Forewarned Is Forearmed: Machine Learning Algorithms for the Prediction of Catheter-Induced Coronary and Aortic Injuries

Jacek Klaudel (), Barbara Klaudel, Michał Glaza, Wojciech Trenkner, Paweł Derejko and Marek Szołkiewicz
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Jacek Klaudel: Department of Invasive Cardiology and Interventional Radiology, St. Adalbert’s Hospital, Copernicus PL, 80-462 Gdańsk, Poland
Barbara Klaudel: Department of Decision Systems and Robotics, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233 Gdańsk, Poland
Michał Glaza: Department of Cardiology, St. Vincent de Paul Hospital, Pomeranian Hospitals, 81-348 Gdynia, Poland
Wojciech Trenkner: Department of Invasive Cardiology and Interventional Radiology, St. Adalbert’s Hospital, Copernicus PL, 80-462 Gdańsk, Poland
Paweł Derejko: Department of Cardiology, Medicover Hospital, 02-972 Warszawa, Poland
Marek Szołkiewicz: Department of Cardiology, St. Vincent de Paul Hospital, Pomeranian Hospitals, 81-348 Gdynia, Poland

IJERPH, 2022, vol. 19, issue 24, 1-14

Abstract: 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.

Keywords: catheter-induced dissection; coronary artery dissection; aortocoronary dissection; iatrogenic complications; dissection predictors; machine-learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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