biostudies-literatureUnknown13(1)Mark ECarlos and Marguerite Mason TrustGeorgia Tech benefactors: William W. George, Andrea Laliberte, Claudia L. and J. Paul Raines, and Richard E. “Rick” and Charlene ZaleskyLaura and John Arnold FoundationWith over 100,000 patients on the kidney transplant waitlist in 2019, it is important to understand if and how the functional status of a patient may change while on the waitlist. Recorded both at registration and just prior to transplantation, the Karnofsky Performance Score measures a patient's functional status and takes on values ranging from 0 to 100 in increments of 10. Using machine learning techniques, we built a gradient boosting regression model to predict a patient's pre-transplant functional status based on information known at the time of waitlist registration. The model's predictions result in an average root mean squared error of 12.99 based on 5 rolling origin cross validations and 12.94 in a separate out-of-time test. In comparison, predicting that the pre-transplant functScientific reports6164https://www.ebi.ac.uk/biostudies/studies/S-EPMC10105757biostudies-literaturePredicting a kidney transplant patient's pre-transplant functional status based on information from waitlist registration.PMC10105757Sokol JMark EKeskinocak PGoldsman DGurbaxani BfalsePredicting a kidney transplant patient's pre-transplant functional status based on information from waitlist registration.With over 100,000 patients on the kidney transplant waitlist in 2019, it is important to understand if and how the functional status of a patient may change while on the waitlist. Recorded both at registration and just prior to transplantation, the Karnofsky Performance Score measures a patient's functional status and takes on values ranging from 0 to 100 in increments of 10. Using machine learning techniques, we built a gradient boosting regression model to predict a patient's pre-transplant functional status based on information known at the time of waitlist registration. The model's predictions result in an average root mean squared error of 12.99 based on 5 rolling origin cross validations and 12.94 in a separate out-of-time test. In comparison, predicting that the pre-transplant funct2023-01-01T00:00:00Z2023 Apr2025-04-04T20:46:10.678Z2025-04-04T20:46:10.678ZS-EPMC101057573706152510.1038/s41598-023-33117-y