Background Only the incidence, management, and prognosis of catheter-induced coronary artery and aortic dissections have been systematically studied until now. We sought to evaluate their mechanisms, risk factors, and propagation causes. Methods Electronic databases containing 76,104 procedures and complication registries from 2000–2020 were searched and relevant cineangiographic studies adjudicated. Results Ninety-six dissections were identified. The overall incidence was 0.126%, and 0.021% for aortic injuries. The in-hospital mortality rate was 4.2%, and 6.25% for aortic dissections. Compared to the non-complicated population, patients with dissection were more often female (48% vs. 34%, p = 0.004), with a higher prevalence of comorbidities such as hypertension (56% vs. 25%, p < 0.001) or chronic kidney disease (10% vs. 4%, p = 0.002). They more frequently presented with acute myocardial infarction (72% vs. 43%, p < 0.001), underwent percutaneous coronary intervention (85% vs. 39%, p < 0.001), and were examined with a radial approach (77% vs. 65%, p = 0.011). The most prevalent predisposing factor was small ostium diameter and/or atheroma. Deep intubation for support, catheter malalignment, and vessel prodding were the most frequent precipitating factors. Of the three dissection mechanisms, ‘wedged contrast injection’ was the commonest (the exclusive mechanism of aortic dissections). The propagation rate was 30.2% and led to doubling of coronary occlusions and aortic extensions. The most frequent progression triggers were repeat injections and unchanged catheter. In 94% of cases, dissections were inflicted by high-volume operators, with ≥ 5-year experience in 84% of procedures. The annual dissection rate increased over a 21-year timespan. Conclusions Catheter-induced dissection rarely came unheralded and typically occurred during urgent interventions performed in high-risk patients by experienced operators.

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Catheter-induced dissections (CID) of coronary arteries and/or the aorta are among the most dangerous complications of percutaneous coronary procedures, yet the data on their risk factors are anecdotal. Logistic regression and five more advanced machine learning techniques were applied to determine the most significant predictors of dissection. Model performance comparison and feature importance ranking were evaluated. We identified 124 cases of CID in electronic databases containing 84,223 records of diagnostic and interventional coronary procedures from the years 2000–2022. Based on the f1-score, Extreme Gradient Boosting (XGBoost) was found to have the optimal balance between positive predictive value (precision) and sensitivity (recall). As by the XGBoost, the strongest predictors were the use of a guiding catheter (angioplasty), small/stenotic ostium, radial access, hypertension, acute myocardial infarction, prior angioplasty, female gender, chronic renal failure, atypical coronary origin, and chronic obstructive pulmonary disease. Risk prediction can be bolstered with machine learning algorithms and provide valuable clinical decision support. Based on the proposed model, a profile of ‘a perfect dissection candidate’ can be defined. In patients with ‘a clustering’ of dissection predictors, a less aggressive catheter and/or modification of the access site should be considered.