Transcatheter Aortic Valve Implantation: Insights into Clinical Complications

__Abstract__ Transcatheter Aortic Valve Implantation (TAVI) has emerged as a viable and safe treatment for patients with severe aortic stenosis (AS) who are considered ineligible or at prohibitive risk for Surgical Aortic Valve Replacement (SAVR)1–4. The aim of the present thesis was to evaluate the in-hospital complications and the determinants or factors associated with outcome after TAVI, thereby, offering insight into the pathophysiology of complications that in turn may help to propose recommendations to improve the planning, execution and follow-up of TAVI

Machine learning-based analysis of non-invasive measurements for predicting intracardiac pressures

Aims: Early detection of congestion has demonstrated to improve outcomes in heart failure (HF) patients. However, there is limited access to invasively haemodynamic parameters to guide treatment. This study aims to develop a model to estimate the invasively measured pulmonary capillary wedge pressure (PCWP) using non-invasive measurements with both traditional statistics and machine learning (ML) techniques. Methods and results: The study involved patients undergoing right-sided heart catheterization at Erasmus MC, Rotterdam, from 2017 to 2022. Invasively measured PCWP served as outcomes. Model features included non-invasive measurements of arterial blood pressure, saturation, heart rate (variability), weight, and temperature. Various traditional and ML techniques were used, and performance was assessed using R2 and area under the curve (AUC) for regression and classification models, respectively. A total of 853 procedures were included, of which 31% had HF as primary diagnosis and 49% had a PCWP of 12 mmHg or higher. The mean age of the cohort was 59 ± 14 years, and 52% were male. The heart rate variability had the highest correlation with the PCWP with a correlation of 0.16. All the regression models resulted in low R2 values of up to 0.04, and the classification models resulted in AUC values of up to 0.59. Conclusion: In this study, non-invasive methods, both traditional and ML-based, showed limited correlation to PCWP. This highlights the weak correlation between traditional HF monitoring and haemodynamic parameters, also emphasizing the limitations of single non-invasive measurements. Future research should explore trend analysis and additional features to improve non-invasive haemodynamic monitoring, as there is a clear demand for further advancements in this field.</p

Machine learning-based analysis of non-invasive measurements for predicting intracardiac pressures

Aims: Early detection of congestion has demonstrated to improve outcomes in heart failure (HF) patients. However, there is limited access to invasively haemodynamic parameters to guide treatment. This study aims to develop a model to estimate the invasively measured pulmonary capillary wedge pressure (PCWP) using non-invasive measurements with both traditional statistics and machine learning (ML) techniques. Methods and results: The study involved patients undergoing right-sided heart catheterization at Erasmus MC, Rotterdam, from 2017 to 2022. Invasively measured PCWP served as outcomes. Model features included non-invasive measurements of arterial blood pressure, saturation, heart rate (variability), weight, and temperature. Various traditional and ML techniques were used, and performance was assessed using R2 and area under the curve (AUC) for regression and classification models, respectively. A total of 853 procedures were included, of which 31% had HF as primary diagnosis and 49% had a PCWP of 12 mmHg or higher. The mean age of the cohort was 59 ± 14 years, and 52% were male. The heart rate variability had the highest correlation with the PCWP with a correlation of 0.16. All the regression models resulted in low R2 values of up to 0.04, and the classification models resulted in AUC values of up to 0.59. Conclusion: In this study, non-invasive methods, both traditional and ML-based, showed limited correlation to PCWP. This highlights the weak correlation between traditional HF monitoring and haemodynamic parameters, also emphasizing the limitations of single non-invasive measurements. Future research should explore trend analysis and additional features to improve non-invasive haemodynamic monitoring, as there is a clear demand for further advancements in this field.</p

Photoplethysmography and intracardiac pressures:early insights from a pilot study

Aims: Invasive haemodynamic monitoring of heart failure (HF) is used to detect deterioration in an early phase thereby preventing hospitalizations. However, this invasive approach is costly and presently lacks widespread accessibility. Hence, there is a pressing need to identify an alternative non-invasive method that is reliable and more readily available. In this pilot study, we investigated the relation between wrist-derived photoplethysmography (PPG) signals and the invasively measured pulmonary capillary wedge pressure (PCWP). Methods and results: Fourteen patients with aortic valve stenosis who underwent transcatheter aortic valve replacement with concomitant right heart catheterization and PPG measurements were included. Six unique features of the PPG signals [heart rate, heart rate variability, systolic amplitude (SA), diastolic amplitude, crest time (CT), and large artery stiffness index (LASI)] were extracted. These features were used to estimate the continuous PCWP values and the categorized PCWP (low &lt; 12mmHg vs. high ≥ 12mmHg). All PPG features resulted in regression models that showed low correlations with the invasively measured PCWP. Classification models resulted in higher performances: the model based on the SA and the model based on the LASI both resulted in an area under the curve (AUC) of 0.86 and the model based on the CT resulted in an AUC of 0.72. Conclusion: These results demonstrate the capability to non-invasively classify patients into clinically meaningful categories of PCWP using PPG signals from a wrist-worn wearable device. To enhance and fully explore its potential, the relationship between PPG and PCWP should be further investigated in a larger cohort of HF patients.</p

Photoplethysmography and intracardiac pressures:early insights from a pilot study

Aims: Invasive haemodynamic monitoring of heart failure (HF) is used to detect deterioration in an early phase thereby preventing hospitalizations. However, this invasive approach is costly and presently lacks widespread accessibility. Hence, there is a pressing need to identify an alternative non-invasive method that is reliable and more readily available. In this pilot study, we investigated the relation between wrist-derived photoplethysmography (PPG) signals and the invasively measured pulmonary capillary wedge pressure (PCWP). Methods and results: Fourteen patients with aortic valve stenosis who underwent transcatheter aortic valve replacement with concomitant right heart catheterization and PPG measurements were included. Six unique features of the PPG signals [heart rate, heart rate variability, systolic amplitude (SA), diastolic amplitude, crest time (CT), and large artery stiffness index (LASI)] were extracted. These features were used to estimate the continuous PCWP values and the categorized PCWP (low &lt; 12mmHg vs. high ≥ 12mmHg). All PPG features resulted in regression models that showed low correlations with the invasively measured PCWP. Classification models resulted in higher performances: the model based on the SA and the model based on the LASI both resulted in an area under the curve (AUC) of 0.86 and the model based on the CT resulted in an AUC of 0.72. Conclusion: These results demonstrate the capability to non-invasively classify patients into clinically meaningful categories of PCWP using PPG signals from a wrist-worn wearable device. To enhance and fully explore its potential, the relationship between PPG and PCWP should be further investigated in a larger cohort of HF patients.</p

The different risk of new-onset, chronic, worsening, and advanced heart failure:A systematic review and meta-regression analysis

Aims: Heart failure (HF) is a chronic and progressive syndrome associated with a poor prognosis. While it may seem intuitive that the risk of adverse outcomes varies across the different stages of HF, an overview of these risks is lacking. This study aims to determine the risk of all-cause mortality and HF hospitalizations associated with new-onset HF, chronic HF (CHF), worsening HF (WHF), and advanced HF. Methods and results: We performed a systematic review of observational studies from 2012 to 2022 using five different databases. The primary outcomes were 30-day and 1-year all-cause mortality, as well as 1-year HF hospitalization. Studies were pooled using random effects meta-analysis, and mixed-effects meta-regression was used to compare the different HF groups. Among the 15 759 studies screened, 66 were included representing 862 046 HF patients. Pooled 30-day mortality rates did not reveal a significant distinction between hospital-admitted patients, with rates of 10.13% for new-onset HF and 8.11% for WHF (p = 0.10). However, the 1-year mortality risk differed and increased stepwise from CHF to advanced HF, with a rate of 8.47% (95% confidence interval [CI] 7.24–9.89) for CHF, 21.15% (95% CI 17.78–24.95) for new-onset HF, 26.84% (95% CI 23.74–30.19) for WHF, and 29.74% (95% CI 24.15–36.10) for advanced HF. Readmission rates for HF at 1 year followed a similar trend. Conclusions: Our meta-analysis of observational studies confirms the different risk for adverse outcomes across the distinct HF stages. Moreover, it emphasizes the negative prognostic value of WHF as the first progressive stage from CHF towards advanced HF.</p

The different risk of new-onset, chronic, worsening, and advanced heart failure:A systematic review and meta-regression analysis

Aims: Heart failure (HF) is a chronic and progressive syndrome associated with a poor prognosis. While it may seem intuitive that the risk of adverse outcomes varies across the different stages of HF, an overview of these risks is lacking. This study aims to determine the risk of all-cause mortality and HF hospitalizations associated with new-onset HF, chronic HF (CHF), worsening HF (WHF), and advanced HF. Methods and results: We performed a systematic review of observational studies from 2012 to 2022 using five different databases. The primary outcomes were 30-day and 1-year all-cause mortality, as well as 1-year HF hospitalization. Studies were pooled using random effects meta-analysis, and mixed-effects meta-regression was used to compare the different HF groups. Among the 15 759 studies screened, 66 were included representing 862 046 HF patients. Pooled 30-day mortality rates did not reveal a significant distinction between hospital-admitted patients, with rates of 10.13% for new-onset HF and 8.11% for WHF (p = 0.10). However, the 1-year mortality risk differed and increased stepwise from CHF to advanced HF, with a rate of 8.47% (95% confidence interval [CI] 7.24–9.89) for CHF, 21.15% (95% CI 17.78–24.95) for new-onset HF, 26.84% (95% CI 23.74–30.19) for WHF, and 29.74% (95% CI 24.15–36.10) for advanced HF. Readmission rates for HF at 1 year followed a similar trend. Conclusions: Our meta-analysis of observational studies confirms the different risk for adverse outcomes across the distinct HF stages. Moreover, it emphasizes the negative prognostic value of WHF as the first progressive stage from CHF towards advanced HF.</p

Defective recovery of QT dispersion following transcatheter aortic valve implantation: Frequency, predictors and prognosis

Background: Corrected QT dispersion (cQTD) has been correlated with non-uniform ventricular repolarisation and increased mortality. In patients with aortic stenosis, cQTD has been shown improved after surgical valve replacement, but the effects of transcatheter aortic valve implantation (TAVI) are unknown. Therefore, we sought to explore the frequency, predictors and prognostic effects of defective cQTD recovery at 6 months after TAVI. Methods: A total of 222 patients underwent TAVI with the Medtronic-CoreValve System between November 2005 and January 2012. Patients who were on class I or III antiarrhythmics or on chronic haemodialysis or who developed atrial fibrillation, a new bundle branch block or became pacemaker dependent after TAVI were excluded. As a result, pre-, post- and follow-up ECG (median: 6 months) analysis was available in 45 eligible patients. Defective cQTD recovery was defined as any progression beyond the baseline cQTD at 6 months. Results: In the 45 patients, the mean cQTD was 47 ± 23 ms at baseline, 45 ± 17 ms immediately after TAVI and 40 ± 16 ms at 6 months (15% reduction, P = 0.049). Compared to baseline, cQTD at 6 months was improved in 60% of the patients whereas defective cQTD recovery was present in 40%. cQTD increase immediately after TAVI was an independent predictor of defective cQTD recovery at 6 months (per 10 ms increase; OR: 1.89, 95% CI: 1.15-3.12). By univariable analysis, defective cQTD recovery was associated with late mortality (HR: 1.52, 95% CI: 1.05-2.17). Conclusions: Despite a gradual reduction of cQTD after TAVI, 40% of the patients had defective recovery at 6 months which was associated with late mortality. More detailed ECG analysis after TAVI may