Evaluation of a clinical decision support system for detection of patients at risk after kidney transplantation
graft failure
kidney transplantation
SDG 3 – Goede gezondheid en welzijn
Decision Support Systems, Clinical
Kidney Transplantation
3. Good health
Machine Learning
03 medical and health sciences
machine learning
0302 clinical medicine
SDG 3 - Good Health and Well-being
Humans
ddc:610
Public Health
rejection
Public aspects of medicine
RA1-1270
decision support (DS)
DOI:
10.3389/fpubh.2022.979448
Publication Date:
2022-10-25T07:12:07Z
AUTHORS (12)
ABSTRACT
Patient care after kidney transplantation requires integration of complex information to make informed decisions on risk constellations. Many machine learning models have been developed for detecting patient outcomes in the past years. However, performance metrics alone do not determine practical utility. We present a newly developed clinical decision support system (CDSS) for detection of patients at risk for rejection and death-censored graft failure. The CDSS is based on clinical routine data including 1,516 kidney transplant recipients and more than 100,000 data points. In a reader study we compare the performance of physicians at a nephrology department with and without the CDSS. Internal validation shows AUC-ROC scores of 0.83 for rejection, and 0.95 for graft failure. The reader study shows that predictions by physicians converge toward the CDSS. However, performance does not improve (AUC–ROC; 0.6413 vs. 0.6314 for rejection; 0.8072 vs. 0.7778 for graft failure). Finally, the study shows that the CDSS detects partially different patients at risk compared to physicians. This indicates that the combination of both, medical professionals and a CDSS might help detect more patients at risk for graft failure. However, the question of how to integrate such a system efficiently into clinical practice remains open.
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