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Liver Simulated Allocation Modeling: Were the Predictions Accurate for Share 35?
- Source :
- Transplantation. 102(5)
- Publication Year :
- 2018
-
Abstract
- The liver simulated allocation model (LSAM) can be used to study likely effects of liver transplant allocation policy changes on organ offers, acceptance, waitlist survival, and posttransplant survival. Implementation of Share 35 in June 2013 allowed for testing how well LSAM predicted actual changes.LSAM projections for 1 year of liver transplants before and after the Share 35 policy change were compared with observed data during the same period. Numbers of organs recovered, organ sharing, transplant rates, and waitlist mortality rates (per 100 waitlist years) were evaluated by LSAM and compared with observed data.Candidate, recipient, and donor characteristics in the LSAM cohorts were similar to those in the observed population before and after Share 35. LSAM correctly predicted more accepted organs and fewer discarded organs with Share 35. LSAM also predicted increased regional and national sharing, consistent with observed data, although the magnitude was overestimated. Transplant rates were correctly projected to increase and waitlist death rates to decrease.Although the absolute number of transplants was underestimated and waitlist deaths overestimated, the direction of change was consistent with observed data. LSAM correctly predicted change in discarded organs, regional and national sharing, waitlist mortality, and transplants after Share 35 implementation.
- Subjects :
- Male
Time Factors
Tissue and Organ Procurement
Waiting Lists
Computer science
030230 surgery
Machine learning
computer.software_genre
Decision Support Techniques
03 medical and health sciences
0302 clinical medicine
Postoperative Complications
Risk Factors
Humans
Computer Simulation
Policy Making
Transplantation
business.industry
Process Assessment, Health Care
Middle Aged
Tissue Donors
United States
Liver Transplantation
Treatment Outcome
030211 gastroenterology & hepatology
Female
Artificial intelligence
business
computer
Subjects
Details
- ISSN :
- 15346080
- Volume :
- 102
- Issue :
- 5
- Database :
- OpenAIRE
- Journal :
- Transplantation
- Accession number :
- edsair.doi.dedup.....a4427866d3e6d461f00911a38fe20551