Clinical trial NCT05735288: Haemodialysis Outcomes & Patient Empowerment Study 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
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Volume Overload
Dialysis
Keywords: Dialysis
Volume Overload
Machine Learning
Bioimpedance
Lead sponsor: Royal College of Surgeons, Ireland
Sponsor class: OTHER
Collaborators: name: patientMpower Ltd.
First posted: 2023-02-21
Enrollment: 24
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients requiring maintenance haemodialysis in an ambulatory care setting.

Brief 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.

Detailed 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.

Eligibility criteria: 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.

Primary outcomes:
measure: The primary objective is to determine the validity of the machine learning model in estimating bioimpedance-determined dry weight in haemodialysis patients.
timeFrame: 8 weeks
Secondary outcomes:
measure: Acceptability
timeFrame: 8 weeks
Other outcomes:
Locations:
city: Dublin
country: Ireland
facility: Beaumont Hospital
state: Leinster
city: Dublin
country: Ireland
facility: Beaumont Hospital
state: Leinster
Study officials:
affiliation: Royal College of Surgeons in Ireland
name: O'Seaghdha
role: PRINCIPAL_INVESTIGATOR
References:
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citation: 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.
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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.
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.