The safety of MBS hinges on careful patient evaluation and optimization. Irrespective of baseline diabetes status, knowledge of patient A1C values prior to MBS influences perioperative care. Indeed, the ASMBS endorses that, while there is no A1C value above which surgery is prohibitive, patients undergoing MBS should receive an A1C test (Mechanick et al. 2019; Carter et al. 2021). Our study using six years of data from the MBSAQIP demonstrates that A1C testing has not yet become a universal practice. While the proportion of patients tested has steadily increased over time, until more recent years, less than half of patients undergoing MBS had their A1C measured preoperatively. Notably, there has been differential practice in which patients receive a test based on baseline diabetes status. As a whole, these results add to a body of literature assessing patient work up prior to MBS, but uniquely focus on glycemic evaluation (Jatana et al. 2023; Gudzune et al. 2013). In doing so, we highlight an area for quality improvement in the preoperative process.

Our findings with regard to glycemic evaluation prior to MBS exist in a broader landscape of challenges surrounding preoperative evaluation of surgical patients. Outside of bariatric surgery, practices of both under- and over-testing of surgical patients have been attributed to issues of uncertainty about who on the team is responsible for ordering tests as well as inability of clinicians to access/review patient records and consult/communicate with colleagues about preoperative decisions (Hall et al. 2022; Patey et al. 2012). Additionally, clinician concern about delaying surgery and the cost-effectiveness of testing have been cited as barriers to adequate evaluation (Hall et al. 2022; Patey et al. 2012). As MBS is the effort of a multi-disciplinary team involving surgeons, anesthesiologists, nurses, and other clinicians that often spans the course of several months, it is possible that many of these challenges extend to patient evaluation before bariatric surgery. To explore this, qualitative studies are needed to understand the individual, team, and organizational dogmas at bariatric programs that may impact the preoperative patient evaluation process.

In our study, low prevalence of A1C testing may be attributable to a degree of measurement bias. It is possible that we are capturing inadequacies in reporting of A1C in the MBSAQIP database as opposed to deficiencies in preoperative evaluation. Indeed, large medical databases are fraught with issues of data completeness, a data quality metric that is often measured through frequency of missing data entries (Aziz et al. 2020; Yang et al. 2021). Interestingly, however, data quality in the MBSAQIP appears to be acceptable and consistent. In a recent analysis, Clapp et al. studied the 2015 to 2019 MBSAQIP datasets and demonstrated that data were completed at a rate of over 97.5% with no significant differences across years (Clapp et al. 2024). The authors, however, only evaluated variables with mandatory reporting (i.e., age, race, ethnicity, BMI, ASA class, and preoperative weight) and acknowledged that laboratory values and other non-mandatory variables might have shown higher percentages of missing values. A better understanding of MBSAQIP data completeness will require analysis of non-mandatory variable collection which then would allow for contextualization of completeness of the A1C variable.

Nonetheless, the prevalence of A1C testing reported in our results is comparatively lower than that of other screening practices prior to MBS, many of which have also been assessed with non-mandatory MBSAQIP variables. The ASMBS and International Federation for the Surgery of Obesity and Metabolic Disorders (IFSO), for example, encourage screening for and optimization of substance abuse disorders and, in an analysis of the 2021 MBSAQIP database, Jatana et al. demonstrated that over 60% of patients received substance abuse screening (Jatana et al. 2023). Similarly, both societies recommend screening for cigarette smoking and, when appropriate, counseling on cessation. In a study of the 2015 to 2018 MBSAQIP datasets, approximately 90% of patients had a documented smoking status (Janik and Aryaie 2021). In a separate study examining associations between postoperative outcomes and preoperative albumin, a marker of nutritional status, over 70% of patients undergoing bariatric surgery had a registered preoperative albumin value in the 2015–2019 MBSAQIP databases (Hart et al. 2022). The comparatively higher prevalence of preoperative substance abuse, smoking, and nutrition evaluation evident in these studies underscores the opportunity for improvement when it comes to glycemic evaluation.

Although baseline comorbidities were largely comparable between those who were tested and those who were not, we found that patients with baseline diabetes had disproportionately higher odds of receiving an A1C test. In some ways, this may be an expected finding that reflects efforts to optimize those with a known risk for postoperative hyperglycemia. Although no clear association has been demonstrated between preoperative A1C and adverse outcomes after MBS, studies have demonstrated that preoperative A1C predicts early postoperative hyperglycemia, avoidance of which can improve outcomes after MBS (Basishvili et al. 2021; Perna et al. 2012; Rawlins et al. 2013; Wysocki et al. 2019). In this regard, knowledge of preoperative A1C values for those patients whose glycemic control is the most challenging at baseline (i.e., insulin-dependent patients) allows for the opportunity to adjust medications, consult medical specialists, and take other actions necessary to ensure perioperative glycemic optimization and favorable postoperative outcomes.

The concern, however, is that the benefits of testing those without a history of diabetes are being squandered. A subset of patients presenting for MBS are not diagnosed with prediabetes or diabetes until the time of preoperative A1C evaluation, a phenomenon likely related to general healthcare avoidance in the face of widespread obesity stigma (McGuigan and Wilkinson 2015; Mensinger et al. 2018). These patients can benefit from perioperative optimization in accordance with American Association of Clinical Endocrinology guidelines (Garber et al. 2020; Lee et al. 2020) and, for some, surgery may need to be delayed to optimize outcomes. An important consideration is that preoperative A1C testing may worsen the time and financial toxicity that many MBS patients experience (Ju et al. 2019; Alvarez et al. 2018). Since patients already present to clinics for standard preoperative lab work, A1C testing would likely not contribute substantially to the time burden of preoperative workup. Furthermore, many insurance policies include coverage of A1C testing prior to bariatric surgery (Gebran et al. 2020). For patients who are found to have elevated A1C levels, early glycemic control is associated with considerable reductions in healthcare costs (Lage and Boye 2020). Thus, regardless of baseline diabetes status, preoperative A1C values contribute to the individualized care plans of all patients and thus ensuring universal measurement can lead to earlier diagnosis and management of at risk patients.

While the frequency of testing was overall low, the proportion of patients receiving an A1C test did progressively increase each year from 35.5% in 2017 to 56.0% in 2022. Further studies will be needed to understand the reasons for this improvement, but possible explanations include better documentation, increased awareness of society guidelines, and changes to program-specific policy. Additionally, the inclusion of the “HEMO” variable in the MBSAQIP in 2017 could have been viewed by bariatric centers as a quality performance indicator. Performance indicators are powerful drivers of individual and organizational decisions in healthcare, a phenomenon summarized by the adage “what gets measured, gets done”(Goodreau 2007; Barbazza et al. 2021). As a result, the mere inclusion of the variable may have been an impetus for A1C testing.

Our study results should be interpreted in the context of key limitations. It is possible that A1C testing was completed at an external facility and not documented in the records of the hospital where the patient underwent surgery. The test result, therefore, would not have been available to the MBSCR to register into the database and, as a result, the MBSAQIP “HEMO” variable would underestimate the true A1C testing frequency. A retrospective chart review at the institution level is an alternative method that may have been able to capture these tests. Using the MBSAQIP database, however, allowed us to describe trends in preoperative A1C testing at the national level, which, in turn, can prompt more nuanced institution-specific reviews of current practice.

Additional study limitations are related to the data elements available in the MBSAQIP. Most comorbidities in the database are binary variables defined by either the absence or presence of the comorbidity. In this regard, we were unable to describe how patients who were tested and those who were not differed in regard to severity of comorbidities. Additionally, while we accounted for patient physical status by adjusting for ASA class in our regression model, the ASA variable in the database may have issues with misclassification (Curatolo et al. 2017; Nafiu et al. 2021). Indeed, our study included a proportion of patients with ASA classifications not concordant with elective bariatric surgery (e.g., ASA class V). Nevertheless, the multitude of baseline clinical and sociodemographic variables available in the MBSAQIP allowed us to extensively describe the characteristics of our study cohort.