Development and validation of explainable machine learning models for risk of mortality in transcatheter aortic valve implantation: TAVI risk machine scores.

AIMS Identification of high-risk patients and individualized decision support based on objective criteria for rapid discharge after transcatheter aortic valve implantation (TAVI) are key requirements in the context of contemporary TAVI treatment. This study aimed to predict 30-day mortality following TAVI based on machine learning (ML) using data from the German Aortic Valve Registry. METHODS AND RESULTS Mortality risk was determined using a random forest ML model that was condensed in the newly developed TAVI Risk Machine (TRIM) scores, designed to represent clinically meaningful risk modelling before (TRIMpre) and in particular after (TRIMpost) TAVI. Algorithm was trained and cross-validated on data of 22 283 patients (729 died within 30 days post-TAVI) and generalisation was examined on data of 5864 patients (146 died). TRIMpost demonstrated significantly better performance than traditional scores [C-statistics value, 0.79; 95% confidence interval (CI)] [0.74; 0.83] compared to Society of Thoracic Surgeons (STS) with C-statistics value 0.69; 95%-CI [0.65; 0.74]). An abridged (aTRIMpost) score comprising 25 features (calculated using a web interface) exhibited significantly higher performance than traditional scores (C-statistics value, 0.74; 95%-CI [0.70; 0.78]). Validation on external data of 6693 patients (205 died within 30 days post-TAVI) of the Swiss TAVI Registry confirmed significantly better performance for the TRIMpost (C-statistics value 0.75, 95%-CI [0.72; 0.79]) compared to STS (C-statistics value 0.67, CI [0.63; 0.70]). CONCLUSION TRIM scores demonstrate good performance for risk estimation before and after TAVI. Together with clinical judgement, they may support standardised and objective decision-making before and after TAVI

How cancer cells hijack DNA double-strand break repair pathways to gain genomic instability

DNA double-strand breaks (DSBs) are a significant threat to the viability of a normal cell, since they can result in loss of genetic material if mitosis or replication is attempted in their presence. Consequently, evolutionary pressure has resulted in multiple pathways and responses to enable DSBs to be repaired efficiently and faithfully. Cancer cells, which are under pressure to gain genomic instability, have a striking ability to avoid the elegant mechanisms by which normal cells maintain genomic stability. Current models suggest that in normal cells DSB repair occurs in a hierarchical manner that promotes rapid and efficient rejoining first, with the utilisation of additional steps or pathways of diminished accuracy if rejoining is unsuccessful or delayed. We evaluate the fidelity of DSB repair pathways and discuss how cancer cells promote the utilisation of less accurate processes. Homologous recombination serves to promote accuracy and stability during replication, providing a battlefield for cancer to gain instability. Non-homologous end-joining, a major DSB repair pathway in mammalian cells, usually operates with high fidelity and only switches to less faithful modes if timely repair fails. The transition step is finely tuned and provides another point of attack during tumour progression. In addition to DSB repair, a DSB signalling response activates processes such as cell cycle checkpoint arrest, which enhance the possibility of accurate DSB repair. We will consider the ways by which cancers modify and accost these processes to gain genomic instabilit

Outcomes in Transcatheter Aortic Valve Replacement for Bicuspid Versus Tricuspid Aortic Valve Stenosis

Cardiolog

The Impact of Non-Lethal Single-Dose Radiation on Tumor Invasion and Cytoskeletal Properties

Irradiation is the standard therapy for glioblastoma multiforme. Glioblastoma are highly resistant to radiotherapy and the underlying mechanisms remain unclear. To better understand the biological effects of irradiation on glioblastoma cells, we tested whether nonlethal irradiation influences the invasiveness, cell stiffness, and actin cytoskeleton properties. Two different glioblastoma cell lines were irradiated with 2 Gy and changes in mechanical and migratory properties and alterations in the actin structure were measured. The invasiveness of cell lines was determined using a co-culture model with organotypic hippocampal slice cultures. Irradiation led to changes in motility and a less invasive phenotype in both investigated cell lines that were associated with an increase in a ”generalized stiffness” and changes in the actin structure. In this study we demonstrate that irradiation can induce changes in the actin cytoskeleton and motility, which probably results in reduced invasiveness of glioblastoma cell lines. Furthermore, “generalized stiffness” was shown to be a profound marker of the invasiveness of a tumor cell population in our model