Quality Improvement in Bariatric Surgery: The Impact of Reducing Postoperative Complications on Medicare Payments

Children's surgery verification and value-based care in pediatric surgery

With the prevailing focus on increasing value in healthcare, understanding the different components of the value equation is of primary importance. Michael E. Porter's writings on the value agenda and the use of integrated practice units (IPUs) have provided easy correlation to adult disease entities with large populations sharing common pathways and providers in the diagnosis and care of these patients. In pediatric surgery, with smaller populations and larger numbers of rare or unique conditions and anatomic challenges, utilizing the concept of an IPU is more challenging. The literature has generally shown the improvements in quality of care through participation in various programs through the American College of Surgeons (ACS) such as trauma verification, or the National Surgical Quality Improvement Project (NSQIP), but that participation alone does not guarantee better outcomes. Use of these programs in conjunction with participation in quality collaboratives have tended to show favorable returns on investment for these programs. We seek to demonstrate how the Children's Surgery Verification (CSV) program provides pediatric surgeons an effective vehicle with which to engage the value agenda, evaluating and improving care over the care continuum in order to improve the function of children's hospitals as larger integrated units.

Selection of Robotic Bariatric Surgery Candidates: a Nationwide Analysis

INTRODUCTION

This study aims to identify risk factors associated with 30-day major complications, readmission, and delayed discharge for patients undergoing robotic bariatric surgery.

METHODS

From the metabolic and bariatric surgery and accreditation quality improvement program (2015-2018) datasets, adult patients who underwent elective robotic bariatric operations were included. Predictors for 30-day major complications, readmission, and delayed discharge (hospital stay ≥ 3 days) were identified using univariable and multivariable analyses.

RESULTS

Major complications in patients undergoing robotic bariatric surgery were associated with both pre-operative and intraoperative factors including pre-existing cardiac morbidity (OR = 1.41, CI = [1.09-1.82]), gastroesophageal reflux disease [GERD] (OR = 1.23, CI = [1.11-1.38]), pulmonary embolism (OR = 1.51, CI = [1.02-2.22]), prior bariatric surgery (OR = 1.66, CI = [1.43-1.94]), increased operating time (OR = 1.003, CI = [1.002-1.004]), gastric bypass or duodenal switch (OR = 1.58, CI = [1.40-1.79]), and intraoperative drain placement (OR = 1.28, CI = [1.11-1.47]). With regard to 30-day readmission, non-white race (OR = 1.25, CI = [1.14-1.39]), preoperative hyperlipidemia (OR = 1.16, CI = [1.14-1.38]), DVT (OR = 1.48, CI = [1.10-1.99]), therapeutic anticoagulation (OR = 1.48, CI = [1.16-1.89]), limited ambulation (OR = 1.33, CI = [1.01-1.74]), and dialysis (OR = 2.14, CI = [1.13-4.09]) were significantly associated factors. Age ≥ 65 (OR = 1.18, CI = [1.04-1.34]), female gender (OR = 1.21, CI = [1.10-1.32]), hypertension (OR = 1.08, CI = [1.01-1.15]), renal insufficiency (OR = 2.32, CI = [1.69-3.17]), COPD (OR = 1.49, CI = [1.23-1.82]), sleep apnea (OR = 1.10, CI = [1.03-1.18]), oxygen dependence (OR = 1.47, CI = [1.10-2.0]), steroid use (OR = 1.26, CI = [1.02-1.55]), IVC filter (OR = 1.52, CI = [1.15-2.0]), and BMI ≥ 40 (OR = 1.12, CI = [1.04-1.21]) were risk factors associated with delayed discharge.

CONCLUSION

When selecting patients for bariatric surgery, surgeons early in their learning curve for utilizing robotics should avoid individuals with pre-existing cardiac or renal morbidities, venous thromboembolism, and limited functional status. Patients who have had previous bariatric surgery or require technically demanding operations are at higher risk for complications. An evidence-based approach in selecting bariatric candidates may potentially minimize the overall costs associated with adopting the technology.

The development of machine learning in bariatric surgery

Background

Machine learning (ML), is an approach to data analysis that makes the process of analytical model building automatic. The significance of ML stems from its potential to evaluate big data and achieve quicker and more accurate outcomes. ML has recently witnessed increased adoption in the medical domain. Bariatric surgery, otherwise referred to as weight loss surgery, reflects the series of procedures performed on people demonstrating obesity. This systematic scoping review aims to explore the development of ML in bariatric surgery.

Methods

The study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR). A comprehensive literature search was performed of several databases including PubMed, Cochrane, and IEEE, and search engines namely Google Scholar. Eligible studies included journals published from 2016 to the current date. The PRESS checklist was used to evaluate the consistency demonstrated during the process.

Results

A total of seventeen articles qualified for inclusion in the study. Out of the included studies, sixteen concentrated on the role of ML algorithms in prediction, while one addressed ML's diagnostic capacity. Most articles (n = 15) were journal publications, whereas the rest (n = 2) were papers from conference proceedings. Most included reports were from the United States (n = 6). Most studies addressed neural networks, with convolutional neural networks as the most prevalent. Also, the data type used in most articles (n = 13) was derived from hospital databases, with very few articles (n = 4) collecting original data via observation.

Conclusions

This study indicates that ML has numerous benefits in bariatric surgery, however its current application is limited. The evidence suggests that bariatric surgeons can benefit from ML algorithms since they will facilitate the prediction and evaluation of patient outcomes. Also, ML approaches to enhance work processes by making data categorization and analysis easier. However, further large multicenter studies are required to validate results internally and externally as well as explore and address limitations of ML application in bariatric surgery.

Higher Adherence to ERAS Society® Recommendations is Associated with Shorter Hospital Stay Without an Increase in Postoperative Complications or Readmissions in Bariatric Surgery: the Association Between Use of Enhanced Recovery After Surgery Protocols and Postoperative Complications after Bariatric Surgery (POWER 3) Multicenter Observational Study

PURPOSE

The effectiveness of enhanced recovery after surgery (ERAS) pathways in patients undergoing bariatric surgery remains unclear. Our objective was to determine the effect of the ERAS elements on patient outcomes following elective bariatric surgery.

MATERIALS AND METHODS

Prospective cohort study in adult patients undergoing elective bariatric surgery. Each participating center selected a single 3-month data collection period between October 2019 and September 2020. We assessed the 24 individual components of the ERAS pathways in all patients. We used a multivariable and multilevel logistic regression model to adjust for baseline risk factors, ERAS elements, and center differences RESULTS: We included 1419 patients. One hundred and fourteen patients (8%) developed postoperative complications. There were no differences in the incidence of overall postoperative complications between the self-designated ERAS and non-ERAS groups (54 (8.7%) vs. 60 (7.6%); OR, 1.14; 95% CI, 0.73-1.79; P = .56), neither for moderate-to-severe complications, readmissions, re-interventions, mortality, or hospital stay (2 [IQR 2-3] vs. 3 [IQR 2-4] days, 0.85; 95% CI, 0.62-1.17; P = .33) Adherence to the ERAS elements in the highest adherence quartile (Q1) was greater than 72.2%, while in the lowest adherence quartile (Q4) it was less than 55%. Patients with the highest adherence rates had shorter hospital stay (2 [IQR 2-3] vs. 3 [IQR 2-4] days, 1.54; 95% CI, 1.09-2.17; P = .015), while there were no differences in the other outcomes CONCLUSIONS: Higher adherence to ERAS Society® recommendations was associated with a shorter hospital stay without an increase in postoperative complications or readmissions.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03864861.

Reoperation Incidence and Severity Within 6 Months After Bariatric Surgery: a Propensity-Matched Study from Nationwide Data

BACKGROUND

Data about incidence and severity of reoperations up to 6 months after bariatric surgery are currently limited. The aim of this cohort study was to evaluate the incidence and severity of reoperations after initial bariatric surgical procedures and to compare this between the 3 most frequent current surgical procedures (sleeve, gastric bypass, gastric banding).

STUDY DESIGN

Nationwide observational cohort study using data from French Hospital Information System (2013-2015) to evaluate incidence and severity of reoperations within 6 months after bariatric surgery. Hazard ratios (HR) of longitudinal comparison between historical propensity-matched cohorts were estimated from a Fine and Gray's model using competing risk of death.

RESULTS

Cumulative reoperation rates increased from postoperative day-30 to day-180. Consequently, 31.1 to 90.0% of procedures would have been missed if the reoperation rate was based solely on a 30-day follow-up. Reoperation rate at 6 months was significantly higher after gastric bypass than after sleeve (HR 0.64; IC 95% [0.53-0.77]) and corresponded to moderate-risk reoperations (HR 0.65; IC 95% [0.53-0.78]). Reoperation rate at 6 months was significantly higher after gastric banding than after sleeve (HR 0.08; IC 95% [0.07-0.09]) and corresponded to moderate-risk reoperations (HR 0.08; IC 95% [0.07-0.10]).

CONCLUSION

Cumulative incidence of reoperations increased from 30 days to 6 months after sleeve, gastric bypass, or gastric banding and corresponded to moderate-risk surgical procedures. Consequently, 30-day reoperation rate should no longer be considered when evaluating complications and surgical performance after bariatric surgery.

Development and validation of machine learning models to predict gastrointestinal leak and venous thromboembolism after weight loss surgery: an analysis of the MBSAQIP database

BACKGROUND

Postoperative gastrointestinal leak and venous thromboembolism (VTE) are devastating complications of bariatric surgery. The performance of currently available predictive models for these complications remains wanting, while machine learning has shown promise to improve on traditional modeling approaches. The purpose of this study was to compare the ability of two machine learning strategies, artificial neural networks (ANNs), and gradient boosting machines (XGBs) to conventional models using logistic regression (LR) in predicting leak and VTE after bariatric surgery.

METHODS

ANN, XGB, and LR prediction models for leak and VTE among adults undergoing initial elective weight loss surgery were trained and validated using preoperative data from 2015 to 2017 from Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program database. Data were randomly split into training, validation, and testing populations. Model performance was measured by the area under the receiver operating characteristic curve (AUC) on the testing data for each model.

RESULTS

The study cohort contained 436,807 patients. The incidences of leak and VTE were 0.70% and 0.46%. ANN (AUC 0.75, 95% CI 0.73-0.78) was the best-performing model for predicting leak, followed by XGB (AUC 0.70, 95% CI 0.68-0.72) and then LR (AUC 0.63, 95% CI 0.61-0.65, p < 0.001 for all comparisons). In detecting VTE, ANN, and XGB, LR achieved similar AUCs of 0.65 (95% CI 0.63-0.68), 0.67 (95% CI 0.64-0.70), and 0.64 (95% CI 0.61-0.66), respectively; the performance difference between XGB and LR was statistically significant (p = 0.001).

CONCLUSIONS

ANN and XGB outperformed traditional LR in predicting leak. These results suggest that ML has the potential to improve risk stratification for bariatric surgery, especially as techniques to extract more granular data from medical records improve. Further studies investigating the merits of machine learning to improve patient selection and risk management in bariatric surgery are warranted.

Factors associated with switching between low and super utilization in the surgical population: A study in medicare expenditure

BACKGROUND

Considered the top 5% of healthcare utilizers, "super-utilizers" are estimated to consume as much as 40-55% of all healthcare costs. The aim of this study was to identify factors associated with switching between low- and super-utilization.

METHODS

Low and super-utilizers who underwent abdominal aortic aneurysm (AAA) repair, coronary artery bypass graft (CABG), colectomy, total hip arthroplasty (THA), total knee arthroplasty (TKA), or lung resection between 2013 and 2015 were identified from 100% Medicare Inpatient Standard Analytic Files.

RESULTS

Among 1,049,160 patients, 788,488 (75.1%) and 21,700 (2.1%) patients were low- or super-utilizers prior to surgery, respectively. Among patients who were super-utilizers before surgery, 23% remained super-utilizers post-operatively, yet 26.8% patients became low-utilizers after surgery. Factors associated with moving from low-to super-utilization in the pre-versus post-operative setting included AAA repair, higher Charlson, and pulmonary failure. In contrast, pre-operative super-utilizers who became low-utilizers in the post-operative setting were less likely to be African American or have undergone CABG.

CONCLUSION

While 3% of pre-operative low-utilizers became super-utilizers likely due to complications, nearly one quarter of all pre-operative super-utilizers became low-utilizers following surgery suggesting success of the surgery to resolve underlying conditions associated with preoperative super-utilization.