Erick Trejo López

Robotic-assisted minimally invasive surgery has emerged as a transformative advancement in general surgery, combining enhanced precision, stability, and visualization to optimize clinical outcomes. This review analyzes twenty international studies published between 2021 and 2025, integrating data from Mexico, Colombia, and Ecuador to provide a comprehensive evaluation of the safety, efficacy, cost-effectiveness, and training models associated with robotic-assisted procedures. Using a methodological framework based on the DMAIC cycle (Define, Measure, Analyze, Improve, Control), the study identifies the strengths and limitations of robotic systems in procedures such as hernia repair, cholecystectomy, fundoplication, bariatric, pancreatic, adrenal, and esophageal surgeries. Results indicate that robotic-assisted techniques offer superior ergonomics, lower complication rates, and shorter hospital stays compared with conventional laparoscopic and open surgery, despite slightly longer operative times and higher direct costs. However, long-term economic analyses suggest that these costs are offset by decreased morbidity, improved recovery, and better institutional efficiency. In Latin America, the progressive incorporation of robotics, supported by structured academic programs and regional cooperation, demonstrates that equitable technological adoption is achievable even in middle-income countries. The study concludes that robotic-assisted general surgery represents not only a technological innovation but also a strategic step toward value-based, patient-centered, and globally accessible healthcare.

The emergence of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and dual glucose-dependent insulinotropic polypeptide/glucagon-like peptide-1 receptor agonists (GIP/GLP-1 RAs) has redefined cardiovascular prevention and metabolic care. This review analyzes the cardiovascular, renal, and metabolic outcomes associated with these therapies, integrating evidence from major outcome trials—LEADER, SUSTAIN-6, REWIND, AMPLITUDE-O, and SELECT—and emerging dual incretin studies such as SUMMIT and SURPASS-CVOT. Across these trials, GLP-1 RAs consistently reduced major adverse cardiovascular events (MACE), cardiovascular mortality, and kidney disease progression, demonstrating benefits that extend beyond glycemic control. Mechanistic data reveal anti-inflammatory, endothelial, and neurohumoral pathways that collectively explain their cardioprotective profile. The new generation of dual incretin agonists, exemplified by tirzepatide, provides unprecedented improvements in weight reduction, insulin sensitivity, and heart failure symptoms, marking a new frontier in cardiometabolic therapeutics. For Latin American countries, including Mexico, Colombia, and Ecuador, the clinical translation of these findings faces barriers such as high cost, limited access, and physician unfamiliarity. However, strategic health policies and regional collaboration could integrate incretin-based therapy into national cardiovascular prevention frameworks. Ultimately, incretin therapy represents a paradigm shift from glucose-centric management toward comprehensive, preventive, and equitable cardiometabolic care with global implications.

Next-generation cardiothoracic surgery is being redefined by three major technological domains: three-dimensional (3D) printing, robotic-assisted procedures, and artificial heart systems, each contributing uniquely to surgical planning, operative precision, and long-term physiological support. This review synthesizes evidence from twenty primary scientific studies and five academic books published between 2005 and 2025, examining the current state, clinical impact, and limitations of these technologies across diverse healthcare settings. Results show that 3D printing enhances preoperative visualization and promotes more accurate decision-making, robotic-assisted surgery improves dexterity and reduces invasiveness in complex procedures, and artificial heart technologies—particularly magnetically levitated LVADs and evolving total artificial heart designs—offer essential support for patients with advanced heart failure. The analysis also reveals substantial barriers to implementation, including high costs, steep learning curves, and limited availability, especially in middle-income countries. These findings underscore the need for strategic, region-specific adoption models supported by specialized training and institutional investment. Future directions include AI-driven planning, cost-reduction initiatives, biohybrid artificial heart development, and multicenter evaluations of long-term outcomes. Collectively, this evidence highlights an evolving ecosystem in which engineering, computational modeling, and surgical expertise converge to redefine the future of cardiothoracic care.

Cardiovascular diseases (CVDs) remain the leading cause of death worldwide, yet conventional predictive models often fail to capture the complex interplay of genetic and physiological risk factors in diverse populations. This study evaluated a hybrid predictive model integrating polygenic risk scores (PRS), artificial intelligence (AI) applied to electrocardiography (ECG), and traditional clinical variables to enhance cardiovascular risk prediction across Latin American cohorts from Mexico, Colombia, and Ecuador. A total of 6,450 participants aged 30 to 75 years were analyzed. PRS were derived from genome-wide association data, and ECGs were processed using deep convolutional neural networks. Model performance was assessed using AUROC, F1-score, and calibration metrics, with interpretability achieved through SHAP (Shapley Additive Explanations) analysis. The hybrid model demonstrated superior predictive accuracy (AUROC 0.91; F1-score 0.87; calibration slope 0.97) compared with the clinical (0.72), PRS (0.78), and AI-ECG (0.86) models. The most influential predictors were PRS, systolic blood pressure, age, and AI-derived electrical age. Subgroup analyses revealed consistent performance across ancestries (Mestizo 0.91; Amerindian 0.89; European 0.93; Afro-descendant 0.87) and countries, confirming the model’s generalizability and fairness. These findings demonstrate that integrating genetic and AI-based physiological data significantly improves cardiovascular risk assessment, enabling early, equitable, and personalized prevention. The proposed hybrid framework provides a scalable foundation for implementing AI-driven genomic precision medicine in multi-ancestry populations, marking an essential step toward reducing cardiovascular disparities in Latin America.

This multicenter study evaluated the role of glucagon-like peptide-1 receptor agonists (GLP-1RAs), specifically semaglutide and tirzepatide, as secondary therapy in patients with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD). A total of 3,000 adults (mean age 61 years, 46% women) were enrolled across centers in Mexico, Colombia, and Ecuador and were randomized to GLP-1RA therapy or standard care. Baseline characteristics, including obesity, hypertension, dyslipidemia, and moderate CKD (mean eGFR ~55 mL/min/1.73 m²), were well balanced between groups. Over a 36-month follow-up, GLP-1RA therapy was associated with a slower decline in renal function (final eGFR 52.8 vs. 50.2 mL/min/1.73 m²), a lower incidence of composite renal outcomes (15.2% vs. 22.8%), and greater reductions in albuminuria (−35% vs. −14%). In addition, participants receiving GLP-1RAs experienced fewer major adverse cardiovascular events (11.5% vs. 15.9%), improved glycemic control (HbA1c reduced from 8.2% to 7.0% vs. 7.7% in controls), and sustained weight loss (−8 kg vs. −2 kg). Safety outcomes showed higher rates of gastrointestinal adverse events but lower hypoglycemia risk and no significant difference in pancreatitis. These findings demonstrate that GLP-1RAs provide integrated renal, cardiovascular, and metabolic benefits, supporting their inclusion as part of comprehensive therapy for high-risk patients with T2DM and CKD.