Glucagon-Like Peptide-1 Receptor Agonist Use and Residual Gastric Content Before Anesthesia

Sudipta Sen; Paul P Potnuru; Nadia Hernandez; Christina Goehl; Caroline Praestholm; Srikanth Sridhar; Omonele O Nwokolo

doi:10.1001/jamasurg.2024.0111

. 2024 Mar 6;159(6):660–667. doi: 10.1001/jamasurg.2024.0111

Glucagon-Like Peptide-1 Receptor Agonist Use and Residual Gastric Content Before Anesthesia

Sudipta Sen ^1,^✉, Paul P Potnuru ¹, Nadia Hernandez ¹, Christina Goehl ¹, Caroline Praestholm ², Srikanth Sridhar ¹, Omonele O Nwokolo ¹

PMCID: PMC10918573 PMID: 38446466

This cross-sectional study investigates the association between glucagon-like peptide-1 receptor agonist use and prevalence of increased residual gastric content on gastrointestinal ultrasonography among fasted patients undergoing elective procedures under anesthesia.

Key Points

Question

Is glucagon-like peptide-1 receptor agonist (GLP-1 RA) use associated with increased residual gastric content (RGC) in fasted patients presenting for elective procedures under anesthesia?

Findings

In this cross-sectional study of 124 patients who fasted for the guideline-recommended duration, the prevalence of increased RGC on gastric ultrasonography was 56% in GLP-1 RA users compared with 19% in nonusers, a significant difference after confounder adjustment.

Meaning

Patients taking a GLP-1 RA had a higher prevalence of increased RGC despite fasting for the guideline-recommended duration.

Abstract

Importance

Glucagon-like peptide-1 receptor agonist (GLP-1 RA) use is rapidly increasing in the US, driven by its expanded approval for weight management in addition to hyperglycemia management in patients with type 2 diabetes. The perioperative safety of these medications, particularly with aspiration risk under anesthesia, is uncertain.

Objective

To assess the association between GLP-1 RA use and prevalence of increased residual gastric content (RGC), a major risk factor for aspiration under anesthesia, using gastric ultrasonography.

Design, Setting, and Participants

This cross-sectional study prospectively enrolled patients from a large, tertiary, university-affiliated hospital from June 6 through July 12, 2023. Participants followed preprocedural fasting guidelines before an elective procedure under anesthesia. Patients with altered gastric anatomy (eg, from previous gastric surgery), pregnancy, recent trauma (<1 month), or an inability to lie in the right lateral decubitus position for gastric ultrasonography were excluded.

Exposure

Use of a once-weekly GLP-1 RA.

Main Outcomes and Measures

The primary outcome was the presence of increased RGC, defined by the presence of solids, thick liquids, or more than 1.5 mL/kg of clear liquids on gastric ultrasonography. Analysis was adjusted for confounders using augmented inverse probability of treatment weighting, a propensity score–based technique. Secondarily, the association between the duration of drug interruption and the prevalence of increased RGC was explored.

Results

Among the 124 participants (median age, 56 years [IQR, 46-65 years]; 75 [60%] female), the prevalence of increased RGC was 56% (35 of 62) in patients with GLP-1 RA use (exposure group) compared with 19% (12 of 62) in patients who were not taking a GLP-1 RA drug (control group). After adjustment for confounding, GLP-1 RA use was associated with a 30.5% (95% CI, 9.9%-51.2%) higher prevalence of increased RGC (adjusted prevalence ratio, 2.48; 95% CI, 1.23-4.97). There was no association between the duration of GLP-1 RA interruption and the prevalence of increased RGC (adjusted odds ratio, 0.86; 95% CI, 0.65-1.14).

Conclusions and Relevance

Use of a GLP-1 RA was independently associated with increased RGC on preprocedural gastric ultrasonography. The findings suggest that the preprocedural fasting duration suggested by current guidelines may be inadequate in this group of patients at increased risk of aspiration under anesthesia.

Introduction

Glucagon-like peptide-1 receptor agonist (GLP-1 RA) use is rapidly increasing in the US.¹ Initially approved for managing type 2 diabetes, GLP-1 RA drugs have since received expanded approval for long-term weight management in adults with overweight or obesity.² Despite their increasing use, the safety of GLP-1 RAs in the perioperative period remains uncertain.^3,4

Anecdotal reports attribute an increased risk of pulmonary aspiration in patients undergoing anesthesia to GLP-1 RA use.^5,6,7 Use of GLP-1 RAs has been associated with slowed gastric emptying,⁸ leading to increased residual gastric content (RGC) in patients presenting for elective procedures despite following recommended preoperative fasting guidelines.⁹ Increased RGC is one of the main factors associated with aspiration risk under anesthesia.¹⁰ Recent consensus-based guidance from the American Society of Anesthesiologists (ASA) offers expert-opinion recommendations to address the aspiration risk associated with GLP-1 RA based on sparse evidence, noting the urgent need for more evidence.³

In this cross-sectional study, we used gastric ultrasonography (GUS) to characterize the association between GLP-1 RA use and RGC in fasted patients presenting for elective procedures under anesthesia. Gastric ultrasonography is a validated point-of-care technique to qualitatively and quantitatively assess gastric content and identify patients at risk for aspiration under anesthesia.^11,12 We hypothesized that GLP-1 RA use would be independently associated with a higher prevalence of increased RGC on GUS examination in fasted patients. Secondarily, we performed an exploratory analysis of the association between the duration of GLP-1 RA discontinuation and increased RGC.

Methods

This cross-sectional study of prospectively enrolled patients received approval from the institutional review board at The University of Texas Health Science Center at Houston. All study participants received information about the study and gave written informed consent to the investigators before participating. We adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.¹³

Participants

We recruited a convenience sample of patients presenting for elective procedures under anesthesia at our tertiary university-affiliated hospital from June 6 through July 12, 2023. Eligible study participants were patients aged 18 years or older who had an ASA physical status classification of I to IV¹⁴ and had fasted for at least 2 hours for clear liquids, 6 hours for a light meal, and 8 hours for a full, heavy meal.

As point-of-care GUS is only validated in patients with normal gastric anatomy, we excluded patients with large hiatal hernia or any history of gastric surgery (including gastrectomy, Roux-en-Y gastrojejunostomy, gastric bands, and fundoplication). Other exclusion criteria were pregnancy, recent trauma within 1 month, and inability to lie in the right lateral decubitus position. Study staff prescreened all patients on the anesthesia schedule by medical record review and approached eligible patients based on the availability of a qualified study anesthesiologist to perform the GUS examination.

Exposure

The primary exposure was using a once-weekly GLP-1 RA. Specifically, we included patients using semaglutide, dulaglutide, or tirzepatide in the exposure group, with half-lives of 7 days, 5 days, and 5 days, respectively.¹⁵ Semaglutide and dulaglutide are GLP-1 RAs, and tirzepatide is a coagonist of the GLP-1 and glucose-dependent insulinotropic polypeptide receptors.¹⁶ We did not study the once-daily–dosed GLP-1 RAs exenatide, lixisenatide, and liraglutide, as these drugs have shorter half-lives of 1.4 hours, 2.5 hours, and 13.5 hours, respectively.¹⁷

Covariates

We collected demographic information for each patient on age and sex. We recorded clinical data on body mass index (BMI; calculated as weight in kilograms divided by height in meters squared), ASA physical status classification, diabetes, gastroesophageal reflux disease (GERD), opioid use, and the time of last oral intake. Due to low counts of patients with a physical status classification of ASA I, we grouped patients with ASA physical status I and II into a single category representing patients without severe systemic disease.¹⁴ Pain was categorized into 3 categories based on patient-reported pain scores on the pain visual analog scale: no pain (0), mild-moderate pain (<7), and severe pain (≥7).¹⁸ We chose prognostically important covariates associated with GLP-1 RA use and increased RGC a priori based on previous literature and clinical plausibility.^19,20 We constructed a directed acyclic graph (DAG) to delineate associations between the exposure, outcome, and covariates (eFigure 1 in Supplement 1).^21,22 This DAG assisted in covariate selection for confounder adjustment.^21,22 For patients in the GLP-1 RA group, we obtained information about the specific drug type.

Outcomes

The primary outcome was increased RGC, defined as the presence of solids, thick liquids, or more than 1.5 mL/kg of clear liquids on GUS. The ultrasonography technique is detailed in the eMethods in Supplement 1, with representative images in eFigures 2 to 4 in Supplement 1. Anesthesiologists (S. Sen, C.G.) with expertise in point-of-care gastric ultrasonography blinded to the group assignments performed all examinations. A second blinded anesthesiologist (N.H.) independently assessed the gastric contents by reviewing the recorded ultrasonography images. A third blinded anesthesiologist adjudicated any discrepancies in outcome classification. For ethical and safety reasons, we disclosed the preoperative scan results to the patient and the anesthesiologist caring for the patient.

Statistical Analysis

We used descriptive data analysis techniques to examine the distribution of all covariates. We summarized categorical variables as counts and percentages and continuous variables as medians with IQRs due to nonparametric distributions. We compared the baseline imbalance between the control and exposure groups by computing the absolute standardized difference (ASD) for each variable²³; an ASD greater than 0.1 represents a significant imbalance between groups.²⁴

We first performed a crude analysis of the association between GLP-1 RA use and increased RGC (primary outcome) by estimating the unadjusted prevalence ratio using a generalized linear model with a log link, Poisson distribution, and robust error variance.^25,26 We then adjusted for confounding using augmented inverse probability of treatment weighting (IPTW), a doubly robust propensity score–based technique.²⁷ Using a logistic regression model, we first estimated each patient’s propensity score—the probability of GLP-1 RA use conditional on the patient’s baseline covariates. Based on the DAG in eFigure 1 in Supplement 1, we identified a minimally sufficient adjustment set of confounding variables,²⁸ including age, sex, BMI, ASA physical status classification, diabetes, home opioid use, and time since last oral intake. We specifically did not adjust for GERD after identifying it as a collider variable—a consequence of both the exposure (GLP-1 RA use) and outcome (increased RGC).²⁹ Incorrectly adjusting for GERD can introduce additional selection bias and create a spurious association between GLP-1 RA use and increased RGC.³⁰ Lastly, we adjusted for time since the last oral intake of either solids or liquids (whichever was shorter), a prognostically important covariate associated only with the outcome (ie, a precision covariate).³¹ We visually verified sufficient overlap in the distribution of the propensity scores between the treatment and control groups (eFigure 5 in Supplement 1).³² Applying the IPTWs computed from the propensity score, we assessed the balance between the weighted GLP-1 RA and control groups by calculating the weighted frequencies (for categorical variables), medians with IQRs (for continuous variables), and ASD for all variables in the model; an ASD less than 0.1 indicated an acceptable balance between the groups (eFigure 6 in Supplement 1).

We then used estimating-equation estimators to calculate the average treatment effect (ATE) estimates for the association of GLP-1 RA with increased RGC in the weighted sample. The ATE in our model represents the absolute difference in the prevalence of increased RGC between the GLP-1 RA and control groups. We also estimated the adjusted prevalence ratio of increased RGC between the GLP-1 RA and control groups.³³ We used bootstrapping to estimate the 95% CIs for the ATE and the adjusted prevalence ratio as recommended for our sample size.³⁴

In a post hoc exploratory analysis of the exposure group, we examined the association between increased RGC and days since the last dose of the GLP-1 RA. For this secondary analysis, we fit a logistic regression model with the same covariates as the primary analysis but with the addition of days since the last dose of the GLP-1 RA as a continuous variable. We truncated the time since the last dose at 7 days since the drugs we studied are dosed weekly and the current consensus guidance recommends a 7-day hold.³ After fitting this adjusted logistic regression model, we estimated the marginal effect estimate for the association of each additional day of drug discontinuation with the prevalence of increased RGC. We also explored the association between GLP-1 RA type and prevalence of increased RGC (eTable 2 in Supplement 1).

We performed post hoc sensitivity analyses of our adjustment technique and unmeasured confounding. For sensitivity analysis of the adjustment technique (augmented IPTW), we repeated the primary analysis using multivariable logistic regression and overlap weighting.^35,36 Multivariable logistic regression can outperform IPTW under certain conditions, such as a high event rate per covariate.³⁷ Compared with IPTW, overlap weighting results in less extreme weights and an exact mean balance of covariates.³⁸ However, as a result, the target estimand with overlap weighting is the ATE in the overlap population, which can differ from the ATE in the whole study population, the target of the augmented IPTW technique used for our primary analysis. In our primary analysis, we did not adjust for fasting time for solids and liquids separately due to potential multicollinearity. We assessed for bias from this assumption by repeating the analysis and replacing time to last oral intake of solids or liquids with time to last oral intake of solids alone. All adjustment techniques in observational studies can only account for measured confounding.³⁹ To assess the potential effects of unmeasured confounding on our findings, we computed the E-values for the point estimate of the primary outcome’s prevalence ratio and the lower bound of its 95% CI.⁴⁰ In the context of our study, the E-value represents the minimum risk ratio that an unmeasured confounder needs to have in association with both GLP-1 RA use and increased RGC to fully explain away the observed association of GLP-1 RA use with increased RGC.

All statistical analyses were performed using Stata, version 16 (StataCorp LLC). We used 2-tailed tests and specified a statistical significance threshold of P < .05.

Based on previous studies using GUS to assess gastric content in fasted patients presenting for elective procedures,^41,42 we estimated a prevalence of increased RGC (primary outcome) of 6.2% in the unexposed group. To detect a prespecified clinically meaningful 20% higher prevalence⁹ of increased RGC in the GLP-1 RA group with α = .05 and a power of 0.80, we estimated a total sample size of 124, with 62 patients in each group.

Results

Our study included 124 patients (median age, 56 years [IQR, 46-65 years]; 75 [60%] female; 49 [40%] male) undergoing elective procedures under anesthesia, with 62 patients (50%) receiving a once-weekly GLP-1 RA (exposure group) and 62 patients (50%) without GLP-1 RA use (control group). Study enrollment is outlined in Figure 1. The overall study cohort had a median (IQR) BMI of 33.9 (30.7-39.2). A comparison of patient characteristics between the 2 groups is presented in Table 1. Compared with the control group, patients in the GLP-1 RA group were older and had higher ASA physical status classifications and a longer time since the last oral intake. The GLP-1 RA group also had a higher prevalence of diabetes, GERD, and home opioid use.

Figure 1. — GLP-1 RA indicates glucagon-like peptide-1 receptor agonist; GUS, gastric ultrasonography.

Table 1. Baseline Characteristics of the Study Groups.

Characteristic	Patients^a
	Unadjusted analysis			Adjusted analysis
	Control group (n = 62)	GLP-1 RA group (n = 62)	ASD	Control group (n = 62)	GLP-1 RA group (n = 62)	ASD
Age, median (IQR), y	53 (43-64)	59 (48-65)	0.399	59 (47-68)	59 (47-63)	0.089
Sex
Female	38 (61)	37 (60)	0.033	40 (65)	41 (66)	0.068
Male	24 (39)	25 (40)	0.033	22 (35)	21 (34)	0.068
BMI, median (IQR)	34.2 (31.2-39.0)	33.3 (30.2-39.9)	0.092	34.0 (30.6-39)	34.9 (30.9-39.4)	0.075
ASA physical status class
I-II	21 (34)	7 (11)	0.699	13 (21)	15 (24)	0.085
III	40 (64)	46 (74)		46 (74)	42 (68)
IV	1 (2)	9 (15)		3 (5)	5 (8)
Diabetes	15 (24)	44 (71)	1.051	29 (47)	29 (47)	0.039
GERD^b	21 (34)	33 (53)	0.395	27 (44)	35 (56)	0.083
Home opioid use	6 (10)	3 (5)	0.186	5 (8)	4 (6)	0.061
Pain
None	35 (56)	43 (69)	0.180	35 (56)	36 (58)	0.003
Mild-moderate	23 (37)	14 (23)		22 (35)	21 (34)
Severe	4 (6)	6 (10)		5 (8)	5 (8)
Time since last oral intake, median (IQR), h	3.8 (2.8-6.3)	4.5 (3.0-9.0)	0.333	4.0 (2.8-7.3)	4.0 (2.8-7.0)	0.023

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Abbreviations: ASA, American Society of Anesthesiologists; ASD, absolute standardized difference; BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); GERD, gastroesophageal reflux disease; GLP-1 RA, glucagon-like peptide-1 receptor agonist.

^{^a}

Data are presented as the number (percentage) of participants unless otherwise indicated.

^{^b}

Excluded from confounder adjustment because GERD is a collider variable (ie, associated with both the exposure and the outcome) on the associative pathway between GLP-1 RA and increased residual gastric content.

In the GLP-1 RA group, 39 patients (63%) were receiving semaglutide, followed by 14 (23%) receiving dulaglutide and 9 (14%) receiving tirzepatide. Most patients took their last dose of GLP-1 RA within 5 days before their procedure (eFigure 7 in Supplement 1).

The crude prevalence of increased RGC was 37 percentage points higher in the GLP-1 RA group (35 patients [56%]) compared with the control group (12 patients [19%]) (gastric content type is detailed in eTable 1 in Supplement 1). In the unadjusted analysis of the primary outcome, GLP-1 RA use was associated with increased RGC (prevalence ratio, 2.92; 95% CI, 1.67-5.08). After adjustment for confounding, GLP-1 RA use was associated with a 30.5% (95% CI, 9.9%-51.2%) higher prevalence of increased RGC than in the control group (adjusted prevalence ratio, 2.48; 95% CI, 1.23-4.97).

Our exploratory analysis showed a decreasing prevalence of increased RGC with each additional day of drug discontinuation (Figure 2). However, there was no association between duration of discontinuation and prevalence of increased RGC (adjusted odds ratio, 0.86; 95% CI, 0.65-1.14). There was also no association between GLP-1 RA type and the prevalence of increased RGC (eTable 2 in Supplement 1).

In our sensitivity analyses, the point estimates from the alternative adjustment models for the ATE and prevalence ratio ranged from 28.3% to 29.0% and 2.22 to 2.34, respectively. Fasting times for liquids and solids by GLP-1 RA use and increased RGC are presented in eTable 4 in Supplement 1. The E-values for the prevalence ratio point estimate and the lower bound of its 95% CI were 4.44 and 1.74, respectively.

Discussion

Pulmonary aspiration under anesthesia is a rare but devastating complication with an incidence of 1 in 3000 to 4000 elective procedures.⁴³ It is associated with substantial morbidity and is the most common cause of anesthesia-related mortality.^44,45 Case reports of pulmonary aspiration under anesthesia attributed to GLP-1 RA use have prompted editorials and consensus recommendations that raise serious perioperative safety concerns with the increasing popularity of these drugs.^5,6,46 Glucagon-like peptide-1 RAs are associated with delayed gastric emptying resulting in increased RGC and adverse gastrointestinal effects such as nausea, vomiting, and gastroesophageal reflux.^47,48 Current fasting guidelines are intended to decrease the risk of pulmonary aspiration under anesthesia by reducing preprocedural gastric volume and acidity.⁴⁹ Despite following the recommended fasting time, 56% of GLP-1 RA users in our study had increased RGC, one of the main factors associated with aspiration risk under anesthesia.^10,20

There is a paucity of data on the increased aspiration risk associated with GLP-1 RA use. To date, only a handful of studies have reported RGC in patients taking GLP-1 RA drugs, with mixed results. A retrospective study of 59 GLP-1 RA users undergoing esophagogastroduodenoscopy (EGD) found food retention in 6.8% of GLP-1 RA users compared with 1.7% of matched controls, but this difference was not significant (odds ratio, 4.22; 95% CI, 0.87-20.34).¹⁵ In contrast, our findings align with 2 retrospective studies of GLP-1 RA users undergoing elective EGD. Silveria et al⁹ identified an association between semaglutide use and increased prevalence of RGC (prevalence ratio, 5.15; 95% CI, 1.92-12.92) in 33 patients taking semaglutide. Similarly, Kobori et al⁵⁰ identified an association between GLP-1 RA use and gastric residue in patients with diabetes undergoing EGD (odds ratio, 11.57; 95% CI, 1.48-90.44) among 205 GLP-1 RA users. Patients undergoing EGD may not represent the broader population of patients undergoing elective procedures under anesthesia due to confounding factors, such as the indication for the EGD and prolonged fasting times.⁵¹ A small prospective observational study in 20 volunteers without obesity (10 GLP-1 RA users) identified a significantly higher prevalence of solid contents on GUS in GLP-1 RA users (prevalence ratio, 7.36; 95% CI, 1.13-47.7) compared with nonusers.⁵²

Our finding of a significantly higher prevalence of increased RGC in GLP-1 RA users provides important insight into their potential increased risk of aspiration with anesthesia. The control group in our study had a 19% prevalence of increased RGC on GUS, markedly higher than the 5% to 6.2% prevalence reported in GUS studies from France and Belgium.^41,42 This discrepancy is likely attributable to differences in the patient populations. For example, the sample in our study had a 10-point higher mean BMI and greater than 3-fold higher incidence of diabetes, which are significant risk factors for increased RGC.^53,54 The association between increased RGC and GLP-1 RA use was independent of diabetes status in our study (Table 2 and eTable 3 in Supplement 1).

Table 2. Association Between GLP-1 RA Use and Increased Residual Gastric Content.

	Unadjusted analysis			Adjusted analysis^a
	Control group (n = 62)	GLP-1 RA group (n = 62)	Unadjusted prevalence ratio (95% CI)	Average treatment effect, % (95% CI)	Adjusted prevalence ratio (95% CI)
Increased residual gastric content, No. (%)^b	12 (19)	35 (56)	2.92 (1.67-5.08)	30.5 (9.9-51.2)	2.48 (1.23-4.97)
Sensitivity analyses
Overlap weighting with all covariates^a	NA	NA	NA	28.4 (14.4-55.9)	2.25 (1.22-4.16)
Multivariable logistic regression with all covariates^a	NA	NA	NA	28.3 (14.6-54.6)	2.22 (1.22-4.05)
Primary analysis with time since last oral intake of solids^c	NA	NA	NA	29 (7.6-50.4)	2.34 (1.20-4.55)

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Abbreviations: GLP-1 RA, glucagon-like peptide-1 receptor agonist; NA, not applicable.

^{^a}

A doubly robust augmented inverse probability treatment weighting technique was used to adjust for the following set of covariates: age, sex, body mass index, American Society of Anesthesiologists physical status classification, diabetes, opioid use, pain score, and time since last oral intake.

^{^b}

Defined as the presence of solids, thick liquids, or more than 1.5 mL/kg of clear fluids on gastric ultrasonography.

^{^c}

Time to last oral intake of solids or liquids (whichever was shorter) from the primary analysis was replaced with time to last oral intake of solids in this sensitivity analysis. Other adjustment covariates remained the same.

The prevalence of increased RGC was elevated even after 7 days of drug discontinuation in our study. Given the long half-life of once-weekly GLP-1 RA drugs, we expected this result with a 1-week hold. Our exploratory analysis showed a decreasing prevalence of increased RGC with each additional day of drug discontinuation (Figure 2). However, there was no association (adjusted odds ratio, 0.86; 95% CI, 0.65-1.14) in our study, which was limited by a small sample size for each day of drug discontinuation and likely underpowered to detect such a difference. A simplistic approach of holding the GLP-1 RA for longer intervals (3-5 weeks) may not be tenable, especially when prescribed for glycemic control. However, this may be a viable strategy for GLP-1 RA use in weight management.

The GLP-1 RA drug market is expected to grow rapidly.^55,56 Increasing use of GLP-1 RA drugs due to increasing prevalence of diabetes, rising cases of overweight and obesity, off-label use for addiction treatment, and metabolic dysfunction–associated fatty liver disease has fueled the market growth of these drugs.^57,58 We expect health care professionals will encounter these classes of drugs with increasing frequency in the perioperative period. Perioperative physicians, including anesthesiologists, surgeons, and primary care physicians, should be well informed about the safety implications of GLP-1 RA drugs. We recommend reporting any adverse events related to GLP-1 RA use and anesthesia to hospital adverse event recording systems and national databases to guide further studies and guidelines.

Limitations

Certain limitations must be considered in interpreting the findings of our cross-sectional study. First, the primary outcome (prevalence of increased RGC) is a surrogate for aspiration risk. Although increased RGC is one of the main factors associated with aspiration risk, the exact threshold of gastric volume at which aspiration risk is meaningfully increased remains elusive.¹⁰ Instead, a “safe” baseline gastric volume has been estimated based on data from healthy fasted patients with a negligible risk of clinically significant aspiration under anesthesia.¹⁰ Second, we did not directly assess aspiration events, which are rare.⁴³ For safety and ethical reasons, we disclosed the results of the GUS to the patient and clinicians. We did not collect data on case cancellations or the use of rapid sequence intubation, as data collection for the study ended after disclosing the scan results. Third, due to the study’s observational nature, an unknown degree of bias from unmeasured confounders should be considered probable. Our sensitivity analysis to estimate this bias resulted in an E-value of 1.74 for the lower bound of the 95% CI of the primary outcome’s adjusted prevalence ratio. This value represents the minimum strength of association between GLP-1 RA use and increased RGC that an unmeasured confounder would need to cancel the exposure-outcome association we identified in this study.⁴⁰ Fourth, since our sample size calculation was performed for the primary analysis, the results of the secondary exploratory analyses are potentially underpowered. Fifth, the study’s nonprobability sampling method (convenience sampling) could introduce selection bias. More approached patients in the control group declined to participate in the study compared with approached patients in the GLP-1 RA use group (Figure 1).

Conclusions

In this cross-sectional study of fasted patients presenting for elective procedures under anesthesia, once-weekly GLP-1 RA use was associated with a significantly higher prevalence of increased RGC on preprocedural GUS. Additionally, we found that GLP-1 RA interruption for up to 7 days was not associated with a decrease in the prevalence of increased RGC back to the baseline prevalence observed in similar patients without GLP-1 RA use. Future studies are needed to evaluate safe discontinuation intervals and preprocedural fasting times for these agents before elective procedures under anesthesia.

Supplement 1.

eFigure 1. Causal Diagram

eMethods. Gastric Ultrasonography Technique

eFigure 2. Empty Stomach on Gastric Ultrasonography

eFigure 3. Solid Content on Gastric Ultrasonography

eFigure 4. Clear Fluids on Gastric Ultrasonography

eFigure 5. Distribution of Propensity Scores for GLP-1 RA Use

eFigure 6. Balance of Variables Between GLP-1 RA Group and Control Group Before and After Inverse Probability of Treatment Weighting

eFigure 7. Distribution of Days Since Last Dose of Drug Among Patients Taking an GLP-1 RA

eTable 1. Prevalence of Increased Residual Gastric Content by Type of Gastric Content

eTable 2. Association Between Type of GLP-1 RA and Increased Residual Gastric Content

eTable 3. Prevalence of Increased RGC by GLP-1 RA Use and Diabetes

eTable 4. Time Since Last Oral Intake of Solids and Liquids by GLP-1 RA Use and Presence of Increased Residual Gastric Content on Ultrasonography

eReferences

jamasurg-e240111-s001.pdf^{(688.7KB, pdf)}

Supplement 2.

Data Sharing Statement

jamasurg-e240111-s002.pdf^{(16.5KB, pdf)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eFigure 1. Causal Diagram

eMethods. Gastric Ultrasonography Technique

eFigure 2. Empty Stomach on Gastric Ultrasonography

eFigure 3. Solid Content on Gastric Ultrasonography

eFigure 4. Clear Fluids on Gastric Ultrasonography

eFigure 5. Distribution of Propensity Scores for GLP-1 RA Use

eFigure 6. Balance of Variables Between GLP-1 RA Group and Control Group Before and After Inverse Probability of Treatment Weighting

eFigure 7. Distribution of Days Since Last Dose of Drug Among Patients Taking an GLP-1 RA

eTable 1. Prevalence of Increased Residual Gastric Content by Type of Gastric Content

eTable 2. Association Between Type of GLP-1 RA and Increased Residual Gastric Content

eTable 3. Prevalence of Increased RGC by GLP-1 RA Use and Diabetes

eTable 4. Time Since Last Oral Intake of Solids and Liquids by GLP-1 RA Use and Presence of Increased Residual Gastric Content on Ultrasonography

eReferences

jamasurg-e240111-s001.pdf^{(688.7KB, pdf)}

Supplement 2.

Data Sharing Statement

jamasurg-e240111-s002.pdf^{(16.5KB, pdf)}

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[soi240005r15] 15.Stark JE, Cole JL, Ghazarian RN, Klass MJ. Impact of glucagon-like peptide-1 receptor agonists (GLP-1RA) on food content during esophagogastroduodenoscopy (EGD). Ann Pharmacother. 2022;56(8):922-926. doi: 10.1177/10600280211055804 [DOI] [PubMed] [Google Scholar]

[soi240005r16] 16.Ussher JR, Drucker DJ. Glucagon-like peptide 1 receptor agonists: cardiovascular benefits and mechanisms of action. Nat Rev Cardiol. 2023;20(7):463-474. doi: 10.1038/s41569-023-00849-3 [DOI] [PubMed] [Google Scholar]

[soi240005r17] 17.Kuwata H, Yabe D, Murotani K, et al. Effects of glucagon-like peptide-1 receptor agonists on secretions of insulin and glucagon and gastric emptying in Japanese individuals with type 2 diabetes: a prospective, observational study. J Diabetes Investig. 2021;12(12):2162-2171. doi: 10.1111/jdi.13598 [DOI] [PMC free article] [PubMed] [Google Scholar]

[soi240005r18] 18.Myles PS, Myles DB, Galagher W, et al. Measuring acute postoperative pain using the visual analog scale: the minimal clinically important difference and patient acceptable symptom state. Br J Anaesth. 2017;118(3):424-429. doi: 10.1093/bja/aew466 [DOI] [PubMed] [Google Scholar]

[soi240005r19] 19.Joshi GP, Abdelmalak BB, Weigel WA, et al. 2023 American Society of Anesthesiologists practice guidelines for preoperative fasting: carbohydrate-containing clear liquids with or without protein, chewing gum, and pediatric fasting duration—a modular update of the 2017 American Society of Anesthesiologists practice guidelines for preoperative fasting. Anesthesiology. 2023;138(2):132-151. doi: 10.1097/ALN.0000000000004381 [DOI] [PubMed] [Google Scholar]

[soi240005r20] 20.Perlas A, Arzola C, Van de Putte P. Point-of-care gastric ultrasound and aspiration risk assessment: a narrative review. Can J Anaesth. 2018;65(4):437-448. doi: 10.1007/s12630-017-1031-9 [DOI] [PubMed] [Google Scholar]

[soi240005r21] 21.Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10(1):37-48. doi: 10.1097/00001648-199901000-00008 [DOI] [PubMed] [Google Scholar]

[soi240005r22] 22.Lipsky AM, Greenland S. Causal directed acyclic graphs. JAMA. 2022;327(11):1083-1084. doi: 10.1001/jama.2022.1816 [DOI] [PubMed] [Google Scholar]

[soi240005r23] 23.Ali MS, Groenwold RH, Pestman WR, et al. Propensity score balance measures in pharmacoepidemiology: a simulation study. Pharmacoepidemiol Drug Saf. 2014;23(8):802-811. doi: 10.1002/pds.3574 [DOI] [PubMed] [Google Scholar]

[soi240005r24] 24.Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med. 2009;28(25):3083-3107. doi: 10.1002/sim.3697 [DOI] [PMC free article] [PubMed] [Google Scholar]

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[soi240005r26] 26.Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46(3):399-424. doi: 10.1080/00273171.2011.568786 [DOI] [PMC free article] [PubMed] [Google Scholar]

[soi240005r27] 27.Kurz CF. Augmented inverse probability weighting and the double robustness property. Med Decis Making. 2022;42(2):156-167. doi: 10.1177/0272989X211027181 [DOI] [PMC free article] [PubMed] [Google Scholar]

[soi240005r28] 28.Textor J, Hardt J, Knüppel S. DAGitty: a graphical tool for analyzing causal diagrams. Epidemiology. 2011;22(5):745. doi: 10.1097/EDE.0b013e318225c2be [DOI] [PubMed] [Google Scholar]

[soi240005r29] 29.Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004;15(5):615-625. doi: 10.1097/01.ede.0000135174.63482.43 [DOI] [PubMed] [Google Scholar]

[soi240005r30] 30.Holmberg MJ, Andersen LW. Collider bias. JAMA. 2022;327(13):1282-1283. doi: 10.1001/jama.2022.1820 [DOI] [PubMed] [Google Scholar]

[soi240005r31] 31.Laubach ZM, Murray EJ, Hoke KL, Safran RJ, Perng W. A biologist’s guide to model selection and causal inference. Proc Biol Sci. 2021;288(1943):20202815. doi: 10.1098/rspb.2020.2815 [DOI] [PMC free article] [PubMed] [Google Scholar]

[soi240005r32] 32.Desai RJ, Franklin JM. Alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners. BMJ. 2019;367:l5657. doi: 10.1136/bmj.l5657 [DOI] [PubMed] [Google Scholar]

[soi240005r33] 33.Cummings P. Methods for estimating adjusted risk ratios. Stata J. 2009;9(2):175-196. doi: 10.1177/1536867X0900900201 [DOI] [Google Scholar]

[soi240005r34] 34.Austin PC. Bootstrap vs asymptotic variance estimation when using propensity score weighting with continuous and binary outcomes. Stat Med. 2022;41(22):4426-4443. doi: 10.1002/sim.9519 [DOI] [PMC free article] [PubMed] [Google Scholar]

[soi240005r35] 35.Tolles J, Meurer WJ. Logistic regression: relating patient characteristics to outcomes. JAMA. 2016;316(5):533-534. doi: 10.1001/jama.2016.7653 [DOI] [PubMed] [Google Scholar]

[soi240005r36] 36.Thomas LE, Li F, Pencina MJ. Overlap weighting: a propensity score method that mimics attributes of a randomized clinical trial. JAMA. 2020;323(23):2417-2418. doi: 10.1001/jama.2020.7819 [DOI] [PubMed] [Google Scholar]

[soi240005r37] 37.Cepeda MS, Boston R, Farrar JT, Strom BL. Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders. Am J Epidemiol. 2003;158(3):280-287. doi: 10.1093/aje/kwg115 [DOI] [PubMed] [Google Scholar]

[soi240005r38] 38.Li F, Thomas LE, Li F. Addressing extreme propensity scores via the overlap weights. Am J Epidemiol. 2019;188(1):250-257. [DOI] [PubMed] [Google Scholar]

[soi240005r39] 39.Amoah J, Stuart EA, Cosgrove SE, et al. Comparing propensity score methods versus traditional regression analysis for the evaluation of observational data: a case study evaluating the treatment of gram-negative bloodstream infections. Clin Infect Dis. 2020;71(9):e497-e505. doi: 10.1093/cid/ciaa169 [DOI] [PMC free article] [PubMed] [Google Scholar]

[soi240005r40] 40.Haneuse S, VanderWeele TJ, Arterburn D. Using the e-value to assess the potential effect of unmeasured confounding in observational studies. JAMA. 2019;321(6):602-603. doi: 10.1001/jama.2018.21554 [DOI] [PubMed] [Google Scholar]

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PERMALINK

Glucagon-Like Peptide-1 Receptor Agonist Use and Residual Gastric Content Before Anesthesia

Sudipta Sen, MD

Paul P Potnuru, MD

Nadia Hernandez, MD

Christina Goehl, MD

Caroline Praestholm, MS

Srikanth Sridhar, MD

Omonele O Nwokolo, MD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Exposure

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Participants

Exposure

Covariates

Outcomes

Statistical Analysis

Results

Figure 1. Patient Screening and Enrollment.

Table 1. Baseline Characteristics of the Study Groups.

Figure 2. Time Since Glucagon-Like Peptide-1 Receptor Agonist (GLP-1 RA) Drug Interruption and Prevalence of Increased Residual Gastric Content (RGC) Measured by Gastric Ultrasonography.

Discussion

Table 2. Association Between GLP-1 RA Use and Increased Residual Gastric Content.

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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