TY - JOUR
T1 - Artificial intelligence to improve ischemia prediction in Rubidium Positron Emission Tomography—a validation study
AU - Frey, Simon M.
AU - Bakula, Adam
AU - Tsirkin, Andrew
AU - Vasilchenko, Vasily
AU - Ruff, Peter
AU - Oehri, Caroline
AU - Amrein, Melissa Fee
AU - Huré, Gabrielle
AU - Rumora, Klara
AU - Schäfer, Ibrahim
AU - Caobelli, Federico
AU - Haaf, Philip
AU - Mueller, Christian E.
AU - Remppis, Bjoern Andrew
AU - Rocca, Hans Peter Brunner La
AU - Zellweger, Michael J.
N1 - Funding Information:
Open access funding provided by University of Basel Simon M. Frey received funding from the University Basel Research Fund (3MS1089). Laboratory tests were funded by a grant of the Basel Cardiology Foundation, Switzerland.
Publisher Copyright:
© 2023, The Author(s).
PY - 2023/12
Y1 - 2023/12
N2 - 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: 1)The MPA model improved individual prediction of ischemia significantly and could safely exclude ischemia based on readily available variables without advanced testing (“predictive”).2)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”).3)Consequently, the MPA model could transform ischemia testing towards a more personalised diagnostic algorithm (“personalised”).
AB - 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: 1)The MPA model improved individual prediction of ischemia significantly and could safely exclude ischemia based on readily available variables without advanced testing (“predictive”).2)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”).3)Consequently, the MPA model could transform ischemia testing towards a more personalised diagnostic algorithm (“personalised”).
KW - Artificial intelligence
KW - Coronary artery disease (CAD)
KW - Gatekeeper
KW - Improved individual outcome
KW - Ischemia
KW - Patient stratification
KW - Positron emission tomography (PET)
KW - Predictive preventive personalised medicine (PPPM/3PM)
KW - Pretest probability (PTP)
KW - Risk stratification
U2 - 10.1007/s13167-023-00341-5
DO - 10.1007/s13167-023-00341-5
M3 - Article
SN - 1878-5077
VL - 14
SP - 631
EP - 643
JO - The EPMA Journal
JF - The EPMA Journal
IS - 4
ER -