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Model to predict the risk of cardiac death based on clinical characteristics and Gated-SPECT parameters
Grethel Rodríguez Cabalé1, Eduardo Rodríguez Cabalé2, Virginia Pubul Núñez3
1Especialista en Medicina Interna, Hospital General de Granollers, Barcelona, Spain.
Insights
This study found that male sex, peripheral artery disease, diabetes, and specific gated-SPECT parameters like reduced ejection fraction predict cardiac death. A predictive model combining clinical factors and imaging results demonstrated high accuracy in assessing cardiac death risk.
Area of Science:
- Cardiology
- Medical Imaging
- Predictive Analytics
Background:
- Coronary artery disease (CAD) is a leading cause of mortality.
- Electrocardiogram-synchronized single-photon emission computed tomography (gated-SPECT) myocardial perfusion imaging is crucial for CAD diagnosis and staging.
- Predicting adverse events requires integrating clinical data with imaging parameters, an area with limited research.
Purpose of the Study:
- To investigate the relationship between clinical characteristics and gated-SPECT parameters with cardiac death.
- To develop a predictive model for cardiac death risk using these combined factors.
Main Methods:
- An observational, longitudinal, retrospective study of 2,230 patients with suspected CAD.
- Collected data included clinical characteristics, gated-SPECT parameters, and cardiac death events.
- Logistic regression modeling was employed to analyze variable relationships and predict cardiac death probability.
Main Results:
- Male sex, peripheral arterial disease, and diabetes mellitus were associated with increased cardiac death risk.
- Gated-SPECT parameters such as low ejection fraction (EF < 50%), increased left ventricular end-diastolic volume (VTD ≥ 140 ml), and enlarged ventricular size (VTS ≥ 70 ml) significantly predicted cardiac death.
- The developed logistic regression model exhibited excellent predictive performance (AUC = 0.9656).
Conclusions:
- Clinical factors and gated-SPECT parameters are significant predictors of cardiac death in patients with suspected CAD.
- The developed model effectively predicts cardiac death risk, offering valuable insights for patient management and risk stratification.
Background:
Coronary artery disease is a complex, multifactorial process with high prevalence and morbidity-mortality. Single photon emission computed tomography (SPECT) myocardial perfusion imaging synchronized with the electrocardiogram (gated-SPECT) is a non-invasive imaging technique that has demonstrated high sensitivity and specificity for diagnosis and staging. To better predict the risk of adverse events, it is necessary to analyze the simultaneous behavior of clinical elements and diagnostic tests, a type of study that is scarce in the current literature. This research evaluated the relationship between clinical characteristics and gated-SPECT myocardial perfusion parameters with progression to cardiac death; subsequently, a model was built to predict the risk of such an outcome.
Methods:
An observational, longitudinal, and retrospective study was conducted with 2 230 patients who underwent this test due to suspected coronary artery disease. Clinical characteristics, test parameters, and progression to cardiac death were collected and the relationships between them were studied. A logistic regression model was built to study the relationships between the variables and their influence on the probability of progression to cardiac death.
Results:
Clinical characteristics associated with a higher probability of cardiac death were male sex (OR = 5.104, p = 0.004), peripheral arterial disease (OR = 7.175, p < 0.001), and diabetes mellitus (OR = 3.159, p = 0.013). The gated-SPECT parameters associated with a higher risk of this outcome were VTS ≥70 ml (OR = 12.257, p < 0.001), EF < 50% (OR = 10.757, p < 0.001), VTD ≥140 ml (OR = 8.884, p < 0.001), ventricular dilation (OR = 8.959, p < 0.001), and reversible defects (OR = 7.454, p = 0.001). Fixed defects, parietal motility abnormalities, the presence of both reversible and fixed defects, and the hyperdynamic gated state were also associated with a higher risk of cardiac death but with lower ORs. The logistic regression model showed good overall performance and high ability to determine progression to cardiac death, close to perfect predictive capacity (AUC = 0.9656).
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