Corinne Brunner, Martin Theiler, Hansjörg Znoj, Agnes Schwieger-Briel, Isabelle Luchsinger, Lisa Weibel, Brigitte Seliner
Corticosteroid fear in parents of children with atopic dermatitis
Pflege
accurately predicting kidney outcomes iga nephropathy crucial clinical decision making insufficient use longitudinal data previous studies limited accuracy interpretability prediction models failing reflect chronic nature iga nephropathy study aimed establishing multivariable dynamic deep learning model using comprehensive longitudinal data prediction kidney outcomes iga nephropathy retrospective cohort study 2056 iga nephropathy patients 18 kidney centers total 28317 data points collected sliding window method among 15462 windows single center randomly assigned training 80 validation 20 sets 8797 windows 18 kidney centers assigned independently test set interpretable multivariable long shortterm memory imvlstm deep learning model implemented predict kidney outcomes kidney failure 50 decline kidney function based timeinvariant variables measured biopsy timevariant variables measured followup risk performance evaluated using kaplanmeier analysis c statistic trajectory analysis performed assess various trends clinical variables followup model achieved higher c statistic 093 95 ci 092095 test set xgboost prediction model developed previous study using baseline information c statistic 084 95 ci 080088 kaplanmeier analysis showed groups lower predicted risks full model survived longer groups higher risks timevariant variables demonstrated higher importance scores timeinvariant variables within timevariant variables recent measurements showed higher importance scores interpretation showed certain trajectory groups timevariant variables serum creatinine urine protein associated elevated risks adverse outcomes iga nephropathy deep learning model used accurately dynamically predict kidney prognosis based longitudinal data timevariant variables show strong ability predict kidney outcome