Artificial intelligence to improve ischemia prediction in Rubidium Positron Emission Tomography-a validation study

Simon M Frey; Adam Bakula; Andrew Tsirkin; Vasily Vasilchenko; Peter Ruff; Caroline Oehri; Melissa Fee Amrein; Gabrielle Huré; Klara Rumora; Ibrahim Schäfer; Federico Caobelli; Philip Haaf; Christian E Mueller; Bjoern Andrew Remppis; Hans-Peter Brunner-La Rocca; Michael J Zellweger

doi:10.1007/s13167-023-00341-5

Artificial intelligence to improve ischemia prediction in Rubidium Positron Emission Tomography-a validation study

EPMA J. 2023 Nov 15;14(4):631-643. doi: 10.1007/s13167-023-00341-5. eCollection 2023 Dec.

Authors

Simon M Frey^{1

2}, Adam Bakula^{1

3}, Andrew Tsirkin⁴, Vasily Vasilchenko⁴, Peter Ruff⁴, Caroline Oehri⁴, Melissa Fee Amrein², Gabrielle Huré², Klara Rumora², Ibrahim Schäfer², Federico Caobelli^{1

3}, Philip Haaf^{1

2}, Christian E Mueller^{1

2}, Bjoern Andrew Remppis⁵, Hans-Peter Brunner-La Rocca⁶, Michael J Zellweger^{1

2}

Affiliations

¹ Department of Cardiology, University Hospital Basel, University of Basel, Petersgraben 4, CH-4031 Basel, Switzerland.
² Cardiovascular Research Institute Basel (CRIB), University Hospital Basel, University of Basel, Spitalstrasse 2, CH-4056 Basel, Switzerland.
³ University Hospital Bern, University of Bern, Freiburgstrasse 18, CH-3010 Bern, Switzerland.
⁴ Exploris Health AG, Industriestrasse 44, CH-8304 Wallisellen, Switzerland.
⁵ Department of Cardiology, Herz- Und Gefässzentrum Bad Bevensen, Römstedter Straße 25, 29549 Bad Bevensen, Germany.
⁶ Department of Cardiology, Maastricht UMC+, Minderbroedersberg 4-6, Maastricht, 6211 LK The Netherlands.

Abstract

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.

Keywords: Artificial intelligence; Coronary artery disease (CAD); Gatekeeper; Improved individual outcome; Ischemia; Patient stratification; Positron emission tomography (PET); Predictive preventive personalised medicine (PPPM/3PM); Pretest probability (PTP); Risk stratification.