Waitlist hospital admissions predict resource utilization and survival after renal transplantation

Raymond J. Lynch, Zhang Rebecca, Rachel E. Patzer, Christian P. Larsen, Andrew B. Adams

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Objective: To determine whether fitness for transplant can be determined by candidates' hospitalizations although waitlisted. Background: Renal transplantation must increasingly serve a population of multiply comorbid patients in an environment defined by organ scarcity and premiums on value-based care. Determining those at excess risk for transplant is critical to these imperatives. Methods: United States Renal Data Systems patient and claims data for all adult renal transplant recipients between 2000 and 2010 with continuous primary Medicare coverage for 1 year before and after transplantation were examined. Outcomes included readmissions within the first-year post-transplant and 3-year graft and patient survival. Chi-square statistics, Kaplan- Meier methods (log-rank test), and goodness of fit calculations (c-statistics) were performed for models of transplant outcome. Results: Among 37,623 patients, the percentages of patients admitted for 0, 1 to 7, 8 to 14, or 15 or more days in the pretransplant year were 51%, 25%, 11%, and 13%. Overall readmission-free survival at 1 year was 31%. Heavily preadmitted patients were more likely to have a greater length of stay during their transplant admission, and had a greater service needs at discharge. Pretransplant admission strongly predicted more frequent post-transplant admission. Among all factors studied, preadmission was the strongest predictor of post-transplant death, and had a dose-dependent effect on both death and graft loss. Conclusions: In summary, hospitalization in the year before transplant is an objective, readily ascertainable, and powerful predictor of excess resource utilization and inferior outcome. Incorporation of a rolling assessment of patient hospitalization has potential policy implications for maximizing value in renal transplantation.

Original languageEnglish (US)
Pages (from-to)1168-1173
Number of pages6
JournalAnnals of surgery
Volume264
Issue number6
DOIs
StatePublished - Nov 28 2016
Externally publishedYes

Bibliographical note

Funding Information:
ABA and REP are supported in part by a grant from Bristol-Myers Squibb.

Publisher Copyright:
Copyright © 2016 Wolters Kluwer Health, Inc. All rights reserved.

Keywords

  • Kidney transplant
  • Renal transplant risk stratification
  • Transplant

Fingerprint

Dive into the research topics of 'Waitlist hospital admissions predict resource utilization and survival after renal transplantation'. Together they form a unique fingerprint.

Cite this