Dialysis Clinical Trial
— HOPE-03Official title:
Pilot-scale, Single-arm, Observational Study to Assess the Utility of a Machine Learning Algorithm in Assessing Fluid Status in Haemodialysis Patients
NCT number | NCT05735288 |
Other study ID # | 21/82 |
Secondary ID | |
Status | Completed |
Phase | |
First received | |
Last updated | |
Start date | February 14, 2023 |
Est. completion date | April 27, 2023 |
Verified date | November 2022 |
Source | Royal College of Surgeons, Ireland |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
This is a prospective, single-arm observational study that aims to assess the validity and reproducibility of an algorithm for assessing fluid status in a cohort of dialysis patients. The study will externally validate an existing algorithm for dry weight prediction in real-time in a cohort of dialysis patients.
Status | Completed |
Enrollment | 24 |
Est. completion date | April 27, 2023 |
Est. primary completion date | April 27, 2023 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years and older |
Eligibility | Inclusion Criteria: - Receiving maintenance haemodialysis in an ambulatory care setting - Aged at least 18 years - Demonstrates understanding of the study requirements. - Willing to give written informed consent. Exclusion Criteria: - Conditions precluding accurate use of bioimpedance (e.g. limb amputations,severe malnourishment, pregnancy, cardiac resynchronisation devices, pacemakers). - Significant confusion or any concomitant medical condition, which would limit the ability of the patient to record symptoms or other parameters. |
Country | Name | City | State |
---|---|---|---|
Ireland | Beaumont Hospital | Dublin | Leinster |
Ireland | Beaumont Hospital | Dublin | Leinster |
Lead Sponsor | Collaborator |
---|---|
Royal College of Surgeons, Ireland | patientMpower Ltd. |
Ireland,
Collins AJ, Foley RN, Herzog C, Chavers BM, Gilbertson D, Ishani A, Kasiske BL, Liu J, Mau LW, McBean M, Murray A, St Peter W, Guo H, Li Q, Li S, Li S, Peng Y, Qiu Y, Roberts T, Skeans M, Snyder J, Solid C, Wang C, Weinhandl E, Zaun D, Arko C, Chen SC, Dalleska F, Daniels F, Dunning S, Ebben J, Frazier E, Hanzlik C, Johnson R, Sheets D, Wang X, Forrest B, Constantini E, Everson S, Eggers PW, Agodoa L. Excerpts from the US Renal Data System 2009 Annual Data Report. Am J Kidney Dis. 2010 Jan;55(1 Suppl 1):S1-420, A6-7. doi: 10.1053/j.ajkd.2009.10.009. No abstract available. — View Citation
Flythe JE, Chang TI, Gallagher MP, Lindley E, Madero M, Sarafidis PA, Unruh ML, Wang AY, Weiner DE, Cheung M, Jadoul M, Winkelmayer WC, Polkinghorne KR; Conference Participants. Blood pressure and volume management in dialysis: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int. 2020 May;97(5):861-876. doi: 10.1016/j.kint.2020.01.046. Epub 2020 Mar 8. — View Citation
Guo X, Zhou W, Lu Q, Du A, Cai Y, Ding Y. Assessing Dry Weight of Hemodialysis Patients via Sparse Laplacian Regularized RVFL Neural Network with L2,1-Norm. Biomed Res Int. 2021 Feb 4;2021:6627650. doi: 10.1155/2021/6627650. eCollection 2021. — View Citation
Lee H, Yun D, Yoo J, Yoo K, Kim YC, Kim DK, Oh KH, Joo KW, Kim YS, Kwak N, Han SS. Deep Learning Model for Real-Time Prediction of Intradialytic Hypotension. Clin J Am Soc Nephrol. 2021 Mar 8;16(3):396-406. doi: 10.2215/CJN.09280620. Epub 2021 Feb 11. — View Citation
Tomasev N, Glorot X, Rae JW, Zielinski M, Askham H, Saraiva A, Mottram A, Meyer C, Ravuri S, Protsyuk I, Connell A, Hughes CO, Karthikesalingam A, Cornebise J, Montgomery H, Rees G, Laing C, Baker CR, Peterson K, Reeves R, Hassabis D, King D, Suleyman M, Back T, Nielson C, Ledsam JR, Mohamed S. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature. 2019 Aug;572(7767):116-119. doi: 10.1038/s41586-019-1390-1. Epub 2019 Jul 31. — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | The primary objective is to determine the validity of the machine learning model in estimating bioimpedance-determined dry weight in haemodialysis patients. | Dry weight (kg) estimated by the machine learning estimation model will be compared with the bioimpedance normohydration weight in kg. | 8 weeks | |
Secondary | Acceptability | The acceptability of the machine learning model's outputs from a clinical healthcare perspective will be assessed.
The patient's clinical fluid status will be assessed via a survey administered to haemodialysis nursing staff every 2 weeks pre-dialysis. The survey will ask the haemodialysis nursing staff to define a patient's fluid overload in kg. The mean difference between fluid overload in kg as defined by healthcare staff and fluid overload in kg as defined by the algorithmic output will be assessed. |
8 weeks |
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