Precision Medicine in Interventional Cardiology

Patient-specific in silico 3D coronary model in cardiac catheterisation laboratories

Coronary artery disease is caused by the buildup of atherosclerotic plaque in the coronary arteries, affecting the blood supply to the heart, one of the leading causes of death around the world. X-ray coronary angiography is the most common procedure for diagnosing coronary artery disease, which uses contrast material and x-rays to observe vascular lesions. With this type of procedure, blood flow in coronary arteries is viewed in real-time, making it possible to detect stenoses precisely and control percutaneous coronary interventions and stent insertions. Angiograms of coronary arteries are used to plan the necessary revascularisation procedures based on the calculation of occlusions and the affected segments. However, their interpretation in cardiac catheterisation laboratories presently relies on sequentially evaluating multiple 2D image projections, which limits measuring lesion severity, identifying the true shape of vessels, and analysing quantitative data. In silico modelling, which involves computational simulations of patient-specific data, can revolutionise interventional cardiology by providing valuable insights and optimising treatment methods. This paper explores the challenges and future directions associated with applying patient-specific in silico models in catheterisation laboratories. We discuss the implications of the lack of patient-specific in silico models and how their absence hinders the ability to accurately predict and assess the behaviour of individual patients during interventional procedures. Then, we introduce the different components of a typical patient-specific in silico model and explore the potential future directions to bridge this gap and promote the development and utilisation of patient-specific in silico models in the catheterisation laboratories.

Low ALT values amongst hospitalized patients are associated with increased risk of hypoglycemia and overall mortality: a retrospective, big-data analysis of 51 831 patients

BACKGROUND

Sarcopenia and frailty influence clinical patients' outcomes. Low alanine aminotransferase (ALT) serum activity is a surrogate marker for sarcopenia and frailty. In-hospital hypoglycemia is associated, also with worse clinical outcomes.

AIM

We evaluated the association between low ALT, risk of in-hospital hypoglycemia and subsequent mortality.

DESIGN

This was a retrospective cohort analysis.

METHODS

We included patients hospitalized in a tertiary hospital between 2007 and 2019. Patients' data were retrieved from their electronic medical records.

RESULTS

The cohort included 51 831 patients (average age 70.88). The rate of hypoglycemia was 10.8% (amongst diabetics 19.4% whereas in non-diabetics 8.3%). The rate of hypoglycemia was higher amongst patients with ALT < 10 IU/l in the whole cohort (14.3% vs. 10.4%, P < 0.001) as well as amongst diabetics (24.6% vs. 18.8%, P < 0.001). Both the overall and in-hospital mortality were higher in the low ALT group (57.7% vs. 39.1% P < 0.001 and 4.3% vs. 3.2%, P < 0.001). A propensity score matching, after which a regression model was performed, showed that patients with ALT levels < 10 IU/l had higher risk of overall mortality (HR = 1.21, CI 1.13-1.29, P < 0.001).

CONCLUSIONS

Low ALT values amongst hospitalized patients are associated with increased risk of in-hospital hypoglycemia and overall mortality.

High fidelity blood flow in a patient-specific arteriovenous fistula

An arteriovenous fistula, created by artificially connecting segments of a patient’s vasculature, is the preferred way to gain access to the bloodstream for kidney dialysis. The increasing power and availability of supercomputing infrastructure means that it is becoming more realistic to use simulations to help identify the best type and location of a fistula for a specific patient. We describe a 3D fistula model that uses the lattice Boltzmann method to simultaneously resolve blood flow in patient-specific arteries and veins. The simulations conducted here, comprising vasculatures of the whole forearm, demonstrate qualified validation against clinical data. Ongoing research to further encompass complex biophysics on realistic time scales will permit the use of human-scale physiological models for basic and clinical medicine.