Effect of transcatheter aortic valve implantation on left ventricular pressure overload indicated by inflammatory biomarkers in high-risk patients

Background

Severe aortic stenosis (AS) is associated with left ventricular (LV) pressure overload that leads to myocardial remodelling and inflammatory processes. Interleukin 6 (IL6) is secreted by leukocytes as an early response to infection and tissue damage as well as high senisitve C-reactive Protein (hsCRP), which is subsequent in the same pathway. Several studies have suggested an association of elevated serum levels with a higher risk of cardiovascular events. GDF-15 and MR-proADM are also associated with inflammatory processes in cardiovascular diseases and are predictors for adverse events and mortality in patients with AS. The aim of the present study was to evaluate their potential prognostic value regarding the patients all-cause mortality.

Methods

A total of 92 consecutive patients (mean age: 80,8 [±5,3] years) undergoing TAVI were included in this study. TAVI was performed according to standard clinical practice. Venous blood samples for biomarker analysis were collected prior to and 6 months after TAVI, these were processed immediately and frozen at −80°C until the assay was performed. Safety events, physiological- and echocardiographical parameters, were assessed at the baseline and the 6-month follow-up. Furthermore, we compiled the all-cause mortality of our patients after two years.

Results

TAVI was performed successfully in all patients. During the two-year follow-up period 24 patients met the endpoint of all-cause mortality. At baseline, serum levels of the inflammatory biomarkers were significantly higher in patients who died within the follow-up period, when compared to survivors (IL6:14,450pg/ml [IQR:7,550; 42,150] vs. 4,200pg/ml [IQR:2,515; 13,875],p=0,0004; hsCRP:5,360 mg/l [IQR:2,248; 26,790] vs. 2,900mg/l [IQR:1,208; 8,210],p=0,022); MR-proADM:1,347nmol/l [IQR:1,038–1,678] vs. 0,922nmol/l [IQR:0,706; 1,202],p=0,0003 and GDF-15:2770,0pg/ml [IQR:2401,0; 3701,0] vs. 1675,2pg/ml [IQR:1141,6; 2524,4],p=0,001). The area under the curve was 0,767 for IL-6, 0,665 for hsCRP, 0,735 for MR-proADM and 0,735 for GDF-15. In addition, there was a significant decrease of IL-6 (baseline: 4,200pg/ml [IQR:2,525; 13,875] vs. 6FU:2,600pg/ml [IQR:1,500; 7,000],p<0,0001), hsCRP (baseline:2,900mg/l [IQR:1,208; 8,210] vs. FU: 2,101 mg/l [IQR: 0,980; 4,540],p=0,002) and MR-proADM (baseline:0,922nmol/l [IQR:0,706–1,202] vs. FU: 0,828nmol/l [IQR:0,642–1,132],p=0,01) serum levels in survivors after a follow-up of 6 months after TAVI, when compared to baseline values. While the median serum levels of GDF-15 (baseline:1675,2pg/ml [IQR:1141,6; 2524,4] vs. FU: 1663,8pg/ml [IQR:1176,5; 2538,1],p=0,563) remained stable.

Conclusions

In the present study there was a significant decrease of inflammatory biomarkers after TAVI in high risk patients with severe aortic stenosis and good clinical outcome. In this regard, IL-6, hsCRP, MR-proADM and GDF-15 were predictors of all-cause mortality in patients, who underwent TAVI.

Funding Acknowledgement

Type of funding sources: None.

Software supported robotic PCI in coronary arteries – comparison from the ROBO.pci trial

Background

Robotic percutaneous coronary interventions (rPCI) have proven to be efficient and safe. The robotics software has recently been updated and now allows precise and fully controlled navigation of devices and wires to fit the various morphologic conditions of coronary arteries and calcified segments.

Objectives

In the present study we aimed to compare the different robotic wiring modalities supported by the new software.

Study design

rPCI performed using the updated robotics software was compared with rPCI using the manually executed wire positioning. Lesion crossing was performed with each method under identical circumstances, crossing the same lesion and measuring the wire times. Measurements were started at the ostium of the guiding catheter and were stopped at a pre-defined individual coronary anatomical landmark.

Results

Eleven consecutive patients with 11 lesions were enrolled in this trial. The mean age of patients was 63±6 years with typical cardiovascular comorbidities and 54% (6/11) presented with three-vessel disease. All patients underwent PCI in an elective setting with stable chronic coronary syndrome. The majority of lesions were located in the LCX (45%, 5/11) followed by the LAD (27%, 3/11) and the RCA (27%, 3/11). All lesions were de novo ACC/AHA grade B2/C lesions, and the procedure was performed by experienced (high-volume) interventional cardiologists. The lesion crossing times were not significantly different (manual crossing 15.00 [IQR: 40] sec vs. rPCI without software support 18.00 [IQR: 18] sec vs. rPCI with software support 13.00 [IQR: 47] sec; p=n.s.).

Summary

rPCI without software support tends to be slower than rPCI with software support. There was no statistical significance. rPCI was found to be safe and feasible and the new software tends to reduced procedural time, which could reduce operators' health risks and improve patients' outcomes. Further randomized controlled trials are needed to evaluate the long-term benefits.

Funding Acknowledgement

Type of funding sources: None.

Artificial intelligence to improve decision making in transcatheter aortic valve implantation

Background

The current state of the art in the diagnosis and treatment of cardiovascular diseases has been based on evidence resulting from traditional trials as well as years of clinical experience. Due to interindividual differences and a huge number of possible cofounders, in interventional cardiology a linear algorithm is usually not able to precisely estimate individual risk, therapy, or outcome. With the technological evolution in deep machine learning (ML) and artificial intelligence (AI), clinicians may now address aspects that might not have been investigated previously, as supercomputers may handle the plethora of data that are generated as part of treatment. Ultimately, treatment recommendations and decisions may be made on a personalized level.

Purpose

The aim of this study was to apply AI to routine clinical practice to improve decision making in transcatheter aortic valve implantation (TAVI) to determine the best type and size of prosthesis personalized for each patient with pre-procedural risk stratification.

Methods

All patients included in the study were undergoing TAVI. To predict the clinical outcomes we applied a random forest classification, a ML method with high interpretability. For baseline data 58 features were chosen, including valve type and size used. After removing highly collinear features with a certain variance inflation factor, feature selection was based on impurity-based feature importance as well as permutation importance. The performance of the estimators was evaluated by a five-fold nested stratified cross-validation. To evaluate the model ROC and mean AUC scores were chosen.

Results

A total of 3882 patient datasets were included in this trial. The baseline characteristics were consistent with a high cardiovascular risk typical of this collective. Device success was achieved in 83.3%, pacemaker implantation was necessary in 12.2%, and aortic valvular insufficiency was observed in 2.5%. The 30-day mortality was 3.4% and one-year mortality was 12.7%. The mean AUC for the outcome parameters device success, aortic valvular insufficiency, any pacemaker operation, and 30-day and one-year mortality after five-fold cross validation were 0.61±0.03, 0.71±0.04, 0.66±0.04, 0.67±0.03, and 0.69±0.01, respectively.

Conclusions

We report preliminary data concerning a promising method to improve decision making in the context of TAVI evaluation and planning using ML algorithm. We showed the feasibility with acceptable AUC values for all outcome parameters. Thus, the integration of AI in TAVI strategy planning process offers a valuable tool providing patient focused personalized therapy.

Funding Acknowledgement

Type of funding sources: None.

Shift in transcriptional landscape of human right ventricle in chronic thromboembolic pulmonary arterial hypertension

Background

Chronic thromboembolic pulmonary hypertension (CTEPH) is a sub group of pulmonary hypertension (PH). CTEPH is characterized by the existence of thromboemboli and vascular remodeling in pulmonary vessels. The effect of increase in pulmonary artery pressures causes right ventricle (RV) hypertrophy and dilatation and finally leads to right heart failure and death. Surgical intervention in operable patients makes the CTEPH as an only curable and unique form of ph. Pulmonary endarterectomy (PEA) is the surgical procedure to remove the thromboembolic clots from the pulmonary vasculature, which restores RV function back to normal with significant improvements in cardiovascular magnetic resonance.

Purpose

The aim of this study is to use transcriptomic profiling to identify signaling pathways, master regulators, and potentially new biomarkers that specifically indicate the effect of PEA on the RV of patients with chronic thromboembolic pulmonary hypertension.

Results

RNA -sequencing (RNA-seq) was performed on RV biopsies obtained from CTEPH patients at PEA baseline (before PEA surgery) and the results were compared with those from RV biopsies obtained during follow-up evaluation. Bioinformatic analysis of RNA-seq data identified 2799 genes (n=14, −0.585 ≤ Log2 fold change ≥0.585, FDR ≤0.05) differentially regulated between the PEA baseline and follow-up sample groups. The great number of genes (2799) differentially expressed after PEA surgery in CTEPH patients confirms a major shift in the transcriptional landscape of RV in these patients. To further identify potential biomarker candidates from the large pool of 2799 differentially expressed genes (DEGs), extensive bioinformatic analysis of different data sets shortlisted 250 DEGs that were functionally associated with cardiovascular development or disease. The findings of this study reveal prominent transcriptional changes that occur in response to PEA. Gene ontology enrichment and pathway analysis confirmed altered regulation of hypoxia-inducible factor 1 (HIF-1) signaling, advanced glycation end products and their receptors (AGE-RAGE), mitogen-activated protein kinase (MAPK) signaling, hippo signaling, the Janus kinase/ signal transducers and activators of transcription (Jak-STAT) signaling pathway, and proteoglycans after PEA compared with before PEA.

Conclusion

Comparison of the results of RNA-seq analysis of RV biopsies of CTEPH patients, pre and post PEA, revealed a major shift in the transcriptional landscape of these patients after reducing the pressure overload of the RV by PEA.

Funding Acknowledgement

Type of funding source: Foundation. Main funding source(s): German Research Foundation (DFG)