Analysis of acute pancreatitis associated with SGLT-2 inhibitors and predictive factors of the death risk: Based on food and drug administration adverse event report system database
Bibliographic record
Abstract
Background and objectives: The US FDA and Health Canada have successively published potential red flags for acute pancreatitis caused by sodium-dependent glucose transporter 2 inhibitors (SGLT-2i). However, existing studies have focused on case reports. We aimed to assess the possible association of SGLT-2i with acute pancreatitis by analyzing postmarketing adverse events reported in the FDA adverse event reporting system (FAERS), to explore risk factors for SGLT-2i-related acute pancreatitis death, and to build a nomogram. Methods and Results: We used a disproportionality analysis of suspected acute pancreatitis-related reports in the FAERS database of patients from the use of SGLT-2i from the first quarter of 2013 to the fourth quarter of 2021. Single-factor and multi-factor logistic regression analyses were performed using the relevant clinical information of patients, and risk factors were combined with the age of patients to construct a SGLT-2i risk prediction model for acute pancreatitis-related death. A total of 757 reports were retrieved. The largest number of acute pancreatitis-related cases were caused by canagliflozin (317 reports), which was also the strongest agent associated with acute pancreatitis, with the information component (IC 2.41, lower 95% one-sided confidence interval 2.16), the reporting odds ratio (ROR 5.37, 95% two-sided confidence interval 4.8–5.99), and the empirical Bayesian geometric mean (EBGM 5.32, lower 90% one-sided confidence interval 4.85). The median time to acute pancreatitis was 54 (interquartile range [IQR] 14–131) days, and approximately 83% of adverse events occurred within 6 months. Odds ratio(OR) adjusted by acute pancreatitis and the coadministration of SGLT-2i with dipeptidyl peptidase 4 inhibitor (DPP-4i), glucagon-like peptide 1 analog (GLP-1RA), and angiotensin converting enzyme inhibitor (ACEIs) was 1.39, 1.97, and 1.34, respectively, all of which were statistically significant. Logistic regression analysis showed that different SGLT-2i type and their combinations with statins were independent risk factors for acute pancreatitis mortality in the patients ( p < 0.05). The mortality risk prediction model showed good discrimination and clinical applicability in both the training set (AUC 0.708) and the validation set (AUC 0.732). Conclusion: SGLT-2i may increase the risk of acute pancreatitis especially within the first 6 months of drug administration. Combination with DPP-4i, GLP-1RA or ACEIs significantly increases the risk of acute pancreatitis. In addition, different SGLT-2i type and their combination with statins are risk factors that can predict the risk of death following acute pancreatitis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".