Routine Screening for Central and Primary Adrenal Insufficiency during Immune-Checkpoint Inhibitor Therapy: An Endocrinology Perspective for Oncologists
Bibliographic record
Abstract
BACKGROUND: Immune checkpoint inhibitor (ICI)-associated hypothalamic-pituitary-adrenal axis disruption can lead to hypocortisolism. This is a life-threatening but difficult to diagnose condition, due to its non-specific symptoms that overlap with symptoms of malignancy. Currently, there is no consensus on how to best screen asymptomatic patients on ICI therapy for hypophysitis with serum cortisol. METHODS: A retrospective chart review of patients treated with ICI in a tertiary care centre was conducted to assess the rate of screening with cortisol and whether this had an impact on diagnosis of ICI-hypophysitis in the preclinical stage. Patients were identified as having hypophysitis with an adrenocorticotropin hormone (ACTH) deficiency based on chart review of patients with cortisol values ≤ 140 nmol/L (≤5 mcg/dL). We also assessed what proportion of cortisol values were drawn at the correct time for interpretation (between 6 AM and 10 AM). RESULTS: Two hundred and sixty-five patients had 1301 cortisol levels drawn, only 40% of which were drawn correctly (between 6 and 10 AM). Twenty-two cases of hypophysitis manifesting with ACTH deficiency were identified. Eight of these patients were being screened with cortisol following treatment and were detected in the outpatient setting. The remaining 14 patients were not screened and were diagnosed when symptomatic, after an emergency room visit or hospital admission. Sixty percent of the cortisol tests were uninterpretable as they were not drawn within the appropriate time window. CONCLUSION: Measuring morning serum cortisol in asymptomatic patients on ICI therapy is a fast and inexpensive way to screen for hypophysitis and should become the standard of care. Random serum cortisol measurement has no clinical value. Education needs to be provided on when to correctly perform the test and how to interpret it and we provide an algorithm for this purpose. The adoption and validation of such an algorithm as part of routine practice could significantly reduce morbidity and mortality in patients, especially as ICI therapy is becoming increasingly commonplace.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".