation of apoptosis and inflammatory processes ,43,44. Val cells . Tacrolial cells ,46, causal cells ,48.After BD, central nervous system injury is associated with a massive inflammatory response syndrome, occurring early after injury; it is responsible for the activation of inflammatory processes in organs. In the present study, the inflammatory markers were markedly upregulated in both the RV and LV after BD, with increased expression of inflammatory cytokines IL-1β, the IL-6-to-IL-10 ratio, and associated receptors and adhesion molecule VCAM-1, together with myocardial neutrophil infiltration. This is consistent with the global inflammatory response syndrome observed after BD and reinforces the relevance of testing the preventive strategies targeting inflammatory and immune pathways to preserve RV function in BD. In addition, TLRs, which are able to generate innate immune responses, have been implicated in cardiac dysfunction . TLR4, wMyocardial hypertrophy and fibrosis often observed in transplanted human hearts are known to have adverse effects on long-term cardiac function . CardiacIn conclusion, tacrolimus prevented pulmonary hypertension and RV dysfunction observed after brain death, with the associated reduced activation of apoptosis, inflammatory, and immune systems. Tacrolimus should therefore be considered as a potential therapeutic agent, which can improve cardiac graft function and minimize the deleterious impact of brain death after cardiac transplantation.This study was conducted in accordance with the Guiding Principles in Care and Use of Animals of the American Physiologic Society and was approved by the Institutional Ethics Committee on Animal Welfare of the Faculty of Medicine of the Université Libre de Bruxelles . Animals were delivered by an approved breeder .n = 7) or to receive a BD procedure (n = 17). BD-randomized animals were randomly assigned to placebo  or 0.25 mg.kg−1 tacrolimus  administered orally with food twice a day.The day before the experiment, twenty-four pigs  were randomly assigned to a control group  of 0.4–1.0 to maintain an arterial oxygen saturation (SaO2) > 90%, a respiratory rate of 12–20 breaths·min−1, and a tidal volume of 15–25 mL·kg−1 to achieve an arterial PaCO2 between 35 and 45 mmHg, and positive end-expiratory pressure between 5 and 8 cm H2O.As previously reported ,29,54,55A flow-directed balloon-tipped pulmonary artery catheter  was inserted via the left external jugular vein and positioned in the pulmonary artery to measure pulmonary artery pressure (PAP), right atrial pressure (RAP), thermodilution cardiac output (CO), and central body temperature, and for mixed venous blood sampling. A polyethylene catheter was inserted in the thoracic aorta via the right carotid artery to measure systemic artery pressure (SAP) and left atrial pressure (LAP) and to sample arterial blood. A left thoracotomy was performed, and an ultrasonic flow probe  was placed around the main pulmonary artery and aorta to measure the instantaneous pulmonary blood flow (Q); high-fidelity 5F manometer-tipped catheters  were introduced in the right and left ventricles and in the pulmonary artery and aorta to measure instantaneous ventricular flow outputs and compute instantaneous PAP and SAP.−1.h−1 to maintain an RAP of 6–8 mmHg. If SAP was below 65 mmHg with a maximal RAP value of 10 mmHg, a continuous infusion of norepinephrine was started to maintain blood pressure and titration [Balanced crystalloid and modified liquid gelatin solutions were perfused at 10 mL.kgitration . Heart r−1; 240 min) in the parenchyma through an 18-gauge SURFLOÒ catheter  subdurally inserted to induce BD and to record instantaneous intracranial pressure. Approximately 1 h after the Cushing reflex (CR) response, which lasted 30–60 min, BD was objectivated with standard criteria, including: (a) deep coma, excluding reversible factors; (b) absence of oculo-pupillary and corneal reflexes; (c) positive apnea testing; (d) no increase in HR after administration of atropine (1 mg) when monitored for 5–15 min; and (e) disappearance of the “tic reaction” [−1 and targeted plateau pressures below 30 cm H2O. Periodic deep inspirations were administered to prevent atelectasis.After providing anesthesia to the animals and placing the monitoring equipment, one hole was drilled in the temporoparietal cranium. Autologous blood was slowly infused ; systemic arterial blood sampling; and lung biopsies, as previously reported [®, Oak, Lake Forest, IL, USA) overdose, and cardiac tissue  was sampled. Transmural myocardial sections were immediately snap-frozen and stored at −80 °C for pathobiological evaluation, and, after overnight fixation, they were embedded in paraffin for immunohistochemistry. Control animals were euthanized after baseline data set acquisition.Each data set included blood gas analysis; measurements of SAP, PAP, LAP, RAP, and CO, using a Bioboxreported ,28,29. A®, Brussels, Belgium). Five consecutive end-expiratory heartbeats were analyzed for each data set. The “single-beat method” was used to evaluate RV- and LV-arterial couplings, computing the end-systolic elastance (Ees) as the slope of end-systolic pressure–volume relationship, the effective artery elastance (Ea) as the slope of end-diastolic–to-end-systolic relationship, and the coupling efficiency as the Ees/Ea ratio [Instantaneous pulmonary and aortic pressures and flow waves were sampled at 200 Hz and analyzed with data-analysis software 1.0 sus scrofa pro-apoptotic Bax and anti-apoptotic Bcl-2 and Bcl-XL mitochondrial membrane transport regulators, interleukin (IL)-1β and its receptor antagonist (IL-1RN), IL-6, its receptor (IL-6 R) and its signal transducer GP130, IL-10 and its receptor (IL-10 R), tumor necrosis factor-α (TNF-α), heme oxygenase (HO)-1, inducible nitric oxide synthase , intercellular adhesion molecules (ICAM)-1 and -2, vascular cell adhesion molecule (VCAM)-1, β-1, -2 and -3 adrenergic receptors, sarcoplasmic/endoplasmic reticulum Ca2+-ATPase 2a (SERCA2A), phospholamban (PLB), ryanodine receptor 2 (RyR2), voltage-gated Ca2+ channel subunit alpha1 C (CACNA1C), Na+/Ca2+ exchanger 1 (NCX1), Ca2+/calmodulin-dependent protein kinase II, delta (CaMKIIδ), α-1 skeletal muscle actin (ACTA1), α- and β-myosin heavy chains (MYH6 and MYH7), natriuretic peptide A (NPPA) and brain natriuretic peptide (NPPB), endothelial NOS , glucose transporters (GLUT) 1 and 4, thrombospondin receptor CD36 (CD36), Toll-like receptors (TLR-) 2 and 4, and NLR family pyrin domain containing three (NLRP3) mRNA sequences , as previously described [Cardiomyocytes undergoing apoptosis were detected by TUNEL staining using the ApopTagescribed ,62. Nega2 were then calculated as the total score for each specimen. Cells were counted by two investigators who were blinded to the assigned group.Myocardial-infiltrating neutrophils were myeloperoxidase (MPO)-stained, as previously described ,44. Myoc® 5600 Integrated System . Serum levels of C-reactive protein (CRP) were evaluated using the immunoturbidimetric method (DIAGAM® C-reactive protein Gold Latex). The serum concentration of highly sensitive cardiac troponin I was measured by an immunometric immunoassay (VITROS® Immunodiagnostic Products HS Troponin I) using a chemiluminescent reaction. Serum urea and creatinine levels were measured using the enzymatic method (VITROS® Chemistry Products UREA Slides) with spectrophotometric detection. All serum measurements were performed using the Vitros® version 5 . All data are expressed as mean ± standard error of the mean. Data were analyzed using a two-factor (group × time) repeated measures analysis of variance. When the F ratio reached a critical value <0.05, Fisher’s exact t-tests were performed with a Bonferroni correction for multiple comparisons. p-values < 0.05 were considered statistically significant. Analyses were performed using PRISMUsing univariable and multivariable analyses with Cox proportional hazard model linear regression analyses, we tested the relationships of the biological variables for apoptosis, inflammation, ventricular function, and myeloperoxidase-positive inflammatory cell infiltrate with right and left ventriculo-arterial couplings. Asmae Belhaj had full access to all the data in the study and takes responsibility for its integrity and the data analysis.