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
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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