Outcomes of patients older than 55 years undergoing abdominoplasty after bariatric surgery

Comparing the Efficacy of Robotic Versus Laparoscopic Sleeve Gastrectomy: A Systematic Review and Meta-Analysis

BACKGROUND

Laparoscopic sleeve gastrectomy (LSG) has emerged as the predominant metabolic bariatric surgery. With a growing number of studies evaluating the feasibility of robotic sleeve gastrectomy (RSG), it becomes imperative to ascertain whether the outcomes of both techniques are comparable. This study endeavors to synthesize existing evidence and juxtapose the surgical outcomes of LSG and RSG.

METHODS

We collected articles comparing LSG and RSG published between 2011 and 2024. The compiled data included author names, study duration, sample size, average age, gender distribution, geographical location, preoperative body mass index (BMI), bougie diameter, duration of hospitalization, surgical duration, readmission rates, conversion rates, costs, postoperative percentage of excess weight loss (%EWL), postoperative BMI, mortality rates, and complications.

RESULTS

We incorporated 21 articles. Both the RSG and LSG cohorts exhibited comparable rates of readmission, conversion, mortality, and incidence of complications (p > 0.05). Moreover, the efficacy of weight loss was similar between RSG and LSG. Nonetheless, RSG was linked to longer operative duration (WMD, -27.50 minutes; 95% confidence interval [CI], -28.82 to -26.18; p < 0.0001), prolonged hospitalization (WMD, -0.15 days; 95% CI, -0.25 to -0.04; p = 0.006), and elevated expenses (WMD, -5830.9 dollars; 95% CI, -8075.98 to -3585.81; p < 0.0001).

CONCLUSIONS

While both RSG and LSG demonstrated positive postoperative clinical outcomes, RSG patients experienced extended hospital stays, longer operative times, and increased hospitalization costs compared to LSG patients. Using the robotic platform for sleeve gastrectomy (SG) in patients with obesity did not appear to offer any clear benefits.

A Procedure and Complication-Specific Assessment of Smoking in Aesthetic Surgery: A Systematic Review and Meta-Analysis

Background: The popularity of aesthetic surgery is on the rise, as is patients' expectations towards excellent surgical results. In order to meet these expectations, risk factors that hinder desired outcomes, such as smoking, need to be identified and addressed. To that end, the present study summarizes an updated systematic review focused on the effects of smoking on cosmetic surgical procedures and outcomes. Methods: A systematic review of studies comparing aesthetic surgical outcomes by procedure, between tobacco smokers and non-smokers was carried out, querying PubMed, Embase and the Cochrane databases. Data regarding surgical outcomes were extracted and meta-analyzed by a random effects model in conjunction with the Mantel-Haenszel statistical method. Results: Eighty-two studies were included in the final synthesis. Abdominoplasty/panniculectomy (n = 19 cohorts) and breast reduction (n = 27 cohorts) were the most common types of procedures included in this review. Other than mastopexy and rhinoplasty, smoking conferred a statistically significant increased risk of overall complications for all studied aesthetic procedures. Conclusions: The data demonstrates that smoking is a clear risk factor for the vast majority of aesthetic plastic surgeries studied. Although our meta-analysis suggests that smoking is not a risk factor for complications in mastopexies and rhinoplasties, these two specific analyses may have been biased, and should therefore be re-evaluated with future additional evidence. The results of this systematic review confirm the importance of smoking cessation and education relative to the outcomes of common cosmetic surgical procedures.

Machine Learning Prediction Algorithm for In-Hospital Mortality following Body Contouring

BACKGROUND

Body contouring is a common procedure, but it is worth attention because of concern for a variety of complications, and even the potential for death. As a result, the purpose of this study was to determine the key predictors following body contouring and create models for the risk of mortality using diverse machine learning (ML) models.

METHODS

The National Inpatient Sample database from 2015 to 2017 was queried to identify patients undergoing body contouring. Candidate predictors, such as demographics, comorbidities, personal history, postoperative complications, and operative features, were included. The outcome was in-hospital mortality. Models were compared by area under the curve, accuracy, sensitivity, specificity, positive and negative predictive values, and decision curve analysis.

RESULTS

Overall, 8214 patients undergoing body contouring were identified, among whom 141 (1.72%) died in the hospital. Variable importance plot demonstrated that sepsis was the variable with greatest importance across all ML algorithms, followed by Elixhauser Comorbidity Index, cardiac arrest, and so forth. The naive Bayes model had a higher predictive performance (area under the curve, 0.898; 95% CI, 0.884 to 0.911) among these eight ML models. Similarly, in the decision curve analysis, the naive Bayes model also demonstrated a higher net benefit (ie, the correct classification of in-hospital deaths considering a tradeoff between false-negatives and false-positives) compared with the other seven models across a range of threshold probability values.

CONCLUSION

The ML models, as indicated by this study, can be used to predict in-hospital death for patients at risk who undergo body contouring.