Higher Risk of Low Blood Sugar After Gastric Bypass Surgery in People Without Diabetes

This retrospective cohort study utilized the TriNetX database (TriNetX LLC, Cambridge, MA, USA), a global federated health research network providing access to electronic health records (EHRs). The TriNetX Global Collaborative network encompasses more than 275 million individuals across over 120 health care organizations (HCOs) worldwide, with established data usage and publication agreements. It offers comprehensive datasets including patient demographics, diagnoses, procedures, medications, and laboratory results, primarily collected from HCOs' EHRs. This extensive data collection ensures a holistic view of patient health and related outcomes, contributing to the robustness of our analyses. However, as with any EHR-based dataset, it may face typical data quality challenges such as incomplete or inaccurate entries, under-reporting of certain conditions, limited granularity, and potential data exclusions. To mitigate these issues, TriNetX employs rigorous data validation processes, including regular quality checks to identify and correct discrepancies, validation against external benchmarks for consistency and accuracy, and collaboration with data contributors to resolve issues and continuously improve data quality. The database provides continuous, comprehensive, up-to-the-month data, ensuring the most current information is available for analysis. For this study, the database was last accessed on September 22, 2024.

The study included patients aged 18 years or older with a body mass index (BMI) of 30 kg/m² or higher. Patients who underwent RYGB surgery were identified using Current Procedural Terminology (CPT) codes 43,644 and 43,846. The non-surgical control group included patients with obesity who did not undergo bariatric surgery. Patients with a history of diabetes (ICD-10-CM codes E08-E13), GLP-1 receptor agonist use prior to the index date, or chromosomal and genetic disorders were excluded, Supplementary Table S1. The index date was defined as the date of RYGB surgery for the surgical group and a randomly assigned date for the non-surgical group.

Baseline characteristics, including age, sex, race, and ethnicity, were extracted from the EHRs. Associated medical problems and medication use were assessed based on ICD-10-CM diagnosis codes and RxNorm medication codes, respectively.

The study included patients who underwent two specific types of Roux-en-Y gastric bypass (RYGB) procedures: Laparoscopic gastric restrictive surgery combined with gastric bypass and a Roux-en-Y gastroenterostomy, where the Roux limb measures 150 cm or less. Gastric restrictive surgery with gastric bypass specifically for morbid obesity, also utilizing a short Roux-en-Y gastroenterostomy, defined as 150 cm or less.

This approach allows for a comprehensive understanding of the impact of surgical variations on patient results.

To adjust for potential confounding factors, propensity score matching was performed using a 1:1 nearest-neighbor matching algorithm without replacement. Propensity scores were calculated using logistic regression, with treatment assignment (RYGB surgery vs. non-surgical control) as the dependent variable. Demographic factors included age, sex, race, and ethnicity. Socioeconomic determinants were represented by problems related to medical facilities and other health care, employment and unemployment, and housing and economic circumstances. Lifestyle factors comprised lack of physical exercise, problems related to sleep, overweight and obesity, dietary counseling, and smoking status. A range of associated medical problems were considered, including alcohol-related disorders, hypertensive diseases, metabolic disorders, malnutrition, vitamin deficiencies, acute and chronic kidney disease, hepatic failure, and neoplasms. Specific risk factors for hypoglycemia were also included: fibrosis and cirrhosis of liver, alcoholic liver disease, primary adrenocortical insufficiency, Addisonian crisis, hypopituitarism, severe sepsis with septic shock, and disorders of pyruvate metabolism and gluconeogenesis. Finally, the matching process accounted for medication use, specifically quinolones, glucagon, ACE inhibitors, and beta blockers. The matched cohorts were used for subsequent analyses.

The primary outcome was the incidence of hypoglycemia, defined by the presence of ICD-10-CM diagnosis codes (E16.1, E16.2) or laboratory values (glucose ≤ 70 mg/dL) during the follow-up period. The time-to-event analysis was conducted for overall incidence and at various time points to assess both short-term and long-term risk. Short-term risk was evaluated at 1 week, 1 month, 3 months, and 6 months after the index date, while long-term risk was assessed at 5 years and 10 years after the index date.

A subgroup analysis was conducted on 2,810 individuals from the surgery cohort who developed hypoglycemia. This analysis evaluated the impact on hospitalization rates and mortality within 30 days following a hypoglycemic event, as well as the length of stay for the first hospital admission. The aim was to quantify the acute clinical outcomes associated with hypoglycemic events in post-RYGB patients.

Baseline characteristics were compared between the RYGB surgery and non-surgical control groups using chi-square tests for categorical variables and t-tests for continuous variables. The prevalence of hypoglycemia was calculated in each group. Cox regression analysis was employed, and hazards ratio (HR) and 95% confidence intervals (CI) were reported. For the subgroup analysis, descriptive statistics were used to present hospitalization rates, length of hospital stay, and mortality in the surgery group. A two-sided p-value < 0.05 was considered statistically significant. All analyses were performed using the TriNetX platform.

A subgroup analysis was conducted on the 2,810 individuals from the RYGB cohort who developed hypoglycemia to quantify the acute clinical outcomes associated with hypoglycemic events. Within 30 days of a hypoglycemic event, 21.3% (n = 598) of these patients were hospitalized. The median length of stay for the first hospital admission was 1.5 days (IQR: 1–3 days). The 30-day mortality rate following a hypoglycemic event was 0.71% (n = 20).

These findings highlight the importance of long-term monitoring and management of hypoglycemia in patients undergoing RYGB surgery, especially in the absence of preexisting diabetes. The consistently elevated risk of hypoglycemia throughout the 10-year follow-up period emphasizes the need for ongoing vigilance. Pre-operative assessment for patients' liability to hypoglycemia should also be essential along with proper patient education about the possibility of such a complication. Furthermore, the significant rate of hospitalization and mortality associated with hypoglycemic events in our subgroup analysis underscores the need for prompt recognition and management of this complication.

The study's strengths include the large sample size, long-term follow-up, and use of propensity score matching to control for potential confounding factors. Additionally, our subgroup analysis provides valuable insights into the acute clinical outcomes associated with hypoglycemic events in post-RYGB patients. However, the study also has some limitations. First, the retrospective nature of the study may introduce bias due to unmeasured confounding factors. Second, the definition of hypoglycemia based on ICD-10-CM codes and laboratory values may not capture all cases, potentially underestimating the true incidence of hypoglycemia. Moreover, due to the nature of the TriNetX database, which provides aggregated data rather than individual patient information, our ability to investigate specific reasons for each hospitalization along with directly assessing missing or incomplete data at the individual patient level was limited. Finally, while our subgroup analysis provides some insight into the clinical impact of hypoglycemic events, we were unable to assess the full spectrum of severity and long-term consequences of these events.