Disparities in donor heart acceptance between the USA and Europe: clinical implications.

BACKGROUND AND AIMS: Given limited evidence and lack of consensus on donor acceptance for heart transplant (HT), selection practices vary widely across HT centres in the USA. Similar variation likely exists on a broader scale-across countries and HT systems-but remains largely unexplored. This study characterized differences in heart donor populations and selection practices between the USA and Eurotransplant-a consortium of eight European countries-and their implications for system-wide outcomes. METHODS: Characteristics of adult reported heart donors and their utilization (the percentage of reported donors accepted for HT) were compared between Eurotransplant (n = 8714) and the USA (n = 60 882) from 2010 to 2020. Predictors of donor acceptance were identified using multivariable logistic regression. Additional analyses estimated the impact of achieving Eurotransplant-level utilization in the USA amongst donors of matched quality, using probability of acceptance as a marker of quality. RESULTS: Eurotransplant reported donors were older with more cardiovascular risk factors but with higher utilization than in the USA (70% vs. 44%). Donor age, smoking history, and diabetes mellitus predicted non-acceptance in the USA and, by a lesser magnitude, in Eurotransplant; donor obesity and hypertension predicted non-acceptance in the USA only. Achieving Eurotransplant-level utilization amongst the top 30%-50% of donors (by quality) would produce an additional 506-930 US HTs annually. CONCLUSIONS: Eurotransplant countries exhibit more liberal donor heart acceptance practices than the USA. Adopting similar acceptance practices could help alleviate the scarcity of donor hearts and reduce waitlist morbidity in the USA.

Assessment of Artificial Intelligence in Echocardiography Diagnostics in Differentiating Takotsubo Syndrome From Myocardial Infarction.

Importance: Machine learning algorithms enable the automatic classification of cardiovascular diseases based on raw cardiac ultrasound imaging data. However, the utility of machine learning in distinguishing between takotsubo syndrome (TTS) and acute myocardial infarction (AMI) has not been studied. Objectives: To assess the utility of machine learning systems for automatic discrimination of TTS and AMI. Design, Settings, and Participants: This cohort study included clinical data and transthoracic echocardiogram results of patients with AMI from the Zurich Acute Coronary Syndrome Registry and patients with TTS obtained from 7 cardiovascular centers in the International Takotsubo Registry. Data from the validation cohort were obtained from April 2011 to February 2017. Data from the training cohort were obtained from March 2017 to May 2019. Data were analyzed from September 2019 to June 2021. Exposure: Transthoracic echocardiograms of 224 patients with TTS and 224 patients with AMI were analyzed. Main Outcomes and Measures: Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the machine learning system evaluated on an independent data set and 4 practicing cardiologists for comparison. Echocardiography videos of 228 patients were used in the development and training of a deep learning model. The performance of the automated echocardiogram video analysis method was evaluated on an independent data set consisting of 220 patients. Data were matched according to age, sex, and ST-segment elevation/non-ST-segment elevation (1 patient with AMI for each patient with TTS). Predictions were compared with echocardiographic-based interpretations from 4 practicing cardiologists in terms of sensitivity, specificity, and AUC calculated from confidence scores concerning their binary diagnosis. Results: In this cohort study, apical 2-chamber and 4-chamber echocardiographic views of 110 patients with TTS (mean [SD] age, 68.4 [12.1] years; 103 [90.4%] were female) and 110 patients with AMI (mean [SD] age, 69.1 [12.2] years; 103 [90.4%] were female) from an independent data set were evaluated. This approach achieved a mean (SD) AUC of 0.79 (0.01) with an overall accuracy of 74.8 (0.7%). In comparison, cardiologists achieved a mean (SD) AUC of 0.71 (0.03) and accuracy of 64.4 (3.5%) on the same data set. In a subanalysis based on 61 patients with apical TTS and 56 patients with AMI due to occlusion of the left anterior descending coronary artery, the model achieved a mean (SD) AUC score of 0.84 (0.01) and an accuracy of 78.6 (1.6%), outperforming the 4 practicing cardiologists (mean [SD] AUC, 0.72 [0.02]) and accuracy of 66.9 (2.8%). Conclusions and Relevance: In this cohort study, a real-time system for fully automated interpretation of echocardiogram videos was established and trained to differentiate TTS from AMI. While this system was more accurate than cardiologists in echocardiography-based disease classification, further studies are warranted for clinical application.

Quantification of mitral regurgitation during percutaneous mitral valve repair: added value of simultaneous hemodynamic and 3D echocardiographic assessment.

The objective of this study was to investigate the usefulness of intraprocedural hemodynamic monitoring for MR evaluation during pMRV. Assessment of mitral regurgitation (MR) during percutaneous mitral valve repair (pMVR) procedure is challenging. 3D color Doppler allows exact quantification of MR, but is technically demanding. Sixty patients with moderate to severe MR (14 with structural and 46 functional MR) were included in the study. Intraprocedural pressure curves were continuously obtained in the left atrium (LA) and left ventricle (LV). Transesophageal echocardiography was performed using 3D color Doppler derived mean vena contracta area (VCAmean) and mitral regurgitation volume (RegVol) to quantify MR severity before and after each clip implantation. In the entire patient group, strongest correlations were observed firstly between VCA and the raise of the ascending limb of the left atrial V pressure wave (Vascend; r = 0.58, p < 0.001) and secondly between the difference of peak V wave pressure and mean LA pressure divided by systolic LV pressure [(Vpeak - LAmean) - LVsystole; r = 0.53, p < 0.001]. In patients with structural MR, the highest area under the ROC curve for prediction of mild MR (VCAmean < 0.2 cm² and RegVol < 30 ml) after clip implantation was found for Vascend (AUC 0.89, p < 0.001) whereas in functional MR calculation of (Vpeak - LAmean) - LVsystole showed the highest predictive value (AUC 0.69, p = 0.003). Invasive pressure monitoring can give a direct feedback with regard to the success of clip placement during pMVR.

Regional differences in recipient waitlist time and pre- and post-transplant mortality after the 2006 United Network for Organ Sharing policy changes in the donor heart allocation algorithm.

OBJECTIVES: This study examined the impact of the United Network for Organ Sharing (UNOS) policy changes for regional differences in waitlist time and mortality before and after heart transplantation. BACKGROUND: The 2006 UNOS thoracic organ allocation policy change was implemented to allow for greater regional sharing of organs for heart transplantation. METHODS: We analyzed 36,789 patients who were listed for heart transplantation from January 1999 through April 2012. These patients were separated into 2 eras centered on the July 12, 2006 UNOS policy change. Pre- and post-transplantation characteristics were compared by UNOS regions. RESULTS: Waitlist mortality decreased nationally (up to 180 days: 13.3% vs. 7.9% after the UNOS policy change, p < 0.001) and within each region. Similarly, 2-year post-transplant mortality decreased nationally (2-year mortality: 17.3% vs. 14.6%; p < 0.001) as well as regionally. Waitlist time for UNOS status 1A and 1B candidates increased nationally 17.8 days on average (p < 0.001) with variability between the regions. The greatest increases were in Region 9 (59.2-day increase, p < 0.001) and Region 4 (41.2-day increase, p < 0.001). Although the use of mechanical circulatory support increased nearly 2.3-fold nationally in Era 2, significant differences were present on a regional basis. In Regions 6, 7, and 10, nearly 40% of those transplanted required left ventricular assist device bridging, whereas only 19.6%, 22.3%, and 15.5% required a left ventricular assist device in regions 3, 4, and 5, respectively. CONCLUSIONS: The 2006 UNOS policy change has resulted in significant regional heterogeneity with respect to waitlist time and reliance on mechanical circulatory support as a bridge to transplantation, although overall both waitlist mortality and post-transplant survival are improved.

Preoperative assessment of high-risk candidates to predict survival after heart transplantation.

BACKGROUND: Alternate waiting list strategies expand listing criteria for patients awaiting heart transplantation (HTx). We retrospectively analyzed clinical events and outcome of patients listed as high-risk recipients for HTx. METHODS AND RESULTS: We analyzed 822 adult patients who underwent HTx of whom 111 patients met high-risk criteria. Clinical data were collected from medical records and outcome factors calculated for 61 characteristics. Significant factors were summarized in a prognostic score. Age >65 years (67%) and amyloidosis (19%) were the most common reasons for alternate listing. High-risk recipients were older (63.2±10.2 versus 51.4±11.8 years; P<0.001), had more renal dysfunction, prior cancer, and smoking. Survival analysis revealed lower post-HTx survival in high-risk recipients (82.2% versus 87.4% at 1-year; 59.8% versus 76.3% at 5-year post-HTx; P=0.0005). Prior cerebral vascular accident, albumin <3.5 mg/dL, re-HTx, renal dysfunction (glomerular filtration rate <40 mL/min), and >2 prior sternotomies were associated with poor survival after HTx. A prognostic risk score (CARRS [CVA, albumin, re-HTx, renal dysfunction, and sternotomies]) derived from these factors stratified survival post-HTx in high-risk (3+ points) versus low-risk (0-2 points) patients (87.9% versus 52.9% at 1-year; 65.9% versus 28.4% at 5-year post-HTx; P<0.001). Low-risk alternate patients had survival comparable with regular patients (87.9% versus 87.0% at 1-year and 65.9% versus 74.5% at 5-year post-HTx; P=0.46). CONCLUSIONS: High-risk patients had reduced survival compared with regular patients post-HTx. Among patients previously accepted for alternate donor listing, application of the CARRS score identifies patients with unacceptably high mortality after HTx and those with a survival similar to regularly listed patients.

Dynamic assessment of ventilatory efficiency during recovery from peak exercise to enhance cardiopulmonary exercise testing.

BACKGROUND: While cardiopulmonary exercise testing (CPX) assessment is generally regarded as an optimal means to assess functional capacity in heart failure (HF) patients, strength parameters are omitted. CPX indices collected in recovery may provide additional insight regarding function, including strength. DESIGN AND METHODS: We performed a cross-sectional controlled study. Systolic HF patients (aged ≥ 50 years) and age-matched controls were assessed using CPX and strength evaluations. Standard CPX indices were assessed during exercise (peak oxygen consumption [VO2], first ventilatory threshold [1stVT], and ventilatory efficiency [VE/VCO2 slope]) as well as indices at 1-minute recovery (1 min VO2, 1 min VE/VCO2, and 1 min heart rate recovery [HRR]) and differences between peak and 1-minute recovery (ΔVO2 and ΔVE/VCO2). Lower extremity strength was evaluated using the 1-repetition maximum (1RM) and power. RESULTS: Seventy adults (31 HF; 39 controls), mean age 66.2 ± 9.7 years were evaluated. Peak VO2 (15.4 ± 4.2 versus 23.4 ± 6.6 mlO2·kg(-1)·min(-1), p < 0.0001) and 1stVT (10.9 ± 2.1 versus 14.4 ± 4.0 mlO2·kg(-1)·min(-1), p < 0.0001) were diminished in HF versus controls and VE/VCO2 slope was increased (42.3 ± 12.2 versus 35.4 ± 8.3, p < 0.01). HF patients had reduced 1 minVO2 (13.1 ± 2.9 versus 16.3 ± 3.7 mlO2·kg(-1)·min(-1), p < 0.0001), 1 min HRR (6.7 ± 11.4 versus 12.4 ± 7.6 beats, p < 0.02), and ΔVO2 (2.43 ± 2.3 versus 7.3 ± 5.0 mlO2·kg(-1)·min(-1), p < 0.0001) as well as increased 1 min VE/VCO2 (37 ± 7.5 versus 31.5 ± 4.4, p < 0.001) and ΔVE/VCO2 (1.17 ± 3.0 versus -0.5 ± 1.3, p < 0.0001). Strength parameters were relatively lower in HF. While CPX exercise parameters correlated with strength, stronger correlations were observed between CPX recovery parameters and strength. CONCLUSIONS: CPX recovery indices corroborate disease-specific aerobic differences and distinguish differences in strength. Recovery ventilatory efficiency enhances CPX's value as a comprehensive physical function tool.