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.

Elderly patients benefit from minimally invasive mitral valve surgery: perioperative risk management matters.

OBJECTIVES: The goal was to assess the single-centre results of minimally invasive mitral valve surgery (MIMVS) in the elderly population. METHODS: All patients referred for minimally invasive valve surgery underwent a standardized preoperative screening. We performed a retrospective analysis of 131 consecutive elderly patients (≥75 years) who underwent endoscopic MIMVS through a right mini-thoracotomy. Survival and postoperative course were assessed in 2 groups: a repair group and a replacement group. RESULTS: Eighty-five patients underwent mitral valve repair, and 46 had mitral valve replacement. The mean age was 79 ± 2.9 years, and the median follow-up duration was 3.8 years. The cardiopulmonary bypass time (128.7 min vs 155.9 min, P = 0.012) and the cross-clamp time (84.9 min vs 124.1 min, P = 0.005) were significantly longer in the replacement group. Except for more reinterventions for bleeding in the replacement group (10.9% vs 0%, P = 0.005), there were no significant differences in the postoperative course between the 2 groups. Low mortality rates at the midterm follow-up were observed in both groups, and no differences were observed between the 4-and the 12-month follow-up. Survival rates after 1 year and 5 years were 97.6% and 88.6%, respectively, with no significant differences between the 2 groups. CONCLUSIONS: MIMVS is an excellent treatment option in vulnerable elderly patients with excellent short- and long-term results. Although other studies suggest that repair could be superior to replacement even in older patients, our experience suggests that replacement is equivalent to repair in terms of mortality and major adverse cardiac and cerebrovascular events. Experience and standardized preoperative screening are mandatory to achieve optimal results.

The evolution of technical prerequisites and local boundary conditions for optimization of mitral valve interventions-Emphasis on skills development and institutional risk performance.

This viewpoint report describes how the evolution of transcatheter mitral valve intervention (TMVI) is influenced by lessons learned from three evolutionary tracks: (1) the development of treatment from mitral valve surgery (MVS) to transcutaneous procedures; (2) the evolution of biomedical engineering for research and development resulting in predictable and safe clinical use; (3) the adaptation to local conditions, impact of transcatheter aortic valve replacement (TAVR) experience and creation of infrastructure for skills development and risk management. Thanks to developments in computer science and biostatistics, an increasing number of reports regarding clinical safety and effectiveness is generated. A full toolbox of techniques, devices and support technology is now available, especially in surgery. There is no doubt that the injury associated with a minimally invasive access reduces perioperative risks, but it may affect the effectiveness of the treatment due to incomplete correction. Based on literature, solutions and performance standards are formulated with an emphasis in technology and positive outcome. Despite references to Heart Team decision making, boundary conditions such as hospital infrastructure, caseload, skills training and perioperative risk management remain underexposed. The role of Biomedical Engineering is exclusively defined by the Research and Development (R&D) cycle including the impact of human factor engineering (HFE). Feasibility studies generate estimations of strengths and safety limitations. Usability testing reveals user friendliness and safety margins of clinical use. Apart from a certification requirement, this information should have an impact on the definition of necessary skills levels and consequent required training. Physicians Preference Testing (PPT) and use of a biosimulator are recommended. The example of the interaction between two Amsterdam heart centers describes the evolution of a professional ecosystem that can facilitate innovation. Adaptation to local conditions in terms of infrastructure, referrals and reimbursement, appears essential for the evolution of a complete mitral valve disease management program. Efficacy of institutional risk management performance (IRMP) and sufficient team skills should be embedded in an appropriate infrastructure that enables scale and offers complete and safe solutions for mitral valve disease. The longstanding evolution of mitral valve therapies is the result of working devices embedded in an ecosystem focused on developing skills and effective risk management actions.

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.

Prosthesis-patient mismatch affects late survival after valve surgery for severe aortic stenosis.

BACKGROUND: The effect of prosthesis-patient mismatch (PPM) on late survival after aortic valve replacement (AVR) in patient with symptomatic severe aortic stenosis (AS) remains unclear. Also, late follow-up in previous studies is confined to only one decade. We aimed to determine the effect of PPM on late survival after isolated AVR for symptomatic severe AS during 25 years of follow-up. METHODS: In this retrospective cohort study, Kaplan-Meier survival analysis was performed to determine late survival in 404 consecutive patients with moderate PPM (N.=86), severe (N.=11), or no/mild PPM (N.=307) after isolated AVR for symptomatic severe AS during a mean follow-up of 25.0±2.9 years. Moderate, severe, and no/mild PPM were defined as indexed effective orifice area of >0.65≤0.85, ≤0.65, and >0.85 cm2/m2, respectively. Multivariable analysis was performed to identify possible independent predictors of decreased late survival, including moderate or severe PPM. RESULTS: Late survival of patients with severe PPM was worse in comparison with those with no/mild PPM: 7.4±2.6 (95% confidence interval 2.2-12.5) vs. 13.6±0.5 (95% confidence interval 12.6-14.6) years, respectively; P=0.020. Late survival of patients with moderate PPM was similar to those with no/mild PPM. Severe PPM was an independent predictor of decreased late survival: hazards ratio 4.002 (95% confidence interval 1.869-8.569); P<0.001. Moderate PPM was not an independent predictor of decreased late survival. CONCLUSIONS: Severe PPM was independently associated with decreased late survival after isolated AVR for symptomatic severe AS during a mean follow-up of 25.0±2.9 years. Therefore, severe PPM should be prevented as much as possible.

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.

What Can We Learn from the Past by Means of Very Long-Term Follow-Up after Aortic Valve Replacement?

BACKGROUND: Studies on very long-term outcomes after aortic valve replacement are sparse. METHODS: In this retrospective cohort study, long-term outcomes during 25.1 ± 2.8 years of follow-up were determined in 673 patients who underwent aortic valve replacement with or without concomitant coronary artery bypass surgery for severe aortic stenosis and/or regurgitation. Independent predictors of decreased long-term survival were determined. Cumulative incidence rates of major adverse events in patients with a mechanical versus those with a biologic prosthesis were assessed, as well as of major bleeding events in patients with a mechanical prosthesis under the age of 60 versus those above the age of 60. RESULTS: Impaired left ventricular function, severe prosthesis-patient mismatch, and increased aortic cross-clamp time were independent predictors of decreased long-term survival. Left ventricular hypertrophy, a mechanical or biologic prosthesis, increased cardiopulmonary bypass time, new-onset postoperative atrial fibrillation, and the presence of symptoms did not independently predict decreased long-term survival. The risk of major bleeding events was higher in patients with a mechanical in comparison with those with a biologic prosthesis. Younger age (under 60 years) did not protect patients with a mechanical prosthesis against major bleeding events. CONCLUSIONS: Very long-term outcome data are invaluable for careful decision-making on aortic valve replacement.

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.

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.

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.

Effect of aortic cross-clamp time on late survival after isolated aortic valve replacement.

OBJECTIVES: Longer aortic cross-clamp (ACC) time is associated with decreased early survival after cardiac surgery. Because maximum follow-up in previous studies on this subject is confined to 28 months, it is unknown whether this adverse effect is sustained far beyond this term. We aimed to determine whether longer ACC time was independently associated with decreased late survival after isolated aortic valve replacement in patients with severe aortic stenosis during 25 years of follow-up. METHODS: In this retrospective cohort study, multivariable analysis was performed to identify possible independent predictors of decreased late survival, including ACC and cardiopulmonary bypass (CPB) time, in a cohort of 456 consecutive patients with severe aortic stenosis, who had undergone isolated aortic valve replacement between 1990 and 1993. RESULTS: Mean follow-up was 25.3 ± 2.7 years. Median (interquartile range) and mean ACC times were normal: 63.0 (20.0) and 64.2 ± 16.1 min, respectively. Age, operative risk scores and New York Heart Association class were similar in patients with ACC time above, versus those with ACC time below the median. Longer ACC time was independently associated with decreased late survival: hazards ratio (HR) 1.01 per minute increase of ACC time (95% confidence interval [CI] 1.00-1.02; P = 0.012). Longer CPB time was not associated with decreased late survival (HR 1.00 per minute increase of CPB time [95% CI 1.00-1.00; P = 0.30]). CONCLUSIONS: Longer ACC time, although still within normal limits, was independently associated with decreased late survival after isolated aortic valve replacement in patients with severe aortic stenosis.

qTICI: Quantitative assessment of brain tissue reperfusion on digital subtraction angiograms of acute ischemic stroke patients.

BACKGROUND: The Thrombolysis in Cerebral Infarction (TICI) scale is an important outcome measure to evaluate the quality of endovascular stroke therapy. The TICI scale is ordinal and observer-dependent, which may result in suboptimal prediction of patient outcome and inconsistent reperfusion grading. AIMS: We present a semi-automated quantitative reperfusion measure (quantified TICI (qTICI)) using image processing techniques based on the TICI methodology. METHODS: We included patients with an intracranial proximal large vessel occlusion with complete, good quality runs of anteroposterior and lateral digital subtraction angiography from the MR CLEAN Registry. For each vessel occlusion, we identified the target downstream territory and automatically segmented the reperfused area in the target downstream territory on final digital subtraction angiography. qTICI was defined as the percentage of reperfused area in target downstream territory. The value of qTICI and extended TICI (eTICI) in predicting favorable functional outcome (modified Rankin Scale 0-2) was compared using area under receiver operating characteristics curve and binary logistic regression analysis unadjusted and adjusted for known prognostic factors. RESULTS: In total, 408 patients with M1 or internal carotid artery occlusion were included. The median qTICI was 78 (interquartile range 58-88) and 215 patients (53%) had an eTICI of 2C or higher. qTICI was comparable to eTICI in predicting favorable outcome with area under receiver operating characteristics curve of 0.63 vs. 0.62 (P = 0.8) and 0.87 vs. 0.86 (P = 0.87), for the unadjusted and adjusted analysis, respectively. In the adjusted regression analyses, both qTICI and eTICI were independently associated with functional outcome. CONCLUSION: qTICI provides a quantitative measure of reperfusion with similar prognostic value for functional outcome to eTICI score.

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.

Non-physiologic closing of bi-leaflet mechanical heart prostheses requires a new tri-leaflet valve design.

Mechanical heart valve prostheses are based on older designs without changes during the last 40 years. Today, there is an unmet need for less thrombogenic mechanical prostheses. Analysis of the relationship between flow characteristics and thromboembolic complications is possible using numerical and biomolecular flow studies that have shown that the reverse rather than the forward flow is responsible for local platelet activation and thrombosis. After peak flow, leaflets experience flow deceleration and the leaflets are still widely open when the flow becomes zero. The closure of the valve starts with the onset of reverse flow. Therefore, the valve closes extremely fast with most of the leaflet traveling angle occurring in <10 ms with excessively high reverse flow velocities. The pivoting spaces, so-called "Hot Spots" should be eliminated to prevent pathologic shear stress that result in thrombosis. A novel tri-leaflet valve combines favorable hemodynamics with the durability of mechanical heart valve. This valve closes within 60 ms, much slower than bi-leaflet valves and similar to the closing mode of a tissue valve. Micro-particle image velocimetry did not show critical regions of flow stagnation and zones of excessive shear in the pivoting region suggesting low potential for thrombogenic events that should allow to avoid long-term anticoagulation.

Attenuated cardiac function degradation in ex vivo pig hearts.

Isolated hearts offer the opportunity to evaluate heart function, treatments, and diagnostic tools without in vivo factor interference. However, the early loss of cardiac function and edema occur over time and do limit the duration of the experiment. This research focuses on delaying these limitations using optimal blood control. This study examines whether blood conditioning by means of the combination of blood predilution and hemodialysis can significantly reduce cardiac function degradation. Slaughterhouse porcine hearts were revived in the PhysioHeart™ platform to restore physiological cardiac performance. Twelve hearts were divided into a control group and a dialysis group; in the latter group, hemodialysis was attached to the blood reservoir. Cardiac hemodynamics and blood parameters were recorded and evaluated. Blood conditioning significantly reduced the loss of cardiac pump function (control group vs dialysis group, -14.9 ± 6.3%/h vs -9.7 ± 2.7%/h) and loss of cardiac output (control group vs dialysis group, -11.8 ± 3.4%/h vs -5.9 ± 2.0%/h). Hemodialysis resulted in physiological and stable blood parameters, whereas in the control group ions reached pathological values, while interstitial edema still occurred. The combination of blood predilution and hemodialysis significantly attenuated ex vivo cardiac function degradation and delayed the loss of cardiac hemodynamics. We hypothesized that besides electrolyte and metabolic control, the hemodialysis-accompanied increase in hematocrit resulted in improved oxygen transport. This could have temporarily compensated the deleterious effect of an increased oxygen-diffusion distance due to edema in the dialysis group and resulted in less progression of cell decay. Clinically validated measures delaying edema might improve the effectiveness of the PhysioHeart™ platform.