Incidence of Hypothyroidism and Thyroid Function Monitoring After Immune Checkpoint Inhibitor Therapy Completion for Lung Cancer: A Nationwide Analysis of a Japanese Claims Database
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) improve lung cancer prognosis but are associated with immune-related adverse events, most commonly thyroid dysfunction. While prior studies and guidelines have focused on thyroid dysfunction during ICI therapy, data on hypothyroidism and its monitoring after ICI therapy remain limited. We aimed to investigate hypothyroidism incidence and implementation of thyroid function monitoring after ICI therapy completion in patients with lung cancer. We conducted a retrospective observational study using the DeSC claims database of approximately 12 million individuals in Japan. Patients with lung cancer who received ICI therapy between April 2014 and August 2023 were included; those with a history of thyroid hormone replacement or insufficient follow-up were excluded. Among 6883 eligible patients, 277 (4.0%) developed hypothyroidism requiring hormone replacement post-ICI therapy completion (median onset, 67.0 d). Risk factors included ICI plus bevacizumab therapy and a history of myasthenia gravis, while steroid use for ≥28 d during ICI therapy lowered the risk. Post-ICI therapy completion thyroid monitoring was performed in 73.7% of patients, with test date distribution showing a median of 126.0 d and mode of 21.0 d. Hypothyroidism was frequently found to develop within 2 months post-ICI therapy completion, highlighting the need for continued thyroid monitoring and prospective studies to establish optimal surveillance strategies.
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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 | 0.001 | 0.001 |
| 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".