The findings from this study underscore the importance of population-level approaches to the prevention and treatment of obesity across the life course of individuals.
Supplemental Digital Content is Available in the Text.Individuals with obesity report significantly more prescription opioid use than nonobese individuals according to data from NHANES (2003-2016).
The ultrasonic Harmonic scalpel has demonstrated clinical and surgical benefits in dissection and coagulation. To evaluate its use in gastrectomy, we conducted a systematic review and meta-analysis of randomized controlled trials comparing the Harmonic scalpel to conventional techniques in gastrectomy for patients with gastric cancer. International databases were searched without language restrictions for comparisons in open or laparoscopic gastrectomy and lymphadenectomy. The meta-analysis used a random-effects model for all outcomes; continuous variables were analyzed for mean differences and dichotomous variables were analyzed for risk ratios. Sensitivity analyses were conducted for study quality, type of conventional technique, and imputation of study results. Ten studies (N = 935) met the inclusion criteria. Compared with conventional hemostatic techniques, the Harmonic scalpel demonstrated significant reductions in operating time (−27.5 min; P < 0.001), intraoperative blood loss (−93.2 mL; P < 0.001), and drainage volume (−138.8 mL; P < 0.001). Results were numerically higher for conventional techniques for hospital length of stay, complication risk, and transfusions but did not reach statistical significance. Results remained robust to sensitivity analyses. This meta-analysis demonstrates the clear advantages of using the Harmonic scalpel compared to conventional techniques, with improvements demonstrated across several outcome measures for patients undergoing gastrectomy and lymphadenectomy.
Objectives: Laparoscopic metabolic surgery (MxS) can lead to remission of type 2 diabetes (T2D); however, treatment response to MxS can be heterogeneous. Here, we demonstrate an open-source predictive analytics platform that applies machine-learning techniques to a common data model; we develop and validate a predictive model of antihyperglycemic medication cessation (validated proxy for A1c control) in patients with treated T2D who underwent MxS. Methods: We selected patients meeting the following criteria in 2 large US healthcare claims databases (Truven Health MarketScan Commercial [CCAE]; Optum Clinformatics [Optum]): underwent MxS between January 1, 2007, to October 1, 2013 (first = index); aged $18 years; continuous enrollment 180 days pre-index (baseline) to 730 days postindex; baseline T2D diagnosis and treatment. The outcome was no antihyperglycemic medication treatment from 365 to 730 days after MxS. A regularized logistic regression model was trained using the following candidate predictor categories measured at baseline: demographics, conditions, medications, measurements, and procedures. A 75% to 25% split of the CCAE group was used for model training and testing; the Optum group was used for external validation. Results: 13 050 (CCAE) and 3477 (Optum) patients met the study inclusion criteria. Antihyperglycemic medication cessation rates were 72.9% (CCAE) and 70.8% (Optum). The model possessed good internal discriminative accuracy (area under the curve [AUC] = 0.778 [95% CI = 0.761-0.795] in CCAE test set N = 3527) and transportability (external AUC = 0.759 [95% CI = 0.741-0.777] in Optum N = 3477).
Conclusion:The application of machine learning techniques to real-world healthcare data can yield useful predictive models to assist patient selection. In future practice, establishment of prerequisite technological infrastructure will be needed to implement such models for real-world decision support.
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