DETECT-PD -- Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis
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
Sponsor: Tuen Mun Hospital

DETECT-PD -- Dialysis Efficiency and Transporter Evaluation Computational Tool 
in Peritoneal Dialysis 
To develop and investigate possibility and performance of artificial intelligence in 
predicting peritoneum transporter status and dialysis efficiency 
V 0.1 – updated 8/1/2025 
Study co-ordination Center: Tuen Mun Hospital 
Principal Investigator: Ka Chun Leung, Resident Specialist, Department of 
Medicine and Geriatrics, Tuen Mun Hospital  
Study Management Group 
Principal Investigator: 
Ka Chun Leung 
Resident Specialist 
Department of Medicine and Geriatrics 
Tuen Mun Hospital 
kc.leung4@ha.org.hk 
Statistician: 
Ka Chun Leung 
Resident Specialist 
Department of Medicine and Geriatrics 
Tuen Mun Hospital 
kc.leung4@ha.org.hk 
Study Management: 
Ka Chun Leung 
Resident Specialist 
Department of Medicine and Geriatrics 

Tuen Mun Hospital 
kc.leung4@ha.org.hk  
Table of Contents 
Study Management Group 2 
Table of Contents 3 
Glossary of Abbreviations 
4 
Keywords 
4 
Study Summary 
4 
Introduction 5 
Background 5 
Rationale for the current study 5 
Study Objectives 6 
Study Design 6 
Study Outline 6 
Data Collection 
7 
Study Outcomes 8 
Statistics and Data Analysis 9 
Data Analysis 9 
Data Storage 9 
Participant entry 10 
Pre-registration evaluations 10 
Assessment and Follow-up 11 
Regulatory issues 11 
Consent 11 

Confidentiality 
11 
Study Management 
12 
Publication Policy 12 
References 
13 
 
  
Glossary of Abbreviations 
PD Peritoneal Dialysis 
ESKD 
End-Stage Kidney Disease 
PET Peritoneal Equilibration Test 
AI 
Artificial Intelligence 
TMH  
Tuen Mun Hospital 
RRT Renal Replacement Therapy 
 
Keywords 
Peritoneal dialysis 
Artificial intelligence 
Dialysis adequacy 
Peritoneum transporter status 
Renal replacement therapy 
Study Summary 
TITLE 
DETECT-PD -- Dialysis Efficiency and Transporter Evaluation 
Computational Tool in Peritoneal Dialysis 
DESIGN Prospective and diagnostic test (correlation) study 

AIMS 
To develop and investigate possibility and performance of artificial 
intelligence in predicting peritoneum transporter status and dialysis efficiency 
OUTCOME MEASURES Peritoneum transporter status measured with 
peritoneum equilibrium test and dialysis adequacy measured as Kt/V 
POPULATION Patients treated with peritoneal dialysis 
ELIGIBILITY 
All adult patients both incident and prevalent to peritoneal dialysis 
DURATION 
Twelve months following study enrolment 
  
Introduction  
Background  
Peritoneal dialysis (PD) is a widely utilized renal replacement therapy (RRT) for 
patients with end-stage kidney disease (ESKD), accounting for approximately 9% 
of such therapies worldwide(1). PD offers several advantages over hemodialysis, 
including lower cost, ease of learning at home, and reduced need for intensive 
nursing support. These factors make it a viable alternative to hemodialysis, 
particularly in regions with limited healthcare resources. 
A key aspect of PD management is the regular assessment of dialysis adequacy 
and peritoneal membrane transporter status. These measurements are crucial 
for optimizing dialysis regimens, preserving residual renal function, and 
improving patient outcomes and quality of life(2–5). Traditionally, the peritoneal 
equilibration test (PET) and the assessment of dialysis adequacy, often require 
patients to store drained peritoneal fluid or undergo standardized dwell studies 
(e.g., a 4-hour dwell), which are both labor- and time-intensive(6,7). As 
continuous peritoneal monitoring becomes increasingly important, these 
conventional methods pose challenges for both patients and healthcare 
providers. 
Multiple attempts have tried to reduce the time and manpower burden of the 
tests(8). The recent development of artificial intelligence (AI) makes the 
prediction of dialysis adequacy and transporter status become possible(9). 

However, there is still a lack of a model that can help renal physicians and 
patients to monitor dialysis adequacy and peritoneum transportation with 
simple measurements. 
This study aims to develop and evaluate the possibility and performance of 
models in assessing the dialysis adequacy and peritoneum transporter status in 
peritoneal dialysis patients. 
Rationale for the current study 
Current methods for assessing PD adequacy and transporter status are time-
consuming and inconvenient for both patients and healthcare providers. 
Artificial intelligence offers a potential solution to simplify these assessments, 
reducing the burden on patients and clinicians. 
  
Study Objectives 
The primary objective of this research protocol is to develop an AI model and 
evaluate its performance in predicting peritoneal dialysis adequacy and 
peritoneal transporter status of PD patients. 
 
Study Design 
This double-blind diagnostic test (correlation) study will examine a prospective 
cohort of patients treated with PD for their ESKD. 
Patients recruited for this study will be randomized into two arms: a 
training/validation arm and a test arm in the ratio of 4:1. Around 350 participants 
will be recruited, with 280 patients allocated to the training/validation arm and 
70 patients to the test arm. Randomization will be performed using a computer-
generated randomization sequence to ensure an unbiased and equitable 
allocation of participants. The Principal Investigator and participants will not 
know the allocation details. The assessor will not know whether the samples are 
originated from participants of this study. 
All participants in the study 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). There will be no 
change to the clinical care of patients participating in this study. All medication 
and renal replacement therapy (RRT) prescription changes will be made solely by 
the clinical team caring for the patient, without reference to the predictions of AI 
models. 
Participants randomized to the training/validation arm will have their data used 
for model development, including the training and validation phases. Those 
randomized to the test arm will also undergo the same investigations and care 
but will have their data isolated and reserved exclusively for evaluating the 
performance of the final AI model. This ensures a robust and unbiased 
assessment of the model while maintaining equity in clinical care across all 
participants. 
This study will involve an additional collection of peritoneal dialysate and spot 
urine samples. These samples are essential for developing and testing the AI 
model but do not impact the patients' standard treatment or care protocols. 
Study Outline 
The outline of the study will be presented in this session. 
Patient consent will be obtained during their routine dialysis adequacy and 
peritoneal transporter status assessments at Tuen Mun Hospital (TMH). After 
consent, patients will be randomly assigned to either the training/validation arm 
or the test arm. Both investigators and patients will remain blinded to the group 
assignments. 
An extra sample of peritoneal dialysate from the patient’s latest pre-exchange 
outflow dwell and an extra sample of urine will be collected. If the patient is 
anuric, the anuric condition will also be recorded as one of the features. The 
patient will then undergo the routine dialysis adequacy and peritoneum 
equilibrium test. 
The latest blood investigation data will be collected in the coming renal specialty 

clinic follow-up, and no extra blood test will be performed. 
Data Collection 
The following data will be collected throughout the study from the training of AI 
model: 
Demographic Data 
- 
Age 
- 
Gender 
- 
Body Height 
- 
Body Weight 
Past Medical History 
- 
History of hypertension 
- 
History of diabetes 
- 
History of cardiovascular events(10) 
- 
Cause of ESKD 
- 
Date of Tenckhoff Catheter insertion 
PD related data 
- 
Latest outflow volume 
- 
Latest dwell bag dextrose/icodextrin concentration 
- 
Latest dwell time 
Biochemical Data 
- 
Blood 
- 
Serum Creatinine 
- 
Serum Urea 
- 
Serum Glucose 

- 
Glycosylated Haemoglobin (Hba1c) 
- 
Urine 
- 
Anuric or not 
- 
Spot urine protein-creatinine ratio 
- 
Peritoneal dialysate 
- 
Dialysate Creatinine 
- 
Dialysate Urea 
- 
Dialysate Glucose 
Study Outcomes 
Primary Outcome: Peritoneal Equilibration Test (PET) Parameters 
 AI-predicted vs. actual 2-hour and 4-hour dialysate-to-plasma creatinine 
ratio (D/P Cr) 
 AI-predicted vs. actual dialysate-to-baseline dialysate glucose 
concentration ratio (D/D0 Glu) 
 Performance Metrics: 
 Mean Absolute Error (MAE) 
 Mean Squared Error (MSE) 
 Coefficient of Determination (R²) 
 Intraclass Correlation Coefficient (ICC) 
 Time Frame: Baseline (measured at study enrollment) 
 
Secondary Outcome: 
 Dialysis Adequacy (Kt/V) Parameters 
 AI-predicted vs. actual total weekly Kt/V 
 Performance Metrics: 

 MAE, MSE, R², ICC 
 Time Frame: Baseline (measured at study enrollment) 
 
 Discriminative Ability of AI Model 
 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) 
 Area Under the Precision-Recall Curve (AUC-PR) 
 Sensitivity, Specificity, F1-score, and Accuracy 
 Time Frame: Baseline (measured at study enrollment) 
 Calibration Performance of AI Model 
 Model-predicted vs. actual transporter status and dialysis adequacy (Kt/V) 
 Performance Metrics: 
 Calibration Slope 
 Calibration-in-the-large (Mean Calibration Error) 
 Brier Score 
 Time Frame: Baseline (measured at study enrollment) 
Statistics and Data Analysis 
The local data showed that the mean and standard deviation of the 4-hour 
dialysate to plasma creatinine ratio is 0.747 ± 0.115 with Kt/V 1.99 ± 0.52(11). 
Assuming a zero dropout rate, 15 features are used in the model to make 
predictions, a 0.5 R-square value and a 0.9 level of shrinkage. A minimum of 280 
samples are required for model training and validation(12). Considering 20% of 
the total sample will be required for model testing, a total of 350 patients will be 
recruited. 

Data Analysis 
Descriptive statistics will be used in assessing outcome measures. Python 
version 3.11 and Pytorch 2.41 will be used for model development and 
evaluation. 
Data Storage 
Data and all appropriate documentation will be stored for 7 years after the 
completion of the study, including the follow-up period. To protect participants’ 
privacy, all research and personal data would be handled in line with HA / 
Hospital’s policy in handling / storage / destruction of patients’ medical records. 
Hard copies will be locked in cabinet during the course of the study while the 
soft copy would be encrypted/ password-protected.  The Principal Investigator 
and HA Central Institutional Review Board (Central IRB) to access the personal 
data and study data for monitoring purpose issue. All research and personal data 
will be destroyed and discarded within 7 years after the study is completed using 
data destruction software. Hard copies will be discarded as confidential waste 
while the soft copy would be deleted and unrecoverable after completion of the 
study. 
 
Participant entry 
Pre-registration evaluations 
No pre-registration evaluations are required. All patients with kidney failure 
treated with peritoneal dialysis are eligible. 
Inclusion criteria 
- 
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. 
Exclusion criteria 

- 
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. 
 
  
Assessment and Follow-up 
All candidates will be followed up for 12 months following initial PD fluid and 
urine sampling. The formal end of this study will be 12 months after the PD fluid 
and urine sampling of the 350th patient. Incidental findings will not be recorded 
and reported in this study. Only the outcomes mentioned in previous sessions 
will be recorded and analysed. Participants will be advised to report incidental 
findings to their General Practitioners and clinical care team as per local 
guidelines or standards of care. 
 
Regulatory issues 
Consent 
Consent to enter the study must be sought from each participant only after a full 
explanation has been given, an information leaflet offered and time allowed for 
consideration. Signed participant consent should be obtained. The right of the 
participant to refuse to participate without giving reasons must be respected. All 
participants are free to withdraw at any time without giving reasons and without 
prejudicing present and further treatment. 
Confidentiality 
The Principal Investigator will preserve the confidentiality of participants taking 
part in the study. Information will be pseudonymised when possible. Only the 

pseudonymized form of data will be shared with the study management team 
statistician.  
All participants' information will be stored securely and analysed using a 
password-protected local computer with strict arrangements for access and 
use, ensuring that the information is used only for health and care research or to 
contact participants about future research opportunities. No cloud service will 
be involved. All identifiable personal data will be anonymised and will follow the 
HA policy on handling of patient data privacy. Subjects will be voluntary and they 
can withdraw at any time without providing any reason.  This will not affect their 
present or future medical care and the legal rights. 
 
  
Study Management 
The day-to-day management of the study will be co-ordinated through Dr Ka 
Chun Leung with contact details listed above. 
 
Publication Policy 
- 
The study results will be disseminated through publication in peer-reviewed 
scientific journals, presentation at scientific conferences, and other appropriate 
channels.  
- 
The study team will develop a summary of the findings in plain language to 
be made available to study participants, as well as to healthcare professionals 
and patient groups. 
- 
All publications and presentations will acknowledge the contribution of 
study participants and comply with ethical standards for authorship. 
- 
The study team will ensure that the results are communicated in a timely 
manner to stakeholders, including healthcare providers, policymakers, and 
patient groups. 
- 
The study team will encourage and support the uptake of the study findings 

into clinical practice, where appropriate. 
- 
The study team will comply with all relevant laws and regulations governing 
the dissemination of research findings.  
References 
1.  Bello AK, Okpechi IG, Osman MA, Cho Y, Cullis B, Htay H, et al. 
Epidemiology of peritoneal dialysis outcomes. Nature Reviews Nephrology. 
2022;18(12): 779–793. https://doi.org/10.1038/s41581-022-00623-7. 
2.  Twardowski ZJ. Clinical value of standardized equilibration tests in CAPD 
patients. Blood Purification. 1989;7(2–3): 95–108. 
https://doi.org/10.1159/000169582. 
3.  Cnossen TT, Smit W, Konings CJAM, Kooman JP, Leunissen KM, Krediet RT. 
Quantification of free water transport during the peritoneal equilibration test. 
Peritoneal Dialysis International: Journal of the International Society for 
Peritoneal Dialysis. 2009;29(5): 523–527. 
4.  Lin YL, Lee YC, Lee CC, Wu MH. Role of Peritoneal Equilibration Test in 
Assessing Folate Transport During Peritoneal Dialysis. Journal of Renal Nutrition. 
2024;34(5): 463–468. https://doi.org/10.1053/j.jrn.2024.02.003. 
5.  Chen JB, Lam KK, Su YJ, Lee WC, Cheng BC, Kuo CC, et al. 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 Nephrology. 2012;13(1): 39. https://doi.org/10.1186/1471-2369-
13-39. 
6.  Blake PG, Bargman JM, Brimble KS, Davison SN, Hirsch D, McCormick BB, et 
al. Clinical Practice Guidelines and Recommendations on Peritoneal Dialysis 
Adequacy 2011. Peritoneal Dialysis International. 2011; 
https://doi.org/10.3747/pdi.2011.00026. 
7.  Morelle J, Stachowska-Pietka J, Öberg C, Gadola L, Milia VL, Yu Z, et al. ISPD 
recommendations for the evaluation of peritoneal membrane dysfunction in 
adults: Classification, measurement, interpretation and rationale for 
intervention. Peritoneal Dialysis International. 2021; 

https://doi.org/10.1177/0896860820982218. 
8.  Gu J, Bai E, Ge C, Winograd J, Shah AD. Peritoneal equilibration testing: Your 
questions answered. Peritoneal Dialysis International. 2022; 
https://doi.org/10.1177/08968608221133629. 
9.  Chen CA, Lin SH, Hsu YJ, Li YC, Wang YF, Chiu JS. Neural network modeling 
to stratify peritoneal membrane transporter in predialytic patients. Internal 
Medicine (Tokyo, Japan). 2006;45(9): 663–664. 
https://doi.org/10.2169/internalmedicine.45.1419. 
10.  The SPRINT Research Group. A Randomized Trial of Intensive versus 
Standard Blood-Pressure Control. New England Journal of Medicine. 
2015;373(22): 2103–2116. https://doi.org/10.1056/NEJMoa1511939. 
11.  Szeto CC, Wong TYH, Chow KM, Leung CB, Li PKT. Dialysis adequacy and 
transport test for characterization of peritoneal transport type in chinese 
peritoneal dialysis patients receiving three daily exchanges. American Journal of 
Kidney Diseases. 2002;39(6): 1287–1291. 
https://doi.org/10.1053/ajkd.2002.33405. 
12.  Riley RD, Ensor J, Snell KIE, Harrell FE, Martin GP, Reitsma JB, et al. 
Calculating the sample size required for developing a clinical prediction model. 
BMJ. 2020; m441. https://doi.org/10.1136/bmj.m441.