Ketoacidosis in a Patient with Type 2 Diabetes Requiring Alpelisib: Learnings and Observations Regarding Alpelisib Initiation and Rechallenge
Bibliographic record
Abstract
Background: Diabetic ketoacidosis (DKA) is a rare complication of alpelisib, but cases of DKA are reported. Alpelisib's safety in patients with long-standing, suboptimally controlled diabetes is unclear since clinical trials of alpelisib did not include them. Case: A case is presented on a patient with metastatic breast cancer and type 2 diabetes admitted for DKA eleven days after starting alpelisib. Since DKA is implicated in antihyperglycemics that inhibit sodium-glucose cotransporter-2 (SGLT2) inhibitors, her empagliflozin was discontinued. Alpelisib was also held since it was recently initiated. After the DKA resolved, she was discharged and restarted alpelisib. Within 4 hours of taking the first dose, the patient developed a second episode of DKA, and alpelisib treatment was stopped permanently. Conclusion: Patients with long-standing type 2 diabetes are at high risk of alpelisib-induced Grade 3 and 4 hyperglycemia, including DKA. It is essential to communicate with non-oncology stakeholders about the risk of DKA with alpelisib as it can be overlooked for more common causes. Restarting alpelisib can result in severe hyperglycemia or DKA within 24 hours of the first dose. In this population, the risks associated with rechallenging alpelisib must be heavily weighed against its benefits. Before restarting alpelisib, a thorough evaluation of the appropriateness of the patient's antihyperglycemics and diet must occur to anticipate and mitigate a second event. Antihyperglycemics independent of the PI3K/AKT/mTOR pathway may be preferred agents. A plan should be in place to quickly respond to rising glycemia and early referral to a diabetologist or endocrinologist is recommended. Continuous glucose monitoring and hospital admission are recommended during rechallenge. A better understanding of alpelisib-induced hyperglycemia, especially in patients with diabetes, is required to navigate alpelisib treatment safely. Emphasis should be placed on patient education of symptoms and monitoring parameters.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".