Analyzing the impact of sleep duration on atrial fibrillation risk: a comprehensive systematic review and meta-analysis

BACKGROUND

Recent studies have suggested an association between sleep duration and cardiovascular diseases; however, the link to AF is inconclusive. This study aimed to explore the relationship between sleep duration and AF by conducting a systematic review and meta-analysis of primary studies to provide evidence of the link between insufficient sleep and AF.

METHODS

A review of the existing literature was conducted to identify the primary studies that examined the association between AF and sleep duration. The inquiry spanned databases, including PubMed, Embase, MEDLINE, and Google Scholar, from their inception through October 2023.

RESULTS

Meta-analysis revealed a statistically significant association (p < 0.01) between shorter sleep duration and the incidence of AF (hazard ratio (HR), 1.18; 95% CI, 1.03-1.34; I2 = 89%). Conversely, longer sleep duration did not exhibit a statistically significant association with the incidence of AF (HR, 1.03; 95% CI, 0.92-1.14; I2 = 66%, p = 0.02). The sensitivity analysis demonstrated reduced heterogeneity after excluding specific studies.

CONCLUSION

Insufficient sleep duration was associated with an increased risk of AF, whereas a longer sleep duration did not show a significant correlation. Standardized methods for sleep assessment and AF diagnosis as well as adjustments for confounding factors are suggested for future studies to improve the clarity and understanding of these associations.

Future of Artificial Intelligence in Surgery: A Narrative Review

Artificial intelligence (AI) is the capability of a machine to execute cognitive processes that are typically considered to be functions of the human brain. It is the study of algorithms that enable machines to reason and perform mental tasks, including problem-solving, object and word recognition, and decision-making. Once considered science fiction, AI today is a fact and an increasingly prevalent subject in both academic and popular literature. It is expected to reshape medicine, benefiting both healthcare professionals and patients. Machine learning (ML) is a subset of AI that allows machines to learn and make predictions by recognizing patterns, thus empowering the medical team to deliver better care to patients through accurate diagnosis and treatment. ML is expanding its footprint in a variety of surgical specialties, including general surgery, ophthalmology, cardiothoracic surgery, and vascular surgery, to name a few. In recent years, we have seen AI make its way into the operating theatres. Though it has not yet been able to replace the surgeon, it has the potential to become a highly valuable surgical tool. Rest assured that the day is not far off when AI shall play a significant intraoperative role, a projection that is currently marred by safety concerns. This review aims to explore the present application of AI in various surgical disciplines and how it benefits both patients and physicians, as well as the current obstacles and limitations facing its seemingly unstoppable rise.

Role of deep brain stimulation (DBS) in addiction disorders

Background

Addiction disorders pose significant challenges to public health, necessitating innovative treatments. This assesses deep brain stimulation (DBS) as a potential intervention for addiction disorders.

Methods

A literature review was carried out with a focus on the role of DBS in addiction disorders and its future implications in neurosurgical research.

Results

The online literature shows that DBS precisely modulates certain brain regions to restore addiction-related neural circuits and promote behavioral control.

Conclusion

Preclinical evidence demonstrates DBS's potential to rebalance neural circuits associated with addiction, and early clinical trials provide encouraging outcomes in enhancing addiction-related outcomes. Ethical considerations, long-term safety, and personalized patient selection require further investigation.

Frailty in Aging HIV-Positive Individuals: An Evolving Healthcare Landscape

The life expectancy of people living with HIV (PLWH) has greatly increased due to advancements in combination antiretroviral treatment (cART). However, this longer life has also increased the prevalence of age-related comorbidities, such as frailty, which now manifest sooner in this group. Frailty, a term coined by the insurance industry, has been broadened to include physical, cognitive, and emotional elements and has been recognized as a critical predictor of negative health outcomes. With the median age of PLWH now in the mid-50s, treating frailty is critical given its link to chronic diseases, cognitive decline, and even death. Frailty assessment tools, such as the Frailty Phenotype (FP) and the Frailty Index (FI), are used to identify vulnerable people. Understanding the pathophysiology of frailty in PLWH indicates the role of immunological mechanisms. Frailty screening and management in this group have progressed, with specialized clinics and programs concentrating on multidisciplinary care. Potential pharmacotherapeutic solutions, as well as novel e-health programs and sensors, are in the future of frailty treatment, but it is critical to ensure that frailty evaluation is not exploited to perpetuate ageist healthcare practices. This narrative review investigates the changing healthcare environment for older people living with HIV (OPLWH), notably in high-income countries. It emphasizes the significance of identifying and managing frailty as a crucial feature of OPLWH's holistic care and well-being.

Hyperbaric Oxygen and Outcomes Following the Brain Injury: A Systematic Review

Introduction

Hyperbaric oxygen therapy (HBO2) aims to address ischemia resulting from brain injury by subjecting patients to an atmosphere that dramatically raises the concentration of inspired oxygen (100% O2 at greater than 1 ATA). This results in elevated levels of oxygen in the plasma, which in turn boosts the delivery of oxygen for diffusion to the brain tissue.

Objective

To study the efficacy of hyperbaric oxygen (HBO)-based modalities in brain injury.

Method

Preferred reporting items for systematic reviews protocol was applied to perform literature search regarding this analytical review.

Results

In our study, fifteen studies are included in this review, involving 1067 people. The mean age group of patients enrolled was 57.0±11.6 and the mean NIHSS score was 10.5±8.7, of which 21 participants had moderate to severe neurological impairment. The total number of HBO treatments was 8 to 70 times (28.3±17.9), at the end of the 6-month follow-up period. mRS (modified Rankin scale) ≤3 was found in 25 cases, of which 12 patients with high-grade aSAH recovered. Poor prognosis was prevalent in patients who experienced delayed cerebral ischemia, this was true for 22.7% of patients in this study. In 3 studies conducted by Rockswold, ICP (mm Hg) was significantly lower in the HBO2 group after the treatment than pretreatment. (p<0.05). 4 studies showed an improvement in GCS score after HBO2 therapy.One trial (Imai 2006) reported that three patients in the HBO group died due to pneumonia (two) and heart failure (one) and one patient died in the control group due to heart failure. Overall, it is relatively safe to use HBO in the treatment of brain-related haemorrhage, strokes, and injury as there were no major complications reported.

Conclusion

This systematic review demonstrates that HBO2 has significant clinical potential in treatment of brain related haemorrhages, stroke and injury.

The future of artificial intelligence in neurosurgery: A narrative review

Background:

Artificial intelligence (AI) and machine learning (ML) algorithms are on the tremendous rise for being incorporated into the field of neurosurgery. AI and ML algorithms are different from other technological advances as giving the capability for the computer to learn, reason, and problem-solving skills that a human inherits. This review summarizes the current use of AI in neurosurgery, the challenges that need to be addressed, and what the future holds.

Methods:

A literature review was carried out with a focus on the use of AI in the field of neurosurgery and its future implication in neurosurgical research.

Results:

The online literature on the use of AI in the field of neurosurgery shows the diversity of topics in terms of its current and future implications. The main areas that are being studied are diagnostic, outcomes, and treatment models.

Conclusion:

Wonders of AI in the field of medicine and neurosurgery hold true, yet there are a lot of challenges that need to be addressed before its implications can be seen in the field of neurosurgery from patient privacy, to access to high-quality data and overreliance on surgeons on AI. The future of AI in neurosurgery is pointed toward a patient-centric approach, managing clinical tasks, and helping in diagnosing and preoperative assessment of the patients.