Forecasting of Patient-Specific Kidney Transplant Function With a Sequence-to-Sequence Deep Learning Model.

Autor:	Van Loon E; Department of Microbiology, Immunology and Transplantation, Nephrology and Kidney Transplantation Research Group, KU Leuven, Leuven, Belgium.; Department of Nephrology and Kidney Transplantation, University Hospitals Leuven, Leuven, Belgium., Zhang W; ESAT STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium., Coemans M; Department of Microbiology, Immunology and Transplantation, Nephrology and Kidney Transplantation Research Group, KU Leuven, Leuven, Belgium., De Vos M; ESAT STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.; Department of Development and Regeneration, KU Leuven, Leuven, Belgium., Emonds MP; Department of Microbiology, Immunology and Transplantation, Nephrology and Kidney Transplantation Research Group, KU Leuven, Leuven, Belgium.; Histocompatibility and Immunogenetic Laboratory, Red Cross Flanders, Mechelen, Belgium., Scheffner I; Department of Nephrology, Hannover Medical School, Hannover, Germany., Gwinner W; Department of Nephrology, Hannover Medical School, Hannover, Germany., Kuypers D; Department of Microbiology, Immunology and Transplantation, Nephrology and Kidney Transplantation Research Group, KU Leuven, Leuven, Belgium.; Department of Nephrology and Kidney Transplantation, University Hospitals Leuven, Leuven, Belgium., Senev A; Department of Microbiology, Immunology and Transplantation, Nephrology and Kidney Transplantation Research Group, KU Leuven, Leuven, Belgium.; Histocompatibility and Immunogenetic Laboratory, Red Cross Flanders, Mechelen, Belgium., Tinel C; Department of Microbiology, Immunology and Transplantation, Nephrology and Kidney Transplantation Research Group, KU Leuven, Leuven, Belgium., Van Craenenbroeck AH; Department of Microbiology, Immunology and Transplantation, Nephrology and Kidney Transplantation Research Group, KU Leuven, Leuven, Belgium.; Department of Nephrology and Kidney Transplantation, University Hospitals Leuven, Leuven, Belgium., De Moor B; ESAT STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium., Naesens M; Department of Microbiology, Immunology and Transplantation, Nephrology and Kidney Transplantation Research Group, KU Leuven, Leuven, Belgium.; Department of Nephrology and Kidney Transplantation, University Hospitals Leuven, Leuven, Belgium.
Jazyk:	angličtina
Zdroj:	JAMA network open [JAMA Netw Open] 2021 Dec 01; Vol. 4 (12), pp. e2141617. Date of Electronic Publication: 2021 Dec 01.
DOI:	10.1001/jamanetworkopen.2021.41617
Abstrakt:	Importance: Like other clinical biomarkers, trajectories of estimated glomerular filtration rate (eGFR) after kidney transplant are characterized by intra-individual variability. These fluctuations hamper the distinction between alarming graft functional deterioration or harmless fluctuation within the patient-specific expected reference range of eGFR. Objective: To determine whether a deep learning model could accurately predict the patient-specific expected reference range of eGFR after kidney transplant. Design, Setting, and Participants: A multicenter diagnostic study consisted of a derivation cohort of 933 patients who received a kidney transplant between 2004 and 2013 with 100 867 eGFR measurements from University Hospitals Leuven, Belgium, and 2 independent test cohorts: with 39 999 eGFR measurements from 1 170 patients, 1 from University Hospitals Leuven, Belgium, receiving transplants between 2013 and 2018 and 1 from Hannover Medical School, Germany, receiving transplants between 2003 and 2007. Patients receiving a single kidney transplant, with consecutive eGFR measurements were included. Data were analyzed from February 2019 to April 2021. Exposures: In the derivation cohort 100 867 eGFR measurements were available for analysis and 39 999 eGFR measurements from the independent test cohorts. Main Outcomes and Measures: A sequence-to-sequence model was developed for prediction of a patient-specific expected range of eGFR, based on previous eGFR values. The primary outcome was the performance of the deep learning sequence-to-sequence model in the 2 independent cohorts. Results: In this diagnostic study, a total of 933 patients in the training sets (mean [SD] age, 53.5 [13.3] years; 570 male [61.1%]) and 1170 patients in the independent test sets (cohort 1 [n = 621]: mean [SD] age, 58.5 [12.1] years; 400 male [64.4%]; cohort 2 [n = 549]: mean [SD] age, 50.1 [13.0] years; 316 male [57.6%]) who received a single kidney transplant most frequently from deceased donors, the sequence-to-sequence models accurately predicted future patient-specific eGFR trajectories within the first 3 months after transplant, based on the previous graft eGFR values (root mean square error, 6.4-8.9 mL/min/1.73 m2). The sequence-to-sequence model predictions outperformed the more conventional autoregressive integrated moving average prediction model, at all input/output number of eGFR values. Conclusions and Relevance: In this diagnostic study, a sequence-to-sequence deep learning model was developed and validated for individual forecasting of kidney transplant function. The patient-specific sequence predictions could be used in clinical practice to guide physicians on deviations from the expected intra-individual variability, rather than relating the individual results to the reference range of the healthy population.
Databáze:	MEDLINE
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