ArticleJournal of inflammation research2024
Prediction Model and Decision Analysis for Early Recognition of SDNS/FRNS in Children.
Article in Journal of inflammation research, 2024. 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
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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.
- Establishment and validation of a machine learning-based prediction model for sepsis-induced coagulopathy.Journal of thrombosis and thrombolysis · 2026Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This study identified factors that identification of progression-predicting utility from steroid-sensitive nephrotic syndrome(SSNS) to steroid-dependent or frequently relapsing nephrotic syndrome (SDNS/FRNS) in patients and developed a corresponding predictive model. Patients and Methods: This retrospective study analyzed clinical data from 756 patients aged 1 to 18 years, diagnosed with SSNS, who received treatment at the Department of Nephrology, Children's Hospital of Chongqing Medical University, between November 2007 and May 2023. We developed a shrinkage and selection operator (LASSO) - logistic regression model, which was visualized using a nomogram. The model's performance, validity, and clinical utility were evaluated through receiver operating characteristic curve analysis, confusion matrix, calibration plot, and decision curve analysis. Results: The platelet-to-lymphocyte ratio (PLR) was identified as an independent risk factor for progression, with an odds ratio (OR) of 1.01 (95% confidence interval (CI) = 1.01-1.01, p = 0.009). Additionally, other significant factors included the time for urinary protein turned negative (OR = 1.17, 95% CI = 1.12-1.23, p < 0.001), estimated glomerular filtration rate(eGFR) (OR = 0.99, 95% CI = 0.98-0.99, p < 0.001), low-density lipoprotein (OR = 0.90, 95% CI = 0.83-0.97, p = 0.006), thrombin time (OR = 1.22, 95% CI = 1.07-1.39, p = 0.003), and neutrophil absolute counts (OR = 1.10, 95% CI = 1.02-1.18, p = 0.009). The model's performance was assessed through internal validation, yielding an area under the curve of 0.78 (0.73-0.82) for the training set and 0.81 (0.75-0.87) for the validation set. Conclusion: PLR, eGFR, the time for urinary protein turned negative, low-density lipoprotein, thrombin time, and neutrophil absolute counts may be effective predictors for identifying SSNS patients at risk of progressing to SDNS/FRNS. These findings offer valuable insights for early detection and support the use of precision medicine strategies in managing SDNS/FRNS.
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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.