Improving prediction of outcome and management after out of hospital cardiac arrest

Abstract

Out of Hospital Cardiac Arrest (OOHCA) is an important cause of mortality and morbidity in developed countries and represents a significant public health burden. A primary cardiac aetiology is common in OOHCA patients and so patients are increasingly brought to specialist cardiac centres for consideration of coronary angiography, percutaneous coronary intervention (PCI) and mechanical circulatory support. However, irrespective of the aetiology and subsequent treatment, the mortality remains high, primarily from hypoxic brain injury. While prognostication for hypoxic brain injury is currently performed 72 hours after the event, this is too delayed to guide decision making on arrival to a heart attack centre. The original research described in this thesis aims to improve prediction of outcome after OOHCA, and guide much earlier and more streamlined decision making for this complex cohort of patients when they arrive at a heart attack centre. This was investigated using a multi-pronged approach. Firstly, we created the King’s Out of Hospital Cardiac Arrest Registry (KOCAR) - a high-fidelity registry composed of primary cardiac aetiology patients admitted to King’s College Hospital between 2012 and 2017. We then used conventional prediction modelling techniques to derive and validate a risk score to predict poor long-term neurological outcome after OOHCA. Secondly, we collaborated with data science experts to employ machine learning techniques in order to develop a decision tree to predict the presence of a culprit lesion in patients presenting with OOHCA. Thirdly, we validated the novel Society for Cardiovascular Angiography and Interventions (SCAI) Cardiogenic shock classification on arrival to the cardiac catheterisation laboratory, its association with outcome and relative effect of invasive therapies. Finally, we conducted an observational study to understand the baseline and early serial release kinetics of cardiac and neurological proteins and circulating micro-RNA (miRNA/miR) biomarkers in patients with OOHCA.

Date of Award	1 Aug 2021
Original language	English
Awarding Institution	King's College London
Supervisor	Philip MacCarthy (Supervisor) & Jonathan Byrne (Supervisor)