A novel learning algorithm to predict individual survival after liver transplantation for primary sclerosing cholangitis
Deciding who should receive a liver transplant (LT) depends on both urgency and utility. Most survival scores are validated through discriminative tests, which compare predicted outcomes between patients. Assessing post-transplant survival utility is not discriminate, but should be “calibrated” to be effective. There are currently no such calibrated models. We developed and validated a novel calibrated model to predict individual survival after LT for Primary Sclerosing Cholangitis (PSC). We applied a software tool, PSSP, to adult patients in the Scientific Registry of Transplant Recipients (n=2769) who received a LT for PSC between 2002 and 2013; this produced a model for predicting individual survival distributions for novel patients. We also developed an appropriate evaluation measure, D-calibration, to validate this model. The learned PSSP model showed an excellent D-calibration (p=1.0), and passed the single-time calibration test (Hosmer-Lemeshow p-value of over 0.05) at 0.25, 1, 5 and 10 years. In contrast, the model based on traditional Cox regression showed worse calibration on long-term survival and failed at 10 years (Hosmer-Lemeshow p value=0.027). The calculator and visualizer are available at: http://pssp.srv.ualberta.ca/calculator/liver_transplant_2002. In conclusion we present a new tool that accurately estimates individual post liver transplantation survival.
Citation
A. Andres,
A. Montano-Loza,
R. Greiner,
M. Uhlich,
P. Jin,
B. Hoehn,
D. Bigam,
J. Shapiro,
N. Kneteman.
"A novel learning algorithm to predict individual survival after liver transplantation for primary sclerosing cholangitis".
PLoS One, 13(3), pp e0193523, March 2018.
| Keywords: |
liver transplant, survival prediction, D-calibration, PSSP |
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BibTeX
@article{Andres+al:PLoSONE18,
author = {Axel Andres and Aldo Montano-Loza and Russ Greiner and Max Uhlich
and Ping Jin and Bret Hoehn and David Bigam and James Shapiro and Norman
Kneteman},
title = {A novel learning algorithm to predict individual survival after
liver transplantation for primary sclerosing cholangitis},
Volume = "13",
Number = "3",
Pages = { e0193523},
journal = {PLoS One},
year = 2018,
}Last Updated: March 07, 2020
Submitted by Sabina P 
