Assessing Predictors of Early and Late Hospital Readmission after Kidney Transplantation

Julien Hogan, Michael D. Arenson, Sandesh M. Adhikary, Kevin Li, Xingyu Zhang, Rebecca Zhang, Jeffrey N. Valdez, Raymond J. Lynch, Jimeng Sun, Andrew B. Adams, Rachel E. Patzer

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Article numbere479
JournalTransplantation Direct
Issue number8
StatePublished - Aug 1 2019
Externally publishedYes

Bibliographical note

Funding Information:
Received 24 May 2019. Accepted 1 June 2019. 1Department of Surgery, Emory Transplant Center, Emory University School of Medicine, Atlanta, GA. 2College of computing, Georgia Institute of Technology, Atlanta, GA. 3Department of Biostatistics, Rollins School of Public Health, Atlanta, GA. 4Department of Epidemiology, Rollins School of Public Health, Atlanta, GA. J.H. and R.E.P. participated in the conception of the paper, analysis of the data, and writing of the manuscript. M.D.A., S.M.A., K.L., X.Z., and R.Z. participated in the data analysis. A.B.A. participated in the conception of the paper and writing of the manuscript. J.F., R.J.L., and J.S. participated in the writing of the manuscript. All the authors have revised the article and approved the final version. The authors have no conflicts of interest to disclose. This work was supported by R01 MD011682. M.D.A. was supported in part by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002378 as well as TL1TR002382.

Publisher Copyright:
© 2019 The Author(s). Transplantation Direct. Published by Wolters Kluwer Health, Inc.

Fingerprint Dive into the research topics of 'Assessing Predictors of Early and Late Hospital Readmission after Kidney Transplantation'. Together they form a unique fingerprint.

Cite this