Incidence and risk factors of late right heart failure in chronic mechanical circulatory support.

BACKGROUND: Late right heart failure (LRHF) is a common complication during long-term left ventricular assist device (LVAD) support. We aimed to identify risk factors for LRHF after LVAD implantation. METHODS: Patients undergoing primary LVAD implantation between 2006 and 2019 and surviving the perioperative period were included for this study (n = 261). Univariate Cox proportional hazards analysis was used to assess the association of clinical covariates and LRHF, stratified for device type. Variables with p < 0.10 entered the multivariable model. In a subset of patients with complete echocardiography or right catheterization data, this multivariable model was extended. Postoperative cardiopulmonary exercise test data were compared in patients with and without LRHF. RESULTS: Nineteen percentage of patients suffered from LRHF after a median of 12 months, of which 67% required hospitalization. A history of atrial fibrillation (AF) (HR: 2.06 [1.08-3.93], p = 0.029), a higher preoperative body mass index (BMI) (HR: 1.07 [1.01-1.13], p = 0.023), and intensive care unit (ICU) duration (HR: 1.03 [1.00-1.06], p = 0.025) were independent predictors of LHRF in the multivariable model. A significant relation between the severity of tricuspid regurgitation (TR) and LRHF (HR: 1.91 [1.13-3.21], p = 0.016) was found in patients with echocardiographic data. Patients with LRHF demonstrated a lower maximal workload and peak VO2 at 6 months postoperatively. CONCLUSION: A history of AF, BMI, and longer ICU stay may help identify patients at high risk for LRHF. Severity of TR was significantly related to LRHF in a subset of patients.

A data mining-based cross-industry process for predicting major bleeding in mechanical circulatory support.

Aims: Over a third of patients, treated with mechanical circulatory support (MCS) for end-stage heart failure, experience major bleeding. Currently, the prediction of a major bleeding in the near future is difficult because of many contributing factors. Predictive analytics using data mining could help calculating the risk of bleeding; however, its application is generally reserved for experienced researchers on this subject. We propose an easily applicable data mining tool to predict major bleeding in MCS patients. Methods and results: All data of electronic health records of MCS patients in the University Medical Centre Utrecht were included. Based on the cross-industry standard process for data mining (CRISP-DM) methodology, an application named Auto-Crisp was developed. Auto-Crisp was used to evaluate the predictive models for a major bleeding in the next 3, 7, and 30 days after the first 30 days post-operatively following MCS implantation. The performance of the predictive models is investigated by the area under the curve (AUC) evaluation measure. In 25.6% of 273 patients, a total of 142 major bleedings occurred during a median follow-up period of 542 [interquartile range (IQR) 205-1044] days. The best predictive models assessed by Auto-Crisp had AUC values of 0.792, 0.788, and 0.776 for bleedings in the next 3, 7, and 30 days, respectively. Conclusion: The Auto-Crisp-based predictive model created in this study had an acceptable performance to predict major bleeding in MCS patients in the near future. However, further validation of the application is needed to evaluate Auto-Crisp in other research projects.

One year improvement of exercise capacity in patients with mechanical circulatory support as bridge to transplantation.

AIMS: Mechanical circulatory support (MCS) results in substantial improvement of prognosis and functional capacity. Currently, duration of MCS as a bridge to transplantation (BTT) is often prolonged due to shortage of donor hearts. Because long-term results of exercise capacity after MCS are largely unknown, we studied serial cardiopulmonary exercise tests (CPETs) during the first year after MCS implantation. METHODS AND RESULTS: Cardiopulmonary exercise tests at 6 and 12 months after MCS implantation in BTT patients were retrospectively analysed, including clinical factors related to exercise capacity. A total of 105 MCS patients (67% male, 50 ± 12 years) underwent serial CPET at 6 and 12 months after implantation. Power (105 ± 35 to 114 ± 40 W; P ≤ 0.001) and peak VO2 per kilogram (pVO2/kg) improved significantly (16.5 ± 5.0 to 17.2 ± 5.5 mL/kg/min (P = 0.008)). Improvement in pVO2 between 6 and 12 months after LVAD implantation was not related to heart failure aetiology or haemodynamic severity prior to MCS. We identified maximal heart rate at exercise as an important factor for pVO2. Younger age and lower BMI were related to further improvement. At 12 months, 25 (24%) patients had a normal exercise capacity (Weber classification A, pVO2 > 20 mL/kg/min). CONCLUSIONS: Exercise capacity (power and pVO2) increased significantly between 6 and 12 months after MCS independent of Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) profile or heart failure aetiology. Heart rate at exercise importantly relates to exercise capacity. This long-term improvement in exercise capacity is important information for the growing group of long-term MCS patients as this is critical for the quality of life of patients.