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

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 (

Results

Ischemia was present in 37.1%. The MPA model was most accurate to predict ischemia (AUC: 0.758,

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.

Informations de copyright

© The Author(s) 2023.

Déclaration de conflit d'intérêts

Competing interestsSimon M. Frey: nothing to declare. Adam Bakula: nothing to declare. Andrew Tsirkin: head modeling and development of Exploris Health. Vasily Vasilchenko: developer of MPA model at Exploris Health. Peter Ruff: CEO of Exploris Health, stock owner Exploris Health. Caroline Oehri: chief operating officer at Exploris Health, stock owner Exploris Health. Melissa F. Amrein: nothing to declare. Gabrielle Huré: nothing to declare. Klara Rumora: nothing to declare. Ibrahim Schäfer: nothing to declare. Federico Caobelli: nothing to declare. Philip Haaf: nothing to declare. Christian E. Mueller: no conflict of interest to declare regarding this project. Dr. Mueller has received research support from the Swiss National Science Foundation, the Swiss Heart Foundation, the KTI, the University Hospital Basel, the University of Basel, Abbott, Astra Zeneca, Beckman Coulter, Idorsia, Novartis, Ortho Diagnostics, Quidel, Roche, Siemens, Singulex, SpinChip and Sphingotec, as well as speaker honoraria/consulting honoraria from Amgen, Astra Zeneca, Bayer, Boehringer Ingelheim, BMS, Idorsia, Novartis, Osler, Roche, Sanofi and SpinChip. Bjoern Andrew Remppis: advisory board member Exploris Health. Hans-Peter Brunner-La Rocca: advisory board member Exploris Health, stock owner Exploris Health, unrestricted research grant by and advisor to Roche Diagnostics. Michael J. Zellweger: advisory board member Exploris Health, stock owner Exploris Health.