Conceptio › Archive › ClinicalTrials.gov
ClinicalTrials.gov public full text Public Domain

Oxford Classification and Clinical Remission After Initial Treatments in Patients With IgA Nephropathy

Shenzhen Second People's Hospital · 2022
ClinicalTrials.gov · Datasets · License: Public Domain · 2022
Open Source ↗
IgA nephropathy (IgAN) is the most common form of primary glomerulonephritis and one of the leading causes of end-stage renal disease in China. The clinical manifestations of IgAN varies widely among individuals, and renal pathology is crucial for determining the severity of renal damage and predicting the renal progression. However, the association between renal pathology and patient response to medication has not been reported, and the majority of earlier RCT studies have not taken renal pathology into consideration when enrolling patients. The Kidney Disease: Improving Global Outcomes (KDIGO) Glomerular Diseases Work Group, one of the most prestigious kidney disease organizations in the world, claims that there is not enough evidence to support the use of Oxford Classification to decide whether to administer immunosuppressive therapy to patients with IgAN.Therefore, the goal of this study was to investigate the relationship between Oxford Classification and clinical remission rates following initial teatments in patients with IgAN, with the aim of providing a basis for individualized clinical treatment plans. This study was a single-center prospective cohort study, and patients who were hospitalized in Shenzhen Second People's Hospital from January 2011 to January 2021 and diagnosed as IgAN by renal biopsy were collected continuously and followed up until December 2022. Cox regression models were used to analyze the effect of different Oxford Classifications on the clinical remission rates of patients at 6, 12, 18, and 24 months of treatments, and the relationship between Oxford Classification and secondary outcome indicators such as long-term renal function and urinary protein changes were analyzed using generalized additive mixed models.
Conceptio Open Archive

Analyze & explore related research in Conceptio

Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.

This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗

Related documents

Record · ID 1058773
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive