Revealing Engagement Dynamics Among Diabetic Kidney Disease Patients
Study Details
Study Description
Brief Summary
The study intends to investigate the personal experiences of diabetic kidney disease patients who take part in a separate clinical study including a specific medication intervention. The major focus will be on closely following individuals' rates of trial completion and withdrawal.
The data collected from this study will help improve future outcomes for all diabetic kidney disease as well as those in under-represented demographic groups.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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Study Design
Outcome Measures
Primary Outcome Measures
- Number of patients who decide to enroll in a diabetic kidney disease clinical research [3 months]
- Rate of patients who remain in a diabetic kidney disease clinical trial to trial completion [12 months]
Eligibility Criteria
Criteria
Inclusion Criteria:
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Patients diagnosed with diabetic kidney disease
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Aged ≥ 18 years old and ability to provide written informed consent obtained prior to participation in the study and any related procedures being performed
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Subjects willing and able to comply with the protocol for the duration of the study including undergoing treatment and scheduled visits and examination.
Exclusion Criteria:
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Refusal of consent
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Women of childbearing potential without a negative pregnancy test; or women who are lactating.
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Any serious and/or unstable pre-existing medical disorders
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Power Life Sciences | San Francisco | California | United States | 94107 |
Sponsors and Collaborators
- Power Life Sciences Inc.
Investigators
- Study Director: Michael B Gill, Power Life Sciences Inc.
Study Documents (Full-Text)
More Information
Publications
- Chan L, Nadkarni GN, Fleming F, McCullough JR, Connolly P, Mosoyan G, El Salem F, Kattan MW, Vassalotti JA, Murphy B, Donovan MJ, Coca SG, Damrauer SM. Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease. Diabetologia. 2021 Jul;64(7):1504-1515. doi: 10.1007/s00125-021-05444-0. Epub 2021 Apr 2.
- Schrauben SJ, Shou H, Zhang X, Anderson AH, Bonventre JV, Chen J, Coca S, Furth SL, Greenberg JH, Gutierrez OM, Ix JH, Lash JP, Parikh CR, Rebholz CM, Sabbisetti V, Sarnak MJ, Shlipak MG, Waikar SS, Kimmel PL, Vasan RS, Feldman HI, Schelling JR; CKD Biomarkers Consortium and the Chronic Renal Insufficiency Cohort (CRIC) Study Investigators. Association of Multiple Plasma Biomarker Concentrations with Progression of Prevalent Diabetic Kidney Disease: Findings from the Chronic Renal Insufficiency Cohort (CRIC) Study. J Am Soc Nephrol. 2021 Jan;32(1):115-126. doi: 10.1681/ASN.2020040487. Epub 2020 Oct 29.
- Tofte N, Lindhardt M, Adamova K, Bakker SJL, Beige J, Beulens JWJ, Birkenfeld AL, Currie G, Delles C, Dimos I, Francova L, Frimodt-Moller M, Girman P, Goke R, Havrdova T, Heerspink HJL, Kooy A, Laverman GD, Mischak H, Navis G, Nijpels G, Noutsou M, Ortiz A, Parvanova A, Persson F, Petrie JR, Ruggenenti PL, Rutters F, Rychlik I, Siwy J, Spasovski G, Speeckaert M, Trillini M, Zurbig P, von der Leyen H, Rossing P; PRIORITY investigators. Early detection of diabetic kidney disease by urinary proteomics and subsequent intervention with spironolactone to delay progression (PRIORITY): a prospective observational study and embedded randomised placebo-controlled trial. Lancet Diabetes Endocrinol. 2020 Apr;8(4):301-312. doi: 10.1016/S2213-8587(20)30026-7. Epub 2020 Mar 2.
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