Predicted Risk of Mortality Score predicts 30-day readmission after coronary artery bypass grafting

Joshua M Rosenblum1, Brendan P Lovasik2, John C Hunting3

  • 1Division of Cardiac Surgery, Department of Surgery, The Emory Clinic, Emory University School of Medicine, 1365 Clifton Rd, Suite A2202, Atlanta, GA, 30322, USA. jrosenblum@emory.edu.

Insights

The Society of Thoracic Surgeons Predicted Risk of Mortality (PROM) score is linked to higher 30-day readmission rates after coronary artery bypass grafting (CABG). While a modest predictor, PROM offers valuable insights into patient risk for readmission post-CABG surgery.

Area of Science:

  • Cardiovascular Surgery
  • Health Services Research
  • Predictive Analytics in Medicine

Background:

  • 30-day readmission rates after coronary artery bypass grafting (CABG) are critical quality metrics.
  • Preoperative patient variables influence readmission risk.
  • Identifying accurate predictors for readmission is essential for quality improvement and resource allocation.

Purpose of the Study:

  • To evaluate the predictive capability of the Society of Thoracic Surgeons Predicted Risk of Mortality (PROM) score for 30-day readmissions following isolated CABG.
  • To compare the predictive performance of PROM with other clinical factors.

Main Methods:

  • Retrospective analysis of 21,719 patients undergoing isolated CABG from 2002-2017.
  • Logistic regression to assess the association between STS PROM scores and 30-day readmission.
  • Receiver-operator curve (ROC) analysis to determine predictive accuracy.

Main Results:

  • 9.2% of patients (2,023) were readmitted within 30 days.
  • Higher STS PROM scores were significantly associated with increased odds of readmission (GMR 1.33, p < 0.0001).
  • STS PROM demonstrated modest predictive accuracy for readmission (AUC 0.59), comparable to insurance status and ejection fraction.

Conclusions:

  • STS PROM scores are associated with an increased risk of 30-day readmission after CABG.
  • PROM can aid in identifying patients at higher risk for readmission, informing targeted interventions.
  • Further research may explore integrating PROM with other variables for enhanced predictive models.
Abstract

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