Powering the future: Exploring self-charging cardiac implantable electronic devices and the Qi revolution.

The incidence and prevalence of cardiovascular diseases (CVD) have risen over the last few decades worldwide, resulting in a cost burden to healthcare systems and increasingly complex procedures. Among many strategies for treating heart diseases, treating arrhythmias using cardiac implantable electronic devices (CIEDs) has been shown to improve quality of life and reduce the incidence of sudden cardiac death. The battery-powered CIEDs have the inherent challenge of regular battery replacements depending upon energy usage for their programmed tasks. Nanogenerator-based  energy harvesters have been extensively studied, developed, and optimized continuously in recent years to overcome this challenge owing to their merits of self-powering abilities and good biocompatibility. Although these nanogenerators and others currently used in energy harvesters, such as biofuel cells (BFCs) exhibit an infinite spectrum of uses for this novel technology, their demerits should not be dismissed. Despite the emergence of Qi wireless power transfer (WPT) has revolutionized the technological world, its application in CIEDs has yet to be studied well. This review outlines the working principles and applications of currently employed energy harvesters to provide a preliminary exploration of CIEDs based on Qi WPT, which may be a promising technology for the next generation of functionalized CIEDs.

Disparities and Outcomes in the First and Second Year of the Pandemic on Events of Acute Myocardial Infarction in Coronavirus Disease 2019 Patients.

Background and Objectives: Coronavirus disease 2019 (COVID-19) caused several cardiovascular complications, including acute myocardial infarction (AMI), in infected patients. This study aims to understand the overall trends of AMI among COVID-19 patients during the first two years of the pandemic and the disparities and outcomes between the first and second years. Materials and Methods: The retrospective analysis was conducted via the 2020 and 2021 National Inpatient Sample (NIS) database for hospitalizations between April 2020 and December 2021 being analyzed for adults with a primary diagnosis of COVID-19 who experienced events of AMI. A comparison of month-to-month events of AMI and mortality of AMI patients with concomitant COVID-19 was made alongside their respective patient characteristics. Results: Out of 2,541,992 COVID-19 hospitalized patients, 3.55% experienced AMI. The highest rate of AMI was in December 2021 (4.35%). No statistical differences in trends of AMI mortality were noted over the 21 months. AMI cases in 2021 had higher odds of undergoing PCI (aOR 1.627, p < 0.01). They experienced higher risks of acute kidney injury (aOR 1.078, p < 0.01), acute ischemic stroke (aOR 1.215, p < 0.01), cardiac arrest (aOR 1.106, p < 0.01), need for mechanical ventilation (aOR 1.133, p < 0.01), and all-cause mortality (aOR 1.032, 95% CI 1.001-1.064, p = 0.043). Conclusions: The incidence of AMI among COVID-19 patients fluctuated over the 21 months of this study, with a peak in December 2021. COVID-19 patients reporting AMI in 2021 experienced higher overall odds of multiple complications, which could relate to the exhaustive burden of the pandemic in 2021 on healthcare, the changing impact of the virus variants, and the hesitancy of infected patients to seek care.

Enhancing Awareness and Self-diagnosis of Obstructive Sleep Apnea Using AI-Powered Chatbots: The Role of ChatGPT in Revolutionizing Healthcare.

Since OpenAI (San Francisco, CA) released its generative AI chatbot, ChatGPT, we are on the cusp of technological transformation. The tool is capable of generating text according to the input that the user adds to it. Due to its ability to imitate human speech tone while extracting encyclopedic knowledge, ChatGPT can be a platform for personalized patient interaction. Thus, it has the potential to revolutionize the healthcare system. Our study aims to evaluate how ChatGPT can answer the queries of patients suffering from obstructive sleep apnea and aid in self-diagnosis. By analyzing symptoms and guiding patients' behavior toward prevention, ChatGPT can play a major role in avoiding serious health repercussions that develop in the later course of obstructive sleep apnea.

Artificial Intelligence in Enhancing Syncope Management - An Update.

This review looks into the use of Artificial Intelligence (AI) in the management of syncope, a condition characterized by a brief loss of consciousness caused by cerebral hypoperfusion. With rising prevalence, high costs, and difficulty in diagnosis and risk stratification, syncope poses significant healthcare challenges. AI has the potential to improve symptom differentiation, risk assessment, and patient management. Machine learning, specifically Artificial Neural Networks, has shown promise in accurate risk stratification. AI-powered clinical decision support tools can improve patient evaluation and resource utilization. While AI holds great promise for syncope management, challenges such as data quality, class imbalance, and defining risk categories remain. Ethical concerns about patient privacy, as well as the need for human empathy, complicate AI integration. Collaboration among data scientists, clinicians, and ethics experts is critical for the successful implementation of AI, which has the potential to improve patient outcomes and healthcare efficiency in syncope management.

Characteristics and clinical outcomes of patients with sarcoidosis admitted for ST-elevation myocardial infarction in the United States: a propensity matched analysis from the National Inpatient Sample.

Introduction: Sarcoidosis is a systemic inflammatory disorder characterised by non-caseating granulomas. Cardiac sarcoidosis (CS) normally causes conduction abnormalities, ventricular arrhythmias, and heart failure. Little is known about the characteristics and impact of sarcoidosis in patients admitted with ST-elevation myocardial infarction (STEMI). This study aims to fill this void. Material and methods: Utilising the National Inpatient Sample (NIS) database (2016-2020), individuals with STEMI were identified and categorised based on sarcoidosis presence whilst adjusting for confounders via logistic regression models. Results: Among 851,290 STEMI patients, 1215 had sarcoidosis. Before propensity matching, sarcoidosis patients were notably different in demographics and comorbidities compared to non-sarcoidosis patients. After propensity score matching (PSM), sarcoidosis patients were found to have a higher incidence of supraventricular tachycardia (SVT) (2.5% vs. 1.3%, p = 0.024) and acute kidney injury (AKI) (23.3% vs. 20.8%, aOR = 1.269, 95% CI: 1.02-1.58, p = 0.033) but a lower incidence of undergoing coronary artery bypass graft (CABG) (5.5% vs. 8.5%, aOR = 0.663; 95% CI: 0.472-0.931, p = 0.018), while no significant disparities were noted in PCI, cardiogenic shock, mortality, or mean length of stay (LOS). Conclusions: Using propensity-matched large real-world data of STEMI patients, sarcoidosis was associated with fewer cases of CABG and a greater incidence of AKI and SVT compared to non-sarcoidosis patients.

Impact of Racial, Ethnic, and Gender Disparities in Cardiology.

Literature shows evidence of racial and gender biases in many sub-specialties of medicine including cardiology. Racial, ethnic, and gender disparities exist along the path to cardiology residency, beginning as early as medical school admissions. Approximately 65.62% White, 4.71% Black, 18.06% Asian, and 8.86% Hispanic are cardiologists, while there are a total of 60.1% White, 12.2% Black, 5.6% Asian, and 18.5% Hispanic people in the United States in 2019, showing evident underrepresentation. Gender disparities have an inevitable role in the lack of a diverse cardiovascular workforce. According to a recent study, only 13% of practicing cardiologists in the United States are women, even though the female population in the United States is 50.52% as compared to 49.48% of men. These disparities led to under-represented physicians earning less than their similarly qualified counterparts, decreased equity, increased workplace harassment, and also results in patients facing unconscious bias from their physicians leading to deteriorated clinical outcomes. Implications in the field of research include the under-representation of minorities and the female population despite the increased burden of cardiovascular disease they face. However, efforts are underway to eradicate the disparities that exist in cardiology. This paper aims to increase awareness regarding the issue and inform future policies with the goal of encouraging underrepresented communities to join the cardiology workforce.

Diagnostic Accuracy of Cerebroplacental Ratio in Prediction of Postnatal Outcomes in Oligohydramnios.

BACKGROUND AND AIM: The evidence on isolated oligohydramnios (IO) patients and their postnatal outcomes are inconsistent. Recent research has clarified the connection between that IO and negative outcomes in the postnatal period. Our goal was to analyze the correlation between Doppler measurements and postnatal outcomes in oligohydramnios patients, with a focus on the cerebroplacental ratio (CPR). METHODOLOGY: A cohort study was conducted in the Radiology Department of Khan Research Laboratories (KRL) Hospital from October 2021 to July 2022. One hundred women were chosen as the sample size. For this study, we used the Raosoft sample size calculator with a 95% confidence interval and a 5% margin of error. Both the middle cerebral artery and the umbilical artery were imaged using ultrasound, and the systolic-to-diastolic ratio and peak systolic velocity are recorded. Pulsatility index (PI) and resistive index (RI) were also calculated. If the amniotic fluid index (AFI) is less than 5 cm, the condition is known as oligohydramnios. The newborn's APGAR score was taken immediately after birth as well as after 5 minutes. RESULTS: We have determined that, on average, mothers are 35.45 weeks/248.15 days pregnant. When compared to the reference standard, CPR diagnostic features showed a sensitivity of 92% and a specificity of 77.27. Overall diagnostic accuracy is predicted to be 93.0%, with a 93.50% positive prognosis and a 73.91% negative prognosis. The effect size for the change in APGAR scores before and after the test was -2.38 1.03, with a 95% confidence interval of -2.58 to -2.17 and a significance level of 0.00. CONCLUSION: This study concludes that CPR is an effective screening tool and that it can be used to predict postnatal outcomes in patients with oligohydramnios. Clinical prediction rules were found to be a more effective screening tool, with a sensitivity of 92%, a specificity of 77.27%, and a diagnostic accuracy of 92.3%.