Application of genetic algorithm for hemodialysis schedule optimization. (July 2017)

• Record Type: Journal Article
• Authors: Choi, Jin Woo; Lee, Hajeong; Lee, Jung Chan; Lee, Saram; Kim, Yon Su; Yoon, Hyung-Jin; Kim, Hee Chan
• Is Part Of: Computer methods and programs in biomedicine. Volume 145(2017)
• Page Start: 35
• Page End: 43
• Publication Date: 2017-07
• Subjects: Hemodialysis -- Hemodialysis schedule -- End-stage renal disease -- Genetic algorithm; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.04.003
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Genetic algorithm (GA) and mathematical model of hemodialysis (HD) patient are used to propose a computational method to optimize HD schedule under variable conditions. GA provides optimized schedules to maintain even intervals among dialysis sessions and to overcome the long-weekend HD interval issue. GA shows frequent dialysis is more physiological and effective, but it is not practical considering the burden. The proposed optimization method might contribute to the adoption of frequent dialysis and provide more scheduling options under patient-specific conditions. Abstract: Background: The conventional hemodialysis (HD) schedule has been used for decades, even though new modalities have been introduced. Many reasons limit practices of frequent dialysis, such as patients' environments and unknown optimal schedules for each patient. This research provides a theoretical recommendation of HD schedule through genetic algorithm (GA). Methods: An end-stage renal disease (ESRD) with various dialysis conditions was modeled through a classic variable-volume two-compartment kinetic model to simulate an anuric patient, and GA was implemented to search for an optimal HD schedule for each individual considering and ignoring burden consumption of each dialysis session. The adequacy of the optimized HD schedules through GA was assessed with time average concentration (TAC) and time average deviation (TAD). Results: While ignoring the burden of dialysis sessions, GA returned schedules with slightly improved values of adequacy criteria (EKRc and std Kt/V), compared to the conventional regular uniform HD schedules. The optimized HD schedules also showed decreased TAC and TAD values compared to the conventional regular uniform HD schedules. It showed that frequent dialysis resulted in more effective treatment and higher fitness values. However, when burden was considered, less frequent dialysis schedules showed better fitness value. Conclusions: Through this research, GA confirmed that at least 12 h of dialysis should be conducted for a week. The optimized schedules from GA indicated that evenly distributing the intervals amongst sessions is efficient, and that scheduling a session at the start and end of a week is optimal to overcome a long weekend interval. The theoretical optimal schedule of HD may help distribution of frequent dialysis and provide more schedule options to patients.