ArticleFrontiers in neurology2022
A Nomogram for Predicting Patent Foramen Ovale-Related Stroke Recurrence.
Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed, 3 citations in OpenAlex.
- Clinical research progress on pathogenesis and treatment of Patent Foramen Ovale-associated stroke.Frontiers in neurology · 2025Review
- Calf muscle pump tensing as a novel maneuver to improve the diagnostic performance of detecting patent foramen ovale during transesophageal echocardiography.Frontiers in neurology · 2023Article
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 at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The high prevalence of patent foramen ovale (PFO) in cryptogenic stroke suggested a stroke-causing role for PFO. As risk factors for recurrence of such stroke are not recognized, clinicians cannot sufficiently identify, treat, and follow-up high-risk patients. Therefore, this study aimed to establish a prediction model for PFO-related stroke recurrence. Methods: This study included 392 patients with PFO-related stroke in a training set and 164 patients with PFO-related stroke in an independent validation set. In the training set, independent risk factors for recurrence identified using forward stepwise Cox regression were included in nomogram 1, and those identified using least absolute shrinkage and selection operator(LASSO)regression were included in nomogram 2. Nomogram performance and discrimination were assessed using the concordance index (C-index), area under the curve (AUC), calibration curve, and decision curve analyses (DCA). The results were also validated in the validation set. Results: Nomogram 1 was based on homocysteine (Hcy), high-sensitivity C-reactive protein (hsCRP), and albumin (ALB), and nomogram 2 was based on age, diabetes, hypertension, right-to-left shunt, ALB, prealbumin, hsCRP, and Hcy. The C-index of nomogram 1 was 0.861, which was not significantly different from that of nomogram 2 (0.893). The 2- and 5-year AUCs of nomogram 1 were 0.863 and 0.777, respectively. In the validation set, nomogram 1 still had good discrimination (C-index, 0.862; 2-year AUC, 0.839; 5-year AUC, 0.990). The calibration curve showed good homogeneity between the prediction by nomogram 1 and the actual observation. DCA demonstrated that nomogram 1 was clinically useful. Moreover, patients were successfully divided into two distinct risk groups (low and high risk) for recurrence rate by nomogram 1. Conclusions: Nomogram 1, based on Hcy, hsCRP, and ALB levels, provided a more clinically realistic prognostic prediction for patients with PFO-related stroke. This model could help patients with PFO-related stroke to facilitate personalized prognostic evaluations.
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.