Purpose: The purpose of this study is to develop and validate a statistical model to predict 30-day all-cause readmission after isolated coronary artery bypass grafting (CABG) surgery to guide and direct plan of care.

Methods: This observational cohort study utilized the California CABG Outcomes Reporting Program and the Patient Discharge Data housed by the Office of Statewide Health Planning and Development. A total of 10,783 patients who underwent isolated CABG surgery at 125 California-licensed hospitals in 2013 constituted the study cohort. Fourteen study variables for possible inclusion in a model were examined. The Society of Thoracic Surgeons (STS) 30-day all-cause readmission after coronary bypass measure was used as the baseline risk model to determine the effect of each of these variables on the performance of a risk model. Statistical measures included: (a) standard and hierarchical logistic regressions to study the effect of risk factors on 30-day readmission; (b) the area under the receiver operating characteristic curve (AUC) and the net reclassification improvement (NRI) to determine the effect of the study variables on the baseline risk model; and (c) the bootstrapping technique for model validation. A series of exploratory analyses were performed to revise the baseline risk model for a more optimal revised version. The later was used to develop a new risk model.

Results: Of the 14 variables, the addition of the variable postoperative length of stay to the revised baseline risk model improved the performance of the model in the AUC (c-statistic from 0.671 to 0.677). The addition of the following variables to the final revised baseline risk model resulted in a model that demonstrated improved performance (c-statistic of 0.679): race and ethnicity, payer status, ZIP code median household income greater than $43,000 per annum, postoperative length of stay, and disposition location after CABG. The new multivariable logistic regression risk model was used to derive the readmission risk score.

Conclusion: The readmission risk index may be helpful to identify high-risk patients for readmission. It may be used in practice to guide and direct plan of care to prevent and reduce 30-day readmission after CABG surgery.

LLU Discipline





School of Nursing

First Advisor

Elizabeth Johnston Taylor

Second Advisor

Beate Herrchen Danielsen

Third Advisor

Fayette Nguyen Truax

Degree Name

Doctor of Philosophy (PhD)

Degree Level


Year Degree Awarded


Date (Title Page)




Library of Congress/MESH Subject Headings

Logistic Models; Patient Readmission; Coronary Artery Bypass Grafting



Page Count

xxvii; 318 p.

Digital Format


Digital Publisher

Loma Linda University Libraries

Usage Rights

This title appears here courtesy of the author, who has granted Loma Linda University a limited, non-exclusive right to make this publication available to the public. The author retains all other copyrights.


Loma Linda University Electronic Theses and Dissertations

Collection Website



Loma Linda University. Del E. Webb Memorial Library. University Archives