ArticleDiabetic medicine : a journal of the British Diabetic Association2026
Application of construction and validation of logistic regression model in risk prediction of non-proliferative diabetic retinopathy in type 2 diabetes mellitus.
Article in Diabetic medicine : a journal of the British Diabetic Association, 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
7 authors.
Funding
Abstract
aimsDiabetic retinopathy (DR) is a leading cause of vision impairment in type 2 diabetes mellitus (T2DM), with non-proliferative DR (NPDR) representing its most prevalent form. Early identification of high-risk individuals remains challenging due to the complexity and poor interpretability of existing machine learning models. This study aims to develop a clinically interpretable Logistic regression model for NPDR risk prediction using routinely available clinical indicators.
methodsA retrospective cohort of 421 T2DM patients from a single centre was divided into training (n = 295) and validation (n = 126) sets. Demographic, glycemic (fasting glucose, HbA1c), renal (UACR) and ophthalmologic (macular oedema) data were collected. Univariate and multivariate Logistic regression with stepwise selection identified independent predictors. Model performance was evaluated using area under the ROC curve (AUC), sensitivity, specificity and Hosmer-Lemeshow goodness-of-fit. Internal validation was performed via bootstrapping (1000 replicates), and external validation used an independent cohort.
resultsFour independent predictors were identified: macular oedema (OR = 3.247), fasting glucose (OR = 2.194), HbA1c (OR = 2.799) and UACR (OR = 1.153). The model demonstrated excellent discrimination in the training set (AUC = 0.949, sensitivity = 86.4%, specificity = 95.5%) and good calibration (H-L test, p = 0.358). Bootstrap validation confirmed stability of HbA1c and UACR. External validation yielded an AUC of 0.918, with a positive predictive value of 91.1% and maintained calibration (p = 0.282).
conclusionsThe constructed Logistic regression model accurately predicts NPDR risk using four readily available clinical variables, offering high discriminative power, interpretability and clinical utility for stratifying high-risk T2DM patients in primary care settings.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.