Assessing Predictors of Early and Late Hospital Readmission After Kidney Transplantation.

• Authors: Julien Hogan, Emory University; Michael D. Arenson, Emory University; Sandesh M. Adhikary, Georgia Institute of Technology; Kevin Li, Emory University; Xingyu Zhang, Emory University; Rebecca Zhang, Emory University; Jeffrey N. Valdez, Georgia Institute of Technology; Raymond Lynch, Emory University; Jimeng Sun, Georgia Institute of Technology; Andrew Adams, Emory University; Rachel Elizabeth Patzer, Emory University
• Journal Title: Transplantation Direct
• Volume: Volume 5, Number 8
• Publisher: Lippincott, Williams & Wilkins: Creative Commons Attribution Non-Commercial No Derivatives License | 2019-08, Pages e479-e479
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1097/TXD.0000000000000918
• Subjects: Health Sciences, Medicine and Surgery
• Keywords: posttransplant readmission; risk factors; kidney transplantation; random forest model; transplant factors; donor characteristics

Abstract

Background: A better understanding of the risk factors of posttransplant hospital readmission is needed to develop accurate predictive models. Methods: We included 40 461 kidney transplant recipients from United States renal data system (USRDS) between 2005 and 2014. We used Prentice, Williams and Peterson Total time model to compare the importance of various risk factors in predicting posttransplant readmission based on the number of the readmissions (first vs subsequent) and a random forest model to compare risk factors based on the timing of readmission (early vs late). Results: Twelve thousand nine hundred eighty-five (31.8%) and 25 444 (62.9%) were readmitted within 30 days and 1 year postdischarge, respectively. Fifteen thousand eight hundred (39.0%) had multiple readmissions. Predictive accuracies of our models ranged from 0.61 to 0.63. Transplant factors remained the main predictors for early and late readmission but decreased with time. Although recipients' demographics and socioeconomic factors only accounted for 2.5% and 11% of the prediction at 30 days, respectively, their contribution to the prediction of later readmission increased to 7% and 14%, respectively. Donor characteristics remained poor predictors at all times. The association between recipient characteristics and posttransplant readmission was consistent between the first and subsequent readmissions. Donor and transplant characteristics presented a stronger association with the first readmission compared with subsequent readmissions. Conclusions: These results may inform the development of future predictive models of hospital readmission that could be used to identify kidney transplant recipients at high risk for posttransplant hospitalization and design interventions to prevent readmission.