Highlights
- •The Bariatric Surgical Risk/Benefit Calculator estimates 30-day post-op event risk
- •Predictive models for each of nine outcomes are accurate and well calibrated
- •The tool supports surgical decision-making, communication, and informed consent
- •The Risk/Benefit Calculator also estimates 1-Year BMI and comorbidity remission
Abstract
Background
There is increasing demand for data-driven tools that provide accurate and clearly
communicated patient-specific information. These can aid discussions between practitioners
and patients, promote shared decision-making, and enhance informed consent. The American
College of Surgeons Metabolic and Bariatric Surgery Accreditation and Quality Improvement
Program (MBSAQIP) sought to create a risk calculator for adult patients considering
primary metabolic and bariatric surgery, with multiple prediction features: (1) 30-day
risk; (2) 1-year body mass index projections; and (3) 1-year co-morbidity remission.
Objectives
To evaluate the 30-day risk estimation feature of this tool.
Setting
Not-for-profit organization, international bariatric surgery clinical data registry.
Methods
MBSAQIP data across 5.5 years, 925 hospitals, and 775,291 cases were used to develop
the 30-day risk feature. Logistic regression models were employed to estimate postoperative
risks for 9 outcomes across 4 procedures: laparoscopic Roux-en-Y gastric bypass, laparoscopic
sleeve gastrectomy, laparoscopic adjustable gastric band, and biliopancreatic diversion
with duodenal switch.
Results
The tool showed good discrimination for mortality and surgical site infection models
(c-statistics, .80 and .70, respectively), and was slightly less accurate for the
7 other complications (.62–.69). Graphical representations showed excellent calibration
for all 9 outcomes.
Conclusions
Overall, the 30-day risk models were accurate and well calibrated, with acceptable
discrimination. The MBSAQIP bariatric surgical risk/benefit calculator is publicly
available, with the intent to be integrated into healthcare practice to guide bariatric
surgical decision-making and care planning, and to enhance communication between patients
and their surgical care team.
Keywords
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References
- How and why do we measure surgical risk?.J R Soc Med. 2007; 100: 508-512
- Informed consent: an ethical obligation or legal compulsion?.J Cutan Aesthet Surg. 2008; 1: 33-35
- Communicating risk to patients and the public.Br J Gen Pract. 2012; 62: 213-216
- Designing visual aids that promote risk literacy: a systematic review of health research and evidence-based design heuristics.Human Factors. 2017; 59: 582-627
- Improving risk literacy in surgeons.Patient Educ Couns. 2016; 99: 1156-1161
- Handling missing data by maximum likelihood [paper 312-2012]. SAS Global Forum; 2012 April 23; Orlando, FL.Statistical Horizons, Harverford, PA2012
- Imputing missing data using SAS [paper 3295-2015]. SAS Global Forum; 2015 April 26–9.Statistical Horizons, Dallas, TX. Harverford, PA2015
- ASA physical status classification system. Washington, DC: American Society of Anesthesiologists; c2014 Oct 15 [updated 2020 Dec 13; cited ].(Available from:)
- MBSAQIP bariatric surgical risk/benefit calculator. c2007 [cited August 15, 2019].(Available from:)
- Relevance of the C-statistic when evaluating risk-adjustment models in surgery.J Am Coll Surg. 2012; 214: 822-830
- Receiver operating characteristic curve in diagnostic test assessment.J Thorac Oncol. 2010; 5: 1315-1316
- Assessing the Fit of the Model.in: Applied logistic regression. 2nd ed. Chapter 5. John Wiley and Sons, New York, NY2000: 160-164
- Assessing the calibration of mortality benchmarks in critical care: the Hosmer-Lemeshow test revisited.Crit Care Med. 2007; 35: 2052-2056
- An examination of ACS NSQIP surgical risk calculator accuracy.J Am Coll Surg. 2017; 224: 787-795.e1
- Development and validation of a bariatric surgery mortality risk calculator.J Am Coll Surg. 2012; 214: 892-900
- Development of a sleeve gastrectomy risk calculator.Surg Obes Relat Dis. 2015; 11: 758-764
- Recent improvements in bariatric surgery outcomes.Medical Care. 2009; 47: 531-535
- New guidelines promote expansion of bariatric surgery to treat obesity. 2019 [cited April 2020].(Available from:)
- AACE/TOS/ASMBS/OMA/ASA 2019 Guidelines; clinical practice guidelines for the perioperative nutrition, metabolic, and nonsurgical support of patients undergoing bariatric procedures–2019 update: cosponsored by American Association of Clinical Endocrinologists/American College of Endocrinology, The Obesity Society, American Society for Metabolic & Bariatric Surgery, Obesity Medicine Association, and American Society of Anesthesiologists.Endocr Pract. 2019; 25: 1346-1359
Article info
Publication history
Published online: February 10, 2021
Accepted:
February 4,
2021
Received:
November 18,
2020
Identification
Copyright
© 2021 American Society for Bariatric Surgery. Published by Elsevier Inc. All rights reserved.
ScienceDirect
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- Comment on: The Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program bariatric surgical risk/benefit calculator: 30-day riskSurgery for Obesity and Related DiseasesVol. 17Issue 6
- PreviewThe creation and implementation of the Metabolic and Bariatric Surgery Quality Improvement Program (MBSAQIP) under the auspices of the American College of Surgeons has enabled the field of metabolic and bariatric surgery (MBS) to benefit from, and show the benefits of, big data. The age of the single surgeon or the institutional series is dead; the age of big data involving hundreds of thousands of patients has arrived. Big data is not new but is finally being applied routinely to medicine. How we use this data to improve outcomes for our patients is now the question.
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