ArticleBMC nephrology2026
Interpretable predictive model for deterioration of kidney function in patients with stage 4 cardiovascular-kidney-metabolic syndrome.
Article in BMC nephrology, 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
6 authors.
Funding
Abstract
backgroundCardiovascular-kidney-metabolic (CKM) syndrome is a progressive disease that can affect multiple vital organs. The specific factors contributing to the deterioration of kidney function in stage 4 CKM patients remain unclear, and no relevant clinical prediction model has been established.
methodsA retrospective analysis was conducted on eleven years of inpatient data. Stage 4 CKM Patients who fulfilled the diagnostic criteria and did not meet the exclusion criteria were enrolled. The outcome was kidney function progression, defined as a sustained decline in estimated glomerular filtration rate of ≥ 40% from baseline or initiation of kidney replacement therapy. Based on clinical indicators, predictive models were constructed using Cox regression, LASSO-Cox regression and random survival forest algorithms. Calibration curves, receiver operating characteristic curves, and decision curve analysis were employed to validate the models. The SHapley Additive exPlanations method was used to interpret the final model. Based on the model, a web-based risk calculator was constructed for clinical practice.
resultsA total of 23,014 subjects with stage 4 CKM were included and randomly divided into two cohorts at a ratio of 2:1 to the development cohort and the validation cohort. During follow-up, 1,772 outcomes (11.6%) occurred in the training cohort and 942 (12.3%) in the validation cohort. Key predictors of kidney function deterioration in stage 4 CKM patients included N-terminal pro-B-type natriuretic peptide, serum albumin, left ventricular ejection fraction, hemoglobin, age, blood urea nitrogen, and urinary protein level. Among the three models, the random survival forest model demonstrated the best discrimination and calibration in the validation set (area under the curve = 0.930, 95% confidence interval: 0.925–0.934 in 12 months).
conclusionsThe random survival forest model was highly accurate in identifying the special patients with an elevated risk of kidney function deterioration in stage 4 CKM syndrome patients. Our model can be utilized for prospective monitoring and support personalized management strategies for this high-risk population. CLINICAL TRIAL NUMBER: Not applicable.
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.