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Coronary artery bypass graft (CABG) is a surgical treatment performed to treat coronary artery disease. During CABG, the patient's coronary arteries are cleaned out of obstructions and new, heart-blood-supplying arteries are implanted in their place. Following the CABG, the objective of postoperative care is aimed at fostering the patient's recovery by reducing the risk of complications. Given that a prolonged length of stay may result in a significant financial burden and ineffective management of hospital resources, the length of stay (LOS) in the hospital is an important component for a good organizational structure. The LOS of patients receiving bypass surgery has been predicted in this study using Machine Learning (ML) techniques. Computer systems can automatically learn from data without explicit programming thanks to a combination of mathematical models and statistical techniques called machine learning (ML). The dataset comprises 243 patients having heart bypass surgery in 2019–2020, was obtained from the University Hospital “Federico II” - Complex Operative Unit (C.O.U.) of Cardiology. The data were compared with those obtained from two other hospitals (University Hospital “San Giovanni di Dio e Ruggi d’Aragona” and those of the national hospital “Antonio Cardarelli”) to identify the algorithm with the best performance in predicting length of stay of patient undergoing CABG by analyzing personal and clinical information like age, presence/absence of comorbidities.

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Use of Machine Learning Algorithms to Study the Hospitalization for Coronary Artery Bypass at University Hospital “Federico II”

  • 会议论文

  • 发表日期：

    2025年

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  • Marta Rosaria Marino,
  • Anna Borrelli,
  • Maria Triassi,
  • Giovanni Improta

摘要

Coronary artery bypass graft (CABG) is a surgical treatment performed to treat coronary artery disease. During CABG, the patient's coronary arteries are cleaned out of obstructions and new, heart-blood-supplying arteries are implanted in their place. Following the CABG, the objective of postoperative care is aimed at fostering the patient's recovery by reducing the risk of complications. Given that a prolonged length of stay may result in a significant financial burden and ineffective management of hospital resources, the length of stay (LOS) in the hospital is an important component for a good organizational structure. The LOS of patients receiving bypass surgery has been predicted in this study using Machine Learning (ML) techniques. Computer systems can automatically learn from data without explicit programming thanks to a combination of mathematical models and statistical techniques called machine learning (ML). The dataset comprises 243 patients having heart bypass surgery in 2019–2020, was obtained from the University Hospital “Federico II” - Complex Operative Unit (C.O.U.) of Cardiology. The data were compared with those obtained from two other hospitals (University Hospital “San Giovanni di Dio e Ruggi d’Aragona” and those of the national hospital “Antonio Cardarelli”) to identify the algorithm with the best performance in predicting length of stay of patient undergoing CABG by analyzing personal and clinical information like age, presence/absence of comorbidities.