Hospital length of stay (HLOS) is a critical healthcare metric influencing patient outcomes, resource utilization, and healthcare costs. While reducing HLOS can improve hospital efficiency and patient throughput, it also poses risks such as premature discharge, increased readmission rates, and potential compromise of patient safety. This literature review synthesizes current evidence on the determinants of HLOS, including patient-specific factors such as demographics, comorbidities, and socioeconomic status, as well as hospital-related factors like admission route, resource allocation, and institutional policies. We also examine the relationship between HLOS and key clinical outcomes, including mortality, readmission rates, and healthcare-associated infections. Additionally, we evaluate predictive modeling approaches, including artificial intelligence and machine learning, for forecasting HLOS and guiding early intervention strategies. While interventions such as enhanced recovery after surgery (ERAS) protocols, multidisciplinary care teams, and structured discharge planning have demonstrated efficacy in reducing HLOS, their success varies based on healthcare setting, patient complexity, and resource availability. Predictive analytics, incorporating clinical and non-clinical variables, offer promising avenues for improving hospital efficiency, yet may carry risks related to data quality and model bias. Given the impact of HLOS on clinical and economic outcomes, targeted interventions and predictive models should be applied cautiously, with future research focusing on refining personalized discharge strategies and addressing disparities across diverse patient populations.

Effective postoperative pain management remains a major clinical challenge in spinal surgery, with poorly controlled pain affecting up to 50% of patients and contributing to delayed mobilization, prolonged hospitalization, and risk of chronic postsurgical pain. This review synthesizes current and emerging strategies in postoperative spinal pain management, tracing the evolution from opioid-centric paradigms to individualized, multimodal approaches. Multimodal analgesia (MMA) has become the cornerstone of contemporary care, combining pharmacologic agents, such as non-steroidal anti-inflammatory drugs (NSAIDs), acetaminophen, and gabapentinoids, with regional anesthesia techniques, including erector spinae plane blocks and liposomal bupivacaine. Adjunctive nonpharmacologic modalities like early mobilization, cognitive behavioral therapy, and mindfulness-based interventions further optimize recovery and address the biopsychosocial dimensions of pain. For patients with refractory pain, neuromodulation techniques such as spinal cord and peripheral nerve stimulation offer promising results. Advances in artificial intelligence (AI), biomarker discovery, and nanotechnology are poised to enhance personalized pain protocols through predictive modeling and targeted drug delivery. Enhanced recovery after surgery protocols, which integrate many of these strategies, have been shown to reduce opioid use, hospital length of stay, and complication rates. Nevertheless, variability in implementation and the need for individualized protocols remain key challenges. Future directions include AI-guided analytics, regenerative therapies, and expanded research on long-term functional outcomes. This review provides an evidence-based framework for pain control following spinal surgery, emphasizing integration of multimodal and innovative approaches tailored to diverse patient populations.

Type 2 diabetes mellitus (T2DM) and obesity affect hundreds of millions of adults worldwide and represent leading drivers of cardiovascular disease, chronic kidney disease, and escalating healthcare expenditures. Incretin-based therapies have fundamentally reshaped cardiometabolic disease management, with dual- and triple-receptor agonists extending the benefits of traditional glucagon-like peptide-1 (GLP-1) receptor agonism. By synthesizing clinical, mechanistic, and real-world data, this review examines the evolving therapeutic landscape of GLP-1-based multi-agonists. Dual GLP-1/glucose-dependent insulinotropic polypeptide (GIP) receptor agonists demonstrate superior metabolic efficacy compared with GLP-1 receptor agonists alone, achieving greater reductions in body weight and glycemic indices across diverse patient populations. Emerging triple agonists targeting GLP-1, GIP, and glucagon receptors further enhance metabolic outcomes, with weight loss approaching that observed following bariatric surgery in late-phase clinical trials. Mechanistically, multi-receptor co- agonism produces synergistic effects through complementary pathways, including appetite suppression, glucose-dependent insulin secretion, improved adipose tissue metabolism, increased energy expenditure, enhanced hepatic lipid oxidation, and reductions in hepatic steatosis. Beyond glycemic and weight endpoints, GLP-1-based therapies confer clinically meaningful cardiovascular and renal protection. Trials consistently demonstrate reductions in major adverse cardiovascular events across populations with and without T2DM, while kidney-specific trials show significant slowing of disease progression. However, gastrointestinal adverse events remain common and contribute to substantial treatment discontinuation, particularly in real-world settings. Despite their transformative efficacy, the population-level impact of these therapies is constrained by significant implementation barriers, including high drug costs, limited insurance coverage, restrictive utilization management policies, and pronounced racial and socioeconomic disparities in access. Emerging innovations including oral formulations, longer-acting injectables, and novel peptide combinations look to improve tolerability, adherence, and scalability, while therapeutic indications continue to expand to conditions such as metabolic dysfunction-associated steatohepatitis, chronic kidney disease, obstructive sleep apnea, and neurodegenerative disease. This review provides a comprehensive framework for understanding the clinical potential, mechanistic basis, and real-world challenges of GLP-1-based multi-agonists and outlines key priorities for optimizing implementation and maximizing their impact on global cardiometabolic health.

Background: Down syndrome (DS) is a genetic disorder characterized by an extra copy of chromosome 21, often leading to intellectual disabilities, developmental delays, and an increased risk of various comorbidities, including thyroid dysfunction and mental health disorders. The relationship between thyroid dysfunction and mood disorders, particularly depression in DS populations, requires further investigation. Objective: This study aims to investigate the presence of a correlative relationship between hypothyroidism and depression in 178,840 individuals with DS, utilizing data from the National Inpatient Sample (NIS) to determine if those with comorbid hypothyroidism exhibit higher rates of depression compared to their counterparts without hypothyroidism. Methods: A retrospective analysis of the 2016–2019 NIS dataset was conducted, focusing on patients with DS, hypothyroidism, and depression diagnoses. The diagnoses were determined and labeled based on ICD-10 codes associated with NIS datapoints. Survey-weighted linear regression analyses were employed to assess the association between hypothyroidism and depression within the DS cohort, adjusting for demographic factors such as age, gender, and race. Results: This study found that individuals with DS exhibit a significantly higher prevalence of hypothyroidism (29.88%) compared to the general population (10.28%). Additionally, individuals with DS and comorbid hypothyroidism demonstrated a higher prevalence of depression (8.67%) compared to those without hypothyroidism (3.00%). These findings suggest a significant association between hypothyroidism and increased depression risk among individuals with DS. However, the overall prevalence of depression in DS (4.69%) remains substantially lower than in the general population (12.27%). Conclusions: This study highlights the importance of considering hypothyroidism as a potential contributor to depression in individuals with DS. Further research is needed to explore the underlying mechanisms of this association and potential screening and management strategies to address thyroid dysfunction and its potential psychiatric implications in DS.