Patients are referred to functional coronary artery disease (CAD) testing based on their pre-test probability (PTP) to search for myocardial ischemia. The recommended prediction tools incorporate three variables (symptoms, age, sex) and are easy to use, but have a limited diagnostic accuracy. Hence, a substantial proportion of non-invasive functional tests reveal no myocardial ischemia, leading to unnecessary radiation exposure and costs. Therefore, preselection of patients before ischemia testing needs to be improved using a more predictive and personalised approach. Using multiple variables (symptoms, vitals, ECG, biomarkers), artificial intelligence–based tools can provide a detailed and individualised profile of each patient. This could improve PTP assessment and provide a more personalised diagnostic approach in the framework of predictive, preventive and personalised medicine (PPPM). Consecutive patients (n = 2417) referred for Rubidium-82 positron emission tomography were evaluated. PTP was calculated using the ESC 2013/2019 and ACC 2012/2021 guidelines, and a memetic pattern–based algorithm (MPA) was applied incorporating symptoms, vitals, ECG and biomarkers. Five PTP categories from very low to very high PTP were defined (i.e., < 5

Objectives Predicting the presence or absence of coronary artery disease (CAD) is clinically important. Pretest probability (PTP) and CAD consortium clinical (CAD2) model and risk scores used in the guidelines are not sufficiently accurate as the only guidance for applying invasive testing or discharging a patient. Artificial intelligence without the need of additional non-invasive testing is not yet used in this context, as previous results of the model are promising, but available in high-risk population only. Still, validation in low-risk patients, which is clinically most relevant, is lacking. Design Retrospective cohort study. Setting Secondary outpatient clinic care in one Dutch academic hospital. Participants We included 696 patients referred from primary care for further testing regarding the presence or absence of CAD. The results were compared with PTP and CAD2 using receiver operating characteristic (ROC) curves (area under the curve (AUC)). CAD was defined by a coronary stenosis >50% in at least one coronary vessel in invasive coronary or CT angiography, or having a coronary event within 6 months. Outcome measures The first cohort validating the memetic pattern-based algorithm (MPA) model developed in two high-risk populations in a low-risk to intermediate-risk cohort to improve risk stratification for non-invasive diagnosis of the presence or absence of CAD. Results The population contained 49% male, average age was 65.6 +/- 12.6 years. 16.2% had CAD. The AUCs of the MPA model, the PTP and the CAD2 were 0.87, 0.80, and 0.82, respectively. Applying the MPA model resulted in possible discharge of 67.7% of the patients with an acceptable CAD rate of 4.2%. Conclusions In this low-risk to intermediate-risk population, the MPA model provides a good risk stratification of presence or absence of CAD with a better ROC compared with traditional risk scores. The results are promising but need prospective confirmation.

Heart failure (HF) is one of the most complex chronic disorders with high prevalence, mainly due to the ageing population and better treatment of underlying diseases. Prevalence will continue to rise and is estimated to reach 3% of the population in Western countries by 2025. It is the most important cause of hospitalisation in subjects aged 65 years or more, resulting in high costs and major social impact. The current “one-size-fits-all” approach in the treatment of HF does not result in best outcome for all patients. These facts are an imminent threat to good quality management of patients with HF. An unorthodox approach from a new vision on care is required. We propose a novel predictive, preventive and personalised medicine approach where patients are truly leading their management, supported by an easily accessible online application that takes advantage of artificial intelligence. This strategy paper describes the needs in HF care, the needed paradigm shift and the elements that are required to achieve this shift. Through the inspiring collaboration of clinical and high-tech partners from North-West Europe combining state of the art HF care, artificial intelligence, serious gaming and patient coaching, a virtual doctor is being created. The results are expected to advance and personalise self-care, where standard care tasks are performed by the patients themselves, in principle without involvement of healthcare professionals, the latter being able to focus on complex conditions. This new vision on care will significantly reduce costs per patient while improving outcomes to enable long-term sustainability of top-level HF care.

Known coronary artery disease (CAD) risk scores (e.g., Framingham) estimate the CAD-related event risk rather than presence/absence of CAD. Artificial intelligence (AI) is rarely used in this context.

A1 Predictive and prognostic biomarker panel for targeted application of radioembolisation improving individual outcomes in hepatocellular carcinoma Jella-Andrea Abraham, Olga Golubnitschaja Department of Radiology, University of Bonn, Sigmund-Freud-Str. 25, 53105 Bonn, Germany Correspondence: Olga Golubnitschaja (olga.golubnitschaja@ukb.uni-bonn.de) – Department of Radiology, University of Bonn, Sigmund-Freud-Str. 25, 53105 Bonn, Germany The EPMA Journal 2016, 7(Suppl 1):A1

Chronic diseases are the leading causes of morbidity and mortality in Europe, accounting for more than 2/3 of all death causes and 75 % of the healthcare costs. Heart failure is one of the most prominent, prevalent and complex chronic conditions and is accompanied with multiple other chronic diseases. The current approach to care has important shortcomings with respect to diagnosis, treatment and care processes. A critical aspect of this situation is that interaction between stakeholders is limited and chronic diseases are usually addressed in isolation. Health care in Western countries requires an innovative approach to address chronic diseases to provide sustainability of care and to limit the excessive costs that may threaten the current systems. The increasing prevalence of chronic diseases combined with their enormous economic impact and the increasing shortage of healthcare providers are among the most critical threats. Attempts to solve these problems have failed, and future limitations in financial resources will result in much lower quality of care. Thus, changing the approach to care for chronic diseases is of utmost social importance.

Currently used coronary artery disease (CAD) risk scores estimate CAD-related event risk rather than the presence or the absence of CAD. Newer statistical methods such as neural network systems are rarely used in this context. They could help to diagnose/exclude CAD based on easily available patient data thereby reducing the need of unnecessary tests and related costs. We applied such a memetic pattern based algorithm (MPA) to data of 2 separate patient cohorts using simple clinical variables to determine the diagnostic accuracy and compare it to the Framingham risk score.