Evidence map›Paper›PMID 32968922›Full record

ArticleSurgical endoscopy2021

Correlating actual one-year weight loss with predicted weight loss by the MBSAQIP: bariatric surgical risk/benefit calculator.

Randal Zhou, Lee Ying, Jessica Valle, Jessie Moore, Geoffrey Nadzam, Kurt Roberts, Saber Ghiassi, John Morton, Andrew Duffy

Abstract read
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In one paragraph

Article in Surgical endoscopy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Weight Loss Prediction after Metabolic and Bariatric Surgery.Journal of obesity & metabolic syndrome · 2023
    Review
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Randal ZhouDivision of Bariatric and Minimally Invasive Surgery, Yale University, School of Medicine, New Haven, USA. Randal_Zhou@med.unc.edu.ORCID 0000-0001-6713-4933
Lee YingDivision of Bariatric and Minimally Invasive Surgery, Yale University, School of Medicine, New Haven, USA.
Jessica ValleDivision of Bariatric and Minimally Invasive Surgery, Yale University, School of Medicine, New Haven, USA.
Jessie MooreDivision of Bariatric and Minimally Invasive Surgery, Yale University, School of Medicine, New Haven, USA.
Geoffrey NadzamDivision of Bariatric and Minimally Invasive Surgery, Yale University, School of Medicine, New Haven, USA.
Kurt RobertsDivision of Bariatric and Minimally Invasive Surgery, Yale University, School of Medicine, New Haven, USA.
Saber GhiassiDivision of Bariatric and Minimally Invasive Surgery, Yale University, School of Medicine, New Haven, USA.
John MortonDivision of Bariatric and Minimally Invasive Surgery, Yale University, School of Medicine, New Haven, USA.
Andrew DuffyDivision of Bariatric and Minimally Invasive Surgery, Yale University, School of Medicine, New Haven, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe American Society for Metabolic and Bariatric Surgery has released a Bariatric Surgical Risk/Benefit Calculator, an online tool with which patients and providers can input patient preoperative information and predict their 1-year weight loss. We seek to validate our institutional data with the national database and investigated patient factors that influence lack of treatment effect after bariatric surgery. MATERIALS AND

methodsA retrospective review of all prospectively collected data of bariatric surgeries performed at Yale New Haven Hospital from 2017 to 2018 was conducted. By entering data into the MBSAQIP Calculator, the 1-year predicted Body Mass Index was calculated and compared to the actual weight loss. Statistical analysis was performed using an unpaired t-test with Welch's correction (Prism 8, GraphPad).

resultsThe average difference between the actual and predicted weight loss at 1-year for 327 patients was 3.6 BMI points. When the actual weight loss was compared to predicted BMI at 1 year, a high correlation was found (R = 0.6, P = 0.003). We examined the outliers with a comparison of weight loss for those patients who's BMI fell within 5 points of the predicted versus those whose BMI recorded above 5. It was discovered those patients who had higher than 5 BMI points than predicted, had higher preoperative BMI (46.1 vs 43.6, P = 0.008).

conclusionsThe MBSAQIP calculator is a useful tool to guide surgeons with decision-making and informed consent. Our institution's 1-year weight loss data correlated closely with that predicted. From the outliers, we found that patients who did not meet the predicted weight loss had significantly higher preoperative BMI. This may alter preoperative discussions with class 3 or over obese patients regarding expected weight loss and warrant investigations with the national database to develop modifications of the calculator.

Indexed as

Bariatric SurgeryObesity, MorbidBody Mass IndexHumansObesityRetrospective StudiesRisk AssessmentTreatment OutcomeWeight LossClass 5 obesityMBSAQIP risk/benefit calculatorResponse to surgery

Identifiers

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.