Immune checkpoint inhibitor-associated diabetic ketoacidosis and insulin-dependent diabetes: a case report
Bibliographic record
Abstract
BACKGROUND: Immunotherapy, including the use of immune checkpoint inhibitors such as nivolumab, is increasingly common in cancer treatment and can lead to various immune-related adverse effects, including rare cases of diabetic ketoacidosis. This case report highlights an unique instance of nivolumab-induced diabetic ketoacidosis in a patient without prior history of diabetes, emphasizing the importance of careful monitoring even in those without traditional risk factors. CASE PRESENTATION: We report a case of a 70-year-old Caucasian male with metastatic esophageal adenocarcinoma who developed diabetic ketoacidosis 3 weeks after stopping nivolumab therapy. The patient had no previous history of diabetes, nor had he used sodium-glucose transport protein 2 inhibitors or corticosteroids. Diagnostic tests confirmed diabetic ketoacidosis, and while he was initially treated following the institutional protocol, he continued to require insulin therapy indefinitely. CONCLUSIONS: This case report underscores the risk of diabetic ketoacidosis linked to nivolumab, even in patients without predisposing factors, emphasizing the need for increased vigilance among both oncologists and physicians. It highlights the importance of monitoring for new-onset diabetes and diabetic ketoacidosis, whether immunotherapy is active or discontinued, and ensuring comprehensive care including hospitalization, insulin management, and diabetes education if diabetic ketoacidosis is diagnosed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".