Can donor narratives yield insights? A natural language processing proof of concept to facilitate kidney allocation.

ABSTRACT

Although expedited placement could ameliorate stagnant kidney utilization, precisely identifying difficult-to-place organs is crucial to mitigate potential harms associated with this policy. Existing algorithms have only leveraged structured data from the Organ Procurement and Transplantation Network (OPTN); however, detailed, free text case information about a donor exists. No known research exists about the utility of these data. We developed a model to predict the probability of delay or discard for adult deceased kidney donors between 2010 and 2018, leveraging donor free text data. The resultant model had a c-statistic of 0.75 compared to 0.80 ( Reduced Probability of Delay or Discard [model], r-PODD) and 0.77 ( Kidney Donor Profile Index, KDPI) on the test dataset. Analysis of the top predictive words suggest both known and potentially novel clinical factors (ie, a known factor such as hypertension vs a novel factor such as stents), and nuanced social factors (intravenous drug use) could negatively affect kidney utilization. These findings suggest that donor narratives have utility; the natural language processing (NLP) model is only moderately correlated with existing indices and provides directional evidence about additional cardiovascular risk factors that may affect kidney utilization. More research is needed to understand the potential to enhance existing indices of kidney utilization to better enable and mitigate the effects of policy interventions such as expedited placement.