Abstract
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. To determine whether a deep learning model could accurately predict the patient-specific expected reference range of eGFR after kidney transplant. 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. In the derivation cohort 100 867 eGFR measurements were available for analysis and 39 999 eGFR measurements from the independent test cohorts. 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. 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. 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.
Highlights
There is a wide variability in kidney function, calculated as the estimated glomerular filtration rate between transplant recipients, depending on quality of the donor organ and on recipientrelated factors
The cohorts consisted of 85.5% of first kidney transplant procedures, mostly (77.1%) from donors after brain death, with a male predominance in recipients and donors, and most (85.5%) receiving calcineurin-based immunosuppressive regimens. eFigure 3 in the Supplement depicts the between-patient variability in estimated glomerular filtration rate (eGFR) trajectories in the data set as well as within-patient variability
autoregressive integrated moving average (ARIMA) Modeling and Performance We first applied the conventional forecasting model, ARIMA, with different input and output lengths to forecast patient-specific eGFR values in the derivation cohort applied in the first 3 months after transplant. This ARIMA model demonstrated potential for forecasting eGFR sequences, albeit with remaining inaccuracies compared with the actual eGFR values between 7.5 and 11.4 mL/min/1.73 m2 (Table 2)
Summary
There is a wide variability in kidney function, calculated as the estimated glomerular filtration rate (eGFR) between transplant recipients, depending on quality of the donor organ and on recipientrelated factors. In patients with chronic disease, many parameters are systematically out of the ranges as defined for healthy individuals, including the eGFR values of transplanted kidneys. This maladapted reference range complicates the use of standardized actions and leads to less reproducible clinical estimation of indications for action and could be the reason that alert systems for acute kidney injury often fail in realizing hard end points,[6,7,8] because not taking into account the individual day-to-day variability may lead to generation of numerous less relevant alerts, inducing alert fatigue.[9] Tools that translate time-dependent intra-individual eGFR fluctuations into a useful patient-specific guide for clinical decision-making may have utility. The predicted eGFR sequences could be considered as a patient-specific expected range, against which new measured values could be benchmarked to assess deviations from the predicted range, which could prompt patientspecific alarms in clinical practice
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
