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Published on: December 28, 2014
Artificial intelligence to improve ischemia prediction in Rubidium Positron Emission Tomography-a validation study
Simon M Frey1,2, Adam Bakula1,3, Andrew Tsirkin4
1Department of Cardiology, University Hospital Basel, University of Basel, Petersgraben 4, CH-4031 Basel, Switzerland.
Insights
An artificial intelligence tool accurately predicts myocardial ischemia, improving patient selection for functional testing. This AI approach enhances diagnostic accuracy and reduces unnecessary procedures, radiation, and costs in coronary artery disease management.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Functional coronary artery disease (CAD) testing is used to detect myocardial ischemia.
- Current prediction tools have limited accuracy, leading to unnecessary tests, radiation exposure, and costs.
- Improved patient preselection is needed for more accurate and personalized ischemia testing.
Purpose of the Study:
- To evaluate an artificial intelligence-based tool for improved pre-test probability (PTP) assessment of myocardial ischemia.
- To apply a memetic pattern-based algorithm (MPA) for personalized diagnostic approaches in cardiology.
- To compare the MPA's accuracy against existing clinical guidelines for ischemia prediction.
Main Methods:
- A cohort of 2417 patients referred for Rubidium-82 positron emission tomography was analyzed.
- Pre-test probability (PTP) was calculated using ESC and ACC guidelines and a memetic pattern-based algorithm (MPA).
- The MPA incorporated symptoms, vitals, ECG, and biomarkers to categorize patients into five PTP levels.
Main Results:
- The MPA model demonstrated superior accuracy in predicting ischemia (AUC: 0.758) compared to current guidelines.
- MPA achieved high sensitivity (99.1%) and negative predictive value (96.4%) for ruling out ischemia at a <5% PTP threshold.
- The MPA model reduced the proportion of patients in intermediate PTP categories by up to 51% and accurately predicted prevalence in very low PTP groups.
Conclusions:
- The MPA model significantly improves individual ischemia prediction, enabling safe exclusion of ischemia without advanced testing.
- This AI tool acts as a gatekeeper, reducing unnecessary downstream testing, radiation, and costs.
- The MPA facilitates a personalized diagnostic strategy for ischemia detection, aligning with predictive, preventive, and personalized medicine (PPPM).
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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