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Published on: January 14, 2014
AI Characterisation of Discordance Profiles Between Stress Electrocardiogram and Myocardial Tomoscintigraphy Using
Youness El Maadaoui1, Abdelaziz Belaguid2, Mohamed Aziz Bsiss3
1Electronic Systems Sensors and Nanobiotechnology, National School of Arts and Crafts, Mohammed v University, Rabat, Morocco.
Insights
Artificial intelligence identified key factors like female gender, advanced age, and diabetes predicting discrepancies between stress ECG and Myocardial Perfusion SPECT (MPS) in Coronary Artery Disease (CAD) patients. This helps understand false positive ECG results.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Coronary Artery Disease (CAD) diagnosis often uses stress ECG and Myocardial Perfusion SPECT (MPS).
- Discrepancies between stress ECG and MPS, particularly positive ECG with negative MPS (MPS-/ECG+), present a diagnostic challenge.
- Classic statistical analyses often fail to explain this discordant patient group.
Purpose of the Study:
- To characterize the patient profile associated with MPS-/ECG+ discordance using interpretable Artificial Intelligence (AI).
- To identify predictors of paradoxical results in non-invasive cardiac diagnostics.
Main Methods:
- A cohort of 86 patients with negative MPS was divided into discordant (MPS-/ECG+, n=19) and concordant (MPS-/ECG-, n=67) groups.
- Machine learning models (Random Forest, XGBoost) were trained on rebalanced data (SMOTE).
- Explainability was achieved using SHAP analysis to interpret model predictions.
Main Results:
- Univariate analysis failed to find significant predictors of discordance (p>0.05).
- XGBoost model achieved an AUC of 0.78, with 67% sensitivity and 50% precision for the discordant class.
- SHAP analysis identified female gender, advanced age, and diabetes as key predictors, highlighting multivariate interactions.
Conclusions:
- Explainable AI can decipher complex clinical issues like MPS/ECG discordance.
- A patient profile characterized by female gender, advanced age, and diabetes is associated with MPS-/ECG+ discordance.
- ECG abnormalities without perfusion deficits may indicate underlying pathology, such as microvascular disease, requiring further investigation.
Purpose:
The evaluation of Coronary Artery Disease (CAD) using stress ECG and Myocardial Perfusion SPECT (MPS) frequently reveals discrepancies, particularly in patients with a positive ECG and a negative MPS (MPS-/ECG+). This paradoxical group poses a diagnostic challenge, often unexplained by classic statistical analyses. Our study aims to characterize this profile using interpretable Artificial Intelligence (AI).
Patients And Methods:
A cohort of 86 patients with negative MPS was stratified into a "discordant" group (MPS-/ECG+, n=19) and a "concordant" group (MPS-/ECG-, n=67). A two-pronged analytical approach was used: (1) bivariate statistical analysis and (2) machine learning modelling. Two algorithms, Random Forest and XGBoost, were trained on a dataset rebalanced using SMOTE. Performance was assessed by AUC, and interpretability was ensured by SHAP analysis.
Results:
Conventional univariate statistical analysis did not identify any variable significantly associated with discordance (all p>0.05). In contrast, the XGBoost model, exploiting multivariate interactions, surpassed Random Forest, achieving a moderate but informative performance (AUC = 0.78), with a Sensitivity (Recall) of 67%, a Precision (Positive Predictive Value) of 50% for the discordant class (Class 1), and an overall Accuracy of 76%. SHAP analysis revealed that the most important predictors of discordance were female gender, advanced age, and the presence of diabetes, indicating that the prediction was influenced by the combination of these factors rather than a single one.
Conclusion:
This exploratory study demonstrates the potential value of explainable AI for deciphering complex clinical problems such as MPS/ECG discordance. Our multivariate models identified a potential patient profile associated with of MPS-/ECG+ discordance, characterized by the synergy of clinical factors. These preliminary results suggest that ECG abnormalities in the absence of a perfusion deficit might reflect an underlying pathology (e.g. microvascular disease) rather than a simple false positive, warranting further prospective validation.
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