Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Dec;37(12):4612-4620.
doi: 10.1111/jocs.17110. Epub 2022 Nov 8.

A machine learning approach to high-risk cardiac surgery risk scoring

Michael P Rogers  1 , Haroon Janjua  1 , Gregory Fishberger  1 , Abhinav Harish  1 , Joseph Sujka  1 , Eric M Toloza  2 , Anthony J DeSantis  1 , Robert L Hooker  3 , Ricardo Pietrobon  4 , Lucian Lozonschi  5 , Paul C Kuo  1
Affiliations

A machine learning approach to high-risk cardiac surgery risk scoring

Michael P Rogers et al. J Card Surg. 2022 Dec.

Abstract

Introduction: In patients undergoing high-risk cardiac surgery, the uncertainty of outcome may complicate the decision process to intervene. To augment decision-making, a machine learning approach was used to determine weighted personalized factors contributing to mortality.

Methods: American College of Surgeons National Surgical Quality Improvement Program was queried for cardiac surgery patients with predicted mortality ≥10% between 2012 and 2019. Multiple machine learning models were investigated, with significant predictors ultimately used in gradient boosting machine (GBM) modeling. GBM-trained data were then used for local interpretable model-agnostic explanations (LIME) modeling to provide individual patient-specific mortality prediction.

Results: A total of 194 patient deaths among 1291 high-risk cardiac surgeries were included. GBM performance was superior to other model approaches. The top five factors contributing to mortality in LIME modeling were preoperative dialysis, emergent cases, Hispanic ethnicity, steroid use, and ventilator dependence. LIME results individualized patient factors with model probability and explanation of fit.

Conclusions: The application of machine learning techniques provides individualized predicted mortality and identifies contributing factors in high-risk cardiac surgery. Employment of this modeling to the Society of Thoracic Surgeons database may provide individualized risk factors contributing to mortality.

Keywords: adult cardiac surgery; machine learning; model interpretability; risk scoring.

PubMed Disclaimer

References

REFERENCES

    1. Shahian DM, Jacobs JP, Badhwar V, et al. The society of thoracic surgeons 2018 adult cardiac surgery risk models: part 1-background, design considerations, and model development. Ann Thorac Surg. 2018;105(5):1411-1418.
    1. Khuri SF. The NSQIP: a new frontier in surgery. Surgery. 2005;138(5):837-843.
    1. Orfanoudaki A, Giannoutsou A, Hashim S, Bertsimas D, Hagberg RC. Machine learning models for mitral valve replacement: a comparative analysis with the Society of Thoracic Surgeons risk score. J Card Surg. 2022;37(1):18-28.
    1. Chen JH, Asch SM. Machine learning and prediction in medicine-beyond the peak of inflated expectations. N Engl J Med. 2017;376(26):2507-2509.
    1. Bertsimas D, Dunn J, Velmahos GC, Kaafarani HMA. Surgical risk is not linear: derivation and validation of a novel, user-friendly, and machine-learning-based predictive optimal trees in emergency surgery risk (POTTER) calculator. Ann Surg. 2018;268(4):574-583.