Impact of Patent Foramen Ovale Anatomic Features on Right-to-Left Shunt in Patients with Cryptogenic Stroke

PFO morphology for evaluation of c-TCD and c-TTE RLS grades

Purpose

The purpose of this study was to observe the morphologic characteristics of patent foramen ovale (PFO) by transesophageal echocardiography (TEE), and to analyze its correlation with right-to-left shunt (RLS) of contrast-transthoracic echocardiography (c-TTE) and contrast-transcranial Doppler ultrasonography (c-TCD).

Methods

124 patients with PFO were divided into four groups according to the morphological characteristics of PFO. RLS grade of each group PFO with c-TTE and c-TCD in resting and Valsalva manoeuvre was measured. Anatomical structures influencing RLS grade were analyzed statistically through multivariate logistic analyses and predictive models.

Results

The 124 cases of PFO were divided into four groups: 55 cases (44.4%) with smooth uniform tubular tunnel (SUT), 21 cases (16.9%) with granule uniform tubular tunnel (GUT), 23 cases (18.5%) of right funnelform, 25 cases (20.2%) of left funnelform. Between group comparisons and multivariate logistic analyses revealed that PFO morphotype and interatrial septum(IAS) mobility were influencing factors of RLS degree. During Valsalva, the probability of c-TCD RLS ≥ 2 for the right funnelform PFO was 13.428 times that of the GUT, one unit increase in IAS mobility increased the probability of c-TCD RLS ≥ 2 by a factor of 2.029, model predicted c-TCD RLS ≥ 2 with 78.1% sensitivity and 94.7% specificity; During Valsalva, the probability of c-TCD RLS ≥ 2 for the SUT PFO was 4.244 times that of the GUT, one unit increase in IAS mobility increased the probability of c-TTE RLS ≥ 2 by a factor of 2.392, model predicted c-TTE RLS ≥ 2 with 80.2% sensitivity and 87.9% specificity.

Conclusions

Studies have shown that the morphological structure of PFO is an influencing factor of RLS, and TEE can observe the specific morphological characteristics of PFO, which can further predict the level of RLS, help predict the occurrence of Cryptogenic stroke (CS). The above provides more evidences and surgical options for Interventional device closure indications.

Intelligent Prediction of Cryptogenic Stroke Using Patent Foramen Ovale from TEE Imaging Data and Machine Learning Methods

In spite of the popularity of random forests (RF) as an efficient machine learning algorithm, methods for constructing the potential association for between patent foramen ovale (PFO) and cryptogenic stroke (CS) using this technique are still barely. For the vital regional study areas (atrial septum), RF was used to predict CS in patients with PFO using partial clinical data of patients and remotely sensed imaging examination data obtained from Tee imaging. We validated our method on a dataset of 151 consecutive patients with detected PFO at a large grade A hospital in China from November 2018 to December 2020, we obtained an area under the relative operating characteristic curve of 0.816, with 65% specificity at 73% sensitivity. The RF models accurately represented the relationship between the CS and remotely sensed predictor variables. Therein, maximum mobility, large right-to-left shunt during Valsalva maneuver, size of PFO in diastole and systole, and diastolic length of the tunnel present higher predictive value in CS. Our findings suggest that multi-Doppler sensor data by transesophageal echocardiography (TEE)-detected morphologic and functional characteristics of PFO may play important roles in the occurrence of CS. These results indicate that the established random forest model has the potential to predict CS in patients with PFO and great promise for application to clinical practice.