Simulated and Synthetic Health Data: Improving Clinical Research on Rare Diseases. A Real-World Data Simulation of Autosomal Dominant Polycystic Kidney Disease (ADPKD) Trials. A Retrospective, Observational Study
SAILING-ADPKD
1 other identifier
observational
100
2 countries
2
Brief Summary
This is a no-profit, retrospective observational study involving real-world data (RWD), retrieved from ADPKD-related electronic health records stored at Mario Negri Institute IRCCS. RWD will be used to generate simulated and synthetic datasets, using AI tools. RWD and generated data (GD) will be used to conduct three virtual RCTs, which main outcome is change in Total Kidney Volume (TKV). Statistical tests will be performed to assess quality and privacy preservation of GD compared with RWD. GD will be also evaluated in exploratory sample size estimations.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P50-P75 for all trials
Started Jun 2025
2 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
May 27, 2025
CompletedFirst Posted
Study publicly available on registry
June 11, 2025
CompletedStudy Start
First participant enrolled
June 12, 2025
CompletedPrimary Completion
Last participant's last visit for primary outcome
June 1, 2027
ExpectedStudy Completion
Last participant's last visit for all outcomes
June 1, 2027
September 2, 2025
August 1, 2025
2 years
May 27, 2025
August 29, 2025
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Changes in total kidney volume (TKV)
Changes in TKV in mL.
At baseline, and immediately after data generation procedure.
Study Arms (3)
Real-world data form ADPKD patients
Real-world data from ADPKD-related electronic health records (EHR) stored at the Istituto di Ricerche Farmacologiche Mario Negri IRCCS, primarily based on the ALADIN (NCT00309283) and ALADIN 2 (NCT01377246) studies
Simulated data
Data based on RWD from the ADPKD patients and derived from predefined statistical models (e.g., normal distribution for continuous variables, binomial distribution for categorical variables).
Synthetic data
Data generated from the RWD of the ADPKD patients using generative large languages models (LLMs)
Eligibility Criteria
Data from ADPKD patients collected in the form of electronic health records (EHR) stored at the Istituto di Ricerche Farmacologiche Mario Negri IRCCS, primarily based on two completed studies performed at the Mario Negri Institue i.e., the ALADIN Study (NCT00309283) and the ALADIN 2 Study (NCT01377246), which enrolled ADPKD-patients treated either with Octreotide-LAR or placebo.
You may qualify if:
- Adult (\>18 years) men and women with ADPKD according to Ravine criteria25
- Estimated glomerular filtration rate (eGFR) between 15 and 40 mL/min/1.73 m2 (CKD stage: G3b-G4) or higher (CKD stage: G1-G3a), as calculated by the Modification of Diet in Renal Disease study four variables equation
You may not qualify if:
- confounding factors that could affect renal function loss independent of kidney growth and treatment allocation (i.e., diabetes mellitus, urinary protein excretion rate \>3 g/24 h)
- Abnormal urinalysis suggestive of concomitant, clinically significant glomerular disease, and urinary tract lithiasis or infection
- Patients with major systemic disease
- Patients unable to provide informed consent
- Pregnant, lactating, or potentially childbearing women without adequate contraception
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (2)
Clinical Research Centre for Rare Diseases Aldo e Cele DaccĂ²
Ranica, BG, 24020, Italy
Department of Global Public Health (GPH), Karolinska Institutet
Stockholm, 171 77, Sweden
Related Publications (27)
Bolignano D, Pisano A. Good-quality research in rare diseases: trials and tribulations. Pediatr Nephrol. 2016 Nov;31(11):2017-23. doi: 10.1007/s00467-016-3323-7. Epub 2016 Jan 27.
PMID: 26817476BACKGROUNDHalpern SD, Karlawish JH, Berlin JA. The continuing unethical conduct of underpowered clinical trials. JAMA. 2002 Jul 17;288(3):358-62. doi: 10.1001/jama.288.3.358.
PMID: 12117401BACKGROUNDLilford RJ, Thornton JG, Braunholtz D. Clinical trials and rare diseases: a way out of a conundrum. BMJ. 1995 Dec 16;311(7020):1621-5. doi: 10.1136/bmj.311.7020.1621.
PMID: 8555809BACKGROUNDGagne JJ, Thompson L, O'Keefe K, Kesselheim AS. Innovative research methods for studying treatments for rare diseases: methodological review. BMJ. 2014 Nov 24;349:g6802. doi: 10.1136/bmj.g6802.
PMID: 25422272BACKGROUNDShurin S, Krischer J, Groft SC. Clinical trials In BMT: ensuring that rare diseases and rarer therapies are well done. Biol Blood Marrow Transplant. 2012 Jan;18(1 Suppl):S8-11. doi: 10.1016/j.bbmt.2011.10.030. No abstract available.
PMID: 22226117BACKGROUNDvan der Lee JH, Wesseling J, Tanck MW, Offringa M. Efficient ways exist to obtain the optimal sample size in clinical trials in rare diseases. J Clin Epidemiol. 2008 Apr;61(4):324-30. doi: 10.1016/j.jclinepi.2007.07.008. Epub 2008 Feb 21.
PMID: 18313556BACKGROUNDStone EM. Challenges in genetic testing for clinical trials of inherited and orphan retinal diseases. Retina. 2005 Dec;25(8 Suppl):S72-S73. doi: 10.1097/00006982-200512001-00034. No abstract available.
PMID: 16374347BACKGROUNDBuckley BM. Clinical trials of orphan medicines. Lancet. 2008 Jun 14;371(9629):2051-5. doi: 10.1016/S0140-6736(08)60876-4. No abstract available.
PMID: 18555919BACKGROUNDKinder B, McCormack FX. Clinical trials for rare lung diseases: lessons from lymphangioleiomyomatosis. Lymphat Res Biol. 2010 Mar;8(1):71-9. doi: 10.1089/lrb.2009.0027.
PMID: 20235889BACKGROUNDLagakos SW. Clinical trials and rare diseases. N Engl J Med. 2003 Jun 12;348(24):2455-6. doi: 10.1056/NEJMe030024. No abstract available.
PMID: 12802033BACKGROUNDBerlin JA. N-of-1 clinical trials should be incorporated into clinical practice. J Clin Epidemiol. 2010 Dec;63(12):1283-4. doi: 10.1016/j.jclinepi.2010.05.006. Epub 2010 Aug 30.
PMID: 20800449BACKGROUNDCerqueira FP, Jesus AMC, Cotrim MD. Adaptive Design: A Review of the Technical, Statistical, and Regulatory Aspects of Implementation in a Clinical Trial. Ther Innov Regul Sci. 2020 Jan;54(1):246-258. doi: 10.1007/s43441-019-00052-y. Epub 2020 Jan 6.
PMID: 32008232BACKGROUNDYoon J, Drumright LN, van der Schaar M. Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN). IEEE J Biomed Health Inform. 2020 Aug;24(8):2378-2388. doi: 10.1109/JBHI.2020.2980262. Epub 2020 Mar 12.
PMID: 32167919BACKGROUNDMorris TP, White IR, Crowther MJ. Using simulation studies to evaluate statistical methods. Stat Med. 2019 May 20;38(11):2074-2102. doi: 10.1002/sim.8086. Epub 2019 Jan 16.
PMID: 30652356BACKGROUNDMETROPOLIS N, ULAM S. The Monte Carlo method. J Am Stat Assoc. 1949 Sep;44(247):335-41. doi: 10.1080/01621459.1949.10483310. No abstract available.
PMID: 18139350BACKGROUNDChen RJ, Lu MY, Chen TY, Williamson DFK, Mahmood F. Synthetic data in machine learning for medicine and healthcare. Nat Biomed Eng. 2021 Jun;5(6):493-497. doi: 10.1038/s41551-021-00751-8.
PMID: 34131324BACKGROUNDCaroli A, Perico N, Perna A, Antiga L, Brambilla P, Pisani A, Visciano B, Imbriaco M, Messa P, Cerutti R, Dugo M, Cancian L, Buongiorno E, De Pascalis A, Gaspari F, Carrara F, Rubis N, Prandini S, Remuzzi A, Remuzzi G, Ruggenenti P; ALADIN study group. Effect of longacting somatostatin analogue on kidney and cyst growth in autosomal dominant polycystic kidney disease (ALADIN): a randomised, placebo-controlled, multicentre trial. Lancet. 2013 Nov 2;382(9903):1485-95. doi: 10.1016/S0140-6736(13)61407-5. Epub 2013 Aug 21.
PMID: 23972263BACKGROUNDPerico N, Ruggenenti P, Perna A, Caroli A, Trillini M, Sironi S, Pisani A, Riccio E, Imbriaco M, Dugo M, Morana G, Granata A, Figuera M, Gaspari F, Carrara F, Rubis N, Villa A, Gamba S, Prandini S, Cortinovis M, Remuzzi A, Remuzzi G; ALADIN 2 Study Group. Octreotide-LAR in later-stage autosomal dominant polycystic kidney disease (ALADIN 2): A randomized, double-blind, placebo-controlled, multicenter trial. PLoS Med. 2019 Apr 5;16(4):e1002777. doi: 10.1371/journal.pmed.1002777. eCollection 2019 Apr.
PMID: 30951521BACKGROUNDCornec-Le Gall E, Alam A, Perrone RD. Autosomal dominant polycystic kidney disease. Lancet. 2019 Mar 2;393(10174):919-935. doi: 10.1016/S0140-6736(18)32782-X. Epub 2019 Feb 25.
PMID: 30819518BACKGROUNDChebib FT, Perrone RD, Chapman AB, Dahl NK, Harris PC, Mrug M, Mustafa RA, Rastogi A, Watnick T, Yu ASL, Torres VE. A Practical Guide for Treatment of Rapidly Progressive ADPKD with Tolvaptan. J Am Soc Nephrol. 2018 Oct;29(10):2458-2470. doi: 10.1681/ASN.2018060590. Epub 2018 Sep 18.
PMID: 30228150BACKGROUNDRavine D, Gibson RN, Walker RG, Sheffield LJ, Kincaid-Smith P, Danks DM. Evaluation of ultrasonographic diagnostic criteria for autosomal dominant polycystic kidney disease 1. Lancet. 1994 Apr 2;343(8901):824-7. doi: 10.1016/s0140-6736(94)92026-5.
PMID: 7908078BACKGROUNDLevey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, Coresh J; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration). A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009 May 5;150(9):604-12. doi: 10.7326/0003-4819-150-9-200905050-00006.
PMID: 19414839BACKGROUNDLevey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med. 1999 Mar 16;130(6):461-70. doi: 10.7326/0003-4819-130-6-199903160-00002.
PMID: 10075613BACKGROUNDThiesmeier R, Orsini N. Rolling the DICE (Design, Interpret, Compute, Estimate): Interactive Learning of Biostatistics With Simulations. JMIR Med Educ. 2024 Apr 15;10:e52679. doi: 10.2196/52679.
PMID: 38619866BACKGROUNDPezoulas VC, Zaridis DI, Mylona E, Androutsos C, Apostolidis K, Tachos NS, Fotiadis DI. Synthetic data generation methods in healthcare: A review on open-source tools and methods. Comput Struct Biotechnol J. 2024 Jul 9;23:2892-2910. doi: 10.1016/j.csbj.2024.07.005. eCollection 2024 Dec.
PMID: 39108677BACKGROUNDZhang Z, Yan C, Mesa DA, Sun J, Malin BA. Ensuring electronic medical record simulation through better training, modeling, and evaluation. J Am Med Inform Assoc. 2020 Jan 1;27(1):99-108. doi: 10.1093/jamia/ocz161.
PMID: 31592533BACKGROUNDSun C, Dumontier M. Generating unseen diseases patient data using ontology enhanced generative adversarial networks. NPJ Digit Med. 2025 Jan 3;8(1):4. doi: 10.1038/s41746-024-01421-0.
PMID: 39753917BACKGROUND
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- STUDY DIRECTOR
Giuseppe Remuzzi, M.D.
Istituto Di Ricerche Farmacologiche Mario Negri
Study Design
- Study Type
- observational
- Observational Model
- CASE CONTROL
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
May 27, 2025
First Posted
June 11, 2025
Study Start
June 12, 2025
Primary Completion (Estimated)
June 1, 2027
Study Completion (Estimated)
June 1, 2027
Last Updated
September 2, 2025
Record last verified: 2025-08
Data Sharing
- IPD Sharing
- Will not share