ArticleHeliyon2024
A nomogram to predict cognitive function impairment in patients with chronic kidney disease: A national cross-sectional survey.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
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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, 1 synthesis or guideline pooled it.
- Risk prediction models for cognitive impairment in patients with chronic kidney disease: a systematic review.Frontiers in public health · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Cognitive function impairment (CFI) is common in patients with chronic kidney disease (CKD) and significantly impacts treatment adherence and quality of life. This study aims to create a simplified nomogram for early CFI risk detection. Methods: Data were obtained from the National Health and Nutrition Examination Survey cycles spanning from 1999 to 2002 and again from 2011 to 2014. Stepwise logistic regression was used to select variables and construct a CFI risk prediction model. Furthermore, C-statistic and Brier Score (BS) assessed model performance. Additionally, Kaplan-Meier survival curves were utilised to assess risk group-death prognosis relationships. Results: Of the 545 participants in the CKD model development cohort, a total of 146 (26.8 %) had CFI. The final model included the variables of age, race, education, annual family income, body mass index, estimated glomerular filtration rate, serum albumin and uric acid. The model had a C-statistic of 0.808 (95 % confidence interval (CI): 0.769-0.847) and a BS of 0.149. Furthermore, the 5-fold cross-validation internal C-statistic was 0.764 (interquartile range: 0.763-0.807) and BS was 0.154. Upon external validation, the model's C-statistic decreased to 0.752 (95 % CI: 0.654-0.850) and its BS increased to 0.182. The Kaplan-Meier survival curves demonstrated that intermediate-to-high-risk participants had shorter overall survival time than low-risk participants (log-rank test: p = 0.00042). Conclusions: This study established an effective nomogram for predicting CFI in patients with CKD, which can be used for the early detection of CFI and guide the treatment of patients with CKD.
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Registered trials
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