Predicting the function of transplanted kidney in long-term care processes: Application of a hybrid model

Rashidi Khazaee⁠, P and Niazkhani, Z and Bagherzadeh, J and Pirnejad, H (2019) Predicting the function of transplanted kidney in long-term care processes: Application of a hybrid model. Journal of Biomedical Informatics, XXX. pp. 1-9.

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Abstract

A tool that can predict the estimated glomerular filtration rate (eGFR) in routine daily care can help clinicians to make better decisions for kidney transplant patients and to improve transplantation outcome. In this paper, we proposed a hybrid prediction model for predicting a future value for eGFR during long-term care processes. Methods: Longitudinal, historical data of 942 transplant patients who received a kidney between 2001 and 2016 at Urmia kidney transplant center was used to develop a hybrid model. The model was based on three primary models: multi-layer perceptron (MLP), linear regression (LR), and a model that predicted a smoothed value of eGFR. The hybrid model used at-hand, longitudinal data of physical examinations and laboratory test values available at each visit. Two different datasets, a generalized dataset (GData) and a personalized dataset (PData), were created. Then, in both datasets, two data subsets of development and validation were created. For prediction, all records related to the fourth to tenth previous visits of patients in time order from the target date, i.e., window size (WS)=4–10, were used. The performance of the models was evaluated using Mean Square Error (MSE) and Mean Absolute Error (MAE). The differences between the models were evaluated with the F-test and the Akaike Information Criterion (AIC). Results: The datasets contained 35,066 records, totally. The GData contained 26,210 and 8856 records and the PData had 24,079 and 9103 records in the development and validation datasets, respectively. In the hybrid model, the MSE and MAE were 153 and 8.9 in the GData, and 113 and 7.5 in the PData, respectively. The model performance improved using a wider WS of historical records (from 4 to 10). When the WS of ten was used the MSE and MAE declined to 141 and 8.5 in the GData and to 91 and 6.9 in the PData, respectively. In both datasets, the F-test showed that the hybrid model was significantly different from other models. The AIC showed that the hybrid model had a better performance than that of others. Conclusions: The hybrid model can predict a reliable future value for eGFR. Our results showed that longitudinal covariates help the models to produce better results. Smoothing eGFR values and using a personalized dataset to develop the models also improved the models’ performances. They can be considered as a step forward towards personalized medicine

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