Parviz Rashidi Khazaee1, Jamshid Bagherzadeh1, Zahra Niazkhani2, Habibollah Pirnejad3. 1. Electrical and Computer Engineering Department, Urmia University, Urmia, Iran. 2. Nephrology and Kidney Transplant Research Center, Urmia University of Medical Sciences, Urmia, Iran; Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran. Electronic address: niazkhani.z@umsu.ac.ir. 3. Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran; Patient Safety Research Center, Urmia University of Medical Sciences, Urmia, Iran; Erasmus School of Health Policy & Management (ESHPM), Erasmus University Rotterdam, Rotterdam, the Netherlands.
Abstract
BACKGROUND: Predicting the function of transplanted kidneys would help clinicians in individualized medical interventions. We aimed to develop and validate a predictive tool for a future value of estimated glomerular filtration rate (eGFR) at upcoming visits. METHODS: We used static and time-dependent covariates as inputs of artificial neural network based prediction models for predicting an eGFR value for an upcoming visit. We included 675 kidney recipients, who received transplant in the Urmia kidney transplant center in 2001-2013 and were longitudinally cared for in 2001-2017. The first 75% of records of longitudinal data of each patient were used to develop the prediction models and the remaining last 25% for evaluating its performance. Models' performances were evaluated by Mean Square Error (MSE) and Mean Absolute Error (MAE). RESULTS: The development and validation datasets included 18,773 and 7038 records of historical data, respectively. The most accurate model included 3 static covariates of recipients' gender and donors' age and gender as well as 11 dynamic covariates of recipients including current age, time since transplant, serum creatinine, fasting blood sugar, weight and blood pressures available at each visit time. The performance of prediction models in the validation cohort was improved when history window of time dependent variables' recent values was increased from 1 to 10 (an MSE decline from 161 to 99). CONCLUSIONS: Our best performed model is able to dynamically predict a future eGFR value for kidney recipients' upcoming visits. Integrating such a clinical tool into daily workflow of outpatient clinics can potentially support clinicians in optimal and individualized decision makings.
BACKGROUND: Predicting the function of transplanted kidneys would help clinicians in individualized medical interventions. We aimed to develop and validate a predictive tool for a future value of estimated glomerular filtration rate (eGFR) at upcoming visits. METHODS: We used static and time-dependent covariates as inputs of artificial neural network based prediction models for predicting an eGFR value for an upcoming visit. We included 675 kidney recipients, who received transplant in the Urmia kidney transplant center in 2001-2013 and were longitudinally cared for in 2001-2017. The first 75% of records of longitudinal data of each patient were used to develop the prediction models and the remaining last 25% for evaluating its performance. Models' performances were evaluated by Mean Square Error (MSE) and Mean Absolute Error (MAE). RESULTS: The development and validation datasets included 18,773 and 7038 records of historical data, respectively. The most accurate model included 3 static covariates of recipients' gender and donors' age and gender as well as 11 dynamic covariates of recipients including current age, time since transplant, serum creatinine, fasting blood sugar, weight and blood pressures available at each visit time. The performance of prediction models in the validation cohort was improved when history window of time dependent variables' recent values was increased from 1 to 10 (an MSE decline from 161 to 99). CONCLUSIONS: Our best performed model is able to dynamically predict a future eGFR value for kidney recipients' upcoming visits. Integrating such a clinical tool into daily workflow of outpatient clinics can potentially support clinicians in optimal and individualized decision makings.
Authors: Riddhi Chawla; S Balaji; Raed N Alabdali; Ibrahim A Naguib; Nadir O Hamed; Heba Y Zahran Journal: J Healthc Eng Date: 2022-05-14 Impact factor: 3.822
Authors: Alexandru Burlacu; Adrian Iftene; Daniel Jugrin; Iolanda Valentina Popa; Paula Madalina Lupu; Cristiana Vlad; Adrian Covic Journal: Biomed Res Int Date: 2020-06-10 Impact factor: 3.411