ArticleBMC nephrology2025
Development and validation of a nomogram for predicting hyperphosphatemia in non-dialysis patients with chronic kidney disease.
Article in BMC nephrology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Development and External Validation of a Nomogram for Individualized Risk Prediction of Hyperprolactinemia in Chronic Kidney Disease: A Retrospective Multicenter Study.International journal of general medicine · 2026Article
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
No grant is acknowledged in the PubMed record.
Abstract
backgroundElevated serum phosphate levels are strongly associated with an increased risk of all-cause mortality in patients with chronic kidney disease (CKD). The aim of this study was to identify independent risk factors for hyperphosphatemia in patients with non-dialysis CKD and use the findings to develop and validate a predictive model for assessing hyperphosphatemia risk.
methodsData of patients with CKD discharged from the Department of Nephrology between January 2021 and December 2023 were retrospectively analyzed. Potential predictors were screened from an array of clinical variables using least absolute shrinkage and selection operator regression in conjunction with 10-fold cross-validation. A multivariate logistic regression model was constructed to identify independent risk factors for predicting hyperphosphatemia. The C-index, receiver operating characteristic curve, calibration curve, and decision curve analysis were used to evaluate model predictive power, discriminability, accuracy, and clinical utility. Internal validation was implemented through a comparison of results from a validation set and the entire dataset.
resultsThis study included 216 patients, with 134 (62.04%) individuals who developed hyperphosphatemia. Logistic regression revealed that hemoglobin, blood urea nitrogen, serum creatinine, and parathyroid hormone were independently correlated with hyperphosphatemia. The nomogram C-index was 0.916 (95% confidence interval [CI]: 0.872-0.961). The model demonstrated excellent discriminative ability in the independent validation set (area under the curve [AUC] = 0.953, 95% CI: 0.909-0.998), with the full dataset analysis showing concordant results (AUC = 0.923, 95% CI: 0.889-0.958). The decision and clinical impact curves showed the clinical value of our nomogram for patients with CKD and hyperphosphatemia.
conclusionsThe nomogram model was highly accurate in identifying CKD subpopulations at an elevated risk of serum phosphorus metabolic disorders. Our model can be utilized for prospective monitoring and preventive intervention. Furthermore, through individualized risk assessments, the model can contribute to the development of customized treatment strategies that have the potential to markedly improve long-term prognosis.
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.