1Charité - Universitätsmedizin Berlin CC 11: Med. Klinik für Kardiologie Berlin, Deutschland; 2Universitätsklinikum Frankfurt Med. Klinik III - Kardiologie, Angiologie Frankfurt am Main, Deutschland
Background: Identification and characterization of acute coronary syndrome (ACS)-causing culprit plaques by high-resolution intracoronary optical coherence tomography (OCT) allows personalized management of patients with ACS. This is especially crucial when distinguishing between culprit lesions (CL) with ruptured - (RFC) and intact-fibrous cap (IFC) as such classifications play a pivotal role in clinical decision-making and in evaluating prognosis. While OCT-evaluation of the ACS-causing culprit plaque morphology is rather effective, its invasivity limits broad clinical implementation.
Objective: The aim of this study was to develop a machine-learning-based model that allows effective differentiation between RFC- and IFC-ACS, utilizing routinely available clinical variables of both routine-invasive and non-invasive diagnostic parameters.
Methods: A representative subset of the OPTICO-ACS study (NCT03129503) was investigated. The dataset was split into a training set (70%) and a test set (30%). The optimum discriminatory cutoff point for age, Body-Mass-Index, LDL, creatinine, aortic pulse pressure (APP), pulse wave velocity (PWV), heart rate-corrected augmentation index, intima-media-thickness (IMT) and the distance to the next coronary branch was determined using the Youden's J statistic and included as a binary variable in the prediction models. We used Least Absolute Shrinkage and Selection Operator (LASSO) regressions with 10-fold cross-validation and subsequently assessed their performance using the Area under the Curve (AUC).
We established three distinct models: The Basic model, which incorporates regularly available clinical variables; the Advanced model, which adds arterial stiffness metrics and IMT; and the Full model, which further includes angiographic parameters. Derived from the model coefficients, a normalized risk score (0-100) was defined for the classification of RFC and IFC.
Results: In total, 96 patients (81% male) with RFC-ACS (69%) and IFC-ACS (31%), with a median age of 62.0 years were included in this study. The predictive power improved with the addition of arterial stiffness metrics (Advanced model: AUC = 0.74) and improved even further with the subsequent inclusion of angiographic findings (Full model: AUC = 0.82) (Fig. 1).
Reclassification analysis revealed that a model containing a combination of clinical covariates, arterial stiffness parameters and angiographic findings significantly outperforms its isolated components (Full vs. Basic model: Net reclassification index (NRI) 0.87 [95% CI 0.46-1.27], p<0.001), Integrated discrimination improvement (IDI) 0.16 [95% CI 0.05-0.27], p=0.005; Full vs. Advanced model: NRI 1.13 [95% CI 0.73-1.54], p<0.001, IDI 0.11 [95% CI 0.03-0.18], p=0.004) (Fig. 2).
Conclusion: A model that integrates commonly available clinical data, arterial stiffness parameters, and angiographic findings offers potential as an effective tool for differentiating between RFC- and IFC-ACS and therefore allows for the first time for a personalized, pathophysiology-guided management of patients with ACS.
Fig. 1: Receiver Operating Characteristic (ROC) curves
Fig. 2: Model development and reclassification metrics
Legend: BMI = Body-Mass-Index; DM = Diabetes mellitus; AHT = Arterial hypertension; LDL-C = Low-density lipoprotein cholesterol; PWV = Puls wave velocity; APP = Aortic pulse pressure; AIx@75 = Heart rate-corrected augmentation index; IMT = Intima-media-thickness; MVD = Multivessel disease.