ArticleGlobal heart2025
Construction and Verification of a Predictive Model for the Progression of Aortic Valve Calcification.
Article in Global heart, 2025. 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
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The primary objective of this study is to develop and validate a predictive model assessing the likelihood of disease progression in individuals with aortic valve calcification (AVC). Methods: For the second and third visits, 2,533 patients were followed up. They were randomly assigned to a train set and a validation set at a ratio of 7:3. After employing the Least Absolute Shrinkage and Selection Operator (LASSO) and multiple Cox regression to filter predictors, the selected variables were input into the Cox proportional risk model for model construction. Calibration curve, Consistency Index (C-index), Receiver Operating Characteristic (ROC) curve, and Decision Curve Analysis (DCA) were employed to validate the model. Patients were categorized into low- and high-risk groups based on the model's predicted risk score, and survival analysis was conducted using Kaplan-Meier (K-M) plots. An online platform was used to enhance the clinical utility. Results: The incidence of AVC progression was 9.63%. LASSO-Cox regression analysis identified seven variables significantly correlated with AVC progression. In both the training and validation sets, the Area Under the Curve (AUC) and C-index of the prediction model exceeded 0.8. The calibration curve aligned closely with the diagonal line. Decision Curve Analysis (DCA) underscored the clinical application value of the model. Survival analysis demonstrated a significantly higher progression rate in the high-risk group compared to the low-risk group. The online platform visualized the probability of progression. Conclusion: The developed predictive model has proven reliability and accuracy in forecasting the 2-, 3-, and 4-year progression rates of patients with AVC. It offers a dependable framework for estimating progression and facilitating individualized comprehensive prevention strategies for individuals with AVC.
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.