End-Stage Kidney DiseaseDETECT-PDNCT06842927

Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis

An observational study of End-Stage Kidney Disease and End Stage Renal Disease (ESRD), run by Tuen Mun Hospital at 1 site in Hong Kong.

Enrolling by invitation

Enrolling by invitation. The study team selects and invites participants, usually from an existing group of patients.

Sites and contacts

Official record

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Trial summary

The goal of this prospective diagnostic test (correlation) study is to develop and investigate the performance of artificial intelligence in predicting peritoneum transporter status and dialysis efficiency in adult patients undergoing peritoneal dialysis (PD). Read more about this study

Who is it for?: Ages 18 years and older
Conditions the study is for: End-Stage Kidney Disease, End Stage Renal Disease (ESRD), End Stage Renal Disease on Dialysis (Diagnosis)
What is being tested?: data collection and data report
How many people: 350 participants
Where: 1 siteHong Kong
Study dates: 3 Mar 2025to 31 Mar 2026
Phase: Observational
Are there results?: Not yet: the study is under way

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: End-stage renal failure patients requiring peritoneal dialysis as renal replacement therapy

Who may be able to join

Age 18 years or older
Diagnosis of end-stage renal failure requiring peritoneal dialysis as renal replacement therapy
Ability to give informed consent and comply with study procedures.

Who cannot join

History of hernia or peritoneal leak, including pleuroperitoneal fistula (PPF), patent processus vaginalis (PPV) and retroperitoneal leak
Ongoing PD peritonitis with or without antibiotic therapy
Just finished PD peritonitis antibiotic treatment within recent 4 weeks
Pregnancy
Patient refusal

Trial locations and contacts

1 site in 1 country. Contact the study team or a site near you to ask about taking part.

Hong Kong1 site

Tuen Mun Hospital
Tuenmen

Other trials in Hong Kong

About this study

Official title: DETECT-PD -- Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis

The main questions it aims to answer are:

Can artificial intelligence predict peritoneal transporter status based on simple clinical and biochemical measurements? Can artificial intelligence predict dialysis adequacy (Kt/V) using these features?

Researchers will compare the performance of the AI model with the gold standard Peritoneal Equilibration Test (PET) and Kt/V to evaluate its accuracy and reliability.

Participants will:

Provide peritoneal dialysate and spot urine samples for biochemical analysis. Undergo routine dialysis adequacy and peritoneal equilibration testing (PET). Have clinical and laboratory data collected for AI model training and validation.

The study will recruit approximately 350 peritoneal dialysis patients, with 280 participants in the training/validation arm and 70 participants in the test arm. The study duration is 12 months following enrollment.

Read the detailed description

The DETECT-PD (Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis) study is a double-blind, prospective diagnostic test (correlation) study designed to evaluate the feasibility and effectiveness of artificial intelligence (AI) in predicting peritoneal transporter status and dialysis efficiency in patients undergoing peritoneal dialysis (PD). The study aims to develop a computational model that leverages clinical, biochemical, and peritoneal transport data to provide a non-invasive and efficient assessment tool, ultimately improving dialysis management and patient outcomes.

Patient recruitment and data collection will be conducted during routine dialysis adequacy and peritoneal transporter status assessments. The following clinical and biochemical parameters will be collected:

Demographics \& Medical History Peritoneal Dialysis Data Biochemical Data

The AI model will be developed using Python 3.11 and PyTorch 2.41 for deep learning and predictive analytics.

The key methodological steps include:

Data Preprocessing: Handling missing values, feature scaling, and one-hot encoding for categorical variables.

Feature Selection: Identifying the most predictive clinical and biochemical markers.

Model Training: Using deep learning regression models to predict PET and Kt/V outcomes.

Performance Evaluation: Evaluating model accuracy using:

Mean Absolute Error (MAE) Mean Squared Error (MSE) R² score (coefficient of determination) Bland-Altman plots and correlation coefficients for agreement with measured values.

Conditions: End-Stage Kidney Disease, End Stage Renal Disease (ESRD), End Stage Renal Disease on Dialysis (Diagnosis), End Stage Renal Failure on Dialysis, Peritoneal Dialysis, Peritoneal Dialysis Patients
Being tested: data collection, data report
Keywords: Peritoneal dialysis, Artificial intelligence, Dialysis adequacy, Peritoneum transporter status, Renal replacement therapy
Medical subject headings: Kidney Failure, Chronic, Disease

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

data collectionOther
Also called model training
An additional collection of peritoneal dialysate and spot urine samples will be collected.
Participants randomized to the training/validation arm will have their data used for model development, including the training and validation phases.
data reportOther
Also called model testing
An additional collection of peritoneal dialysate and spot urine samples will be collected. Participants randomized to the test arm will have their data isolated and reserved exclusively for evaluating the performance of the final AI model

Study groups in NCT06842927
Study group	Role	What they receive
Training/Validation		Participants in training/validation arm will receive the same standard investigations and care as part of their routine PD management, including clinical evaluations, biochemical testing, and measurements of peritoneal transporter status via the Peritoneal Equilibrium Test (PET) and dialysis adequacy (Kt/V). Receives: Other: data collection
Test		Participants in training/validation arm will receive the same standard investigations and care as part of their routine PD management, including clinical evaluations, biochemical testing, and measurements of peritoneal transporter status via the Peritoneal Equilibrium Test (PET) and dialysis adequacy (Kt/V). Receives: Other: data report

What is being measured

The primary outcome is the main question the study is designed to answer.

Peritoneal Equilibration Test (PET) Parameters
Predictive Accuracy of AI Model for Peritoneal Equilibration Test (PET) Parameters Outcome: AI-predicted vs. actual 2-hour and 4-hour dialysate-to-plasma creatinine ratio (D/P Cr) Performance Metrics: Mean Absolute Error (MAE) Unit of Measure: Absolute error
Measured: Measured at baseline during study enrollment
Peritoneal Equilibration Test (PET) Parameters
Predictive Accuracy of AI Model for Peritoneal Equilibration Test (PET) Parameters Outcome: AI-predicted vs. actual dialysate-to-baseline dialysate glucose concentration ratio (D/D0 Glu) Performance Metrics: Mean Absolute Error (MAE) Unit of Measure: Absolute error
Measured: Measured at baseline during study enrollment
Peritoneal Equilibration Test (PET) Parameters
Predictive Accuracy of AI Model for Peritoneal Equilibration Test (PET) Parameters Outcome: AI-predicted vs. actual 2-hour and 4-hour dialysate-to-plasma creatinine ratio (D/P Cr) Performance Metrics: Mean Squared Error (MSE) Unit of Measure: Squared Error
Measured: Measured at baseline during study enrollment
Peritoneal Equilibration Test (PET) Parameters
Predictive Accuracy of AI Model for Peritoneal Equilibration Test (PET) Parameters Outcome: AI-predicted vs. actual dialysate-to-baseline dialysate glucose concentration ratio (D/D0 Glu) Performance Metrics: Mean Squared Error (MSE) Unit of Measure: Squared Error
Measured: Measured at baseline during study enrollment
Peritoneal Equilibration Test (PET) Parameters
Predictive Accuracy of AI Model for Peritoneal Equilibration Test (PET) Parameters Outcome: AI-predicted vs. actual 2-hour and 4-hour dialysate-to-plasma creatinine ratio (D/P Cr) Performance Metrics: Coefficient of Determination (R²) Unit of Measure: R² value (range: 0 to 1, higher values indicate better model performance)
Measured: Measured at baseline during study enrollment
Peritoneal Equilibration Test (PET) Parameters
Predictive Accuracy of AI Model for Peritoneal Equilibration Test (PET) Parameters Outcome: AI-predicted vs. actual dialysate-to-baseline dialysate glucose concentration ratio (D/D0 Glu) Performance Metrics: Coefficient of Determination (R²) Unit of Measure: R² value (range: 0 to 1, higher values indicate better model performance)
Measured: Measured at baseline during study enrollment
Peritoneal Equilibration Test (PET) Parameters
Predictive Accuracy of AI Model for Peritoneal Equilibration Test (PET) Parameters Outcome: AI-predicted vs. actual 2-hour and 4-hour dialysate-to-plasma creatinine ratio (D/P Cr) Performance Metrics: Intraclass Correlation Coefficient (ICC) Unit of Measure: ICC value (range: 0 to 1, higher values indicate better agreement)
Measured: Measured at baseline during study enrollment
Peritoneal Equilibration Test (PET) Parameters
Predictive Accuracy of AI Model for Peritoneal Equilibration Test (PET) Parameters Outcome: AI-predicted vs. actual dialysate-to-baseline dialysate glucose concentration ratio (D/D0 Glu) Performance Metrics: Intraclass Correlation Coefficient (ICC) Unit of Measure: ICC value (range: 0 to 1, higher values indicate better agreement)
Measured: Measured at baseline during study enrollment

Secondary outcomes (12)

Dialysis Adequacy (Kt/V) parameters
Predictive Accuracy of AI Model for Dialysis Adequacy (Kt/V) Outcome: AI-predicted vs. actual total weekly Kt/V Performance Metrics: Mean Absolute Error (MAE) Unit of Measure: Absolute error
Measured: Measured at baseline during study enrollment
Dialysis Adequacy (Kt/V) parameters
Predictive Accuracy of AI Model for Dialysis Adequacy (Kt/V) Outcome: AI-predicted vs. actual total weekly Kt/V Performance Metrics: Mean Squared Error (MSE) Unit of Measure: Squared Error
Measured: Measured at baseline during study enrollment
Dialysis Adequacy (Kt/V) parameters
Predictive Accuracy of AI Model for Dialysis Adequacy (Kt/V) Outcome: AI-predicted vs. actual total weekly Kt/V Performance Metrics: Coefficient of Determination (R²) Unit of Measure: R² value (range: 0 to 1, higher values indicate better model performance)
Measured: Measured at baseline during study enrollment
Dialysis Adequacy (Kt/V) parameters
Predictive Accuracy of AI Model for Dialysis Adequacy (Kt/V) Outcome: AI-predicted vs. actual total weekly Kt/V Performance Metrics: Intraclass Correlation Coefficient (ICC) Unit of Measure: ICC value (range: 0 to 1, higher values indicate better agreement)
Measured: Measured at baseline during study enrollment
Discriminative Ability of AI Model
Outcome: Classification of peritoneal transporter type (low, low-average, high-average, high) based on PET Performance Metrics: Area Under the Receiver Operating Characteristic Curve (AUC-ROC) Unit of Measure: AUC-ROC value (range: 0 to 1, higher values indicate better discriminative ability)
Measured: Measured at baseline during study enrollment
Discriminative Ability of AI Model
Outcome: Classification of peritoneal transporter type (low, low-average, high-average, high) based on PET Performance Metrics: Area Under the Precision-Recall Curve (AUC-PR) Unit of Measure: AUC-PR value (range: 0 to 1, higher values indicate better model performance)
Measured: Measured at baseline during study enrollment
Discriminative Ability of AI Model
Outcome: Classification of peritoneal transporter type (low, low-average, high-average, high) based on PET Performance Metrics: Sensitivity Unit of Measure: Sensitivity (%)
Measured: Measured at baseline during study enrollment
Discriminative Ability of AI Model
Outcome: Classification of peritoneal transporter type (low, low-average, high-average, high) based on PET Performance Metrics: F1-score Unit of Measure: F1-score (range: 0 to 1, higher values indicate better balance between precision and recall)
Measured: Measured at baseline during study enrollment
Calibration Performance of AI Model
Outcome: Model-predicted vs. actual transporter status and dialysis adequacy (Kt/V) Performance Metrics: Calibration Slope Unit of Measure: Calibration slope (ideal value = 1)
Measured: Measured at baseline during study enrollment
Calibration Performance of AI Model
Outcome: Model-predicted vs. actual transporter status and dialysis adequacy (Kt/V) Performance Metrics: Calibration-in-the-large (Mean Calibration Error) Unit of Measure: Mean error (lower values indicate better calibration)
Measured: Measured at baseline during study enrollment
Calibration Performance of AI Model
Outcome: Model-predicted vs. actual transporter status and dialysis adequacy (Kt/V)
Performance Metrics:
Calibration Slope Calibration-in-the-large (Mean Calibration Error) Brier Score
Measured: Measured at baseline during study enrollment
Calibration Performance of AI Model
Outcome: Model-predicted vs. actual transporter status and dialysis adequacy (Kt/V) Performance Metrics: Brier Score Unit of Measure: Brier Score (range: 0 to 1, lower values indicate better calibration)
Measured: Measured at baseline during study enrollment

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

3 Mar 202531 Mar 2026

First posted24 Feb 2025
Study start3 Mar 2025
Primary completion28 Feb 2026 (estimated)
Study completion31 Mar 2026 (estimated)

Resources

Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
Publications (12)Papers the sponsor has linked to this study.
Other trials for End-Stage Kidney DiseaseStudies 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: 350 participants (estimated)

IDs, dates and publications

ClinicalTrials.gov ID: NCT06842927
Sponsor's protocol ID: CIRB-2024-569-5
Acronym: DETECT-PD
Status last verified: Apr 2025
First submitted: 2 Feb 2025
Last updated: 9 Apr 2025
Results posted: No
FDA-regulated drug: No
FDA-regulated device: No
Individual data sharing: Undecided

Publications

Riley RD, Ensor J, Snell KIE, Harrell FE Jr, Martin GP, Reitsma JB, Moons KGM, Collins G, van Smeden M. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020 Mar 18;368:m441. doi: 10.1136/bmj.m441. No abstract available.
PubMed 32188600
Szeto CC, Wong TY, Chow KM, Leung CB, Li PK. Dialysis adequacy and transport test for characterization of peritoneal transport type in Chinese peritoneal dialysis patients receiving three daily exchanges. Am J Kidney Dis. 2002 Jun;39(6):1287-99. doi: 10.1053/ajkd.2002.33405.
PubMed 12046043
SPRINT Research Group; Wright JT Jr, Williamson JD, Whelton PK, Snyder JK, Sink KM, Rocco MV, Reboussin DM, Rahman M, Oparil S, Lewis CE, Kimmel PL, Johnson KC, Goff DC Jr, Fine LJ, Cutler JA, Cushman WC, Cheung AK, Ambrosius WT. A Randomized Trial of Intensive versus Standard Blood-Pressure Control. N Engl J Med. 2015 Nov 26;373(22):2103-16. doi: 10.1056/NEJMoa1511939. Epub 2015 Nov 9.
PubMed 26551272
Chen CA, Lin SH, Hsu YJ, Li YC, Wang YF, Chiu JS. Neural network modeling to stratify peritoneal membrane transporter in predialytic patients. Intern Med. 2006;45(9):663-4. doi: 10.2169/internalmedicine.45.1419. Epub 2006 Jun 1. No abstract available.
PubMed 16755101
Gu J, Bai E, Ge C, Winograd J, Shah AD. Peritoneal equilibration testing: Your questions answered. Perit Dial Int. 2023 Sep;43(5):361-373. doi: 10.1177/08968608221133629. Epub 2022 Nov 9.
PubMed 36350033
Morelle J, Stachowska-Pietka J, Oberg C, Gadola L, La Milia V, Yu Z, Lambie M, Mehrotra R, de Arteaga J, Davies S. ISPD recommendations for the evaluation of peritoneal membrane dysfunction in adults: Classification, measurement, interpretation and rationale for intervention. Perit Dial Int. 2021 Jul;41(4):352-372. doi: 10.1177/0896860820982218. Epub 2021 Feb 10.
PubMed 33563110
Blake PG, Bargman JM, Brimble KS, Davison SN, Hirsch D, McCormick BB, Suri RS, Taylor P, Zalunardo N, Tonelli M; Canadian Society of Nephrology Work Group on Adequacy of Peritoneal Dialysis. Clinical Practice Guidelines and Recommendations on Peritoneal Dialysis Adequacy 2011. Perit Dial Int. 2011 Mar-Apr;31(2):218-39. doi: 10.3747/pdi.2011.00026. No abstract available.
PubMed 21427259
Chen JB, Lam KK, Su YJ, Lee WC, Cheng BC, Kuo CC, Wu CH, Lin E, Wang YC, Chen TC, Liao SC. Relationship between Kt/V urea-based dialysis adequacy and nutritional status and their effect on the components of the quality of life in incident peritoneal dialysis patients. BMC Nephrol. 2012 Jun 14;13:39. doi: 10.1186/1471-2369-13-39.
PubMed 22697882
Lin YL, Lee YC, Lee CC, Wu MH. Role of Peritoneal Equilibration Test in Assessing Folate Transport During Peritoneal Dialysis. J Ren Nutr. 2024 Sep;34(5):463-468. doi: 10.1053/j.jrn.2024.02.003. Epub 2024 Mar 13.
PubMed 38490516
Cnossen TT, Smit W, Konings CJ, Kooman JP, Leunissen KM, Krediet RT. Quantification of free water transport during the peritoneal equilibration test. Perit Dial Int. 2009 Sep-Oct;29(5):523-7.
PubMed 19776045
Twardowski ZJ. Clinical value of standardized equilibration tests in CAPD patients. Blood Purif. 1989;7(2-3):95-108. doi: 10.1159/000169582.
PubMed 2663040
Bello AK, Okpechi IG, Osman MA, Cho Y, Cullis B, Htay H, Jha V, Makusidi MA, McCulloch M, Shah N, Wainstein M, Johnson DW. Epidemiology of peritoneal dialysis outcomes. Nat Rev Nephrol. 2022 Dec;18(12):779-793. doi: 10.1038/s41581-022-00623-7. Epub 2022 Sep 16.
PubMed 36114414

Interested in taking part?

The sponsor has not listed a contact. The official record may have more details.

Talk to your doctor first and take this page with you. Only the study team can confirm whether you can join.

Questions to ask the study team

What is the study trying to find out?
What would I need to do, and how often would I visit?
Could I receive a placebo instead of the treatment?
What are the possible risks and benefits for me?
Will it cost me anything? Are travel or time paid for?
Can I keep seeing my own doctor during the study?
What happens when the study ends?

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Study at a glance

From the registry record, last updated by the sponsor on 9 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.

Official title: DETECT-PD -- Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis
ClinicalTrials.gov ID: NCT06842927
Acronym / sponsor ID: DETECT-PD / CIRB-2024-569-5
Conditions: End-Stage Kidney Disease, End Stage Renal Disease (ESRD), End Stage Renal Disease on Dialysis (Diagnosis), End Stage Renal Failure on Dialysis, Peritoneal Dialysis, Peritoneal Dialysis Patients
Keywords: Peritoneal dialysis, Artificial intelligence, Dialysis adequacy, Peritoneum transporter status, Renal replacement therapy
Study type: Observational
Recruitment status: Enrolling by invitation
Enrolment: 350 participants (estimated)
Lead sponsor: Tuen Mun HospitalOther government
Study start: 3 Mar 2025 (actual)
Primary completion: 28 Feb 2026 (estimated)
Study completion: 31 Mar 2026 (estimated)
First posted: 24 Feb 2025 (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.

The main questions it aims to answer are:

Researchers will compare the performance of the AI model with the gold standard Peritoneal Equilibration Test (PET) and Kt/V to evaluate its accuracy and reliability.

Participants will:

Detailed description

Demographics \& Medical History Peritoneal Dialysis Data Biochemical Data

The AI model will be developed using Python 3.11 and PyTorch 2.41 for deep learning and predictive analytics.

The key methodological steps include:

Data Preprocessing: Handling missing values, feature scaling, and one-hot encoding for categorical variables.

Feature Selection: Identifying the most predictive clinical and biochemical markers.

Model Training: Using deep learning regression models to predict PET and Kt/V outcomes.

Performance Evaluation: Evaluating model accuracy using:

Mean Absolute Error (MAE) Mean Squared Error (MSE) R² score (coefficient of determination) Bland-Altman plots and correlation coefficients for agreement with measured values.

Study design

Observational model: Cohort
Time perspective: Prospective
Number of arms: 2

Arm	Type	Receives
Training/Validation	–	Other: data collectionParticipants in training/validation arm will receive the same standard investigations and care as part of their routine PD management, including clinical evaluations, biochemical testing, and measurements of peritoneal transporter status via the Peritoneal Equilibrium Test (PET) and dialysis adequacy (Kt/V).
Test	–	Other: data reportParticipants in training/validation arm will receive the same standard investigations and care as part of their routine PD management, including clinical evaluations, biochemical testing, and measurements of peritoneal transporter status via the Peritoneal Equilibrium Test (PET) and dialysis adequacy (Kt/V).

Who may qualify

Ages: 18 years and older
Sex: All
Healthy volunteers: Not accepted
Age groups: Adult, Older adult
Study population: End-stage renal failure patients requiring peritoneal dialysis as renal replacement therapy

Key inclusion criteria (3 listed)

Age 18 years or older
Diagnosis of end-stage renal failure requiring peritoneal dialysis as renal replacement therapy
Ability to give informed consent and comply with study procedures.

Key exclusion criteria (5 listed)

History of hernia or peritoneal leak, including pleuroperitoneal fistula (PPF), patent processus vaginalis (PPV) and retroperitoneal leak
Ongoing PD peritonitis with or without antibiotic therapy
Just finished PD peritonitis antibiotic treatment within recent 4 weeks
Pregnancy
Patient refusal

Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.

Interventions

Intervention	Type	Arms
data collectionAlso: model trainingAn additional collection of peritoneal dialysate and spot urine samples will be collected. Participants randomized to the training/validation arm will have their data used for model development, including the training and validation phases.	Other	Training/Validation
data reportAlso: model testingAn additional collection of peritoneal dialysate and spot urine samples will be collected. Participants randomized to the test arm will have their data isolated and reserved exclusively for evaluating the performance of the final AI model	Other	Test

Primary outcome

Peritoneal Equilibration Test (PET) Parameters
Time frame: Measured at baseline during study enrollment
Predictive Accuracy of AI Model for Peritoneal Equilibration Test (PET) Parameters Outcome: AI-predicted vs. actual 2-hour and 4-hour dialysate-to-plasma creatinine ratio (D/P Cr) Performance Metrics: Mean Absolute Error (MAE) Unit of Measure: Absolute error
Peritoneal Equilibration Test (PET) Parameters
Time frame: Measured at baseline during study enrollment
Predictive Accuracy of AI Model for Peritoneal Equilibration Test (PET) Parameters Outcome: AI-predicted vs. actual dialysate-to-baseline dialysate glucose concentration ratio (D/D0 Glu) Performance Metrics: Mean Absolute Error (MAE) Unit of Measure: Absolute error
Peritoneal Equilibration Test (PET) Parameters
Time frame: Measured at baseline during study enrollment
Predictive Accuracy of AI Model for Peritoneal Equilibration Test (PET) Parameters Outcome: AI-predicted vs. actual 2-hour and 4-hour dialysate-to-plasma creatinine ratio (D/P Cr) Performance Metrics: Mean Squared Error (MSE) Unit of Measure: Squared Error
Peritoneal Equilibration Test (PET) Parameters
Time frame: Measured at baseline during study enrollment
Predictive Accuracy of AI Model for Peritoneal Equilibration Test (PET) Parameters Outcome: AI-predicted vs. actual dialysate-to-baseline dialysate glucose concentration ratio (D/D0 Glu) Performance Metrics: Mean Squared Error (MSE) Unit of Measure: Squared Error
Peritoneal Equilibration Test (PET) Parameters
Time frame: Measured at baseline during study enrollment
Predictive Accuracy of AI Model for Peritoneal Equilibration Test (PET) Parameters Outcome: AI-predicted vs. actual 2-hour and 4-hour dialysate-to-plasma creatinine ratio (D/P Cr) Performance Metrics: Coefficient of Determination (R²) Unit of Measure: R² value (range: 0 to 1, higher values indicate better model performance)
Peritoneal Equilibration Test (PET) Parameters
Time frame: Measured at baseline during study enrollment
Predictive Accuracy of AI Model for Peritoneal Equilibration Test (PET) Parameters Outcome: AI-predicted vs. actual dialysate-to-baseline dialysate glucose concentration ratio (D/D0 Glu) Performance Metrics: Coefficient of Determination (R²) Unit of Measure: R² value (range: 0 to 1, higher values indicate better model performance)
Peritoneal Equilibration Test (PET) Parameters
Time frame: Measured at baseline during study enrollment
Predictive Accuracy of AI Model for Peritoneal Equilibration Test (PET) Parameters Outcome: AI-predicted vs. actual 2-hour and 4-hour dialysate-to-plasma creatinine ratio (D/P Cr) Performance Metrics: Intraclass Correlation Coefficient (ICC) Unit of Measure: ICC value (range: 0 to 1, higher values indicate better agreement)
Peritoneal Equilibration Test (PET) Parameters
Time frame: Measured at baseline during study enrollment
Predictive Accuracy of AI Model for Peritoneal Equilibration Test (PET) Parameters Outcome: AI-predicted vs. actual dialysate-to-baseline dialysate glucose concentration ratio (D/D0 Glu) Performance Metrics: Intraclass Correlation Coefficient (ICC) Unit of Measure: ICC value (range: 0 to 1, higher values indicate better agreement)

12 secondary outcomes

Dialysis Adequacy (Kt/V) parameters
Time frame: Measured at baseline during study enrollment
Dialysis Adequacy (Kt/V) parameters
Time frame: Measured at baseline during study enrollment
Dialysis Adequacy (Kt/V) parameters
Time frame: Measured at baseline during study enrollment
Dialysis Adequacy (Kt/V) parameters
Time frame: Measured at baseline during study enrollment
Discriminative Ability of AI Model
Time frame: Measured at baseline during study enrollment
Discriminative Ability of AI Model
Time frame: Measured at baseline during study enrollment
Discriminative Ability of AI Model
Time frame: Measured at baseline during study enrollment
Discriminative Ability of AI Model
Time frame: Measured at baseline during study enrollment
Calibration Performance of AI Model
Time frame: Measured at baseline during study enrollment
Calibration Performance of AI Model
Time frame: Measured at baseline during study enrollment
Calibration Performance of AI Model
Time frame: Measured at baseline during study enrollment
Calibration Performance of AI Model
Time frame: Measured at baseline during study enrollment

Results and safety

This study is still under way. Results are not available in the source record yet.

Research links

Official record on ClinicalTrials.govLast updated by the sponsor 9 Apr 2025.
Background: PubMed 32188600Riley RD, Ensor J, Snell KIE, Harrell FE Jr, Martin GP, Reitsma JB, Moons KGM, Collins G, van Smeden M. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020 Mar 18;368:m441. doi: 10.1136/bmj.m441. No abstract available.
Background: PubMed 12046043Szeto CC, Wong TY, Chow KM, Leung CB, Li PK. Dialysis adequacy and transport test for characterization of peritoneal transport type in Chinese peritoneal dialysis patients receiving three daily exchanges. Am J Kidney Dis. 2002 Jun;39(6):1287-99. doi: 10.1053/ajkd.2002.33405.
Background: PubMed 26551272SPRINT Research Group; Wright JT Jr, Williamson JD, Whelton PK, Snyder JK, Sink KM, Rocco MV, Reboussin DM, Rahman M, Oparil S, Lewis CE, Kimmel PL, Johnson KC, Goff DC Jr, Fine LJ, Cutler JA, Cushman WC, Cheung AK, Ambrosius WT. A Randomized Trial of Intensive versus Standard Blood-Pressure Control. N Engl J Med. 2015 Nov 26;373(22):2103-16. doi: 10.1056/NEJMoa1511939. Epub 2015 Nov 9.
Background: PubMed 16755101Chen CA, Lin SH, Hsu YJ, Li YC, Wang YF, Chiu JS. Neural network modeling to stratify peritoneal membrane transporter in predialytic patients. Intern Med. 2006;45(9):663-4. doi: 10.2169/internalmedicine.45.1419. Epub 2006 Jun 1. No abstract available.
Background: PubMed 36350033Gu J, Bai E, Ge C, Winograd J, Shah AD. Peritoneal equilibration testing: Your questions answered. Perit Dial Int. 2023 Sep;43(5):361-373. doi: 10.1177/08968608221133629. Epub 2022 Nov 9.
Background: PubMed 33563110Morelle J, Stachowska-Pietka J, Oberg C, Gadola L, La Milia V, Yu Z, Lambie M, Mehrotra R, de Arteaga J, Davies S. ISPD recommendations for the evaluation of peritoneal membrane dysfunction in adults: Classification, measurement, interpretation and rationale for intervention. Perit Dial Int. 2021 Jul;41(4):352-372. doi: 10.1177/0896860820982218. Epub 2021 Feb 10.
Background: PubMed 21427259Blake PG, Bargman JM, Brimble KS, Davison SN, Hirsch D, McCormick BB, Suri RS, Taylor P, Zalunardo N, Tonelli M; Canadian Society of Nephrology Work Group on Adequacy of Peritoneal Dialysis. Clinical Practice Guidelines and Recommendations on Peritoneal Dialysis Adequacy 2011. Perit Dial Int. 2011 Mar-Apr;31(2):218-39. doi: 10.3747/pdi.2011.00026. No abstract available.
Background: PubMed 22697882Chen JB, Lam KK, Su YJ, Lee WC, Cheng BC, Kuo CC, Wu CH, Lin E, Wang YC, Chen TC, Liao SC. Relationship between Kt/V urea-based dialysis adequacy and nutritional status and their effect on the components of the quality of life in incident peritoneal dialysis patients. BMC Nephrol. 2012 Jun 14;13:39. doi: 10.1186/1471-2369-13-39.
Background: PubMed 38490516Lin YL, Lee YC, Lee CC, Wu MH. Role of Peritoneal Equilibration Test in Assessing Folate Transport During Peritoneal Dialysis. J Ren Nutr. 2024 Sep;34(5):463-468. doi: 10.1053/j.jrn.2024.02.003. Epub 2024 Mar 13.
Background: PubMed 19776045Cnossen TT, Smit W, Konings CJ, Kooman JP, Leunissen KM, Krediet RT. Quantification of free water transport during the peritoneal equilibration test. Perit Dial Int. 2009 Sep-Oct;29(5):523-7.
Background: PubMed 2663040Twardowski ZJ. Clinical value of standardized equilibration tests in CAPD patients. Blood Purif. 1989;7(2-3):95-108. doi: 10.1159/000169582.
Background: PubMed 36114414Bello AK, Okpechi IG, Osman MA, Cho Y, Cullis B, Htay H, Jha V, Makusidi MA, McCulloch M, Shah N, Wainstein M, Johnson DW. Epidemiology of peritoneal dialysis outcomes. Nat Rev Nephrol. 2022 Dec;18(12):779-793. doi: 10.1038/s41581-022-00623-7. Epub 2022 Sep 16.
Individual participant dataSharing planned: Undecided

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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.

The record lists no contact. Use the official record to reach the sponsor.

1 site in 1 country: see every site and its status.

Questions about this trial

What is this clinical trial studying?

An observational study of End-Stage Kidney Disease and End Stage Renal Disease (ESRD), run by Tuen Mun Hospital at 1 site in Hong Kong.

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 3 inclusion and 5 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: enrolling by invitation, as of the sponsor's last update on 9 Apr 2025. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in Hong Kong. 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?

This study is still under way. Results are not available in the source record yet.

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?

Peritoneal Equilibration Test (PET) Parameters (time frame: Measured at baseline during study enrollment). The study lists 8 primary outcomes in all. 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 NCT06842927, which also lists 12 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 9 Apr 2025; AzTrials last refreshed its copy on 31 Oct 2026.