Association between electrocardiographic and echocardiographic atrial abnormalities and prognosis in cryptogenic stroke

Obesity-hyperlipidemia, Hypertension, and Left Atrial Enlargement During Stroke in Young Adults

BACKGROUND

This study investigated the association between left atrial enlargement and stroke severity in young adults. We also studied the differences between the normal and left atrial enlargement groups in clinical data.

METHODS

A total of 135 young stroke patients admitted to the Department of Neurology of the Taizhou People's Hospital were recruited from January 2018 to December 2021. The patients were divided into normal and enlarged groups by left atrial size. The relationship between the left atrial diameter and the National Institutes of Health Stroke Scale score was analyzed apart from the differences in clinical variables.

RESULTS

No relationship was observed between the left atrial diameter and the National Institutes of Health Stroke Scale score ( r =-0.045 P =0.603). The univariate analysis of both groups revealed that hypertension ( P =0.004), hyperlipidemia ( P =0.001), body mass index ( P =0.000), obesity ( P =0.015), and not stroke etiologic subtypes were associated with left atrial enlargement. In binary logistic regression analysis models, hyperlipidemia 3.384 (95% CI, 1.536 to 7.452), hypertension 2.661 (95% CI, 1.066 to 6.639), and obesity 2.858 (95% CI, 1.158 to 7.052) were significantly associated with the young stroke of left atrial enlargement.

CONCLUSIONS

In young adults, obesity-hyperlipidemia and hypertension were significantly associated with left atrial enlargement in stroke.

Artificial Intelligence-Enabled Electrocardiogram Estimates Left Atrium Enlargement as a Predictor of Future Cardiovascular Disease

Background: Left atrium enlargement (LAE) can be used as a predictor of future cardiovascular diseases, including hypertension (HTN) and atrial fibrillation (Afib). Typical electrocardiogram (ECG) changes have been reported in patients with LAE. This study developed a deep learning model (DLM)-enabled ECG system to identify patients with LAE. Method: Patients who had ECG records with corresponding echocardiography (ECHO) were included. There were 101,077 ECGs, 20,510 ECGs, 7611 ECGs, and 11,753 ECGs in the development, tuning, internal validation, and external validation sets, respectively. We evaluated the performance of a DLM-enabled ECG for diagnosing LAE and explored the prognostic value of ECG-LAE for new-onset HTN, new-onset stroke (STK), new-onset mitral regurgitation (MR), and new-onset Afib. Results: The DLM-enabled ECG achieved AUCs of 0.8127/0.8176 for diagnosing mild LAE, 0.8587/0.8688 for diagnosing moderate LAE, and 0.8899/0.8990 for diagnosing severe LAE in the internal/external validation sets. Notably, ECG-LAE had higher prognostic value compared to ECHO-LAE, which had C-indices of 0.711/0.714 compared to 0.695/0.692 for new-onset HTN, 0.676/0.688 compared to 0.663/0.677 for new-onset STK, 0.696/0.695 compared to 0.676/0.673 for new-onset MR, and 0.800/0.806 compared to 0.786/0.760 for new-onset Afib in internal/external validation sets, respectively. Conclusions: A DLM-enabled ECG could be considered as a LAE screening tool and provide better prognostic information for related cardiovascular diseases.