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Background: Bariatric clinical calculators have already been implemented in clinical practice to provide objective predictions of complications and outcomes. The Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) Surgical Risk/Benefit Calculator is the most comprehensive risk calculator in bariatric surgery., Objectives: Evaluate the accuracy of the calculator predictions regarding the 30-day complication risk, 1-year weight loss outcomes, and comorbidity resolution., Setting: MBSAQIP-accredited center., Methods: All adult patients who underwent primary laparoscopic Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy at our institution between 2012 and 2019 were included. Baseline characteristics were used to generate the individualized outcome predictions for each patient through the bariatric risk calculator and were compared to actual patient outcomes. Statistical analysis was performed using c-statistics, linear regression models, and McNemmar chi-square test., Results: One thousand four hundred fifty-three patients with a median age of 45 (37, 55) and consisting of 80.1% females were included in the study. The c-statistics for the complications and comorbidity resolution ranged from .533 for obstructive sleep apnea remission to .675 for 30-day reoperation. The number of comorbidity resolutions predicted by the calculator was significantly higher than the actual remissions for diabetes, hyperlipidemia, hypertension and obstructive sleep apnea (P < .001). On average, the calculator body mass index (BMI) predictions deviated from the observed BMI measurement by 3.24 kg/m 2 . The RYGB procedure (Coef -.89; P = .005) and preoperative BMI (Coef -.4; P = .012) were risk factors associated with larger absolute difference between the predicted and observed BMI., Conclusions: The MBSAQIP Surgical Risk/Benefit Calculator prediction models for 1-year BMI, 30-day reoperation, and reintervention risks were fairly well calibrated with an acceptable level of discrimination except for obstructive sleep apnea remission. The 1-year BMI estimations were less accurate for RYGB patients and cases with very high or low preoperative BMI measurements. Therefore, the bariatric risk calculator constitutes a helpful tool that has a place in preoperative counseling., (Copyright © 2023 American Society for Metabolic and Bariatric Surgery. Published by Elsevier Inc. All rights reserved.)

Purpose: To determine the correlation of the T1-weighted signal intensity ratio (T1 SIR, or T1 Score) and arterial-to-delayed venous enhancement ratio (ADV ratio) of the pancreas with pancreatic fibrosis on histopathology., Methods: Sixty consecutive adult CP patients who had an MRI/MRCP study prior to pancreatic surgery were analyzed. Three blinded observers measured T1 SIR of pancreas to spleen (T1 SIR p/s), pancreas-to-paraspinal muscle (T1 SIR p/m), ADV ratio, and Cambridge grade. Histopathologic grades were given by a gastrointestinal pathologist using Ammann's fibrosis score. Statistical analysis included Spearman's correlation coefficient of the T1 SIR, ADV ratio, Cambridge grade with the fibrosis score, and weighted kappa for interobserver agreement., Results: The study population included 31 female and 29 male patients, with an average age of 52.1 (26-78 years). Correlations between fibrosis score and T1 SIR p/s, T1 SIR p/m, and ADV ratio were ρ = - 0.54 (p = 0.0001), ρ = - 0.19 (p = 0.19), and ρ = - 0.39 (p = 0.003), respectively. The correlation of Cambridge grade with fibrosis score was ρ = 0.26 (p = 0.07). There was substantial interobserver agreement (weighted kappa) for T1 SIR p/s (0.78), T1 SIR p/m (0.71), and ADV ratio (0.64). T1 SIR p/s of ≤ 1.20 provided a sensitivity of 74% and specificity of 50% (AUC: 0.74), while ADV ratio of ≤ 1.10 provided a sensitivity of 75% and specificity of 55% (AUC: 0.68) to detect a fibrosis score of ≥ 6., Conclusion: There is a moderate negative correlation between the T1 Score (SIR p/s) and ADV ratio with pancreatic fibrosis and a substantial interobserver agreement. These parenchymal metrics show a higher correlation than the Cambridge grade., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Despite reliable evidence-based research supporting the COVID-19 vaccines, population-wide confidence and trust remain limited. We sought to expand prior knowledge about COVID-19 vaccine perceptions, while determining which population groups are at greatest risk for not getting a vaccine., Methods: Study participants in the U.S. (79% female, median age group 46-60 years) were recruited through an online Qualtrics survey distributed as a Facebook advertisement from 3/19/21-4/30/21. We assumed that every participant is at risk of COVID-19 infection and should be able to get the vaccine with proper access. Bivariate and multivariable models were performed. Collinearity between variables was assessed., Results: A total of 2,626 responses were generated and 2,259 were included in data analysis. According to our multivariate model analysis, vaccines were perceived as safe by those who had or planned to obtain full vaccination (adjusted odds ratio (aOR) (95% confidence interval) = 40.0 (19.0, 84.2); p< 0.0001) and those who indicated trust in science (aOR = 10.5 (5.1, 21.8); p< 0.0001); vaccines were perceived as not safe by those who self-identified as Republicans vs. self-identified Democrats (aOR = 0.2 (0.1, 0.5); p = 0.0020) and those with high school or lower education (aOR = 0.2 (0.1, 0.4); p = 0.0007). Similarly, according to our multivariate model analysis, the following groups were most likely to reject vaccination based on belief in vaccinations: those with lower income (aOR = 0.8 (0.6, 0.9); p = 0.0106), those who do not know anyone who had been vaccinated (aOR = 0.1 (0.1, 0.4); p< 0.0001), those who are unwilling to get vaccinated even if family and friends had done so (aOR = 0.1 (<0.1, 0.2); p< 0.0001), those who did not trust science (aOR < 0.1 (<0.1, 0.1); p< 0.0001), those who believe that vaccination was unnecessary if others had already been vaccinated (aOR = 2.8 (1.5, 5.1); p = 0.0007), and those who indicate refusal to vaccinate to help others (aOR = 0.1 (0.1, 0.2); p< 0.0001). An alpha of p<0.05 was used for all tests., Conclusion: Level of education and partisanship, but not race/ethnicity, were the most likely factors associated with vaccine hesitancy or likelihood to vaccinate. Also, low vaccination rates among underrepresented minorities may be due to distrust for healthcare industries. Population sub-groups less likely to be vaccinated and/or receptive to vaccines should be targeted for vaccine education and incentives., Competing Interests: The authors have declared that no competing interests exist.