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Development and Validation of an Individualized Nomogram for Predicting Patent Foramen Ovale-Associated Stroke:
Qin Zhang1, Mochao Xiao2, Wuzhu Lu3
1Department of Cardiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China.
Insights
This study found that specific patent foramen ovale (PFO) shapes and other factors can predict cryptogenic stroke (CS) risk. A new nomogram helps identify high-risk individuals for potential device closure.
Area of Science:
- Cardiology
- Neurology
- Medical Diagnostics
Background:
- Patent foramen ovale (PFO) morphology influences right-to-left shunt (RLS) grades.
- Previous research identified four distinct PFO morphological types.
Purpose of the Study:
- To investigate the relationship between PFO morphology and cryptogenic stroke (CS).
- To develop a predictive nomogram for PFO-associated stroke risk.
Main Methods:
- Retrospective observational study of 389 adult patients with PFO.
- Analysis of PFO morphology, RLS grades, and functional parameters.
- Development and validation of a predictive nomogram using multivariable logistic regression.
Main Results:
- The predictive nomogram included PFO morphology, IAS mobility, septum primum thickness, PFO channel length, and c-TTE RLS grade.
- The nomogram demonstrated high predictive accuracy with an AUC of 0.891 in the derivation cohort and 0.935 in the validation cohort.
- Decision curve analysis confirmed the nomogram's clinical applicability.
Conclusions:
- PFO morphological types enhance risk stratification for PFO-associated stroke.
- The developed nomogram effectively identifies high or low-risk PFO individuals.
- The nomogram aids in selecting patients who may benefit from interventional device closure.
Background:
We previously reported four patent foramen ovale (PFO) morphological types that influenced right-to-left shunt (RLS) grades. Herein, we aimed to study the relationship between PFO morphology and cryptogenic stroke (CS). We further developed a nomogram based on four PFO morphological types and functional parameters to guide clinicians in judging the risk of PFO-associated stroke.
Methods:
This was a retrospective observational study involving adult patients with PFO between January 2020 and November 2022. Patients were divided into a PFO-associated stroke group (CS group) and a group without cryptogenic stroke (non-CS group). Four types of PFO and RLS grades were analyzed. Nomograms were made to predict PFO-associated stroke using multivariable logistic regression analysis. The discrimination performance of the model was internally validated and assessed using the receiver operating characteristic.
Results:
We enrolled 389 patients (male, 182 patients; female, 207 patients) with PFO, the mean age was 43.3 ± 8.1 years. The derivation cohort comprised 293 patients (CS group, 186 patients; non-CS group, 107 patients). The predictive nomogram comprised PFO morphological types, interatrial septum (IAS) mobility distance, septum primum thickness, PFO channel length at rest, and contrast-transthoracic echocardiography (c-TTE) RLS grade during the Valsalva maneuver. A validation cohort was established (CS group, 61 patients; non-CS group, 35 patients). The model area under the curve (AUC) was 0.891 (95% confidence interval = 0.855-0.928) in the derivation cohort and 0.935 (95% confidence interval = 0.885-0.986) in the validation cohort. Calibration curve analysis showed that the nomogram had a C-index of 0.891 in the derivation cohort and 0.935 in the validation cohort. The decision curve analysis (DCA) indicated that the nomogram had clinical applicability.
Conclusions:
Adding four PFO morphological types improved the risk stratification capability for PFO-associated stroke. The nomogram can identify high or low-risk PFO individuals and select patients who will likely benefit from interventional device closure.
