Big Data in cardiac surgery: real world and perspectives

J Cardiothorac Surg. 2022 Oct 29;17(1):277. doi: 10.1186/s13019-022-02025-z.


Big Data, and the derived analysis techniques, such as artificial intelligence and machine learning, have been considered a revolution in the modern practice of medicine. Big Data comes from multiple sources, encompassing electronic health records, clinical studies, imaging data, registries, administrative databases, patient-reported outcomes and OMICS profiles. The main objective of such analyses is to unveil hidden associations and patterns. In cardiac surgery, the main targets for the use of Big Data are the construction of predictive models to recognize patterns or associations better representing the individual risk or prognosis compared to classical surgical risk scores. The results of these studies contributed to kindle the interest for personalized medicine and contributed to recognize the limitations of randomized controlled trials in representing the real world. However, the main sources of evidence for guidelines and recommendations remain RCTs and meta-analysis. The extent of the revolution of Big Data and new analytical models in cardiac surgery is yet to be determined.

Keywords: Artificial intelligence; Big Data; Cardiac surgery; Coronary revascularization; Heart failure; Left ventricular assist devices; Machine learning; Valvular heart diseases.

Publication types

  • Meta-Analysis
  • Review

MeSH terms

  • Artificial Intelligence
  • Big Data*
  • Cardiac Surgical Procedures* / methods
  • Electronic Health Records
  • Humans
  • Machine Learning