Predicting Functional Status After Transplantation in Patients With Acute-on-Chronic Liver Failure

Kok, Beverley; Duarte‐Rojo, Andrés; Tandon, Puneeta

doi:10.1016/j.cgh.2019.05.048

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…On the positive side, the usefulness of KPS in predicting post-transplant survival and the likelihood of receiving a kidney has been established in previous literature [2][3][4][5][6][7]43 . Further, other interest in predicting the KPS is illustrated by Kok et al 44 where the authors predict functional status after transplantation for patients with acute-on-chronic liver failure. Hence, even if there is some subjectivity in the recording of KPS at registration, the high predictive accuracy of our model would still lead to useful information and insights for patients and physicians.…”

Section: Discussionmentioning

confidence: 99%

Predicting a kidney transplant patient’s pre-transplant functional status based on information from waitlist registration

Mark

Goldsman

Gurbaxani

et al. 2023

Sci Rep

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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 functional status remains the same as the status at registration, results in average root mean squared errors of 14.50 and 14.11 respectively. The analysis is based on 118,401 transplant records from 2007 to 2019. To the best of our knowledge, there has been no previously published research on building a model to predict kidney pre-transplant functional status. We also find that functional status at registration and total serum albumin, have the most impact in predicting the pre-transplant functional status.

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