Artificial intelligence-enabled 8-lead ECG detection of atrial septal defect among adults: a novel diagnostic tool.

Background: Patients with atrial septal defect (ASD) exhibit distinctive electrocardiogram (ECG) patterns. However, ASD cannot be diagnosed solely based on these differences. Artificial intelligence (AI) has been widely used for specifically diagnosing cardiovascular diseases other than arrhythmia. Our study aimed to develop an artificial intelligence-enabled 8-lead ECG to detect ASD among adults. Method: In this study, our AI model was trained and validated using 526 ECGs from patients with ASD and 2,124 ECGs from a control group with a normal cardiac structure in our hospital. External testing was conducted at Wuhan Central Hospital, involving 50 ECGs from the ASD group and 46 ECGs from the normal group. The model was based on a convolutional neural network (CNN) with a residual network to classify 8-lead ECG data into either the ASD or normal group. We employed a 10-fold cross-validation approach. Results: Statistically significant differences (p < 0.05) were observed in the cited ECG features between the ASD and normal groups. Our AI model performed well in identifying ECGs in both the ASD group [accuracy of 0.97, precision of 0.90, recall of 0.97, specificity of 0.97, F1 score of 0.93, and area under the curve (AUC) of 0.99] and the normal group within the training and validation datasets from our hospital. Furthermore, these corresponding indices performed impressively in the external test data set with the accuracy of 0.82, precision of 0.90, recall of 0.74, specificity of 0.91, F1 score of 0.81 and the AUC of 0.87. And the series of experiments of subgroups to discuss specific clinic situations associated to this issue was remarkable as well. Conclusion: An ECG-based detection of ASD using an artificial intelligence algorithm can be achieved with high diagnostic performance, and it shows great clinical promise. Our research on AI-enabled 8-lead ECG detection of ASD in adults is expected to provide robust references for early detection of ASD, healthy pregnancies, and related decision-making. A lower number of leads is also more favorable for the application of portable devices, which it is expected that this technology will bring significant economic and societal benefits.

Role of the Social Support and Health Status in Living Arrangement Preference of the Elderly in China-A Cross-Sectional Study.

Background: Living arrangement of the elderly is one of the most important components that affect their quality of life in later years. The aging, with the phenomenon of low fertility rate and family structure transformation, has caused changes in the living arrangements of the elderly. This research aimed to find the elderly's living arrangement preferences and influencing factors. Methods: The data were obtained from The Chinese Longitudinal Healthy Longevity Survey (CLHLS) in the 2018, and the sample was comprised of 9,638 individuals aged ≥ 60 years. Independent variables were divided into social support, health status and so-economic status. Chi-square test and binary logistic regression were used to analyze the relationship between the above variates and living arrangement preferences. Results: Currently, in terms of living arrangement preferences, nearly half (45.6%) of the respondents choose not to live with their children. The binary model results showed that elderly who were married (OR = 0.166, 95% CI: 0.147-0.187), experienced more than 6 years of education (OR = 0.600, 95% CI: 0.517-0.695), ability of daily living (ADL) impaired (OR = 0.810, 95% CI: 0.720-0.912), suffering from multiple chronic diseases (OR = 0.803, 95% CI: 0.720-0.912), and obtained community services (OR = 0.884, 95% CI: 0.803-0.972) incline to not live with their children. The elderly who living in rural areas (OR = 1.244, 95% CI: 1.129-1.371), with an income of more than 500,000 yuan per year (OR = 1.557, 95% CI: 1.380-1.757), having children visiting regularly (OR = 1.405, 95% CI: 1.161-1.707) and receiving children's financial support (OR = 1.194, 95% CI: 1.080-1.319) are more likely to choose to live with their children. Conclusions: This study found that the living arrangement preferences of the elderly were affected by social support and health status, and living with children is no longer the only option for the elderly these days. The elderly care services provided by communities or professional care institutions may become the mainstream of taking care of the elderly citizens in the aging society. Improving the types and forms of community nursing services to increase the accessibility of these services; setting up elderly care institutions reasonably and equipping adequate professional nursing staff should be considered as priority measures.

Factors affecting the evolution of Chinese elderly depression: a cross-sectional study.

BACKGROUND: In the past decades, China's rapid economic growth has been accompanied by rapid changes in lifestyle and an increasing prevalence of mental disorders. This study explored the changes and factors associated with depression among the elderly population of China from 2011 to 2018. METHOD: Data were obtained from the China Health and Retirement Longitudinal Study. The total sample size consists of 21,484 individuals aged ≥60 years, and the sample sizes in 2011, 2013, 2015 and 2018 were 5081, 4853, 5207, 6343, respectively. Depressive symptoms were measured by the 10-item Short-Form developed by the Center for Epidemiological Studies Depression. RESULT: We found that the tendency in depression rate of the Chinese elderly from 2011 (36.8%) to 2018 (44.5%). The results showed poor health (OR = 3.553), ADL damage (OR = 2.010), multiple chronic diseases (OR = 1.287), and western (OR = 1.777) are risk factors for depression. CONCLUSION: The rate of depression of the elderly people in China has risen dramatically. Therefore, additional steps to prevent, treat and care for the affected population are needed, Mental health prevention and treatment strategies should be incorporated into China's public health policies in a timely manner to mitigate the serious economic burden caused by the increase of depression.