Optimised versus standard automated peritoneal dialysis regimens pilot study (OptiStAR): A randomised controlled crossover trial

Optimised versus standard automated peritoneal dialysis regimens pilot study (OptiStAR): A randomised controlled crossover trial

  • Post category:Journal Update
  • Reading time:5 mins read

Introduction

The incidence of end-stage renal disease continues to rise worldwide, and the future demand for kidney replacement therapy is predicted to rise. Automated peritoneal dialysis (APD) is an increasingly popular option for cost-effective home-based dialysis. Automatic cycling allows for a wide range of treatments that are adapted to the individual patient’s dialysis needs and preferences. Despite the growing use of non-glucose osmotic agents, dialysis fluids with glucose concentrations 15 to 40 times greater than physiological levels continue to be the most often employed.

Glucose, its breakdown products, and hyperosmolar stress all affect structural and functional membrane characteristics, with consequences for method failure and patient survival. In addition to local membrane effects, systemic glucose absorption is thought to account for 20% of necessary daily energy consumption. Given that 50% of Parkinson’s disease patients acquire a new-onset glucose metabolic problem, it is evident that the use of glucose as an osmotic agent should be improved, decreased, or replaced. Berg and Rippe’s theoretical three-pore model (TPM) simulations imply that using bi-modal, ‘optimised’ APD regimens that combine high glucose ‘ultrafiltration (UF) dwells’ with subsequent extremely low glucose small-solute ‘clearing dwells’ reduces glucose absorption significantly.

Optimized regimens lower absorbed glucose by 20–30% compared to a’normal’ simulated APD prescription, assuming that standard and optimised regimens are equally successful in terms of UF and small solute clearances. In this experimental pilot study, the clinical feasibility of such regimens is tested regarding glucose absorption, UF and small solute clearances.

Methods

Twenty-one prevalent PD patients were enrolled after written informed consent between June and December 2019. Enrolment criteria and allocation flow chart are shown in Figure 1.

We compared a reference (standard) automated PD regimen 6 × 2 L 1.36% glucose (76 mmol/L) over 9 h with a novel, theoretically glucose sparing (optimised) prescription consisting of ‘ultrafiltration cycles’ with high glucose strength (126 mmol/L) and ‘clearance cycles’ with ultra-low, physiological glucose (5mmol/L) for approximately 40% of the treatment time. Twenty-one prevalent PD patients underwent the optimised regimen (7 × 2 L 2.27% glucose + 5 × 2 L 0.1% glucose over 8 h) and the standard regimen in a crossover fashion. Six patients were excluded from data analysis.

Results

Median glucose absorption was 43 g (IQR 41–54) and 44 g (40–55) for the standard and optimised intervention, respectively (p = 1).

Conclusion

Optimized treatments were feasible and well-tolerated in this small pilot study. Despite no difference in absorbed glucose, results indicate possible improvements in ultrafiltration effciency and small solute clearances by optimized regimens. The use of optimized prescriptions as a glucose sparing strategy should be evaluated in larger study populations.

References

  1. Collins, AJ, Foley, RN, Gilbertson, DT, et al. United States Renal Data System public health surveillance of chronic kidney disease and end-stage renal disease. Kidney Int Suppl 2015; 5(1): 2–7.
  2. GBD Chronic Kidney Disease Collaboration . Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2020; 395(10225): 709–733.
  3. Koye, DN, Magliano, DJ, Nelson, RG, et al. The global epidemiology of diabetes and kidney disease. Adv Chronic Kidney Dis 2018; 25(2): 121–132.
  4. Shen, Y, Cai, R, Sun, J, et al. Diabetes mellitus as a risk factor for incident chronic kidney disease and end stage renal disease in women compared with men: a systematic review and meta-analysis. Endocrine 2017; 55(1): 66–76.
  5. Li, PK, Cheung, WL, Lui, SL, et al. Increasing home-based dialysis therapies to tackle dialysis burden around the world: a position statement on dialysis economics from the 2nd Congress of the International Society for Hemodialysis. Hemodial Int 2011; 15(1): 10–14.
  6. Jain, AK, Blake, P, Cordy, P, et al. Global trends in rates of peritoneal dialysis. J Am Soc Nephrol 2012; 23(3): 533–544.
  7. Blake, PG, Brown, EA. Person-centered peritoneal dialysis prescription and the role of shared decision-making. Perit Dial Int 2020; 40(3): 302–309.
  8. Gokal, R, Moberly, J, Lindholm, B, et al. Metabolic and laboratory eects of icodextrin. Kidney Int Suppl 2002; (81): S62–S71.
  9. Li, PK, Culleton, BF, Ariza, A, et al. Randomized, controlled trial of glucose-sparing peritoneal dialysis in diabetic patients. J Am Soc Nephrol 2013; 24(11): 1889–1900.
  10. Holmes, CJ, Shockley, TR. Strategies to reduce glucose exposure in peritoneal dialysis patients. Perit Dial Int 2000; 20(suppl 2): S37–S41.
  11. Nolph, KD, Rosenfeld, PS, Powell, JT, et al. Peritoneal glucose transport and hyperglycemia during peritoneal dialysis. Am J Med Sci 1970; 259(4): 272–281.
  12. Davies, SJ . Longitudinal relationship between solute transport and ultrafiltration capacity in peritoneal dialysis patients. Kidney Int 2004; 66(6): 2437–2445.
  13. Ha, H, Lee, HB. Effect of high glucose on peritoneal mesothelial cell biology. Perit Dial Int 2000; 20(suppl 2): S15–S18.
  14. Krediet, RT . Ultrafiltration failure is a reflection of peritoneal alterations in patients treated with peritoneal dialysis. Front Physiol 2018; 9: 1815.
  15. Churchill, DN, Thorpe, KE, Nolph, KD, et al. Increased peritoneal membrane transport is associated with decreased patient and technique survival for continuous peritoneal dialysis patients. The Canada-USA (CANUSA) Peritoneal Dialysis Study Group. J Am Soc Nephrol 1998; 9(7): 1285–1292.
  16. Williams, JD, Craig, KJ, Topley, N, et al. Morphologic changes in the peritoneal membrane of patients with renal disease J Am Soc Nephrol. 2002; 13(2): 470–479.
  17. Xue, C, Gu, YY, Cui, CJ, et al. New-onset glucose disorders in peritoneal dialysis patients: a meta-analysis and systematic review. Nephrol Dial Transplant 2020; 35(8): 1412–1419.
  18. Szeto, CC, Chow, KM, Kwan, BC, et al. New-onset hyperglycemia in nondiabetic Chinese patients started on peritoneal dialysis. Am J Kidney Dis 2007; 49(4): 524–532.
  19. Öberg, CM, Rippe, B. Optimizing automated peritoneal dialysis using an extended 3-pore model. Kidney Int Rep 2017; 2(5): 943–951.
  20. Bergling, K, de Arteaga, J, Ledesma, F, et al. Optimized vs. Standard Automated Peritoneal Dialysis Regimens (OptiStAR): study protocol for a randomized controlled crossover trial. Pilot Feasibility Stud 2020; 6: 81.
  21. Martus, G, Bergling, K, Simonsen, O, et al. Novel method for osmotic conductance to glucose in peritoneal dialysis. Kidney Int Rep 2020; 5(11): 1974–1981.
  22. Öberg, CM . Optimization of bimodal automated peritoneal dialysis prescription using the three-pore model. Perit Dial Int 2021; 41(4): 381–393.
0