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Machine learning models effectively predict one-year mortality after cardiovascular surgery using pre-operative lab values. Logistic regression showed the best accuracy, highlighting renal function and red cell distribution width as key factors.

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Area of Science:

  • Cardiovascular Surgery
  • Machine Learning
  • Medical Informatics

Background:

  • Post-operative outcomes in cardiovascular surgery are highly variable.
  • Machine learning offers potential for predicting surgical mortality and identifying risk factors.

Purpose of the Study:

  • To estimate all-cause one-year mortality in cardiovascular surgery patients.
  • To identify pre-operative factors associated with mortality using machine learning.

Main Methods:

  • Utilized the MIMIC-IV database, analyzing 11,261 cardiovascular surgery patients.
  • Included patient demographics and pre-operative laboratory values (e.g., electrolytes, eGFR, RDW).
  • Employed machine learning models to predict one-year mortality.

Main Results:

  • Logistic regression demonstrated superior accuracy (85.07%) compared to other models.
  • Achieved high sensitivity (82.89%) and specificity (85.19%).
  • Identified features aligned with established mortality predictors in the literature.

Conclusions:

  • Pre-operative laboratory values are strong predictors of one-year mortality when used with machine learning.
  • Key prognostic factors include renal function, red cell distribution width, leukocytosis, and erythrocyte indices.