title: Simulating inadequate dialysis and its correction using an individualized patient-derived nomogram.
abstract: Computerized kinetic modeling is a valuable automated peritoneal dialysis (APD) prescription tool for optimizing dialysis adequacy. However, non-compliance results in failure to achieve adequacy targets. The aim of this study was to determine if a nomogram could estimate dialysis compensations for shortfalls in simulated non-compliant patients, such that total weekly urea clearance (Kt/V(urea)) targets are met. Individualized nomograms comprising a series of curves were derived from PD Adequest (ver. 2.0)-predicted Kt/V(urea) data (r (2 ) > 0.99) for different APD prescriptions. The nomogram was then used to estimate the (Nomogram-computed) average of the daily Kt/V(urea) in 14 patients. The study comprised three 1-month phases. Patients were compliant to dialysis in phase I, where Adequest-predicted Kt/V(urea) showed good agreement with both measured (r (I) = 0.72), and Nomogram-computed values (r (I) > 0.99) (p < 0.001). Conversely, in non-compliant phase II, Nomogram-computed values were lower than Adequest-predicted values (p < 0.002). In phase III, the nomogram estimated prescription adjustments required to compensate for shortfalls, such that there was significantly less difference between Nomogram-computed and Adequest-predicted Kt/V(urea) than in phase II (p = 0.005). Thus, despite non-compliance, predicted Kt/V(urea) targets were attained using the nomogram to adjust the daily APD prescriptions. This concept is potentially useful for patients desiring to compensate for inadvertent shortfalls, rather than for 'truly non-compliant' patients.
authors.affiliation: Department of Pediatrics, Yong Loo Lin School of Medicine, National University of Singapore, 5 Lower Kent Ridge Road, 119074 Singapore, Singapore.
authors.forename: Alison Joanne
authors.initials: AJ
authors.lastname: Lee
authors.forename: Kevin
authors.initials: K
authors.lastname: Kho
authors.forename: Kee-Seng
authors.initials: KS
authors.lastname: Chia
authors.forename: Tze-Liang
authors.initials: TL
authors.lastname: Oi
authors.forename: Christopher
authors.initials: C
authors.lastname: Yap
authors.forename: Pei-Pei
authors.initials: PP
authors.lastname: Foong
authors.forename: Yew-Weng
authors.initials: YW
authors.lastname: Lau
authors.forename: Lee-Kean
authors.initials: LK
authors.lastname: Lim
authors.forename: Eric
authors.initials: E
authors.lastname: Aragon
authors.forename: Chien-Wyei
authors.initials: CW
authors.lastname: Liew
authors.forename: Hui-Kim
authors.initials: HK
authors.lastname: Yap
mesh_terms: D000293:Adolescent; D003198:Computer Simulation; D003404:Creatinine / Q000097:blood / Q000652:urine; D005260:Female; D005947:Glucose / Q000493:pharmacokinetics; D006801:Humans; D007700:Kinetics; D008137:Longitudinal Studies; D008297:Male; D008657:Metabolic Clearance Rate; D015233:Models, Statistical; D008991:Monitoring, Physiologic; D049451:Nomograms; D010349:Patient Compliance; D010530:Peritoneal Dialysis; D010531:Peritoneal Dialysis, Continuous Ambulatory; D010537:Peritoneum / Q000378:metabolism; D055656:Prescriptions; D013813:Therapy, Computer-Assisted; D014508:Urea / Q000097:blood / Q000652:urine
chemical_list: D014508:Urea; D003404:Creatinine; D005947:Glucose