Which indices of heart rate variability are the best predictors of mortality after acute myocardial infarction? Meta-analysis of observational studies.

BACKGROUND: Cardiovascular disease is the first cause of death globally with myocardial infarction as the main event. Heart rate variability (HRV) has been associated with an increased risk of mortality post-myocardial infarction. However, which indices of heart rate variability are the best predictors for total and cardiac mortality post-myocardial infarction remains unclear. We performed a systematic review to evaluate this association. METHODS AND RESULTS: PubMed, Google Scholar, Embase and Cochrane databases were searched for studies with HRV as a predictive mortality marker. Two authors independently selected papers and extracted data and disagreements were solved with a third author. HRV indices included were SDNN, SDANN, HRV index, Total power, RMSSD, LF, HF, ULF, VLF, and LF/HF. For these clinical and statistical heterogeneity was assessed, forest and funnel plot graphs were made and sensitivity analysis, cumulative and regression meta-analysis were performed. Stata 16 was used for statistical analysis. Out of 19.960 articles found, 332 were initially selected for abstract screening and 27 finally fulfilled the criteria and allowed the extraction of data. After a sensitivity analysis, low values of SDNN, HRV index, ULF, VLF, Total Power, LF, LF/HF ratio and HF showed a statistically significant association with cardiac mortality, but SDNN index had the highest association (RR 4.19, CI95% 3.36-5.22, I2 39.7%). For total mortality, HRV index, VLF, ULF, LF, Total power, SDNNN, LF/HF ratio, HF were significantly associated, but HRV index was the index with highest association, (RR 3.60, CI95% 2.30-5.64, I2 27.5%). CONCLUSIONS: Based on a sensitivity analysis, the best index associated with cardiac mortality post-myocardial infarction is low values of SDNN and for total mortality is low values of HRV index.

Robotic Surgery: A Comprehensive Review of the Literature and Current Trends.

Robotic surgery (RS) is an evolution of minimally invasive surgery that combines medical science, robotics, and engineering. The first robots approved by the Food and Drug Administration (FDA) were the Da Vinci Surgical System and the ZEUS Robotic Surgical System, which have been improving over time. Through the decades, the equipment applied to RS had undergone a wide transformation as a response to the development of new techniques and facilities for the assembly and implementation of the own. RS has revolutionized the field of urology, enabling surgeons to perform complex procedures with greater precision and accuracy, and many other surgical specialties such as gynecology, general surgery, otolaryngology, cardiothoracic surgery, and neurosurgery. Several benefits, such as a better approach to the surgical site, a three-dimensional image that improves depth perception, and smaller scars, enhance range of motion, allowing the surgeon to conduct more complicated surgical operations, and reduced postoperative complications have made robotic-assisted surgery an increasingly popular approach. However, some points like the cost of surgical procedures, equipment-instrument, and maintenance are important aspects to consider. Machine learning will likely have a role to play in surgical training shortly through "automated performance metrics," where algorithms observe and "learn" individual surgeons' techniques, assess performance, and anticipate surgical outcomes with the potential to individualize surgical training and aid decision-making in real time.