Improve Compliance in Automated Peritoneal Dialysis Machine With SHARESOURCE

Overview

This study is to investigate the non-compliance rate of patients undergoing automated peritoneal dialysis by using automated peritoneal dialysis with SHARESOURCE software, and to evaluate if telemonitoring can improve peritoneal dialysis compliance and outcomes in the observation period.

Full Title of Study: “The Impact of Telemonitoring on Compliance and Outcomes of Patients Undergoing Automated Peritoneal Dialysis With SHARESOURCE”

Study Type

  • Study Type: Interventional
  • Study Design
    • Allocation: N/A
    • Intervention Model: Single Group Assignment
    • Primary Purpose: Treatment
    • Masking: None (Open Label)
  • Study Primary Completion Date: January 31, 2021

Detailed Description

The non-compliance of patients receiving automated peritoneal dialysis (APD) is around 10-20%, and was believed to be under-estimated. Recently, a two-way telehealth system, SHARESOURCE software, provide practitioners real-time monitoring and recording of the therapy of APD. By using this APD with SHARESOURCE software, we want to investigate the non-compliance rate of patients undergoing automated peritoneal dialysis, want to see if it can improve peritoneal dialysis compliance and outcomes in the observation period.

Interventions

  • Device: HomeChoice Claria APD machine with SHARESOURCE
    • HomeChoice Claria APD machine with SHARESOURCE software. SHARESOURCE is a software can telemonitor patients’ compliance

Arms, Groups and Cohorts

  • Experimental: APD

Clinical Trial Outcome Measures

Primary Measures

  • Change of baseline patients’ non-compliance rate at 3 months
    • Time Frame: baseline(between week 0 and 12) and 3 months(week 12 and week 24)
    • Non compliance rate was calculated by the days of non-compliance divided by days of APD therapy

Secondary Measures

  • Change of baseline peritoneal dialysis adequacy at 3 and 6 months
    • Time Frame: baseline, week 12 and week 24
    • Dialysis adequacy is to see if dialysis is enough
  • Change of baseline uremic toxin level at 3 and 6 months
    • Time Frame: baseline, week 12 and week 24
    • concentration of uremic toxins(ex: indoxyl sulfate, and p-cresyl sulfate)
  • change of body composition analysis
    • Time Frame: baseline, week 12 and week 24
    • body composition exam (Body Composition Monitor, Fresenius Medical Care, Bad Homburg, Germany) will be done
  • peritonitis rate (patient-month)
    • Time Frame: follow up to week 60
    • calculate the number of peritonitis rate
  • Hospitalization rate
    • Time Frame: follow up to week 60
    • calculate the number of hospitalization rate
  • Change of telephone contact frequency
    • Time Frame: baseline(between week 0 and 12) and 3 months(week 12 and week 24)
    • the telephone contact frequency (from patient to nurse) for peritoneal dialysis-related problems will be collected

Participating in This Clinical Trial

Inclusion Criteria

  • stable received peritoneal dialysis for over 1 year. Use automated peritoneal dialysis machine regularly by him/herself. Exclusion Criteria:

  • Acute hospitalization events due to acute coronary syndrome, stroke, heart failure, liver cirrhosis, systemic infection in 1 month. – Life expectancy <1 year – Peritoneal dialysis prescriptions will be scheduled or expected to change in 3 months

Gender Eligibility: All

Minimum Age: N/A

Maximum Age: N/A

Are Healthy Volunteers Accepted: No

Investigator Details

  • Lead Sponsor
    • E-DA Hospital
  • Provider of Information About this Clinical Study
    • Sponsor
  • Overall Contact(s)
    • Shih-Yuan Hung, +8867-615-0011, ed100367@edah.org.tw

References

Saran R, Robinson B, Abbott KC, Agodoa LYC, Bragg-Gresham J, Balkrishnan R, Bhave N, Dietrich X, Ding Z, Eggers PW, Gaipov A, Gillen D, Gipson D, Gu H, Guro P, Haggerty D, Han Y, He K, Herman W, Heung M, Hirth RA, Hsiung JT, Hutton D, Inoue A, Jacobsen SJ, Jin Y, Kalantar-Zadeh K, Kapke A, Kleine CE, Kovesdy CP, Krueter W, Kurtz V, Li Y, Liu S, Marroquin MV, McCullough K, Molnar MZ, Modi Z, Montez-Rath M, Moradi H, Morgenstern H, Mukhopadhyay P, Nallamothu B, Nguyen DV, Norris KC, O'Hare AM, Obi Y, Park C, Pearson J, Pisoni R, Potukuchi PK, Repeck K, Rhee CM, Schaubel DE, Schrager J, Selewski DT, Shamraj R, Shaw SF, Shi JM, Shieu M, Sim JJ, Soohoo M, Steffick D, Streja E, Sumida K, Kurella Tamura M, Tilea A, Turf M, Wang D, Weng W, Woodside KJ, Wyncott A, Xiang J, Xin X, Yin M, You AS, Zhang X, Zhou H, Shahinian V. US Renal Data System 2018 Annual Data Report: Epidemiology of Kidney Disease in the United States. Am J Kidney Dis. 2019 Mar;73(3 Suppl 1):A7-A8. doi: 10.1053/j.ajkd.2019.01.001. Epub 2019 Feb 21. No abstract available.

Bernardini J, Piraino B. Compliance in CAPD and CCPD patients as measured by supply inventories during home visits. Am J Kidney Dis. 1998 Jan;31(1):101-7. doi: 10.1053/ajkd.1998.v31.pm9428459.

The USRDS Dialysis Morbidity and Mortality Study: Wave 2. United States Renal Data System. Am J Kidney Dis. 1997 Aug;30(2 Suppl 1):S67-85. No abstract available.

Milan Manani S, Crepaldi C, Giuliani A, Virzi GM, Garzotto F, Riello C, de Cal M, Rosner MH, Ronco C. Remote Monitoring of Automated Peritoneal Dialysis Improves Personalization of Dialytic Prescription and Patient's Independence. Blood Purif. 2018;46(2):111-117. doi: 10.1159/000487703. Epub 2018 Apr 25.

Uchiyama K, Washida N, Yube N, Kasai T, Shinozuka K, Morimoto K, Hishikawa A, Inoue H, Urai H, Hagiwara A, Fujii K, Wakino S, Deenitchina S, Itoh H. The impact of a remote monitoring system of healthcare resource consumption in patients on automated peritoneal dialysis (APD): A simulation study . Clin Nephrol. 2018 Nov;90(5):334-340. doi: 10.5414/CN109471.

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