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Background: The reason for autonomic dysfunction in COVID-19 is yet to get clear, though we tend to notice a few dysautonomia cases during post-COVID-19 manifested most commonly in the form of postural orthostatic tachycardia syndrome (POTS). Case Presentation: A case of autonomic dysregulation is presented in a COVID-19 patient as a post-COVID-19 sequelae. On doing an active stand test and two basic autonomic function tests, dysautonomia was diagnosed in the patient, after going through all routine investigations for six months. Cardiac parasympathetic integrity was assessed by measuring the heart rate response to standing by continuous electrocardiographic tracing. The management of these patients is usually conservative. Conclusion: Family physicians must be aware of such cases in future for early diagnosis and management of the patients.
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Background Dysautonomia denotes an alteration in the autonomic nervous system that can significantly lead to multiorgan failure. Conversely, depression not only affects cognitive functions but also poses a risk factor for sudden cardiac death. In our study, the Composite Autonomic Scoring-31 (COMPASS-31) is used to quantify the autonomic symptoms present if any in patients with depression. Materials and methods Forty-two patients with major depressive disorder (MDD) were recruited using a PHQ-9 questionnaire followed by a COMPASS-31 scale to quantify dysautonomia symptoms, and they were compared with healthy controls. Further regression analysis was conducted to establish any relationship between independent variables and COMPASS-31 scores. Results The average COMPASS-31 score in patients with MDD was 22.56±8.42, which was significantly increased compared to healthy controls (p=0.001). Furthermore, the differences persisted across various subdomains of the COMPASS-31 scale relative to severity of depression. Conclusion The study observations could provide a relevant perception regarding the association between depression and autonomic dysfunction with the use of a simple and brief yet validated instrument COMPASS-31, which can be utilized for screening at the primary care level.
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Background and objective ChatGPT is a large language model (LLM) generative artificial intelligence (AI) chatbot trained through deep learning to produce human-like language skills and analysis of simple problems across a wide variety of subject areas. However, in terms of facilitating the transfer of learning in medical education, a concern has arisen that while AI is adept at applying surface-level understanding, it does not have the necessary in-depth knowledge to act at an expert level, particularly in addressing the core concepts. In this study, we explored the efficacy of ChatGPT in solving various reasoning questions based on the five core concepts applied to different modules in the subject of physiology. Materials and methods In this study, a total of 82 reasoning-type questions from six modules applicable to the five core concepts were created by the subject experts. The questions were used to chat with the conversational AI tool and the responses generated at first instance were considered for scoring and analysis. To compare the scores among various modules and five core concepts separately, the Kruskal-Wallis test along with post hoc analysis were used. Results The overall mean score for the modules (60 questions) was 3.72 ±0.26 while the average score obtained for the core concepts (60 questions) was 3.68 ±0.30. Furthermore, statistically significant differences (p=0.05 for modules and p=0.024 for core concepts) were observed among various modules as well as core concepts. Conclusion The significant differences observed in the scores among various modules and core concepts highlight the varying execution of the same software tool, thereby necessitating the need for further evaluation of AI-enabled learning applications to enhance the transfer of learning among undergraduates.
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Cardiovascular disease has become a huge burden to human health. Artificial intelligence (AI)-enabled smartwatches continuously monitor the heart rate, which potentially helps to diagnose unwarranted rhythm and irregularity problems such as tachycardia, bradycardia, and fibrillation. Deep learning (DL), convolutional neural networks (CNN), and support vector machines are various modalities of AI adopted in the field of cardiology extensively, starting from a single echocardiogram to cardiac imaging. Yet, the efficacy and safety of machine learning in healthcare have always raised valid questions for the manufacturers of various healthcare devices. It is thus challenging and promising to see how AI in medicine will affect human lives in tackling various medical conditions along with global threats such as pandemics. A case report on idiopathic ventricular tachycardia (VT) detected by an AI-aided smartwatch is presented in this paper.
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Background: Bhramari Pranayama yogic breathing is a very simple exercise of voluntary breathing which mitigates stress reactivity through autonomic modulation of heart rate. Aim: To study the effects of long-term practice of the yoga breathing exercise on cardiovascular reactivity to head-up tilt (HUT) test. Methods: All the participants were subjected to a 10-item yoga questionnaire. Based on Likert scale scoring, 32 subjects (group I) were selected as long-term yogic breathing practitioners, and autonomic function tests using heart rate variability (HRV) and a provocative stress test and HUT test were conducted. Cardiovascular parameters obtained were compared with controls who had never been engaged in any form of yoga (group II). Results: A highly significant decrease in values of both high (HF) and low frequency (LF) variables was observed in group II as compared to group I. However, a highly significant rise in LF/HF has been observed in group I compared to group I (P = 0.004), further indicating a greater withdrawal of vagal tone during the HUT test and also during recovery (P = 0.001). Discussion: The results and discussion of HRV analysis during provocative tests concluded that autonomic imbalance was present in the subjects who were not exposed to any form of yoga. Thus, it is advisable to practice yoga in any form regularly to mitigate the early onset of autonomic dysfunction.
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Objective: Depressive disorder poses an increased risk for sudden cardiac death in elderly population with existing comorbidities. Also, few international studies suggest the association of decreased stress reactivity with symptoms of depression in old age. Our cross-sectional study has attempted to explore the relationship of daily exercise and stress reactivity with depression in elderly individuals with hypertension. Methods: Depression was assessed using Hamilton depression rating scale or HAM-D. The physical activity was assessed using the global physical activity questionnaire (GPAQ), developed by WHO. Stress reactivity is calculated from various cardiovascular parameters. The relationship of physical activity and stress reactivity scores on depression was explored using regression analysis. Results: The prevalence rate of depressive symptoms in elderly hypertension population was around 40.2%. Significantly lower reactivity scores (P = 0.032) to deep breathing were evident in elderly hypertension population. After proper adjustment of the confounding factors, regression analysis depicted that decreased physical activity (Odds ratio = 4.48; 95% Confidence interval = 2.32-7.69) resulted in heavy risk of developing depressive symptoms besides old age and low educational status. Conclusion: Our ï¬ndings suggest that low physical activity leads to the development of depressive symptoms and decreased stress reactivity among elderly hypertensive patients.
