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Article Abstract
Purpose Of Review: This paper summarizes predictive models developed to determine transplant eligibility over the past 5 years, focusing on application of novel data sources and methodologic approaches.
Recent Findings: The contemporary body of research employing predictive models to inform transplant eligibility mainly relies on pre- or post-transplant patient survival. No studies have sought to assimilate all features collected during the transplant evaluation process to produce a composite prediction of post-transplant success or failure.
Summary: Predictive modeling is a commonly used statistical technique that uses available data on a subset of a target population to estimate the current health state or the probability of developing a future health outcome among individuals in the target population. Modern analytic techniques allow for transformation of vast amounts of data into actionable information but require curated organized well-defined data to deploy. That data is currently lacking for patients referred for transplant.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12360406 | PMC |
| http://dx.doi.org/10.1007/s40472-024-00454-4 | DOI Listing |
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