Haemodialysis Outcomes & Patient Empowerment Study 03

Dialysis Clinical Trial

— HOPE-03  

Official title:

Pilot-scale, Single-arm, Observational Study to Assess the Utility of a Machine Learning Algorithm in Assessing Fluid Status in Haemodialysis Patients

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05735288
• Other study ID #: 21/82
• Status: Completed
• Start date: February 14, 2023
• Est. completion date: April 27, 2023

Study information

• Verified date: November 2022
• Source: Royal College of Surgeons, Ireland
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

Description:

Volume Overload is a contributing factor to the high rates of cardiovascular and all-cause mortality demonstrated in haemodialysis patients. At present, no method exists that can consistently refine volume status and provide patients with feedback to allow adjustments to their fluid intake. Current standards used to assess volume are either poorly predictive of fluid status, cumbersome to use, or lack an adequate patient interface. An automated, accurate and periodic assessment of dry weight would be clinically useful, low-cost, and rapidly scalable. Machine learning methods have been widely studied in nephrology. Large amounts of precise haemodialysis data, collected and stored electronically at regular intervals, have the potential to be leveraged in the prediction of patients' extracellular volume or ideal fluid status. A number of proof-of-concept machine-learning models for the prediction of dry weight in haemodialysis data have been created using retrospective data. This study will evaluate the usability of the machine learning models in managing fluid volume in haemodialysis patients while also assessing their validity and reproducibility against validated measurements; in this instance the Body Composition Monitor (BCM) by Fresenius. As the machine learning model for assessing fluid status was trained and tested on retrospective data, there is sufficient justification for testing the model's performance, acceptability and usability in a controlled, observational prospective study. This will be an 8-week trial with a 2-week run-in period conducted in a single centre in Beaumont, Dublin, Ireland. Bioimpedance measurements using the Fresenius BCM will be performed every 2 weeks. Haemodialysis data will be processed continuously throughout the trial. The algorithm will use haemodialysis data to predict the BCM output. The algorithm prediction will be compared to the BCM prediction to assess its usability.

Recruitment information / eligibility

• 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.

Related Conditions & MeSH terms

• Dialysis
• Volume Overload

Locations

Country	Name	City	State
Ireland	Beaumont Hospital	Dublin	Leinster
Ireland	Beaumont Hospital	Dublin	Leinster

Sponsors (2)

Lead Sponsor	Collaborator
Royal College of Surgeons, Ireland	patientMpower Ltd.

Country where clinical trial is conducted

Ireland, 

References & Publications (5)

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

Outcome

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