The First Exploratory Personalized Medicine Approach to Improve Bariatric Surgery Outcomes Utilizing Psychosocial and Genetic Risk Assessments: Encouraging Clinical Research.

It is predicted that by 2030, globally, an estimated 2.16 billion adults will be overweight, and 1.12 billion will be obese. This study examined genetic data regarding Reward Deficiency Syndrome (RDS) to evaluate their usefulness in counselling patients undergoing bariatric surgery and gathered preliminary data on the potential use in predicting short term (6-month) weight loss outcomes. Methods: Patients undergoing bariatric surgery (n = 34) were examined for Genetic Addiction Risk Severity (GARS) [measures the presence of risk alleles associated with RDS]; as well as their psychosocial traits (questionnaires). BMI changes and sociodemographic data were abstracted from Electronic Health Records. Results: Subjects showed ∆BMI (M = 10.0 ± 1.05 kg/m2) and a mean % excess weight loss (56 ± 13.8%). In addition, 76% of subjects had GARS scores above seven. The homozygote risk alleles for MAO (rs768062321) and DRD1 (rs4532) showed a 38% and 47% prevalence among the subjects. Of the 11 risk alleles identified by GARS, the DRD4 risk allele (rs1800955), was significantly correlated with change in weight and BMI six months post-surgery. We identified correlations with individual risk alleles and psychosocial trait scores. The COMT risk allele (rs4680) showed a negative correlation with EEI scores (r = -0.4983, p < 0.05) and PSQI scores (r = -0.5482, p < 0.05). The GABRB3 risk allele (rs764926719) correlated positively with EEI (r = 0.6161, p < 0.01) and FCQ scores (r = 0.6373, p < 0.01). The OPRM1 risk allele showed a positive correlation with the DERS score (r = 0.5228, p < 0.05). We also identified correlations between DERS and BMI change (r = 0.61; p < 0.01). Conclusions: These data support the potential benefit of a personalized medicinal approach inclusive of genetic testing and psychosocial trait questionnaires when counselling patients with obesity considering bariatric surgery. Future research will explore epigenetic factors that contribute to outcomes of bariatric surgery.

A qualitative analysis of Black men's attitudes toward obesity and bariatric surgery.

BACKGROUND: Metabolic and bariatric surgery (MBS) is a safe and highly effective treatment for morbid obesity and related co-morbidities. While MBS access and insurance coverage have greatly improved, sex and racial disparities remain in utilization of MBS. OBJECTIVE: To identify novel intrinsic factors that may explain Black underutilization of surgical treatments for weight management. SETTING: This study was conducted in metropolitan communities of Western New York. METHODS: We conducted semistructured face-to-face interviews with 27 adult Black men with a history of obesity and at least 2 obesity-related conditions (diabetes, hypertension, and/or chronic kidney disease [CKD]), about their attitudes, beliefs, behaviors, and habits related to obesity and obesity management. Interview transcripts were reviewed using thematic analysis for patterns and themes. RESULTS: Most participants did not perceive obesity as a serious health condition and those who had weight-loss goals did not aim for a healthy body mass index (BMI). Trust and respectful communication with physician were very important in making healthcare decisions. MBS was perceived as extreme and dangerous option for weight loss, and only participants with severe symptoms such as chronic pain were open to discussing MBS with their providers. Participants acknowledged lack of role models of similar background who had successfully undergone MBS for obesity. CONCLUSIONS: This study identified misinformation about risks and benefits of MBS and lack of community role models as important factors contributing to Black men's unwillingness to consider MBS. Further research is needed to facilitate patient-provider communication about weight and improve provider's ability and motivation for weight management in primary care settings.

The impact of disclosure of conflicts of interest in studies comparing robot-assisted and laparoscopic cholecystectomies-a persistent problem.

INTRODUCTION: Accurate disclosure of conflicts of interest (COI) is critical to interpretation of study results, especially when industry interests are involved. We reviewed published manuscripts comparing robot-assisted cholecystectomy (RAC) and laparoscopic cholecystectomy (LC) to evaluate the relationship between COI disclosures and conclusions drawn on the procedure benefits and safety profile. METHODS: Searching Pubmed and Embase using key words "cholecystectomy", laparoscopic" and "robotic"/"robot-assisted" retrieved 345 publications. Manuscripts that compared benefits and safety of RAC over LC, had at least one US author and were published between 2014 and 2020 enabling verification of disclosures with reported industry payments in CMS's Open Payments database (OPD) (up to 1 calendar year prior to publication) were included in the analysis (n = 37). RESULTS: Overall, 26 (70%) manuscripts concluded that RAC was equivalent or better than LC (RAC +) and 11 (30%) concluded that RAC was inferior to LC (RAC-). Six manuscripts (5 RAC + and 1 RAC-) did not have clearly stated COI disclosures. Among those that had disclosure statements, authors' disclosures matched OPD records among 17 (81%) of RAC + and 9 (90%) RAC- papers. All 11 RAC- and 17 RAC + (65%) manuscripts were based on retrospective cohort studies. The remaining RAC + papers were based on case studies/series (n = 4), literature review (n = 4) and clinical trial (n = 1). A higher proportion of RAC + (85% vs 45% RAC-) manuscripts used data from a single institution. Authors on RAC + papers received higher amounts of industry payments on average compared to RAC- papers. CONCLUSIONS: It is imperative for authors to understand and accurately disclose their COI while disseminating scientific output. Journals have the responsibility to use a publicly available resource like the OPD to verify authors' disclosures prior to publication to protect the process of scientific authorship which is the foundation of modern surgical care.

Outcomes following robot-assisted versus laparoscopic sleeve gastrectomy: the New York State experience.

INTRODUCTION: Laparoscopic sleeve gastrectomy (LSG) represents more than half of all bariatric procedures in the USA, and robot-assisted sleeve gastrectomy (RSG) is becoming increasingly common. There is a paucity of evidence regarding postoperative surgical outcomes (> 30 days) in RSG patients, especially as these patients move between multiple hospital systems. METHODS: Using 2012-2018 New York State's inpatient and ambulatory data from the Statewide Planning and Research Cooperative System, bivariate and multivariate analyses were employed to examine patient long-term outcomes, postoperative complications, and charges following RSG versus LSG in unmatched and propensity score-matched (PSM) samples. RESULTS: Among the 72,157 minimally invasive sleeve gastrectomies identified, 2365 (2.6%) were RSGs. In the PSM sample (2365 RSG matched to 23,650 LSG), RSG cases were more likely to be converted to an open procedure (2.3% vs 0.2% LSG patients, p < 0.01) and had a longer mean length of stay (LOS; 2.1 vs. 1.8 days LSG, p < 0.01). Postoperative complications were not different between RSG and LSG patients, but the proportion of emergency room visits resulting in inpatient readmissions was higher among RSG patients (5.5% vs. 4.2% in LSG patients, p < .01). Among the super obese (body mass index ≥ 50) patients, conversions to open procedure and LOS were also significantly higher for RSG versus LSG cases. Average hospital charges for the index admission ($47,623 RSG vs $35,934 LSG) and cumulative changes for 1 year from the date of surgery ($57,484 RSG vs $43,769 LSG) were > 30% higher for RSG patients. CONCLUSIONS: RSG patients were more likely to have conversions to open procedures, longer postoperative stay, readmissions, and higher charges for both the index admission and beyond, compared to LSG patients. No clear advantages emerged for the utilization of the robotic platform for either average risk or extremely obese patients.

Is New York State good at managing hollow viscus injury?

BACKGROUND: There are an estimated 100,000 cases of abdominal injury (ABI) in the USA, annually resulting in over $12 billion in direct medical cost and $18 billion in lost productivity. This study assesses the timeliness, safety, and efficacy of the surgical management of abdominal injuries (ABIs), hollow viscus injuries (HVIs), and colonic injuries (CIs) for patients residing in New York State (NYS). METHODS: Using data from NYS's Statewide Planning and Research Cooperative System (SPARCS), we identified all trauma patients with ABI admitted between 2006 and 2015. We subdivided ABI into HVI and CI using diagnosis and procedure codes and examined processes of care and outcomes adjusting for patient characteristics, injury severity score, structural, and process indicators. RESULTS: We identified 31,043 hospitalized patients with ABI, 71% were incurred from blunt forces. Most patients with ABI (72%) were treated at a Level I/II trauma center (TC) and 7% patients were transferred to Level I/II TC. Failure to be treated at Level I/II TC was associated with 16% increased hazard of death. HVI was diagnosed in 23% of ABI patients (n = 7294); 18% experienced delayed hollow viscus repair (dHVR); dHVR was associated with a 76% increased hazard of death. CI was diagnosed in 9% of ABI patients (n = 2921) and 18% experienced dHVR. Seventy-five percent of CI were repaired primarily (n = 1354). Less than 37% of stomas were reversed by 4 years of index trauma. CONCLUSION: Most abdominal trauma in NYS was caused by motor vehicle accidents, falls, and assault. dHVR and not being treated at Level I/II TC were associated with worse outcomes. More research is needed to reduce under-triage and delays in the operative treatment of blunt abdominal trauma.

Using the Super Learner algorithm to predict risk of 30-day readmission after bariatric surgery in the United States.

BACKGROUND: Risk prediction models that estimate patient probabilities of adverse events are commonly deployed in bariatric surgery. The objective was to validate a machine learning (Super Learner) prediction model of 30-day readmission after bariatric surgery in comparison with a traditional logistic regression. METHODS: This prognostic study for validation of risk prediction models used data from the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program. Patients who underwent elective laparoscopic gastric bypass or laparoscopic sleeve gastrectomy between 2015 and 2018 were included. Models used 5-fold cross-validation and were evaluated using the area under the receiver operating characteristic curve, the net reclassification index, and the integrated discrimination improvement. RESULTS: The 30-day readmission rate among 393,833 patients was 3.9%. Super Learner area under the receiver operating characteristic curve was 0.674 (95% confidence interval 0.670-0.679), compared to 0.650 (95% confidence interval 0.645-0.654) for logistic regression. The net reclassification index was 0.239 (95% confidence interval 0.223-0.254), and 0.252 (95% confidence interval 0.249-0.255) for those who were and were not readmitted within 30 days. The integrated discrimination improvement was 0.0032 (95% confidence interval 0.0030-0.0033). CONCLUSION: The Super Learner outperformed traditional logistic regression in predicting risk of 30-day readmission after bariatric surgery. Machine learning models may help target high-risk patients more optimally and prevent unnecessary readmissions.

The Early (2009-2017) Experience With Robot-assisted Cholecystectomy in New York State.

OBJECTIVE: The aim of this study was to examine real-life patterns of care and patient outcomes associated with robot-assisted cholecystectomy (RAC) in New York State (NYS). BACKGROUND: Although robotic assistance may offer some technological advantages, RACs are associated with higher procedural costs and longer operating times compared to traditional laparoscopic cholecystectomies (LCs). Evidence on long-term patient outcomes after RAC from large population-based datasets remains limited and inconsistent. METHODS: Using NYS inpatient and ambulatory surgery data from the Statewide Planning and Research Cooperative System (2009-2017), we conducted bivariate and multivariate analyses to examine patterns of utilization, complications, and secondary procedures following cholecystectomies. RESULTS: Among 299,306 minimally invasive cholecystectomies performed in NYS between 2009 and 2017, one thousand one hundred eighteen (0.4%) were robot-assisted. Compared to those undergoing LC, RAC patients were older, travelled further for surgery, and were more likely to have public insurance and preoperative comorbidities. RAC versus LC patients were more significantly likely to have conversions to open procedure (4.9% vs 2.8%), bile duct injuries (1.3% vs 0.4%), and major reconstructive interventions (0.6% vs 0.1%), longer median length of stay (3 vs 1 day), readmissions (7.3% vs 4.4%), and higher 12-month post-index surgery hospital charges (P < 0.01 for all estimates). Other postoperative complications decreased over time for LC but remained unchanged for RAC patients. CONCLUSIONS: Patients receiving RAC in NYS experienced higher rates of complications compared to LC patients. Addressing patient-, surgeon-, and system-level factors associated with intra/postoperative complications and applying recently promulgated safe cholecystectomy strategies coupled with advanced imaging modalities like fluorescence cholangiography to RAC may improve patient outcomes.

Teaching surgery novices and trainees advanced laparoscopic suturing: a trial and tribulations.

INTRODUCTION: The benefits of minimally invasive surgery are numerous; however, considerable variability exists in its application and there is a lack of standardized training for important advanced skills. Our goal was to determine whether participation in an advanced laparoscopic curriculum (ALC) results in improved laparoscopic suturing skills. METHODS AND PROCEDURES: Study design was a prospective, randomized controlled trial. Surgery novices and trainees underwent baseline FLS training and were pre-tested on bench models. Participants were stratified by pre-test score and randomized to undergo either further FLS training (control group) or ALC training (intervention group). All were post-tested on the same bench model. Tests for differences between post-test scores of cohorts were performed using least squared means. Multivariable regression identified predictors of post-test score, and Wilcoxon rank sum test assessed for differences in confidence improvement in laparoscopic suturing ability between groups. RESULTS: Between November 2018 and May 2019, 25 participants completed the study (16 females; 9 males). After adjustment for relevant variables, participants randomized to the ALC group had significantly higher post-test scores than those undergoing FLS training alone (mean score 90.50 versus 82.99, p = 0.001). The only demographic or other variables found to predict post-test score include level of training (p = 0.049) and reported years of video gaming (p = 0.034). There was no difference in confidence improvement between groups. CONCLUSIONS: Training using the ALC as opposed to basic laparoscopic skills training only is associated with superior advanced laparoscopic suturing performance without affecting improvement in reported confidence levels. Performance on advanced laparoscopic suturing tasks may be predicted by lifetime cumulative video gaming history and year of training but does not appear to be associated with other factors previously studied in relation to basic laparoscopic skills, such as surgical career aspiration or musical ability.

The costs of supervised classification: The effect of learning task on conceptual flexibility.

Research has shown that learning a concept via standard supervised classification leads to a focus on diagnostic features, whereas learning by inferring missing features promotes the acquisition of within-category information. Accordingly, we predicted that classification learning would produce a deficit in people's ability to draw novel contrasts--distinctions that were not part of training--compared with feature inference learning. Two experiments confirmed that classification learners were at a disadvantage at making novel distinctions. Eye movement data indicated that this conceptual inflexibility was due to (a) a narrower attention profile that reduces the encoding of many category features and (b) learned inattention that inhibits the reallocation of attention to newly relevant information. Implications of these costs of supervised classification learning for views of conceptual structure are discussed.

Prior knowledge enhances the category dimensionality effect.

A study of the combined influence of prior knowledge and stimulus dimensionality on category learning was conducted. Subjects learned category structures with the same number of necessary dimensions but with more or fewer additional, redundant dimensions and with either knowledge-related or knowledge-unrelated features. Minimal-learning models predict that all subjects, regardless of condition, either should learn the same number of dimensions or should respond more slowly to each dimension. Despite similar learning rates and response times, subjects learned more features in the high-dimensional than in the low-dimensional condition. Furthermore, prior knowledge interacted with dimensionality, increasing what was learned, especially in the high-dimensional case. A second experiment confirmed that the participants did, in fact, learn more features during the training phase, rather than simply inferring them at test. These effects can be explained by direct associations among features (representing prior knowledge), combined with feedback between features and the category label, as was shown by simulations of the knowledge resonance, or KRES, model of category learning.

Blocking in category learning.

Many theories of category learning assume that learning is driven by a need to minimize classification error. When there is no classification error, therefore, learning of individual features should be negligible. The authors tested this hypothesis by conducting three category-learning experiments adapted from an associative learning blocking paradigm. Contrary to an error-driven account of learning, participants learned a wide range of information when they learned about categories, and blocking effects were difficult to obtain. Conversely, when participants learned to predict an outcome in a task with the same formal structure and materials, blocking effects were robust and followed the predictions of error-driven learning. The authors discuss their findings in relation to models of category learning and the usefulness of category knowledge in the environment.

Category dimensionality and feature knowledge: when more features are learned as easily as fewer.

Three experiments compared the learning of lower-dimensional family resemblance categories (4 dimensions) with the learning of higher-dimensional ones (8 dimensions). Category-learning models incorporating error-driven learning, hypothesis testing, or limited capacity attention predict that additional dimensions should either increase learning difficulty or decrease learning of individual features. Contrary to these predictions, the experiments showed no slower learning of high-dimensional categories; instead, subjects learned more features from high-dimensional categories than from low-dimensional categories. This result obtained both in standard learning with feedback and in noncontingent, observational learning. These results show that rather than interfering with learning, categories with more dimensions cause individuals to learn more. The authors contrast the learning of family resemblance categories with learning in classical conditioning and probability learning paradigms, in which competition among features is well documented.

Thirty-something categorization results explained: selective attention, eyetracking, and models of category learning.

An eyetracking study testing D. L. Medin and M. M. Schaffer's (1978) 5-4 category structure was conducted. Over 30 studies have shown that the exemplar-based generalized context model (GCM) usually provides a better quantitative account of 5-4 learning data as compared with the prototype model. However, J. D. Smith and J. P. Minda (2000) argued that the GCM is a psychologically implausible account of 5-4 learning because it implies suboptimal attention weights. To test this claim, the authors recorded undergraduates' eye movements while the students learned the 5-4 category structure. Eye fixations matched the attention weights estimated by the GCM but not those of the prototype model. This result confirms that the GCM is a realistic model of the processes involved in learning the 5-4 structure and that learners do not always optimize attention, as commonly supposed. The conditions under which learners are likely to optimize attention during category learning are discussed.

Eyetracking and selective attention in category learning.

An eyetracking version of the classic Shepard, Hovland, and Jenkins (1961) experiment was conducted. Forty years of research has assumed that category learning often involves learning to selectively attend to only those stimulus dimensions useful for classification. We confirmed that participants learned to allocate their attention optimally. We also found that learners tend to fixate all stimulus dimensions early in learning. This result obtained despite evidence that participants were also testing one-dimensional rules during this period. Finally, the restriction of eye movements to only relevant dimensions tended to occur only after errors were largely (or completely) eliminated. We interpret these findings as consistent with multiple-systems theories of learning which maximize information input in order to maximize the number of learning modules involved, and which focus solely on relevant information only after one module has solved the learning problem.