Volume OverloadHOPE-03NCT05735288
Haemodialysis Outcomes & Patient Empowerment Study 03
An observational study of Volume Overload and Dialysis, run by Royal College of Surgeons, Ireland at 2 sites in Ireland.
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Volume Overload, Dialysis
- How many people
- 24 participants
- Where
- 2 sitesIreland
- Study dates
- 14 Feb 2023to 27 Apr 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients requiring maintenance haemodialysis in an ambulatory care setting.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
2 sites in 1 country.
Ireland2 sites
Beaumont Hospital
Dublin, Leinster, 9
Beaumont Hospital
Dublin, Leinster, D09V2N0
About this study
Official title: Pilot-scale, Single-arm, Observational Study to Assess the Utility of a Machine Learning Algorithm in Assessing Fluid Status in Haemodialysis Patients
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.
Read the 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.
- Conditions
- Volume Overload, Dialysis
- Keywords
- Dialysis, Volume Overload, Machine Learning, Bioimpedance
- Medical subject headings
- Edema
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Haemodialysis patients | Haemodialysis patients attending haemodialysis in an outpatient setting in Beaumont Hospital, Ireland. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 8 weeks
Secondary outcomes (1)
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.
Measured: 8 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Feb 2023
- Study start14 Feb 2023
- Primary completion27 Apr 2023
- Study completion27 Apr 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Volume OverloadStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 24 participants (actual)
Sponsor and investigators
- Lead sponsor
- Royal College of Surgeons, Ireland
- Sponsor type
- University, hospital or other
- Collaborators
- patientMpower Ltd.
- Responsible party
- Sponsor
- Investigators
- O'Seaghdha, Principal investigator, Royal College of Surgeons in Ireland
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05735288
- Sponsor's protocol ID
- 21/82
- Acronym
- HOPE-03
- Status last verified
- Nov 2022
- First submitted
- 30 Nov 2022
- Last updated
- 19 May 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 33628794Collins 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.
PubMed 20082919Flythe 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.
PubMed 32278617Tomasev 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.
PubMed 31367026Lee 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.
PubMed 33574056
Study at a glance
From the registry record, last updated by the sponsor on 19 May 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Pilot-scale, Single-arm, Observational Study to Assess the Utility of a Machine Learning Algorithm in Assessing Fluid Status in Haemodialysis Patients
- ClinicalTrials.gov ID
- NCT05735288
- Acronym / sponsor ID
- HOPE-03 / 21/82
- Conditions
- Volume Overload, Dialysis
- Keywords
- Dialysis, Volume Overload, Machine Learning, Bioimpedance
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 24 participants (actual)
- Lead sponsor
- Royal College of Surgeons, IrelandUniversity, hospital or other
- Collaborators
- patientMpower Ltd.
- Study start
- 14 Feb 2023 (actual)
- Primary completion
- 27 Apr 2023 (actual)
- Study completion
- 27 Apr 2023 (actual)
- First posted
- 21 Feb 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Haemodialysis patients | – | –Haemodialysis patients attending haemodialysis in an outpatient setting in Beaumont Hospital, Ireland. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients requiring maintenance haemodialysis in an ambulatory care setting.
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (2 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
The primary objective is to determine the validity of the machine learning model in estimating bioimpedance-determined dry weight in haemodialysis patients.
Time frame: 8 weeks
Dry weight (kg) estimated by the machine learning estimation model will be compared with the bioimpedance normohydration weight in kg.
1 secondary outcome
Acceptability
Time frame: 8 weeks
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 May 2023.
- Background: PubMed 33628794Guo 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.
- Background: PubMed 20082919Collins 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.
- Background: PubMed 32278617Flythe 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.
- Background: PubMed 31367026Tomasev 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.
- Background: PubMed 33574056Lee 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
O'Seaghdha principal investigator
Royal College of Surgeons in Ireland
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Volume Overload and Dialysis, run by Royal College of Surgeons, Ireland at 2 sites in Ireland.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 19 May 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Ireland. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The primary objective is to determine the validity of the machine learning model in estimating bioimpedance-determined dry weight in haemodialysis patients. (time frame: 8 weeks). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05735288, which also lists 5 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 May 2023.