ArticleFrontiers in cardiovascular medicine2026
Prediction model for early left ventricular systolic dysfunction progression in hypertrophic cardiomyopathy.
Article in Frontiers in cardiovascular medicine, 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
No grant is acknowledged in the PubMed record.
Abstract
Background: The risk of early left ventricular systolic dysfunction progression (ELVSDP) among patients with hypertrophic cardiomyopathy (HCM) is heterogeneous, and accurate short-and medium-term prediction tools are lacking. This study aimed to develop and validate a predictive model for the risk of ELVSDP in HCM patients at 6, 12, and 18 months. Methods: A total of 314 HCM patients without ELVSDP at baseline were included and randomly divided into training and validation sets. LASSO-Cox regression was employed to select variables and identify independent predictors, based on which a nomogram and an interactive dynamic prediction tool were constructed. Model performance was evaluated using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Risk stratification was assessed using the Kaplan-Meier method. Results: Age [hazard ratio (HR) = 1.17], smoking history (HR = 2.79), B-type natriuretic peptide (BNP) level (HR = 1.002), and left ventricular outflow tract obstruction (HR = 2.24) were independent predictors of ELVSDP. The model demonstrated strong performance in both the training and validation sets. The time-dependent area under the curve (AUC) exceeded 0.88 at 6, 12, and 18 months, with C-indices of 0.94 and 0.93, respectively. Bootstrap validation confirmed model stability. Calibration curves showed good agreement between predicted and observed outcomes, and DCA indicated a net clinical benefit. The incidence of ELVSDP was significantly higher in the high-risk group compared to the low-risk group ( Conclusion: The nomogram developed in this study accurately predicts short-term ELVSDP risk in HCM patients, facilitating early risk stratification and individualized management. Clinical Trial Registration: This study was registered at the Chinese Clinical Trial Registry http://www.chictr.org.cn/, registration number ChiCTR2500106648 [Registration Date: 2025-07-28].
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.