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Background: 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. Aims: 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). Methods: 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%, 5-15%, 15-50%, 50-85%, > 85%). Ischemia was defined as summed difference score (SDS) ≥ 2. Results: Ischemia was present in 37.1%. The MPA model was most accurate to predict ischemia (AUC: 0.758, p < 0.001 compared to ESC 2013, 0.661; ESC 2019, 0.673; ACC 2012, 0.585; ACC 2021, 0.667). Using the < 5% threshold, the MPA's sensitivity and negative predictive value to rule out ischemia were 99.1% and 96.4%, respectively. The model allocated patients more evenly across PTP categories, reduced the proportion of patients in the intermediate (15-85%) range by 29% (ACC 2012)-51% (ESC 2019), and was the only tool to correctly predict ischemia prevalence in the very low PTP category. Conclusion: The MPA model enhanced ischemia testing according to the PPPM framework:The MPA model improved individual prediction of ischemia significantly and could safely exclude ischemia based on readily available variables without advanced testing ("predictive").It reduced the proportion of patients in the intermediate PTP range. Therefore, it could be used as a gatekeeper to prevent patients from further unnecessary downstream testing, radiation exposure and costs ("preventive").Consequently, the MPA model could transform ischemia testing towards a more personalised diagnostic algorithm ("personalised"). Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00341-5.
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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.