Although the dynamic changes of atrial natriuretic peptide (ANP) expressions in a failing heart are well-documented, the clinical implications of detailed measurements of each ANP molecular form processed from proANP remain unclear.Patients screening was conducted on patients who were eligible for cardiac resynchronization therapy (CRT) between 2014 and 2019 in our institution. Blood samples and echocardiographic parameters were collected on the day before and six months after implantation. Total ANP, proANP, and N-terminal fragment of proANP (NT-proANP) were examined as predictive biomarkers for cardiac death, left ventricular assist device implantation, and heart failure hospitalization following CRT implantation.p = 0.021). NT-proANP levels were associated with left atrial volume and left diastolic functional parameters and decreased in response to echocardiographic improvements at six months post-implantation .A total of 86 subjects  who underwent successful CRT implantation were enrolled. Plasma levels of total ANP, proANP, and NT-proANP were not normally distributed . Over a median follow-up of 2.7 years, 31 patients  reached the endpoints. Among the different ANP forms, only NT-proANP emerged as an independent predictor of the composite outcome (adjusted odds ratio of 2.542 in those with levels above vs. below the median, 95 % confidence interval 1.151–5.615, Pre-implantation NT-proANP levels could serve as a predictive factor for clinical outcomes in recipients of CRT. Nevertheless, robust biomarkers capable of accurately predicting clinical outcomes remains unidentified. In our recent study, we introduced a novel parameter known as the cyclic guanosine monophosphate (cGMP) to mature brain natriuretic peptide (BNP) ratio, which provides a potential prognostic indicator in CRT recipients In humans, four endogenous molecular forms of ANP have been identified: precursor ANP (proANP), mature ANP which includes α-ANP and β-ANP as biologically active forms, and N-terminal fragment (NT) of proANP (NT-proANP) which is an inactive form In the present study, we aimed to clarify the correlations between plasma levels of individual ANP forms and clinical outcomes subsequent to CRT implantation.22.1The study was conducted at our institute between 2014 and 2019, with consecutive candidates for CRT enrolled prospectively. To ensure the inclusion of appropriate subjects, the following criteria were applied: 1) New York Heart Association functional class II-IV, 2) left ventricular ejection fraction ≤ 35 %, and 3) a QRS duration ≥ 120 msec 2.2Plasma levels of total ANP, proANP, and NT-proANP were evaluated as predictive markers. Plasma samples were collected in plastic tubes containing ethylenediaminetetraacetic acid disodium salt (1.25 mg/ml) and aprotinin   the day before CRT implantation and 6 months following the implantation, which were aliquoted and stored at −80 °C before use. Total ANP and proANP were measured using our recently developed plate-based 2.3The following clinical data were extracted from the patients‘ medical records: age, gender, underlying heart disease, comorbidities, medication, laboratory data including total BNP and NT-proBNP, echocardiographic parameters, and 12-lead electrocardiogram.2.4CRT devices were implanted using the standard transvenous approach. The procedure involved the placement of a right ventricular (RV) lead was positioned at the RV septal apex. Subsequently, a left ventricular lead was implanted in a suitable branch of the coronary venous system, specifically in the lateral, posterolateral, or anterolateral region. The selection of the implantation site was based on the ability to achieve an acceptable pacing threshold while avoiding diaphragmatic pacing.2.5CRT devices were programmed to DDD(R) for sinus rhythm and VVI(R) for atrial fibrillation. To optimize the atrioventricular delay, echocardiography guidance was employed. This involved assessing various parameters to determine the optimal atrioventricular delay that would provide the maximum left ventricular filling time. The goal was to avoid interference from atrial wave activity and diastolic mitral regurgitation. Additionally, the inter-ventricular delay was adjusted based on the pulsed wave left ventricular outflow tract velocity.2.6The evaluation of echocardiographic response was conducted at the 6-month mark following implantation. A patient was classified as a CRT responder if there was a reduction of left ventricular end-systolic volume ≥ 15 % and/or an improvement in left ventricular ejection fraction > 10 % compared to the pre-implantation values. The primary clinical outcome was defined as a composite endpoint, comprising cardiac death, left ventricular assist device implantation, or unplanned heart failure hospitalization. The secondary endpoints were defined as individual events within the composite outcome.In addition to echocardiographic and clinical parameters, biomarkers with predictive potential were also assessed. The evaluation focused on analyzing the association between the time-course changes in these biomarkers following implantation and the corresponding echocardiographic responses.Each endpoint was separately analyzed in terms of bundle branch block or QRS width types 2.7p < 0.05 was taken as a threshold for statistical significance. All analyses were performed using JMP 14 software .The results were presented as the mean ± standard deviation or as the median and interquartile range (IQR) for continuous data, depending on the nature of the continuous data. Categorical data were expressed as counts and percentages. Statistical comparisons between categorical variables were performed using the χ2 test or Fisher’s exact test when appropriate. For continuous variables, the Wilcoxon rank-sum test was utilized for group comparisons. Survival analysis was conducted using the Kaplan-Meier method, with the comparison divided by the median of each biomarker. The log-rank test was employed to assess the differences in survival curves between the groups. The effects of covariates on the time to endpoint were investigated using a Cox proportional hazards model. Additionally, a multiple linear regression model was employed to test the influence of multiple covariates on the study outcomes. A value of 33.1Total of 86 patients  were prospectively enrolled in the present study, as detailed in 3.2p = 0.379). However, the former group experienced significantly more favorable outcomes in terms of hospitalization due to worsening heart failure .During a median follow-up of 794 days after CRT implantation (IQR: 384 – 1191 days), the primary outcome was observed in 31 subjects . Kaplan-Meier curves representing the primary outcome, stratified by the median level of each ANP form before CRT implantation, were presented in 2, diabetes mellitus, hemoglobin, and plasma levels above the median of proANP and NT-proANP before CRT implantation were associated with the primary endpoint. In the analysis adjusted for body mass index, diabetes mellitus, and hemoglobin, NT-proANP emerged as a predictor of the primary outcome, whereas proANP did not. Interestingly, total BNP or NT-proBNP were not associated with the primary endpoint.3.3The relationships of NT-proANP levels with clinical characteristics, as well as echocardiographic and electrocardiographic parameters, were assessed . Importa3.4p = 0.052). NT-proANP levels before CRT implantation did not differ significantly between responders and non-responders (3.03 [1.76 – 5.71] nM vs. 4.91 [2.66 – 7.67] nM. p = 0.097). However, CRT responders exhibited a decrease in NT-proANP levels 6 months after implantation, while non-responders showed the opposite pattern of changes   .Fig. 4Pe44.1The present study revealed novel findings regarding the relationship between plasma levels of ANP forms and a failing heart in patients undergoing CRT implantation, as followed; 1) Low plasma levels of NT-proANP before CRT implantation predicted favorable outcomes after the implantation, 2) NT-proANP levels were positively correlated with left atrial volume and left ventricular parameters associated with diastolic dysfunction, and 3) plasma levels of NT-proANP decreased in conjunction with echocardiographic responses to CRT at six months after implantation.4.2In humans, ANP exists in four distinct endogenous molecular forms; proANP, mature ANP, and NT-proANP, with β-ANP being a minor form 4.3Previous studies have demonstrated that NT-proANP can serve as a diagnostic biomarker and a predictor of heart failure In a recent study by Michel T. et al., it was shown that corin cleaves proANP to generate NT-proANP and mature ANP. which are subsequently degraded by meprin B and neprilysin, respectively 4.4In our study, NT-proANP showed associations with left atrial volume, severity of mitral regurgitation, E/A ratio, and E/e’ ration, which are indicators of left atrial function and left ventricular diastolic function. Left ventricular diastolic dysfunction leads to an elevation in left atrial pressure In the follow-up data, we observed a decrease in NT-proANP levels in patients who responded to CRT therapy, whereas non-responders exhibited an increase. These findings suggest that NT-proANP expression fluctuates alongside the reverse remodeling of both the left atrium and left ventricle following CRT implantation. Monitoring NT-proANP levels post-implantation may serve as a viable alternative to frequent echocardiographic assessments.4.5Mature BNP and N-terminal proBNP measurements are widely used in heart failure management. In a previous study, we demonstrated the significance of measuring each BNP molecular form in CRT recipients 5This study has several limitations. Firstly, the sample size of enrolled subjects was small, and the study was conducted at a single-center, limiting the generalizability of the findings. Secondly, the mechanisms of underlying the regulation of each ANP form processing in relation to atrial overload or ventricular diastolic dysfunction remained a topic of future investigation. Thirdly, the study population lacked individuals who did not undergo CRT implantation making it unclear whether the predictive power observed in this study is applicable to a broader population with advanced heart failure. Fourthly, β-ANP levels were not measured in this study due to lack of sufficient plasma sample for condensation and measurements. Finally, the administration of sacubitril/valsartan, which is a standard therapeutic strategy for systolic dysfunction, may affect the proteolytic process of ANP. However, no patients receiving this treatment were included in our study.6A high plasma level of NT-proANP before the procedure was associated with unfavorable outcomes following CRT implantation. Details measurements of each ANP molecular form could provide valuable insights into heart failure with systolic dysfunction.FundingThis work was supported by the Intramural Research Fund of the National Cerebral and Cardiovascular Center .The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Chemotherapy-induced cardiotoxicity has become a prevalent complication. Regular monitoring of patients who received chemotherapy using 3D strain parameters may aid in early detection of myocardial damage and its prevention. The purpose of this study was to evaluate the effectiveness of three-dimensional speckle tracking imaging (3D-STI) in diagnosing and predicting the likelihood of cardiotoxicity. This was achieved by conducting a systematic review of original research articles.To evaluate the role of 3D speckle tracking echocardiography in early detection of cardiotoxicity.Relevant case control studies published prior to December 2022 were extracted to assess cardiotoxicity by 3D STE in patients after chemotherapy.A total of 1991 chemotherapy treated patients and control patients were included in the present review via pooling 22 studies.3D speckle tracking echocardiography has the utility of non-invasive and objective evaluation of changes in left ventricular function in cancer patients undergoing chemotherapy.CRD42023383790 on PROSPERO: International prospective register of systematic reviews.Study ID,  Millions of people are fighting against cancer as one of the leading causes of death . Over thCardiotoxicity often leads to treatment discontinuation with a subsequent increased risk of cancer recurrence and mortality, which has been reported to be as high as 40% over the next 5 years following chemotherapy [Left ventricular ejection fraction (LVEF) values are used in practice to assess and follow patients with susceptible myocardial toxicity but the accuracy and sensitivity in determining myocardial injury at an early stage have lower sensitivity and high variability. Once LVEF declines are detected, advanced myocardial injury has occurred and is likely irreversible , 7.There are advancements in screening modalities to diagnose subclinical myocardial dysfunction. Speckle tracking allows comprehensive quantitative evaluation of LV deformation in terms of strain and strain rate . Post-thEven this threshold still may not be enough to allow early detection of cancer therapy related cardiac dysfunction (CTRCD). Further inclusion of other strain parameters may facilitate comprehensive evaluation of myocardial mechanics that can help in detecting subclinical cardiotoxicity .Three-dimensional speckle tracking imaging (3D-STI) represents an advanced form of echocardiographic technology for detection of myocardial injuries at earlier stages. 3D-STI has higher measurement reproducibility with tracking of out-of-plane speckle motion . Since mLeft ventricle has garnered much attention from researchers, however, right ventricle (RV) evaluation by STE has been recognized as an independent prognostic factor and an asset in recognizing early subclinical changes in the myocardium for routine follow-up in cancer patient populations , 17.Monitoring of chemotherapy related cardiac dysfunction is not only for patients currently under therapy but also for cancer survivors who remain at risk of developing adverse events even after treatment completion, which can last for years . TherefoThis systematic review aims to assess and monitor subclinical cardiotoxicity detection using different 3D strain parameters in cancer patients during and after chemotherapy.This is a systematic review performed according to the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines . A literCancer patients aged 18 years or older who had undergone chemotherapy and had recorded 3D strain parameters, with no restrictions on gender or ethnicity. Participants must also have no evidence of cardiac symptoms or signs, no history of cardiomyopathies, uncontrolled cardiac risk factors, or significant valvular heart disease.Any chemotherapy is known to cause cardiotoxicity.3D based strain parameters in patients who developed and those who did not develop cancer therapy related cardiovascular toxicity.Value of 3D based speckle tracking echocardiography parameters in detecting cardiac affection.Inclusion criteria were as follows: Adult (≥ 18 years) cancer patients undergoing chemotherapy were included. We included all clinical studies , prospective or retrospective observational studies) investigating the ability of 3D speckle tracking based strain analysis to detect cardiotoxicity and cardiac adverse events regardless of its definition. Non-English case reports and articles were not included.Abstracts and full texts were reviewed in a blind standardized manner. Relevant studies from references in selected papers were scanned for possible inclusion. RAYYAN software was used. Before final approval, any disagreements between researchers were resolved by senior contributors.Two authors (MA and MA) extracted the data, using predefined standardized data extraction form. Data extraction included: study characteristics as design and duration, study population as total number of included patients and type of cancer being treated, cardiac therapy related cardiac dysfunction (CTRCD) definition, CTRCD events, investigated 3D speckle tracking based parameters, methods of investigations and results. Microsoft Office Excel was used for data extraction.The included studies were assessed by four reviewers for risk of bias and quality, including the use of the Newcastle–Ottawa Scale  Table 11.Table 1The literature research retrieved 835 studies. After the removal of duplicates, 772 studies remained. After the review of all abstracts, another 684 studies were excluded due to irrelevance. The remaining 79 articles underwent full reading and 19 met the predefined inclusion criteria. Three more studies were included from the bibliography scanning making a total of 22 papers included in the present review  with or without trastuzumab underwent 3D STE at standardized intervals prior to, during, and annually after chemotherapy, left ventricular ejection fraction (LVEF), global circumferential strain (GCS), global longitudinal strain (GLS), principal strain, twist, and torsion were quantified. After a median 2.1 years follow-up period they reported that 3D LVEF, GCS, and GLS, had initial decrement that was followed by modest improvement, though at 2 years these values remained worse than baseline. In contrast, values for 3D twist and torsion plateaued at six months without substantial subsequent change .2), (group 3 at mid-chemotherapy with a dose of 240 mg/m2) and (group 4 at the end of chemotherapy with a dose of 360 mg/m2). GAS, GLS, GCS, and E/A were significantly reduced during mid chemotherapy and at the end of chemotherapy. They reported a significant negative correlation between GAS and anthracycline doses. A GAS of − 31.5% was used as the cut-off value for diagnosing left ventricular systolic dysfunction after receiving chemotherapy. The sensitivity of the previous parameters was 81.9%, and the specificity was 80.3% [Chen et al. conducted a study on 89 breast cancer patients who received a 6‐cycle epirubicin plus cyclophosphamide chemotherapy regimen. They divided patients into four groups according to cumulative epirubicin doses: (group 1 with 0 mg/m2 before chemotherapy),  and underwent 3D STE at baseline, at the end of chemotherapy, and at 12 months after chemotherapy. RV ejection fraction, LV global longitudinal strain (GLS), RV GLS, and RV free wall longitudinal strain were all assessed using 3D STE. An absolute decrease in 3D LVEF of more than 10% to a value less than 50% was defined as cardiotoxicity, while subclinical CTRCD was defined as a reduction in 3D LV GLS of more than 15%. They reported that subclinical CTRCD occurred in 10.5% of patients during follow-up. The 3D GLS of the LV and the GLS and free wall longitudinal strain (FWLS) of the RV both significantly decreased 12 months after treatment compared to baseline. 3D RV GLS and RV FWLS variations had good discrimination for predicting subclinical cardiotoxicity. Only one independent predictor of subclinical CTRCD was the variance of 3D RV FWLS .Wang et al. made the RV their scope and included 64 BC patients who were hospitalized to receive pirarubicin chemotherapy. All patients were evaluated by 3D strain assessment of the RV, and RV GLS, RV GCS, RV GRS and RVGAS were calculated before and after chemotherapy. They reported that RV GLS and RV GAS were significantly reduced after chemotherapy although the conventional parameters for assessment of RV function like TAPSE were not significantly affected .p < 0.05). The degree of decrease in 3D-GCS was G3 > G2 > G1 [Guan et al. enrolled 79 breast cancer patients receiving different tumor treatment regimens divided into three groups: G1 received anthracycline-free and nontargeted regimens, G2 included patients treated with anthracycline-containing regimens without targeted therapy and G3 included patients treated with anthracyclines combined with targeted drugs. CTRCD was defined as an absolute reduction in left ventricular ejection fraction (LVEF) of > 5% to < 53%. All patients underwent echocardiography before chemotherapy (T0) and after the end of chemotherapy cycle 2 (T2), cycle 4 (T4), cycle 6 (T6), and cycle 8 (T8). They reported that in comparison with the basic condition before chemotherapy, 3D-GLS, 3D-GCS, 3D-GAS, and 3D-GRS were significantly decreased. The decrease in 3D-GLS and 3D-GCS in G2 and G3 was more obvious than that in G1, the changes were statistically significant in T6, and 3D-GAS and 3D-GRS were significantly changed in T8. The changes in 3D-GLS, 3D-GAS, and 3D-GRS in the G3 group were significantly higher than those in the G1 and G2 groups  positive patients received trastuzumab treatment.Patients underwent a comprehensive echocardiographic study during the following 5 stages: baseline, after cumulative doses of 120 and 240 mg/m2 of doxorubicin, and then, after 6 months and at least 1 year after anthracyclines. Cardiotoxicity was defined as a decrease in LVEF by more than 10 percentage points to a value lower than 53%. They reported that the 2DGLS presented changes only at the end of the protocol, after the 240 mg/m2 dosage with no change after a lower cumulative dosage of doxorubicin. By 3DSTE they reported changes in most myocardial deformation parameters: 3D GLS, 3D GRS, 3D GCS, and 3D GAS after the cumulative dose of 240 mg/m2 of doxorubicin and no changes in rotation, torsion, or twist in any of the evaluation stages, alternatively, following a lower cumulative dose of doxorubicin (120 mg/m2), the only variable that significantly shifted was 3D GAS, so they decided that The 3D GAS early changed to below normal values was the only parameter that was associated with the subsequent decrease in LVEF during follow-up, while the other variable indices of LV mechanics derived from 2 and 3D speckle tracking analysis, as well as the conventional echocardiographic data were not predictors of CTRCD, another point they highlighted in their study was the proof of accurate reproducibility of 3DSTE, especially in 3D GCS and 3D GAS, demonstrating their dependability and accuracy indices in the assessment of ventricular mechanics .Not only breast cancer and its chemotherapy were the focus of this systematic review, but we were also looking at complementary data for other patients with heterogeneous malignancies who received different chemotherapies.Hong-kui Yu et al. ran a study on 53 anthracycline-treated pediatric cancer survivors  who had finished treatment for more than a year in comparison to 38 control patients. Three-dimensional speckle-tracking echocardiography was performed to assess 3DLV global and segmental strain, time to peak segmental 3D strain, LV torsion, LV systolic desynchrony index (SDI), global performance index (GPI), and ejection fraction.Cancer survivors had significantly reduced LV strain parameters globally  and regionally  when compared to the control group. Global 3D strain, SDI, and GPI were correlated with cumulative anthracycline dose. GPI had sensitivity of 84.9% and specificity of 81.6% with cut-off 10.6°/cm to differentiate patients from controls .Li et al. ran a case control study included 83 cancer survivors  after anthracycline therapy who had been off treatment for more than 5 years and 42 age- and sex-matched healthy subjects . Acute lymphoblastic leukemia was the most frequent cancer diagnosed in 37 out of the 83 survivors.Survivors of both sexes had significantly lower 3D GLS, GCS, GRS, GAS, and LV ejection fraction compared with controls. The absence of sexual dimorphism in these survivors was considered a novel finding. Among 3D strain parameters, GLS is considered the most sensitive parameter in the detection of subclinical cardiotoxicity in survivors with the greatest area under the ROC curve. A 3D STE-derived GLS correlated positively with LV ejection fraction and showed 85.7% sensitivity and 80.7% specificity at a cut-off 16.4% in differentiating survivors from controls .Miyoshi T et al. retrospectively compared 50 anthracycline treated patients with 20 matched healthy volunteers as a control group. Of the 50 patients, 22 had non-Hodgkin lymphoma, 7 had leukemia, 6 had Hodgkin lymphoma, 4 had breast cancer, 2 had osteosarcoma, and 9 had others.2D and 3D strain analysis including 3D LV GLS, GCS, GRS and GAS was performed for both groups and they found that while 3D GRS, 3D GLS, and other 2D speckle tracking parameters were not affected significantly in anthracycline group, only 3D GAS and peak 3D global circumferential strains of the anthracycline group were significantly worse than those of the control group and 3D GAS was the only parameter that could be independently linked to the cumulative doxorubicin dose .Mornoş C et al. ran an observational prospective study on 59 anthracycline treated patients, of these patients 26 had breast cancer, 12 had non-Hodgkin’s lymphoma, 10 had Hodgkin’s lymphoma, 8 had acute lymphoblastic leukemia, 2 had acute myeloblastic leukemia, and 1 had osteosarcoma. 3D echocardiography strain parameters and cardiac biomarkers were assessed before and after 12 and 36 weeks of treatment. A reduction in LVEF by ≥ 5% to < 55% with symptoms of heart failure, or an asymptomatic reduction of LVEF by ≥ 10% to < 55% was indicative of subclinical cardiotoxicity. Eight individuals (13.5%) had cardiotoxicity at the end of follow up according to the criteria. While at 12 weeks after chemotherapy 3D GLS, 3D GCS, and 3D GRS revealed significant changes and 3D GLS emerged as the only independent predictor of later cardiotoxicity .Song, F, et al. prospectively studied 101 patients with diffuse large B-cell lymphoma between December 2012 and August 2015, who had been treated with anthracycline. All patients received 4 cycles of R-CHOP treatment. 3D-STE was done at baseline, after the completion of two cycles and four cycles of the regimen. The study showed that LV apical rotation, basal rotation, twist, and torsion significantly reduced compared with strain values at baseline, two cycles and after four cycles of the regimens. Furthermore, apical-basal rotation delay increased significantly after two cycles as well as after four cycles of the regimen .Another prospective observational study by Song F et al. included 89 diffuse non-Hodgkin lymphoma patients receiving treatment that contained anthracyclines every 21 days for a maximum of eight cycles. 2D and 3D STE were performed and analyzed according to standardized procedures. 3D STE analysis included 3D LV GLS and GCS and RV GLS. All parameters were analyzed at baseline, after four cycles and at the end of the regimen. 3D LV GLS, GCS and RV GLS decreased significantly after four chemotherapeutic cycles, while 2D GLS and GCS of LV showed significant deterioration only at the end of treatment and 2D GLS of RV showed no significant variation during the whole study. Hs-cTnT correlated significantly with 3D GLS of LV and with 3D GLS of RV. The cut off value for 3D LV GLS was − 20.4% with a sensitivity of 81% and specificity of 66% and was − 21.9% for RV GLS by 3D with a sensitivity of 71% and specificity of 74% for differentiating patients after therapy from baselines .Guang-Li Hou et al. conducted a study on 58 lung cancer patients who received pemetrexed and cisplatin chemotherapy and compared them with 50 cases as a control group. 3D GAS after three cycles of the chemotherapy group was significantly lower than that of the control group and changes in 3D GAS correlated with oxidative stress and apoptosis markers .Xu et al. in a retrospective observational single center study evaluated sixty patients with diffuse large B-cell lymphoma who received R-CHOP chemotherapy every 21 days for a maximum of eight cycles. Three-dimensional left ventricular strain parameters were measured at baseline, after two and four cycles of the regimen respectively.There was a statistically significant difference between 3D-GLS at baseline and at the end of four cycles of anthracycline chemotherapy, while there was no significant reduction after completion of the first two cycles, and no significant changes in 3D GCS during the whole study. All longitudinal strains in the middle and apical segments showed a significant decrease after four cycles with an early drop of apical anterior and septal wall LS after two cycles. Serum hs-cTnT levels, as the gold standard diagnostic tool for myocardial injury were significantly increased after two and four cycles, and serum hs-cTnT levels were correlated with 3D-GLS as well as the mean value of 3D-LS of different myocardial segments .Alam et al. in a prospective, single center, observational study included 46 patients diagnosed with different malignancies who received a cumulative dose of doxorubicin of less than 550 mg/m2 from December 2017 to November 2018. Echocardiographic examination was done at baseline and after completion of four cycles of anthracycline based chemotherapy. Most of the patients had breast cancer (40 patients).In 2D echocardiography, there was no statistically significant difference pre and post chemotherapy regarding EF, however of all 2D strain parameters only 2D GLS showed statistically significant difference with mean percent change of 18.33%.All 3D strain parameters were observed to be significantly reduced during follow up. The reduction of 3D global longitudinal strain by 29.19%, circumferential strain by 30.65%, area strain by 21.61%, and radial strain by 29.66% after completion of chemotherapy. EF was significantly reduced by 3D echocardiography, but no patient had a fall of EF to less than 50% by either 2D or 3D .Zhao et al. in a prospective observational single center study enrolled 74 patients with diffuse large B-cell lymphoma who received 6 cycles of intravenously anthracycline-based treatment between May 2014 and May 2015. All patients underwent echocardiography at baseline, after the completion of two, four and six cycles of the chemotherapy regimen respectively. A relative reduction of > 10% in 3D RVEF or a relative reduction of > 5% to a value of < 45% was the main definition of RV cardiotoxicity.3D strain parameters in the form of longitudinal free wall strain RV LFS and longitudinal septal strain LV GLS were statistically significant before and after 4 cycles of chemotherapy and only RV LFS was associated with subsequent RV cardiotoxicity with RVEF decline at the end of follow-up. However, RV LSS and LV GCS were only significantly decreased at T3, suggestive of a late change. Of note, the results showed no significant difference in LVESV, and LVEF from the values at T0 to T3 .Ugur Nadir Karakulak et al. conducted a cross-sectional and observational study on 37 patients with chronic myeloid leukemia, 17 were under dasatinib and 20 were under nilotinib after imatinib failure compared with 50 healthy individuals as a control group. 4D speckle tracking echocardiography derived global longitudinal (GLS), circumferential (GCS), radial (GRS), and area (GAS) strain indices were calculated for patient and control groups.Myocardial deformation parameters did not show any correlation with demographic, clinical, and laboratory characteristics while there is significant reduction in area, longitudinal, and circumferential strain values with no difference between dasatinib and nilotinib groups. Additionally, area and radial strain had a stronger association with the duration of dasatinib treatment .Diana Mihalcea et al. included 110 patients with non‐Hodgkin’s lymphoma scheduled for CHOP chemotherapy from January 2014 to October 2018 in a prospective observational study. 3D myocardial deformation parameters were evaluated at baseline, after third cycle and chemotherapy completion. Only 18 patients (16%) developed cardiotoxicity as defined by guidelines  after the 3rd cycle, while 3D strain echocardiography showed a significant decrease of all deformation parameters LS, CS, RS, and AS in the study group after 3rd cycle and persistent after final cycle of therapy, with more important reduction in patient who developed cardiotoxicity. LS were identified as the best independent predictors for 3D LVEF decrease at the end of chemotherapy .Wang et al. in a prospective observational study included 30 colorectal cancer patients with 19 male participants with no history of cardiac problems who received mFOLFOX6  for a total of 12 cycles. They measured the 3D STE parameters at baseline, and after cycle 1, 6 and 12 respectively. The 3D STE parameters GLS, GAS, MCI and LVtw decreased after the first cycle of chemotherapy. Increasing cumulative dose of mFOLFOX6 correlated with decreases in left ventricular MCI, GLS, GAS, GCS, GRS, and LVtw, with statistically significant differences between pre- and post-chemotherapy. In particular, the decrease in MCI was found to be the most significant with the highest cTnT correlation. Area under the curve of MCI was larger than other parameters, which may be a more sensitive indicator of 3D-STE parameters .Chen et al. included 39 patients with multiple myeloma who received more than 6 PAD chemotherapy cycles  from July 2019 to December 2020, with no radiotherapy or chemotherapy in a prospective observational study. History of cardiac disease or severe chronic disease was considered as an exclusion criterion. Echocardiography assessment after the 2nd, 4th and 6th cardiac cycles showed no significant change by 2D echocardiography till the end of all six cycles, while 3D-STI parameters changed significantly in the earlier stages. After 2 cycles of chemotherapy RVGLS was significantly lower than that before chemotherapy. After 4 cycles of chemotherapy, RVGCS, RVGLS, RVGRS, and LVGLS were significantly lower than that before chemotherapy. Furthermore, there was a significant negative correlation among the RVGCS, RVGLS, RVGRS, and LV GLS. Among them, RVGLS has the strongest correlation. The results showed that the dose of doxorubicin was negatively correlated with RVGCS, RVGLS, RVGRS, and LV GLS. They concluded that 3D-STI is more sensitive for early detection of subtle cardiotoxicity and should be routinely used .Recent advancements in caner chemotherapeutics have led to remarkable improvements in cancer survival rates . CardiotIn the 2022 cardio-oncology European guidelines, two-dimensional global longitudinal strain (2D-GLS) was introduced as an index of chemotherapy-induced cardiotoxicity, and numerous studies have verified that a decrease in two-dimensional left ventricular longitudinal strain (2D-GLS) of over 15% after chemotherapy in patients with an LVEF greater than 50% indicates subclinical myocardial injury , 12. NonThree-dimensional speckle tracking echocardiography (3D-STI) strain parameters reflect myocardial deformation in three spatial directions: longitudinal, radial, and circumferential. It overcomes the issue of angle dependence in two-dimensional plane tracking, and hence is anticipated to provide more sensitive parameter for early detection of subclinical myocardial dysfunction . This imGLS is a measure of myocardial deformation from base to apex caused by contraction of the longitudinal fibers beneath the endocardium. Longitudinal myocardial movement is essential to left ventricle contraction. GCS is the short axis circumferential shortening of left ventricular myocardial fibers. GRS reflects left ventricular cavity deformation towards the center, indicating thickening and thinning of left ventricular wall during cardiac cycle. GAS is a product of longitudinal and circumferential strain as the left ventricular endocardial surface deforms during systole and diastole. It reflects the change in relative area by combining longitudinal and circumferential shortening effects .All 3D strains, which encompass GLS, GCS, GRS, and GAS, exhibited a significant correlation with subsequent LV dysfunction in individuals undergoing chemotherapy. Notably, of the various 3D strain measurements, 3D-GAS displayed a tendency towards a strong association with detection and follow-up LV dysfunction, this can be related to the fact that GAS is combining both longitudinal and circumferential shortening effects .Segmental myocardial affection was also an interesting finding and could enable earlier detection of cardiotoxicity. Xu et al. found that all longitudinal strains on middle and apical segments decreased significantly after four cycles of chemotherapy, with an early drop in apical anterior and septal wall longitudinal strains after only two cycles. In a separate study, Cruz et al. observed impaired contractility in the anterior, inferior, and septal walls of breast cancer patients using 3D strain regional analysis. These findings suggest that myocardial segments supplied by the left anterior descending artery are more susceptible to anthracycline treatment than other segments, but the underlying mechanism remains unclear. Potential factors contributing to this susceptibility may include differences in shear stress and left ventricular geometry, greater anthracycline exposure in end-stage circulatory areas, fibrosis, or local variations in apoptotic activation. Further research is still needed to fully understand the mechanisms behind segmental cardiotoxicity in patients undergoing anthracycline treatment.Incorporating 3D myocardial strain imaging into the follow-up strategy helps in early detection of subclinical cardiotoxicity.In two studies by Hong-kui Yu et al. and Li et al. included pediatric cancer survivors; the area under ROC curve showed that 3D-derived GLS is more sensitive than other 3D and 2D strain measures in the evaluation of chemotherapy -induced cardiotoxicity. Potential advantage may be explained by the simultaneous evaluation of myocardial deformation in several LV myocardial segments and the tracking of speckle motion in a real 3D manner , 35.While most studies focused on LV evaluation, few studies focused on RV evaluation in early detection of subclinical cardiotoxicity. There is growing evidence to suggest that the right ventricle is more susceptible to chemotherapy-induced cardiotoxicity than the left ventricle . ChemothHowever, the available evidence on the impact of chemotherapy treatment on right ventricular 3D strain parameter is limited, only 4 studies reported the outcome on GLS and showed nonsignificant pooled effect size of anthracycline treatment on right ventricle and another two studies reported the effect of chemotherapy on global radial strain (GRS) and circumferential strain (GCS). The pooled mean difference for GRS and GCS was lower after chemotherapy treatment but not statistically significant. While these findings provide preliminary insights into the potential impact of anthracycline treatment on right ventricular function, further research is needed to fully understand these effects.Although 3D echocardiography images have the advantage of being able to measure LV volume, LVEF, and 3D strain simultaneously, it has low temporal and spatial resolution and requires acquisition of 6 consecutive beats. So, a good acoustic window without arrhythmia is mandatory.Most of the studies have a good feasibility and reproducibility of 3D STE, only one study has a concern in 3D feasibility in cancer patient receiving chemotherapy. This was highlighted by Santoro et al. who stated that suboptimal feasibility of 3D echocardiography and in particular of 3D STE emerges as a main disadvantage and strongly limits the routine applicability of this technique in breast cancer patients. However, their result may be related to specific group of patients as left side breast cancer, left side mastectomy or radiotherapy that may limit the 3D study.3D speckle tracking echocardiography has the utility of non-invasive and objective evaluation of changes in left ventricular function in cancer patients undergoing chemotherapy. This allows for early detection, early intervention before symptoms appear and optimization of chemotherapy protocols.With continuous advancement in 3D technology, 3D-STI could be used as a monitoring method in assessment of patients receiving chemotherapy and in predicting their prognosis. However, long-term follow-up and more multicenter studies are needed to determine the predictive and diagnostic threshold for post-chemotherapy myocardial toxicity.


BackgroundHeart failure contributes to the global burden of cardiovascular diseases, with hypertensive heart failure affecting individuals in their productive age group and leading to high economic losses and disability-adjusted life years. The left atrium, on the other hand, contributes significantly to left ventricular filling in heart failure patients, and the left atrial function index is an excellent tool for assessing left atrial function among heart failure patients. The study aimed to evaluate some parameters of systolic and diastolic function as correlates and potential predictors of the left atrial function index among hypertensive heart failure cohorts.Materials and methodsThe study was conducted at Delta State University Teaching Hospital, Oghara. Eighty (80) patients with hypertensive heart failure who met the inclusion criteria were enrolled in the cardiology outpatient clinics. The left atrial function index was calculated using the following formula: LAFI = (LAEF x LVOT-VTI)/LAESVI. . The data were analysed using IBM Statistical Product and Service Solution Version 22. Relationships between variables were determined using analysis of variance, Pearson correlation, and multiple linear regressions. Significance was assessed at p<0.05.ResultIt was discovered that the left atrial function index correlated with ejection fraction , fractional shortening , and the ratio of early transmitral flow to early myocardial contractility, E/E' . However, there was no correlation with stroke volume ; the ratio of early transmitral flow to late transmitral flow, E/A ; isovolumetric relaxation time, IVRT ; and tricuspid annular plane systolic excursion, TAPSE . Of the variables that correlated with left atrial function index, left ventricular ejection fraction and the ratio of early transmitral flow to early myocardial contractility (E/E') were found to be independent predictors of left atrial function index.ConclusionLeft ventricular ejection fraction and the ratio of early transmitral flow to early myocardial contractility reflect changes in the left atrial function index, and as such, they should be used as surrogates for its assessment, especially in low- and medium-income countries where left atrial function index estimation is not routinely done. Heart failure is a global pandemic affecting at least 26 million people worldwide, and its prevalence is on the increase, with reports showing a three-year mortality rate of patients with advanced heart failure of 57.9%-67.1% in Sub-Saharan Africa , 4, 5.2 while functioning in the setting of normal cardiac output, which is a left ventricular output tract velocity time integral greater than 20cm [The left atrium contributes significantly to cardiac function, even in failure, and if the left atrial mechanical and electrical function becomes impaired, cardiac output will be significantly affected . Contrarhan 20cm .In low- and medium-income countries where the left atrial function index is not routinely done , it is e2) were excluded from the work. Hypertensive heart failure was defined as heart failure patients who have a clinical history of hypertension, irrespective of their current blood pressure status, or persistent BP ≥ 140/90 mmHg in those who are not previously known to be hypertensive and/or on antihypertensive medications [The study was conducted at the Delta State University Teaching Hospital, Delta State, Nigeria. The study period was between June 2020 and October 2021. The study population included eighty (80) hypertensive heart failure patients who enrolled in the heart failure follow-up clinic. The study design was cross-sectional. Hypertensive heart failure patients aged 18 years and older who gave consent were included in the study. Those with subclinical echocardiographic views, atrial fibrillation, and atrial flutter who refused or lacked the capacity to grant consent, as well as those with comorbidities such as chronic obstructive pulmonary disease, valvular heart disease, diabetic mellitus, retroviral disease, autoimmune disorders, and chronic kidney disease (estimated glomerular filtration rate (eGFR) < 60mls/min/1.73mications , 12. A sTo obtain data from the heart failure patients, a transthoracic echocardiogram  was used alongside electrocardiographic gating by the researcher at the time of recruiting patients. The left atrial function was determined by the volumetric method using the left atrial function index, as shown in the formula below. The left atrial volumes were estimated at different time points of the cardiac activity: the maximal left atrial volume was calculated at the end of the T wave on the electrocardiogram (ECG), just before the opening of the mitral valve; the minimal left atrial volume was at the beginning of the QRS complex, just at the closure of the mitral valve; and the preceding left atrial contraction volume was conducted at the beginning of the P wave . The lefThe early transmittal velocity by pulsed wave Doppler was obtained with the sample volume set at the tips of the mitral leaflets. Peak velocities in early diastole (E-wave velocity during early left ventricular relaxation) and late diastole  were measured, and the ratio E/A was calculated. When atrial contraction occurs before the mitral deceleration has decreased to zero, the deceleration time is estimated as the time between the peak E-wave velocity and the deceleration slope extrapolated to zero baseline. Isovolumetric relaxation time was estimated from the leading edge of the aortic valve closure point to the leading edge of the mitral valve opening point.In order to determine the ratio of early transmitral flow to early myocardial contractility (E/E’), pulsed wave Doppler tissue imaging was used to measure peak velocities in systole (S’), early diastole (E), and late diastole (A’), with the sample volume placed at the septal annulus . The E/ELeft ventricular systolic function was assessed by ejection fraction, fractional shortening, and stroke volume. In 2D echocardiography , the meaM-mode echocardiography  was usedData were collated and analysed with International Business Machines Statistical Product and Service Solutions (IBM-SPSS) version 22.0. A questionnaire was used to obtain information from participants. Means and standard deviation were used to present continuous variables. Pearson’s correlation test was used to assess the relationship between the left atrial function index and left ventricular function parameters such as ejection fraction, fractional shortening, stroke volume, the ratio of early transmitral flow to early myocardial contractility, tricuspid annular plane systolic excursion, etc. The variables that significantly correlated with the left atrial function index were assessed further for cause and effect with multiple linear regressions. A positive correlation was defined by a correlation coefficient of r <+1. On the other hand, a negative correlation was defined by r < -1. The strength of the correlation was assessed as follows: weak correlation: r = 0.10-0.30; moderate correlation: r = 0.40-0.60; and strong correlation: r ≥0.70. Significance was assessed at p<0.05.Ethical approval was obtained from the Health Research and Ethics Committee of the Delta State University Teaching Hospital, Oghara, with the institutional review board number HREC/PAN/2019/0313. Patients were duly informed, and consent forms were duly signed or thumb printed before they were enrolled in the study. The researcher also maintained confidentiality and the right to exit the study at any time.Sociodemographic characteristics of hypertensive heart failure patientsTable Correlation between left atrial function index and left ventricular systolic functionTable Correlation between left atrial function index and left ventricular diastolic functionTable Figure Independent predictors of left atrial function index among hypertensive heart failure patientsTable In our study, the left atrial function index correlated moderately and positively with left ventricular ejection fraction and fractional shortening, but there was no correlation with stroke volume. This was comparable to a report from another author  that notThe left atrial function index was inversely correlated with the ratio of early transmitral inflow to early myocardial tissue contractility, but there was no correlation with the ratio of early transmitral flow to late transmitral flow or isovolumetric relaxation time. This finding was comparable to a report from another study in the United States  that shoIn addition, this study also demonstrated that the left atrial function index had no correlation with tricuspid annular plane systolic excursion, and this may be due to the fact that the population of this study was mostly patients with chronic stable heart failure who were routinely followed up in the clinic. A study from Nigeria  reportedAlthough ejection fraction and the ratio of early transmitral flow to early myocardial contractility independently predict an abnormal left atrial function index, this was not the case with fractional shortening. Ejection fraction takes into consideration left ventricular myocardial contractility and left ventricular volumes, unlike fractional shortening, which considers only left ventricular myocardial contractility. This may account for the inability of fractional shortening to be an independent predictor of left atrial function among hypertensive heart failure patients.Limitations of the studyThe study was conducted in a single hospital and may not represent the true picture of the population. Also, chronic obstructive lung disease was excluded on clinical grounds, and patients with subclinical conditions may have been included; therefore, they may have had an impact on the right ventricular assessment. Transoesophageal echocardiography, speckle tracking, and cardiac magnetic resonance imaging are better tools to assess left atrial function. This was not used due to challenges with funds.There was a correlation between the left atrial function index and left ventricular ejection fraction, fractional shortening, and the ratio of early transmitral inflow to early myocardial tissue contractility, but there was no correlation between left atrial function index and stroke volume, the ratio of early transmitral inflow to late transmitral inflow, isovolumetric relaxation time, or tricuspid annular plane systolic excursion in our study. Left ventricular ejection fraction and the ratio of early transmitral inflow to early myocardial tissue contractility independently predict abnormal left atrial function index and could be a possible alternative for the left atrial function index, especially in low-and middle-income countries where its calculation is not commonly implemented.


Cardiac pump function arises from a series of highly orchestrated events across multiple scales. Computational electromechanics can encode these events in physics-constrained models. However, the large number of parameters in these models has made the systematic study of the link between cellular, tissue, and organ scale parameters to whole heart physiology challenging. A patient-specific anatomical heart model, or digital twin, was created. Cellular ionic dynamics and contraction were simulated with the Courtemanche-Land and the ToR-ORd-Land models for the atria and the ventricles, respectively. Whole heart contraction was coupled with the circulatory system, simulated with CircAdapt, while accounting for the effect of the pericardium on cardiac motion. The four-chamber electromechanics framework resulted in 117 parameters of interest. The model was broken into five hierarchical sub-models: tissue electrophysiology, ToR-ORd-Land model, Courtemanche-Land model, passive mechanics and CircAdapt. For each sub-model, we trained Gaussian processes emulators (GPEs) that were then used to perform a global sensitivity analysis (GSA) to retain parameters explaining 90% of the total sensitivity for subsequent analysis. We identified 45 out of 117 parameters that were important for whole heart function. We performed a GSA over these 45 parameters and identified the systemic and pulmonary peripheral resistance as being critical parameters for a wide range of volumetric and hemodynamic cardiac indexes across all four chambers. We have shown that GPEs provide a robust method for mapping between cellular properties and clinical measurements. This could be applied to identify parameters that can be calibrated in patient-specific models or digital twins, and to link cellular function to clinical indexes. Cardiac function relies on complex links between the single cell and the whole organ. Digital twins or patient-specific models, e.g. computer models that replicate a patient’s heart, can help understanding these links in healthy or diseased states, and improving cardiac patient care. To build a patient-specific model, first we need to quantify which model parameters affect model outputs, to discard those that have little effect and to understand input-output interactions. This normally requires a lot of expensive model evaluations, making this type of analysis very challenging. We used Gaussian processes emulators (GPEs) to reduce the computational costs of our model. The heart simulator we approximated had 117 initial parameters, and was able to simulate whole heart electrical excitation and contraction, cellular dynamics, as well as the circulatory system and the interaction of the heart with the pericardium. Thanks to the GPEs, we were able to identify the most important 45 parameters at a feasible cost, and to study their effect on a wide range of clinically-measured biomarkers for cardiac function. Our analysis provides a comprehensive assay of how cellular function can impact the whole heart, and can be used to investigate a wide range of cardiac pathologies and treatment. Heart function is a multi-scale process, going from sub-cellular mechanisms initiating single cell excitation and contraction, up to the whole organ and body. The heartbeat starts at the right atrium (RA) at the sino-atrial node, where the cells are able to excite spontaneously. The activation wave then travels to the left atrium (LA) and ventricles through the atrioventricular pathways and the His–Purkinje system, causing every myocyte to undergo the action potential, a rapid sequence of changes in the transmembrane potential. During the action potential, calcium ions are released into the cytosol, where they bind to the Troponin C located on the thin filament. This induces a conformational change in the thin filament which exposes the actin binding sites to the myosyn heads on the thick filament. The myosin heads then bind to the actin and pull the thin filament towards the centre of the sarcomere, leading to sarcomere shortening and ultimately myocyte contraction. Thanks to the structured myocyte orientation within the cardiac muscle, this results into a coordinated contraction of the whole heart, which then pumps oxygenated blood across the aortic valve and into the whole circulation.Cardiac electromechanics models are increasingly used to study heart function in healthy and diseased states . InitialIn order to perform parameter inference and build a digital twin of a human heart, we need to quantify the effect of the input parameters on the model outputs of interest. This not only allows us to exclude the parameters that have little to no effect on model outputs and can therefore be excluded from the analysis, but it also provides information about the extent of the model input-output interactions. In this paper, we used Gaussian processes emulators (GPE) to perform a GSA on a four-chamber electromechanics model. Our simulator accounts for the atrial and ventricular action potential, calcium transient, cross-bridge kinetics, whole heart electrical excitation and contraction, the effect of the pericardium and the coupling with the circulatory system. By applying GPEs and GSA on different components of the electromechanics simulation framework, we were able to reduce the number of parameters from 117 to 45. We used GPEs and history matching (HM) on the tissue electrophysiology and electromechanics cell simulators to identify the areas of the parameter space where the total ventricular and atrial activation times, and cellular calcium and active tension transients for atria and ventricles were physiological. Finally, we performed a GSA on the fully coupled framework with the remaining 45 parameters to quantify the effect of the model parameters on atrial and the ventricular function.Below, we summarize the clinical data and the four-chamber electromechanics framework used in this study. Tables The long-term goal of this work is to construct a digital twin of a specific subject. Therefore, we focused this study on one patient. The clinical data were gathered from a 78 yo female heart failure (HF) patient with atrial fibrillation (AF) selected for cardiac resynchronisation therapy device upgrade from RV pacing to biventricular pacing. Right ventricular (RV) pacing was therefore the baseline rhythm, with a QRS duration of 200 ms measured from a 12-lead electrocardiogram (ECG). The patient underwent ECG-gated CT prior to the upgrade procedure, providing ten CT frames over a cardiac cycle. During the upgrade procedure, the LV pressure was invasively recorded through a pressure wire. After the ectopic baseline beats were removed, 30 beats were left to characterise the patient’s LV haemodynamics. The LV end-diastolic pressure (EDP) and peak in pressure were 2.8±3.2 mmHg and 124.2±7.4 mmHg, while the basic cycle length and the LV pressure systolic duration were 854±9 ms and 467±15 ms, respectively.ex-vivo diffusion tensor MRI dataset onto the endocardial and the epicardial surfaces (The four-chamber heart geometry was generated from the end-diastolic computed tomography (CT). The pipeline to segment and generate 1mm linear tetrahedral four-chamber meshes was described previously , 3. The ta(x) at node location x given V(x) containing the squared local conduction velocities (CV) in the fibres, sheet and normal to sheet directions, and sites of initial activation Γ, which activate at a prescribed time t0:The electrical activation of the heart was simulated with a reaction-Eikonal model without diffusion . The Eikf,V and CVf,A) and an anisotropy ratio , respectively. The remaining regions were passive. To represent fast endocardial activation due to the His–Purkinje system, we defined a one-element thick endocardial layer extending up to 70% in the apico-basal direction in the ventricles [kFEC (kBB (delay). The RA and RV lead locations were selected by segmenting the pacemaker leads from the CT image by thresholding the image intensity.Atrial and ventricular myocardium were represented as transversely isotropic conductive regions and were assigned CV in the fibre direction  computed with the Eikonal model [The action potential of ventricular and atrial myocytes was simulated with the ToR-ORd model with dynamic intracellular cloride , 21 and al model . The intal model  to compuIn the cell electrophysiology models, we selected model parameters that have a biophysical interpretation but can be, at the same time, considered uncertain . Ion chaPrior to the whole organ simulations, the ToR-ORd-Land and the Courtemanche-Land cell models were ran for 500 beats at a basic cycle length of 854.0 ms, consistent with the patient’s heart rate, to bring the models to a steady state. The cell models were then initialised at a steady state in the whole heart simulations.J is the determinant of the deformation gradient, E represents the Cauchy-Green strain tensor and f, s and n are the fibre, sheet and normal to sheet directions. Parameters a, bf, bft and bt are the stiffness parameters, and κ = 1000 kPa is the bulk modulus, penalising volume change and therefore enforcing near incompressibility [Passive material properties of atria and ventricular myocardium were represented with a transversely isotropic Guccione model :Ψ(E)=a2sibility , 28. Allsibility .kperi was included as a free simulator parameter. This value was scaled on the ventricles according to a map derived from motion data [μm and was not considered as a free parameter as this was only imposed to constrain the motion during the unloading of the mesh, performed in the absence of the pericardium.We represented the effect of the pericardium on the heart with normal springs, as described in , 29. Theion data , to consion data , showed V3D and V0D are the volumes of the cavity computed from the deforming 3D mesh and predicted by the 0D model, respectively, t is the time and u is the displacement field. The three-dimensional (3D) four-chamber electromechanics model was coupled with CircAdapt , 32 Fig, a closeshift,LV to shift the LV EDP during unloading. This not only allowed the simulations to achieve higher end-diastolic strains, but it also accounted for uncertainty on the available clinical measurements. The RV end-diastolic pressure was not available, so we added it as a free simulator parameter . The unloading was performed with a backward displacement method [The ventricles of the end-diastolic mesh were unloaded from an end-diastolic LV and RV pressure, while the atria were not unloaded, under the assumption that the active tension in the atrial myocardium balances the pressure . The meat method . During We used the available clinical data to estimate 17 simulator parameters, therefore reducing the computational cost of the final GSA . The aortic valve open orifice area was measured with the same approach, by selecting points on the aortic valve leaflets on the end-systolic CT frame. The leaflets of the tricuspid and pulmonary valves were not visible on the CT, so their open orifice area was assumed to be the same as the mitral and the aortic valve, respectively, again consistent with the CircAdapt model. The systemic and pulmonary orifices areas were computed as the average of the cross-sectional area of the superior and inferior vena cava, and of all pulmonary veins, respectively, all computed from the end-diastolic CT. The reference area for the aorta, the pulmonary artery, the systemic veins and the pulmonary veins tubes were set to be the same as the corresponding adjacent valve. The aortic and the pulmonary artery wall area were then computed assuming a circular cross-section (consistent with CircAdapt) and a wall thickness of 2 mm [The 30 baseline LV pressure beats were used to compute an average basic cycle length (BCL) of 854 ms, corresponding to a heart rate (HR) of 70 bpm. The ratio between the SV and the BCL provided the systemic flow  of 2 mm . The sys of 2 mm  as they N = 500 samples for GPE training. Due to simulator complexity, this requires a lot of computational resources. We therefore selected the numerical tolerances in the mechanics simulations to reduce as much as possible the computational cost of the electromechanical simulations.As shown in Tables -4 for all beats. In We assumed that 5 beats were enough to achieve a near steady state solution. The first three beats were ran by setting the number of Newton iterations to 1. As shown by Augustin et al. , this apAll simulations were run with the cardiac arrhythmia research package (CARP)  on a suphttps://github.com/stelong/Historia.git). Briefly, a GPE provides a statistical model for how a scalar simulator output f(x) varies as a function of D input parameters x = . Under our GPE, the simulator outputs are modelled as being jointly Gaussian with prior mean and covariancek is a positive-definite kernel, and β0, …, βD are the degrees of freedom of the GPE, which need to be estimated. In this case, we chose the exponentiated quadratic kernel, also known as squared exponential or the Gaussian kernel, because it offers a parsimonious model even in situations where the data is limited and noisy [GPEs, GSA and HM methods used in this study were based on Longobardi et al. , using cnd noisy . In S8 Fribed in .R2 and the independent standard error (ISE) between a vector of outputs Y and the posterior predictions of the GPE with expected value and standard deviation R2 provides an error on the point-wise estimate of the GPE, with values close to 1 indicating a low error between predictions and observations. The ISE evaluates the uncertainty quantification of the GPEs, and represents the number of data points that fall within two standard deviations away from the average prediction. As it is presented as a percentage over all evaluated points, an ISE close to 100% indicates that, for most points, the distance between the GPE prediction and the corresponding observation is within the GPE uncertainty. Once the GPEs were validated, we trained an additional GPE for each output using all available simulations that was then used for GSA and HM.GPE accuracy was evaluated by performing a 5-fold cross-validation. For each split, we computed the coefficient of determination ST using the Saltelli method with the SALib Python library. A Saltelli sampling [N = 1000 samples, computed the total effect for each sample and taken the mean of the total effects. To an extent, this allowed us to account for GPE uncertainty in the GSA.Using the GPEs to predict the simulator outputs, we performed a Sobol variance based GSA to quantify the importance of each parameter . We compsampling , 45 was We used the total effects to rank the parameters from most to least important by computing the maximum total effect across all outputs. The maximum total effects for each parameter were normalised to that they would sum up to 1 (representing 100% of output variance), and the parameters that collectively explained 90% of variation in simulator output were included in the analysis, while the others were fixed to values provided in Tables I(x) as:fi is the GPE trained for the ith output. Scalars μi and σi are the measured target value and standard deviation for the ith output, available from the literature or the clinical data. By defining a threshold Ith on the implausibility measure I(x), the test samples are split in plausible (I(x) below the threshold) and implausible (I(x) above the threshold). From the implausible region, we then select a number Nsimul samples where the simulator is evaluated, and we construct a new test parameter set Ntest = 100000 for the next HM iteration (or wave). The GPEs are re-trained by adding the new simulated samples to the training set, and evaluated on the test set constructed at the previous wave. For each wave, we report the percentage of non-implausible points, the mean and maximum I and the mean and maximum ratio of GPE variance prediction over the measured variance σ2 (Vratio). The latter quantity gives a measure of how uncertain the GPEs are, relative to the uncertainty on the data we are trying to match. We set the final implausibility threshold to 3, based on Pukelsheim’s 3-sigma rule [In order to make sure that the ToR-ORd-Land, Courtemanche-Land and the Eikonal models operated within physiological ranges, and to avoid wasting computational effort in the electromechanics simulator, we used HM. HM with GPEs is thoroughly explained in Longobardi et al. , and Figgma rule .Tables In We performed a GSA on the passive mechanics model during inflation alone to exclude unimportant stiffness parameters . We finaIn addition to running a GSA on each simulator component, we used HM to restrict the important parameters to areas in the parameter space where either the outputs were physiological, or where the mechanics simulations were unlikely to be unstable and diverge, therefore leading to significant waste of computational resources. We constrained the ToR-ORd-Land model so that it provided physiological calcium transients based on the literature , 48 S2 , and whedelay was set between 100 and 200 ms, consistently with ranges reported for CRT patients [a were increased based on higher collagen density in the atria compared to the ventricles [psa_select from the python library diversipy to extract N = 500 uniformly distributed samples.In patients . The ranpatients , to accontricles . Similarntricles . The lenntricles , while tntricles . The shintricles . For theWe trained GPEs to predict the following outputs:LV/RV end-diastolic volume (EDV)LV/RV end-diastolic pressure (EDP)LV/RV end-systolic volume (ESV)max)LV/RV peak in pressure (pmax)LV/RV maximum pressure derivative (dp/dtmin)LV/RV minimum pressure derivative LA/RA end-systolic volume at the end of atrial contraction (ESV)vwave)LA/RA maximum volume during the v-wave (EDVmax).LA/RA peak in pressure during atrial contraction  over all outputs (x-axis), with dark colors showing high interaction between inputs and outputs. Below, we provide a detailed analysis of the sensitivity analysis on the ventricular and atrial diastolic and systolic properties. We also show barplots of the total effects of important parameters for each output, multiplied by the sign of the linear regression coefficients βi from LV,shift) and the RV , and by the systemic (Rsys) and the pulmonary (Rpulm) resistances. A higher unloading pressure for the ventricles caused higher strains and therefore higher rest tension, that in turn reduced the EDV simulated at the limit cycle. The systemic resistance increased the LV EDV and the LV EDP. This was caused by increased LA ESV (and therefore decreased LA ejection) in response to increased systemic resistance. Similarly, the RV EDV was positively affected by the pulmonary resistance. The parameters that had a negative effect on the LV EDV had a positive effect on the RV EDV and vice-versa. This was caused by the interaction between the ventricles, modulated by the presence of the pericardium and by the intra-ventricular septum. When the LV EDV is bigger, the septum bulges towards the RV, leading to smaller RV EDV and therefore opposite effects of simulator parameters on the LV EDV and the RV EDV. Finally, passive material properties of the ventricles  positively affected the EDV of the LV. Although this might be counter-intuitive, decreased stiffness causes smaller unloaded volumes and higher initial strains at the beginning of the simulations, leading to higher ventricular rest tension and therefore decreased LV EDV.unload,RV) and the reference tension scaling factor  of the RV had minor positive effects on the LA peak in pressure. In addition to the systemic resistance, the RA peak in pressure was affected by cellular ionic and active tension parameters for velocity dependence , cross-bridge kinetics  and calcium handling . Atrial stiffness  and atrial conduction velocities  also played a minor but significant role in RA peak pressure, with stiffer and slower myocardium causing lower RA pressure values.The systemic and pulmonary peripheral resistances also affected atrial systolic peak in pressure . SimilarThe unloading pressures of the LV and RV affected the EDV of the ventricles the most, as they determined the end-diastolic strains and therefore the ventricular rest active tension. The systemic and pulmonary resistances had significant effects on the EDV as well, due to their effect on atrial ejection, while the AV delay mostly affected the EDP. Parameters for atrial active tension timing, CV in the ventricular endocardium and in the Bachmann bundle showed minor effects on the EDP of the ventricles, due to their effect on timing of RV initial contraction and atrial contraction rise and decay, respectively. Finally, the effect of parameters on the LV EDV was opposite to the effect on the RV EDV due to ventricular interaction modulated by the pericardium.aV) decreased the peak in pressure and increased ventricular ESV of both ventricles due to less significant deformation during active contraction. Parameters for ventricular cellular active tension also had a significant effect on the peak in pressure of the ventricles. Degree of velocity dependence , cross-bridges kinetics parameter  and the reference active tension  had significant effects on the peak in pressure, as they affected the timing and extent of cellular peak in cellular active tension, as shown in Tref,LvRv.The systemic and the pulmonary resistances affected the peak in pressure of the LV and the RV the most , top. InaV and bt,V had minor positive effects on the ESV of both ventricles, therefore leading to smaller LV and RV ejection.LV ESV was positively affected by the systemic resistance and the unloading pressure of the RV. On the other hand, the unloading pressure of the LV negatively impacted LV ESV. The RV ESV was similarly affected by the unloading pressure of the RV and the LV, and by the pulmonary resistance. Increased peripheral resistance downstream to the LV and the RV led to higher ventricular afteload, therefore causing higher ESV. The passive stiffness of the ventricles rs,V, rw,V, ku,V) affected both LV and RV maximum pressure derivative due to their effect on cellular active tension rising time and maximum tension derivative, as we showed in aV) caused slower LV and RV pressure rise in both ventricles. In addition, LV pressure rise was impacted by the unloading pressures of the LV and the RV due to their effect on end-diastolic strains, by the rate of transition from blocked to unblocked cross-bridge binding sites  and by ventricular velocity dependence . The reference tension of the ventricles (Tref) and the scaling for the RV reference tension  had an effect on the maximum pressure derivative of the LV and the RV, respectively, due to their effect on cellular active tension peak and timing. Apart from velocity dependence and ventricular stiffness, all important parameters for the LV and RV rise in pressure also impacted the LV and RV minimum pressure derivative. In addition, ventricular minimum pressure derivative was affected by the maximum troponin concentration Click here for additional data file.S2 FileWe trained GPEs to predict calcium and active tension transient features simulated by the ToR-ORd model coupled with the Land contraction model, and used them to run a GSA to identify important parameters for these dynamics. Then, we used HM to isolate areas in the parameter space where the ventricular calcium and tension were physiological.(PDF)Click here for additional data file.S3 FileWe trained GPEs to predict calcium and active tension transient features simulated by the Courtemanche model coupled with the Land contraction model, and used them to run a GSA to identify important parameters for these dynamics. Then, we used HM to isolate areas in the parameter space where the atrial calcium and tension were physiological.(PDF)Click here for additional data file.S4 FileWe trained GPEs to predict the total atrial and ventricular activation times, and used them to run a GSA to identify important conduction parameters for simulated activation times. Then, we used HM to isolate areas in the parameter space where the total ventricular and atrial activation times were physiological.(PDF)Click here for additional data file.S5 FileWe trained GPEs to predict the maximum volumes for the four chambers and ventricular and atrial fibres strains during a passive inflation, and used them to run a GSA to identify important stiffness parameters.(PDF)Click here for additional data file.S6 FileWe trained GPEs to predict a wide range of pressure and volume output features for all four chambers simulated with CircAdapt, and used them to run a GSA to identify important circulatory parameters for pressure and volume dynamics.(PDF)Click here for additional data file.S7 FileDetailed explanation of how we constructed the Saltelli samples for the GSA.(PDF)Click here for additional data file.S8 FileWe investigated how the choice of kernel affected GPE accuracy and GSA. The GPE training and GSA performed for the ToR-ORd model  were rep(PDF)Click here for additional data file.S9 FileWe performed the GSA on the electrophysiology tissue model with two different ventricular fibre orientation to investigate the effect of myofibre arrengement on electrophysiology simulations and GSA. We also repeated a passive inflation and a four-chamber electromechanics simulation to quantify the effect of ventricular fibres on mechanics model outputs.(PDF)Click here for additional data file.S10 FileWe performed the GSA on the electrophysiology tissue model altering the size of the fast endocardial conducting layer of the ventricles and the Bachmann bundle.(PDF)Click here for additional data file.S11 FileSchematic of how the non-implausible areas from the HM of the sub-models were used to construct the training samples for the GPEs at the whole organ scale.(PDF)Click here for additional data file.S12 FileWe show the mean and standard deviation of the total effects computed for the whole organ sensitivity analysis obtained when sampling the posterior distribution of the emulators.(PDF)Click here for additional data file.