ArticleAmerican journal of clinical and experimental urology2026
A non-invasive predictive model for identifying non-diabetic kidney disease in type 2 diabetes mellitus: development and multicenter validation.
Article in American journal of clinical and experimental urology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to develop a non-invasive, simple, and rapid predictive model for identifying non-diabetic kidney disease (NDKD) in patients with type 2 diabetes mellitus (T2DM).
methodsWe performed a retrospective analysis of clinical data from 117 T2DM patients who underwent renal biopsy at a single medical institution between 2017 and 2022; candidate variables were first prioritized based on clinical relevance, followed by the construction of a predictive framework using logistic regression. Dubbed the RICH model, the final framework integrated four key parameters: red blood cell (RBC) count, immunoglobulin A (IgA) level, cystatin C-derived estimated glomerular filtration rate (eGFR_2), and glycated hemoglobin A1c (HbA1c).
resultsExternal validation was conducted across three independent centers involving 299 T2DM patients (2018-2024), achieving area under the receiver operating characteristic curve (AUC-ROC) values of 0.755, 0.764, and 0.755, which complemented the internal validation AUC-ROC of 0.847; at an optimal threshold probability of 0.559, approximately 20% of patients obtained clinical net benefit from the model, and notably, applying the RICH model for early NDKD screening has the potential to reduce the renal biopsy rate by 42.05%.
conclusionsThe RICH model exhibits robust performance in predicting NDKD among T2DM patients with renal impairment, providing a practical tool for clinical decision-making.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.