Clinical and economical impact of the presence of an extended heart team throughout the balloon-expandable transcatheter aortic valve implantation procedure

The COORDINATE Pilot Study: Impact of a Transcatheter Aortic Valve Coordinator Program on Hospital and Patient Outcomes

The transcatheter aortic valve implantation (TAVI) treatment pathway is complex, leading to procedure-related delays. Dedicated TAVI coordinators can improve pathway efficiency. COORDINATE was a pilot observational prospective registry at three German centers that enrolled consecutive elective patients with severe aortic stenosis undergoing TAVI to investigate the impact a TAVI coordinator program. Pathway parameters and clinical outcomes were assessed before (control group) and after TAVI coordinator program implementation (intervention phase). The number of repeated diagnostics remained unchanged after implementation. Patients with separate hospitalizations for screening and TAVI had long delays, which increased after implementation (65 days pre- vs. 103 days post-implementation); hospitalizations combining these were more efficient. The mean time between TAVI and hospital discharge remained constant. Nurse (p = 0.001) and medical technician (p = 0.008) working hours decreased. Patient satisfaction increased, and more consistent/intensive contact between patients and staff was reported. TAVI coordinators provided more post-TAVI support, including discharge management. No adverse effects on post-procedure or 30-day outcomes were seen. This pilot suggests that TAVI coordinator programs may improve aspects of the TAVI pathway, including post-TAVI care and patient satisfaction, without compromising safety. These findings will be further investigated in the BENCHMARK registry.

Machine learning-based risk prediction of intrahospital clinical outcomes in patients undergoing TAVI

BACKGROUND

Currently, patient selection in TAVI is based upon a multidisciplinary heart team assessment of patient comorbidities and surgical risk stratification. In an era of increasing need for precision medicine and quickly expanding TAVI indications, machine learning has shown promise in making accurate predictions of clinical outcomes. This study aims to predict different intrahospital clinical outcomes in patients undergoing TAVI using a machine learning-based approach. The main clinical outcomes include all-cause mortality, stroke, major vascular complications, paravalvular leakage, and new pacemaker implantations.

METHODS AND RESULTS

The dataset consists of 451 consecutive patients undergoing elective TAVI between February 2014 and June 2016. The applied machine learning methods were neural networks, support vector machines, and random forests. Their performance was evaluated using five-fold nested cross-validation. Considering all 83 features, the performance of all machine learning models in predicting all-cause intrahospital mortality (AUC 0.94-0.97) was significantly higher than both the STS risk score (AUC 0.64), the STS/ACC TAVR score (AUC 0.65), and all machine learning models using baseline characteristics only (AUC 0.72-0.82). Using an extreme boosting gradient, baseline troponin T was found to be the most important feature among all input variables. Overall, after feature selection, there was a slightly inferior performance. Stroke, major vascular complications, paravalvular leakage, and new pacemaker implantations could not be accurately predicted.

CONCLUSIONS

Machine learning has the potential to improve patient selection and risk management of interventional cardiovascular procedures, as it is capable of making superior predictions compared to current logistic risk scores.

Current results and remaining challenges of trans-catheter aortic valve replacement expansion in intermediate and low risk patients

TAVR has become the standard treatment in patients at increased surgical risk (STS or EuroSCORE II ≥4% or logistic EuroSCORE I ≥ 10% or other risk factors not included in these scores such as frailty, porcelain aorta, sequelae of chest radiation) and it is increasingly being performed in patients at intermediate to low (STS or EuroSCORE II <4% or logistic EuroSCORE I < 10%) surgical risk. Although non-inferiority has been demonstrated in intermediate and low-risk patients, several challenges need to be addressed before expansion to younger patients. Current trends, trials results, and remaining challenges are summarized and discussed in this review.