AUTHOR=Wu Zhuonan , Zhang Chuanjing , Liu Nan , Xie Wenqing , Yang Jinjin , Guo Hangyuan , Chi Jufang 

TITLE=A Nomogram for Predicting Patent Foramen Ovale-Related Stroke Recurrence

JOURNAL=Frontiers in Neurology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.903789

DOI=10.3389/fneur.2022.903789

ISSN=1664-2295

ABSTRACT=Abstract
Background: The high prevalence of patent foramen ovale (PFO) in cryptogenic stroke suggested a stroke-causing role for PFOThere is a trend to differentiate patent foramen ovale (PFO)-related stroke from cryptogenic stroke. 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 nomogram1, and those identified using least absolute shrinkage and selection operator（LASSO）regression were included in nomogram2. 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: Nomogram1 was based on homocysteine (Hcy), high-sensitivity C-reactive protein (hsCRP) and albumin (ALB), and nomogram2 was based on Age, Diabetes, Hypertension, right-to-left shunt, ALB, prealbumin, hsCRP and Hcy. The C-index of nomogram1 was 0.861, which was not significantly different from that of nomogram2 (0.893). The 2- and 5-year AUCs of nomogram1 were 0.863 and 0.777, respectively. In the validation set, nomogram1 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 nomogram1 and the actual observation. DCA demonstrated that nomogram1 was clinically useful. Moreover, patients were successfully divided into two distinct risk groups (low and high risk) for recurrence rate by nomogram1.
Conclusions: Nomogram1 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.