Antipsychotic Drugs and Diabetic Ketoacidosis: A Disproportionality Analysis of the FDA Adverse Event Reporting System
Bibliographic record
Abstract
Objectives: To evaluate reports of diabetic ketoacidosis (DKA) associated with antipsychotic drug (APD) use submitted to the U.S. Food and Drug Administration’s Adverse Event Reporting System (FAERS). Methods: A retrospective pharmacovigilance analysis was conducted using FAERS data from January 2000 to December 2022. DKA cases were identified using the MedDRA preferred term “diabetic ketoacidosis” in reports listing antipsychotic drugs as suspect medications. Disproportionality analyses, including the proportional reporting ratio (PRR) and empirical Bayes geometric mean (EBGM), were used to assess reporting patterns. Multiple analyses were performed, including those restricted to primary suspect listed drugs only, expanded to incorporate secondary suspect drugs, and sensitivity analyses excluding reports submitted by legal professionals. Results: Among 19,961 DKA reports in FAERS, 2489 (12.5%) listed atypical antipsychotics as the primary suspect drug, whereas reports involving typical APDs were rare. The majority of reports were submitted by healthcare professionals (74.1%), and nearly half originated from the United States (45.4%). Hospitalization was a frequent outcome, reported in 74.3% of cases. Quetiapine and olanzapine were the most frequently reported atypical APDs, with disproportionality analyses demonstrating strong safety signals when compared to all other drugs in FAERS: olanzapine PRR 13.2 (95% CI: 12.4–14.2) and quetiapine PRR 11.8 (95% CI: 11.1–12.5). The findings remained consistent across multiple sensitivity analyses, including incorporating secondary suspect drugs, when the comparator group was restricted to only psychotropic drugs, and excluding reports submitted by lawyers. Conclusions: This pharmacovigilance analysis highlights a potential safety signal for DKA with atypical antipsychotic drugs, notably quetiapine and olanzapine. While these findings do not establish causality, they underscore the need for further investigation using clinical and epidemiological data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".