Balancing Scaffold Degradation and Neo-Tissue Formation in In Situ Tissue Engineered Vascular Grafts.

An essential aspect of cardiovascular in situ tissue engineering (TE) is to ensure balance between scaffold degradation and neo-tissue formation. We evaluated the rate of degradation and neo-tissue formation of three electrospun supramolecular bisurea-based biodegradable scaffolds that differ in their soft-block backbone compositions only. Scaffolds were implanted as interposition grafts in the abdominal aorta in rats, and evaluated at different time points (t = 1, 6, 12, 24, and 40 weeks) on function, tissue formation, strength, and scaffold degradation. The fully carbonate-based biomaterial showed minor degradation after 40 weeks in vivo, whereas the other two ester-containing biomaterials showed (near) complete degradation within 6-12 weeks. Local dilatation was only observed in these faster degrading scaffolds. All materials showed to some extent mineralization, at early as well as late time points. Histological evaluation showed equal and non-native-like neo-tissue formation after total degradation. The fully carbonate-based scaffolds lagged in neo-tissue formation, presumably as its degradation was (far from) complete at 40 weeks. A significant difference in vessel wall contrast enhancement was observed by magnetic resonance imaging between grafts with total compared with minimal-degraded scaffolds.

Temporal validation of 30-day mortality prediction models for transcatheter aortic valve implantation using statistical process control - An observational study in a national population.

Background: Various mortality prediction models for Transcatheter Aortic Valve Implantation (TAVI) have been developed in the past years. The effect of time on the performance of such models, however, is unclear given the improvements in the procedure and changes in patient selection, potentially jeopardizing the usefulness of the prediction models in clinical practice. We aim to explore how time affects the performance and stability of different types of prediction models of 30-day mortality after TAVI. Methods: We developed both parametric (Logistic Regression) and non-parametric (XGBoost) models to predict 30-day mortality after TAVI using data from the Netherlands Heart Registration. The models were trained with data from 2013 to the beginning of 2016 and pre-control charts from Statistical Process Control were used to analyse how time affects the models' performance on independent data from the mid of 2016 to the end of 2019. The area under the Receiver Operating Characteristics curve (AUC) was used to evaluate the models in terms of discrimination and the Brier Score (BS), which is related to calibration, in terms of accuracy of the predicted probabilities. To understand the extent to which refitting the models contribute to the models' stability, we also allowed the models to be updated over time. Results: We included data from 11,291 consecutive TAVI patients from hospitals in the Netherlands. The parametric model without re-training had a median AUC of 0.64 (IQR 0.54-0.73) and BS of 0.028 (IQR 0.021-0.035). For the non-parametric model, the median AUC was 0.63 (IQR 0.48-0.68) and BS was 0.027 (IQR 0.021-0.036). Over time, the developed parametric model was stable in terms of AUC and unstable in terms of BS. The non-parametric model was considered unstable in both AUC and BS. Repeated model refitting resulted in stable models in terms of AUC and decreased the variability of BS, although BS was still unstable. The refitted parametric model had a median AUC of 0.66 (IQR 0.57-0.73) and BS of 0.027 (IQR 0.020-0.035) while the non-parametric model had a median AUC of 0.66 (IQR 0.57-0.74) and BS of 0.027 (IQR 0.023-0.035). Conclusions: The temporal validation of the TAVI 30-day mortality prediction models showed that the models refitted over time are more stable and accurate when compared to the frozen models. This highlights the importance of repeatedly refitted models over time to improve or at least maintain their performance stability. The non-parametric approach did not show improvement over the parametric approach.

Outpatient treatment of worsening heart failure with intravenous diuretics: first results from a multicentre 2-year experience.

AIMS: The aim of this study is to examine the safety and efficacy of outpatient treatment of worsening heart failure (WHF) with intravenous diuretics. METHODS AND RESULTS: This is a multicentre retrospective observational research study. Patients with all types of heart failure (HF) were included: heart failure with reduced ejection fraction (HFrEF), heart failure with mildly reduced ejection fraction (HFmrEF), and heart failure with preserved ejection fraction (HFpEF). Patients included in this study were 18 years or older, had symptoms of WHF, had weight gain of more than 2 kg, and were not responding to uptitrating of oral diuretic therapy. Patients were treated for one or more days at the outpatient department with administration of intravenous loop diuretics with or without a bolus. In this study, 259 patients were included (mean age of 76 years, mean left ventricular ejection fraction of 41%). Rehospitalization rates for HF were 30.5% and 53.3%, respectively, at 30 days and 1 year. All-cause mortality was 5.8% and 26.3%, respectively, at 30 days and 1 year. Rehospitalization rates for HF and all-cause mortality were highest in patients with HFrEF. In a total of 322 individual outpatient treatments with intravenous diuretics, only one adverse event was registered. CONCLUSIONS: Outpatient treatment with intravenous diuretics of patients with WHF is a safe alternative strategy compared with the same treatment in hospitalized patients. However, only non-randomized data are available and rehospitalization rates for this group with WHF are high. No data are available on the best selection criteria and the cost-effectiveness of outpatient treatment with intravenous diuretics.

Development and validation of a prediction model for early mortality after transcatheter aortic valve implantation (TAVI) based on the Netherlands Heart Registration (NHR): The TAVI-NHR risk model.

BACKGROUND: The currently available mortality prediction models (MPM) have suboptimal performance when predicting early mortality (30-days) following transcatheter aortic valve implantation (TAVI) on various external populations. We developed and validated a new TAVI-MPM based on a large number of predictors with recent data from a national heart registry. METHODS: We included all TAVI-patients treated in the Netherlands between 2013 and 2018, from the Netherlands Heart Registration. We used logistic-regression analysis based on the Akaike Information Criterion for variable selection. We multiply imputed missing values, but excluded variables with >30% missing values. For internal validation, we used ten-fold cross-validation. For temporal (prospective) validation, we used the 2018-data set for testing. We assessed discrimination by the c-statistic, predicted probability accuracy by the Brier score, and calibration by calibration graphs, and calibration-intercept and calibration slope. We compared our new model to the updated ACC-TAVI and IRRMA MPMs on our population. RESULTS: We included 9144 TAVI-patients. The observed early mortality was 4.0%. The final MPM had 10 variables, including: critical-preoperative state, procedure-acuteness, body surface area, serum creatinine, and diabetes-mellitus status. The median c-statistic was 0.69 (interquartile range [IQR] 0.646-0.75). The median Brier score was 0.038 (IQR 0.038-0.040). No signs of miscalibration were observed. The c-statistic's temporal-validation was 0.71 (95% confidence intervals 0.64-0.78). Our model outperformed the updated currently available MPMs ACC-TAVI and IRRMA (p value < 0.05). CONCLUSION: The new TAVI-model used additional variables and showed fair discrimination and good calibration. It outperformed the updated currently available TAVI-models on our population. The model's good calibration benefits preprocedural risk-assessment and patient counseling.

Application of local gentamicin in the treatment of deep sternal wound infection: a randomized controlled trial.

OBJECTIVES: In patients with deep sternal wound infection (DSWI), primary closure of the sternal bone over high negative pressure Redon drains has shown to be a safe and feasible treatment method. Addition of local gentamicin could accelerate healing and improve clinical outcomes. METHODS: We conducted a randomized controlled trial to evaluate the effectiveness of local gentamicin in the treatment of DSWI. In the treatment group, collagenous carriers containing gentamicin were left between the sternal halves during sternal refixation. In the control group, no local antibiotics were used. Primary outcome was hospital stay. Secondary outcomes were mortality, reoperation, wound sterilization time, time till removal of all drains and duration of intravenous antibiotic treatment. RESULTS: Forty-one patients were included in the trial of which 20 were allocated to the treatment group. Baseline characteristics were similar in both groups. Drains could be removed after a median of 8.5 days in the treatment group and 14.5 days in the control group (P-value: 0.343). Intravenous antibiotics were administered for a median of 23.5 days in the treatment group and 38.5 days in the control group (P-value: 0.343). The median hospital stay was 27 days in the treatment group and 28 days in the control group (P-value: 0.873). Mortality rate was 10% in the treatment group and 9.5% in the control group (P-value: 0,959). No side effects were observed. CONCLUSIONS: This randomized controlled trial showed that addition of local gentamicin in the treatment of DSWI did not result in shorter length of stay. CLINICAL TRIAL REGISTRATION NUMBER: 2014-001170-33.

Local and Distributed Machine Learning for Inter-hospital Data Utilization: An Application for TAVI Outcome Prediction.

Background: Machine learning models have been developed for numerous medical prognostic purposes. These models are commonly developed using data from single centers or regional registries. Including data from multiple centers improves robustness and accuracy of prognostic models. However, data sharing between multiple centers is complex, mainly because of regulations and patient privacy issues. Objective: We aim to overcome data sharing impediments by using distributed ML and local learning followed by model integration. We applied these techniques to develop 1-year TAVI mortality estimation models with data from two centers without sharing any data. Methods: A distributed ML technique and local learning followed by model integration was used to develop models to predict 1-year mortality after TAVI. We included two populations with 1,160 (Center A) and 631 (Center B) patients. Five traditional ML algorithms were implemented. The results were compared to models created individually on each center. Results: The combined learning techniques outperformed the mono-center models. For center A, the combined local XGBoost achieved an AUC of 0.67 (compared to a mono-center AUC of 0.65) and, for center B, a distributed neural network achieved an AUC of 0.68 (compared to a mono-center AUC of 0.64). Conclusion: This study shows that distributed ML and combined local models techniques, can overcome data sharing limitations and result in more accurate models for TAVI mortality estimation. We have shown improved prognostic accuracy for both centers and can also be used as an alternative to overcome the problem of limited amounts of data when creating prognostic models.

Prediction of Atrial Fibrillation Recurrence after Thoracoscopic Surgical Ablation Using Machine Learning Techniques.

Thoracoscopic surgical ablation (SA) for atrial fibrillation (AF) has shown to be an effective treatment to restore sinus rhythm in patients with advanced AF. Identifying patients who will not benefit from this procedure would be valuable to improve personalized AF therapy. Machine learning (ML) techniques may assist in the improvement of clinical prediction models for patient selection. The aim of this study is to investigate how available baseline characteristics predict AF recurrence after SA using ML techniques. One-hundred-sixty clinical baseline variables were collected from 446 AF patients undergoing SA in our tertiary referral center. Multiple ML models were trained on five outcome measurements, including either all or a number of key variables selected by using the least absolute shrinkage and selection operator (LASSO). There was no difference in model performance between different ML techniques or outcome measurements. Variable selection significantly improved model performance (AUC: 0.73, 95% CI: 0.68-0.77). Subgroup analysis showed a higher model performance in younger patients (<55 years, AUC: 0.82 vs. >55 years, AUC 0.66). Recurrences of AF after SA can be predicted best when using a selection of baseline characteristics, particularly in young patients.

Machine Learning for Predicting Mortality in Transcatheter Aortic Valve Implantation: An Inter-Center Cross Validation Study.

Current prognostic risk scores for transcatheter aortic valve implantation (TAVI) do not benefit yet from modern machine learning techniques, which can improve risk stratification of one-year mortality of patients before TAVI. Despite the advancement of machine learning in healthcare, data sharing regulations are very strict and typically prevent exchanging patient data, without the involvement of ethical committees. A very robust validation approach, including 1300 and 631 patients per center, was performed to validate a machine learning model of one center at the other external center with their data, in a mutual fashion. This was achieved without any data exchange but solely by exchanging the models and the data processing pipelines. A dedicated exchange protocol was designed to evaluate and quantify the model's robustness on the data of the external center. Models developed with the larger dataset offered similar or higher prediction accuracy on the external validation. Logistic regression, random forest and CatBoost lead to areas under curve of the ROC of 0.65, 0.67 and 0.65 for the internal validation and of 0.62, 0.66, 0.68 for the external validation, respectively. We propose a scalable exchange protocol which can be further extended on other TAVI centers, but more generally to any other clinical scenario, that could benefit from this validation approach.

Improving electrocardiogram-based detection of rare genetic heart disease using transfer learning: An application to phospholamban p.Arg14del mutation carriers.

The pathogenic mutation p.Arg14del in the gene encoding Phospholamban (PLN) is known to cause cardiomyopathy and leads to increased risk of sudden cardiac death. Automatic tools might improve the detection of patients with this rare disease. Deep learning is currently the state-of-the-art in signal processing but requires large amounts of data to train the algorithms. In situations with relatively small amounts of data, like PLN, transfer learning may improve accuracy. We propose an ECG-based detection of the PLN mutation using transfer learning from a model originally trained for sex identification. The sex identification model was trained with 256,278 ECGs and subsequently finetuned for PLN detection (155 ECGs of patients with PLN) with two control groups: a balanced age/sex matched group and a randomly selected imbalanced population. The data was split in 10 folds and 20% of the training data was used for validation and early stopping. The models were evaluated with the area under the receiver operating characteristic curve (AUROC) of the testing data. We used gradient activation for explanation of the prediction models. The models trained with transfer learning outperformed the models trained from scratch for both the balanced (AUROC 0.87 vs AUROC 0.71) and imbalanced (AUROC 0.0.90 vs AUROC 0.65) population. The proposed approach was able to improve the accuracy of a rare disease detection model by transfer learning information from a non-manual annotated and abundant label with only limited data available.

Update and, internal and temporal-validation of the FRANCE-2 and ACC-TAVI early-mortality prediction models for Transcatheter Aortic Valve Implantation (TAVI) using data from the Netherlands heart registration (NHR).

BACKGROUND: The predictive performance of the models FRANCE-2 and ACC-TAVI for early-mortality after Transcatheter Aortic Valve Implantation (TAVI) can decline over time and can be enhanced by updating them on new populations. We aim to update and internally and temporally validate these models using a recent TAVI-cohort from the Netherlands Heart Registration (NHR). METHODS: We used data of TAVI-patients treated in 2013-2017. For each original-model, the best update-method (model-intercept, model-recalibration, or model-revision) was selected by a closed-testing procedure. We internally validated both updated models with 1000 bootstrap samples. We also updated the models on the 2013-2016 dataset and temporally validated them on the 2017-dataset. Performance measures were the Area-Under ROC-curve (AU-ROC), Brier-score, and calibration graphs. RESULTS: We included 6177 TAVI-patients, with 4.5% observed early-mortality. The selected update-method for FRANCE-2 was model-intercept-update. Internal validation showed an AU-ROC of 0.63 (95%CI 0.62-0.66) and Brier-score of 0.04 (0.04-0.05). Calibration graphs show that it overestimates early-mortality. In temporal-validation, the AU-ROC was 0.61 (0.53-0.67).The selected update-method for ACC-TAVI was model-revision. In internal-validation, the AU-ROC was 0.63 (0.63-0.66) and Brier-score was 0.04 (0.04-0.05). The updated ACC-TAVI calibrates well up to a probability of 20%, and subsequently underestimates early-mortality. In temporal-validation the AU-ROC was 0.65 (0.58-0.72). CONCLUSION: Internal-validation of the updated models FRANCE-2 and ACC-TAVI with data from the NHR demonstrated improved performance, which was better than in external-validation studies and comparable to the original studies. In temporal-validation, ACC-TAVI outperformed FRANCE-2 because it suffered less from changes over time.

External validation of existing prediction models of 30-day mortality after Transcatheter Aortic Valve Implantation (TAVI) in the Netherlands Heart Registration.

BACKGROUND: Several mortality prediction models (MPM) are used for predicting early (30-day) mortality following transcatheter aortic valve implantation (TAVI). Little is known about their predictive performance in external TAVI populations. We aim to externally validate established MPMs on a large TAVI dataset from the Netherlands Heart Registration (NHR). METHODS: We included data from NHR-patients who underwent TAVI during 2013-2017. We calculated the predicted mortalities per MPM. We assessed the predictive performance by discrimination (Area Under Receiver Operating-characteristic Curve, AU-ROC); the Area Under Precision-Recall Curve, AU-PRC; calibration (using calibration-intercept and calibration-slope); Brier Score and Brier Skill Score. We also assessed the predictive performance among subgroups: tertiles of mortality-risk for non-survivors, gender, and access-route. RESULTS: We included 6177 TAVI-patients with an observed early-mortality rate of 4.5% (n = 280). We applied seven MPMs (STS, EuroSCORE-I, EuroSCORE-II, ACC-TAVI, FRANCE-2, OBSERVANT, and German-AV) on our cohort. The highest AU-ROCs were 0.64 (95%CI 0.61-0.67) for ACC-TAVI and 0.63 (95%CI 0.60-0.67) for FRANCE-2. All MPMs had a very low AU-PRC of ≤0.09. ACC-TAVI had the best calibration-intercept and calibration-slope. Brier Score values ranged between 0.043 and 0.063. Brier Skill Score ranged between -0.47 and 0.004. ACC-TAVI and FRANCE-2 predicted high mortality-risk better than other MPMs. ACC-TAVI outperformed other MPMs in different subgroups. CONCLUSION: The ACC-TAVI model has relatively the best predictive performance. However, all models have poor predictive performance. Because of the poor discrimination, miscalibration and limited accuracy of the models there is a need to update the existing models or develop new TAVI-specific models for local populations.

Fibrotic aortic valve disease after radiotherapy: an immunohistochemical study in breast cancer and lymphoma patients.

BACKGROUND: Radiation-associated aortic valve (AV) stenosis is frequently seen as a late sequela after thoracic radiotherapy (RT). Although the clinical relationship between thoracic radiotherapy and valvular dysfunction has been established, the process leading to accelerated aortic valve stenosis remains unclear. The aim of this study was to determine whether increased inflammatory cell infiltration, fibrosis, and calcification is present in aortic valves after radiotherapy at the time of aortic valve replacement. METHODS: Stenotic aortic valve specimens from 43 patients were obtained after surgical aortic valve replacement. A total 28 patients had previously undergone radiotherapy for breast cancer or malignant lymphoma. A total 15 patients were included as control. The valve leaflets were assessed by (immuno)histochemistry for inflammatory cell composition (CD3, CD20, CD68, and CD163) and extracellular matrix changes (collagen and calcification). RESULTS: Aortic valve cell density after radiotherapy for lymphoma was markedly decreased when compared with other groups. Irradiated aortic valve show similar (low) degrees of late T and B lymphocyte infiltration as control valves, whereas macrophage marker CD68 was decreased after radiotherapy for breast cancer. Collagen content was increased following radiotherapy. Aortic valves of patients with lymphoma contained significantly less calcified tissue when compared with the other groups. CONCLUSION: High-dose radiation at a young age (patients with lymphoma) results in cell loss and premature fibrotic aortic valve stenosis as opposed to the degenerative calcific stenosis observed in patients with breast cancer. Our findings suggest a possible dose-dependent effect of radiotherapy on aortic valve fibrosis. The active presence of inflammatory cells may be limited to the acute phase after radiotherapy.

Development of an ex vivo aneurysm model for vascular device testing.

An ex vivo aneurysm model that closely resembles the in vivo situation can provide an important tool for testing therapies. The model should mimic a variety of conditions, such as in vivo hemodynamics and native arterial structure and characteristics, avoiding animal experimentation.  Therefore, the aim of this study is to develop an ex vivo aneurysm model by vessel wall stiffening to be used to assess treatment strategies. Porcine carotid arteries from slaughterhouse animals were used to evaluate the acute effect of different concentrations of Rose Bengal on distensibility. This sono-sensitive compound was activated by several ultrasound frequencies, resulting in stiffening of the treated arteries of which the most effective combination was selected. In a pulsatile ex vivo vascular bioreactor treated and control porcine carotid arteries were subjected to physiological conditions for 10 days. During culture, hemodynamics showed increased mean pressure and decreased pulsatility in treated arteries compared to controls. Change in vessel morphology and significant increase of distal diameter was observed in the treated arteries but not in the controls. Histology of treated arteries revealed dissection-like lesions distally and aneurysm-like structure proximally. Finally, a stent graft was deployed in one treated artery and cultured demonstrating the feasibility of testing endovascular devices in the model. In conclusion, we developed an ex vivo model reproducing the onset of aneurysm formation. This could represent a promising tool for early stage device testing thereby reducing the need for animal studies.

Investigating the physiology of normothermic ex vivo heart perfusion in an isolated slaughterhouse porcine model used for device testing and training.

BACKGROUND: The PhysioHeart™ is a mature acute platform, based isolated slaughterhouse hearts and able to validate cardiac devices and techniques in working mode. Despite perfusion, myocardial edema and time-dependent function degradation are reported. Therefore, monitoring several variables is necessary to identify which of these should be controlled to preserve the heart function. This study presents biochemical, electrophysiological and hemodynamic changes in the PhysioHeart™ to understand the pitfalls of ex vivo slaughterhouse heart hemoperfusion. METHODS: Seven porcine hearts were harvested, arrested and revived using the PhysioHeart™. Cardiac output, SaO2, glucose and pH were maintained at physiological levels. Blood analyses were performed hourly and unipolar epicardial electrograms (UEG), pressures and flows were recorded to assess the physiological performance. RESULTS: Normal cardiac performance was attained in terms of mean cardiac output (5.1 ± 1.7 l/min) and pressures but deteriorated over time. Across the experiments, homeostasis was maintained for 171.4 ± 54 min, osmolarity and blood electrolytes increased significantly between 10 and 80%, heart weight increased by 144 ± 41 g, free fatty acids (- 60%), glucose and lactate diminished, ammonia increased by 273 ± 76% and myocardial necrosis and UEG alterations appeared and aggravated. Progressively deteriorating electrophysiological and hemodynamic functions can be explained by reperfusion injury, waste product intoxication (i.e. hyperammonemia), lack of essential nutrients, ion imbalances and cardiac necrosis as a consequence of hepatological and nephrological plasma clearance absence. CONCLUSIONS: The PhysioHeart™ is an acute model, suitable for cardiac device and therapy assessment, which can precede conventional animal studies. However, observations indicate that ex vivo slaughterhouse hearts resemble cardiac physiology of deteriorating hearts in a multi-organ failure situation and signalize the need for plasma clearance during perfusion to attenuate time-dependent function degradation. The presented study therefore provides an in-dept understanding of the sources and reasons causing the cardiac function loss, as a first step for future effort to prolong cardiac perfusion in the PhysioHeart™. These findings could be also of potential interest for other cardiac platforms.

Minimal invasive aortic valve replacement: associations of radiological assessments with procedure complexity.

OBJECTIVES: Limited aortic annulus exposure during minimal invasive aortic valve replacement (mini-AVR) proves to be challenging and contributes to procedure complexity, resulting in longer procedure times. New innovations like sutureless valves have been introduced to reduce procedure complexity. Additionally, preoperative imaging could also contribute to reducing procedure times. Therefore, we hypothesize that Computed Tomography (CT)-image based measurements are associated with mini-AVR complexity. METHODS: One hundred patients who underwent a mini-sternotomy and had a preoperative CT scan were included. With a CT-based mini-AVR planning tool, we measured access distance, access angle, annulus dimensions, and calcium volume. The associations of these measurements with cardiopulmonary bypass (CPB) time and aortic cross-clamp (AoX) time were assessed using univariable and multivariable regression models. In the multivariable models, these measurements were adjusted for age and suture technique. RESULTS: In the univariable regression models, calcium volume and annulus dimensions were associated with longer CPB and AoX time. After adjusting for age and suture technique, increasing calcium volume was still associated with longer CPB (adjusted ß-coefficient 0.002, 95%-CI (0.005, 0.019), p-value = 0.002) and AoX time (adjusted ß-coefficient 0.010, 95%-CI (0.004, 0.016), p-value = 0.002). However, after adjusting for these confounders, the association between annulus dimensions and procedure times lost statistical significance. CONCLUSION: Increase in calcium volume are associated with longer CPB and AoX times, with age and sutureless valve implantation as independent confounders. In contrast to previous studies, access angle was not associated with procedure complexity.

Biomedical Applications of Photo- and Sono-Activated Rose Bengal: A Review.

Objective: The aim of this review is to discuss and compare the extensive range of biomedical applications of photo- and sono-activated Rose Bengal (RB). Background data: RB is a xanthene dye that due to its interesting photo- and sono-sensitive properties is gaining attention in the scientific field. Methods: This study is a literature review using the database PubMed. Results: As a photosensitizer, RB converts the triplet oxygen molecule into reactive oxygen species after irradiation with green light (532 nm). This mechanism allows for the use of photo-activated RB in photochemical tissue bonding, photodynamic therapy, antimicrobial therapy and cancer treatment, photothrombotic animal models, and other applications, including tissue engineering and treatment of tauopathies. As a sono-sensitive compound, RB is applied for sonodynamic therapy, cancer treatment, and antimicrobial therapy. Conclusions: This review outlines the versatility and effectiveness of photo- and sono-activated RB in numerous biomedical applications.

Feasibility of mapping and cannulation of the porcine epicardial lymphatic system for sampling and decompression in heart failure research.

BACKGROUND AND AIM: The cardiac lymphatic system drains excess fluid from the cardiac interstitium. Any impairment or dysfunction of the lymph structures can result in the accumulation of interstitial fluid, and may lead to edema and eventually cardiac dysfunction. Lymph originates directly from the interstitium and carries real-time information about the metabolic state of cells in specific regions of the heart. The detailed anatomy of the epicardial lymphatic system in individuals is broadly unknown. Generally, the epicardial lymphatic system is not taken into consideration during heart surgery. This study investigates the feasibility of detailed mapping and cannulation of the porcine epicardial lymphatic system for use in preservation of explanted hearts and heart failure studies in pigs and humans. METHODS: The anatomy of the epicardial lymphatic systems of forty pig hearts was studied and documented. Using a 27 G needle, India ink was introduced directly into the epicardial lymphatic vessels in order to visualise them. Based on the anatomical findings thus obtained, two cannulation regions for the left and right principal trunks were identified. These regions were cannulated with a 26 G intravenous Venflon cannula-over-needle, and a Galeo Hydro Guide F014 wire was used to verify that the lumen was patent. RESULTS: The main epicardial lymphatic collectors were found to follow the main coronary arteries. Most of the lymph vessels drained into the left ventricular trunk, which evacuates fluid from the left heart and also partially from the right heart. The right trunk was often found to drain into the left trunk anterior basally. Right heart drainage was highly variable compared to the left. In addition, the overall cannulation success rate of the selected cannulation sites was only 57%. CONSLUSIONS: Mapping of the porcine epicardial lymphatic anatomy is feasible. The right ventricular drainage system had a higher degree of variability than the left, and the right cardiac lymph system was found to be partially cleared through the left lymphatic trunk. To improve cannulation success rate, we proposed two sites for cannulation based on these findings and the use of Venflon cannulas (26 G) for cannulation and lymph collection. This method might be helpful for future studies that focus on biochemical sample analysis and decompression. RELEVANCE FOR PATIENTS: Real-time biochemical assessment and decompression of lymph may contribute to the understanding of heart failure and eventually result in preventive measures. First its relevance should be established by additional research in both arrested and working porcine hearts. Imaging and mapping of the epicardial lymphatics may enable sampling and drainage and contribute to the prevention or treatment of heart failure. We envision that this approach may be considered in patients with a high risk of postoperative left and right heart failure during open-heart surgery.

Bileaflet mechanical aortic valves do not alter ascending aortic wall shear stress.

Progressive ascending aortic dilatation has been observed after mechanical aortic valve replacement (mAVR), possibly due to altered blood flow and wall shear stress (WSS) patterns induced by their bileaflet design. We examined the effect of mAVR on WSS in the ascending aorta using time-resolved 4D flow MRI. Fifteen patients with mechanical aortic valve prostheses, 10 patients with bicuspid aortic valve disease and 10 healthy individuals underwent thoracic 4D flow MRI. Peak systolic hemodynamic parameters (velocity and WSS) and vessel diameters were assessed in the ascending aorta. In addition, three-dimensional per-voxel analysis was used to compare velocity and WSS between patient groups and healthy controls. Peak aortic diameters were significantly higher in mAVR and BAV patients compared to healthy controls (p = 0.011). Mean aortic diameters were comparable between mAVR and BAV patients. No differences in 4D flow MRI-derived mean blood flow velocity and peak WSS were found between the three groups. Compared to healthy controls, mean WSS was significantly lower in mAVR patients (p = 0.031). Per-voxel analysis revealed no increased WSS in the ascending aortic wall and significantly lower velocity and WSS values in mAVR patients compared to healthy controls. In contrast, regions of significantly increased outer lumen velocities and WSS in BAV patients compared to healthy controls were found. This study shows that there is no increased ascending aortic WSS after mAVR. Our results suggest that, in contrast to BAV patients, there is no indication for intensified follow-up of the ascending aorta after mAVR.

Full-field analysis of epicardial strain in an in vitro porcine heart platform.

The quantitative assessment of cardiac strain is increasingly performed to provide valuable insights on heart function. Currently, the most frequently used technique in the clinic is ultrasound-based speckle tracking echocardiography (STE). However, verification and validation of this modality are still under investigation and further reference measurements are required to support this activity. The aim of this work was to enable these reference measurements using a dynamic beating heart simulator to ensure reproducible, controlled, and realistic haemodynamic conditions and to validate the reliability of optical-based three-dimensional digital image correlation (3D-DIC) for a dynamic full-field analysis of epicardial strain. Specifically, performance assessment of 3D-DIC was carried out by evaluating the accuracy and repeatability of the strain measurements across multiple cardiac cycles in a single heart and between five hearts. Moreover, the ability of this optical method to differentiate strain variations when different haemodynamic conditions were imposed in the same heart was examined. Strain measurements were successfully accomplished in a region of the lateral left ventricle surface. Results were highly repeatable over heartbeats and across hearts (intraclass correlation coefficient = 0.99), whilst strain magnitude was significantly different between hearts, due to change in anatomy and wall thickness. Within an individual heart, strain variations between different haemodynamic scenarios were greater than the estimated error of the measurement technique. This study demonstrated the feasibility of applying 3D-DIC in a dynamic passive heart simulator. Most importantly, non-contact measurements were obtained at a high spatial resolution (~ 1.5 mm) allowing resolution of local variation of strain on the epicardial surface during ventricular filling. The experimental framework developed in this paper provides detailed measurement of cardiac strains under controlled conditions, as a reference for validation of clinical cardiac strain imaging modalities.

Aortic coarctation repair through left thoracotomy: results in the modern era.

OBJECTIVES: Surgical repair of coarctation of the aorta (CoA) is often possible through left thoracotomy and without the use of cardiopulmonary bypass. Recent studies reporting the outcome after CoA repair through left thoracotomy are limited. Therefore, the aim of this study is to evaluate the results of CoA repair through left thoracotomy in children who were operated on in our centre over the past 21 years. METHODS: From January 1995 to December 2016, 292 patients younger than 18 years underwent primary CoA repair through left thoracotomy at our 2 institutions. Peri- and postoperative data and follow-up data collected from our hospital and the referring hospitals were retrospectively reviewed. RESULTS: Median age at operation was 64 days (range 2 days-17 years). Most patients underwent the resection of the CoA followed by an (extended) end-to-end anastomosis (93%). Six patients died perioperatively and 2 more patients died during the follow-up, of which 7 patients had other major comorbidities. Actuarial survival was 97% at 5 years, 96% at 10 years and 96% at 15 years. Second arch interventions due to recoarctation were performed in 9.9% (n = 29) of patients, consisting of balloon dilatation in all but 2 patients. Recoarctation occurred significantly more often after initial repair in the neonatal period (21%) and could occur as late as 14 years after initial surgery. There were 7 re-recoarctations, and 14% of patients were on hypertensive medication during the follow-up. CONCLUSIONS: Repair of CoA through left thoracotomy is a safe procedure with low rates of mortality. The long-term follow-up is necessary due to the significant risk of recoarctation requiring reintervention.