Telemedicine-based new patient consultations for hernia repair and advanced abdominal wall reconstruction

Anesthesia Patient Monitoring 2050

The monitoring of vital signs in patients undergoing anesthesia began with the very first case of anesthesia and has evolved alongside the development of anesthesiology ever since. Patient monitoring started out as a manually performed, intermittent, and qualitative assessment of the patient's general well-being in the operating room. In its evolution, patient monitoring development has responded to the clinical need, for example, when critical incident studies in the 1980s found that many anesthesia adverse events could be prevented by improved monitoring, especially respiratory monitoring. It also facilitated and perhaps even enabled increasingly complex surgeries in increasingly higher-risk patients. For example, it would be very challenging to perform and provide anesthesia care during some of the very complex cardiovascular surgeries that are almost routine today without being able to simultaneously and reliably monitor multiple pressures in a variety of places in the circulatory system. Of course, anesthesia patient monitoring itself is enabled by technological developments in the world outside of the operating room. Throughout its history, anesthesia patient monitoring has taken advantage of advancements in material science (when nonthrombogenic polymers allowed the design of intravascular catheters, for example), in electronics and transducers, in computers, in displays, in information technology, and so forth. Slower product life cycles in medical devices mean that by carefully observing technologies such as consumer electronics, including user interfaces, it is possible to peek ahead and estimate with confidence the foundational technologies that will be used by patient monitors in the near future. Just as the discipline of anesthesiology has, the patient monitoring that accompanies it has come a long way from its beginnings in the mid-19th century. Extrapolating from careful observations of the prevailing trends that have shaped anesthesia patient monitoring historically, patient monitoring in the future will use noncontact technologies, will predict the trajectory of a patient's vital signs, will add regional vital signs to the current systemic ones, and will facilitate directed and supervised anesthesia care over the broader scope that anesthesia will be responsible for.

Body mass index and the risk of abdominal hernia: a Mendelian randomization study

PURPOSE

Abdominal hernia (AH) is one of the most common clinical diseases. A large number of observational studies have found that obesity is an important risk factor for AH. However, the causal relationship between obesity and AH cannot be determined because of the clinical studies on AH induced by obesity are relatively few and only have some small- or medium-scale observational studies. Observational studies have so many confounding factors and reverse causality due to their shortcomings. From an evidence-based medicine perspective, they are not sufficiently convincing. Therefore, there is still a lack of high-quality, evidence-based medical evidence supporting a causal relationship between obesity and AH. A causal relationship between obesity and AH is also almost impossible to confirm by randomized controlled trials (RCTs). Our study based on Mendelian randomization (MR) may provide a higher level of evidence-based medical support for the relationship between obesity and AH. Body mass index (BMI) is the most common measure used for defining obesity. Finally, we employed two-sample Mendelian randomization (TSMR) to explore the causal relationship between BMI and AH.

METHODS

AH-related single nucleotide polymorphisms (SNPs) data were obtained from the FinnGen Biobank (FB), and BMI-related single nucleotide polymorphisms (SNPs) data were obtained from the UK Biobank (UKB). Genetic loci are used as instrumental variables (IVs), methods such as inverse variance weighted (IVW) were used for two-sample Mendelian randomization analysis, and the odds ratio (OR) value was used to evaluate the causal relationship between BMI and AH.

RESULTS

The results of the horizontal pleiotropy test were calculated by Egger-intercept method: p = 0.34 > 0.05. The Cochran Q test of MR-Egger method and IVW method showed heterogeneity P = 0.03 < 0.05, so the IVW random effect model was used as the gold standard. We found a genetically determined 1-standard deviation (SD) increment of BMI causally increased a 66.0% risk of AH (N = 371 SNPs, OR = 1.66, 95% CI 1.46-1.89, p = 1.55E-14) based on the IVW random effect model which was almost consistent with the results of other seven methods.

CONCLUSIONS

Our MR found genetic evidence for BMI and AH. The risk of developing AH increases with the number of BMI. This finding provides further evidence that maintaining a healthy BMI can prevent the development of AH. In addition, clinicians may need to focus on the potential risk of AH on some high-BMI patients.