Real world chart review of adverse event management in patients taking tyrosine kinase inhibitors (TKIs) for metastatic renal cell carcinoma (mRCC) by line of therapy (LoT).
Bibliographic record
Abstract
521 Background: This is the first real world study to describe adverse events of special interest (AESI) management for all available TKIs by LoT. A better understanding of optimal AE management can positively impact patient care. Methods: A chart review was conducted largely in community oncology practices in Canada and the US. Patients with mRCC were eligible if they started TKI monotherapy between Nov 15, 2010-Nov 15, 2013 and had ≥ 1 of 5 AESI onset ≤ 3 months post TKI start. AESI included fatigue, hypertension, diarrhea, hand-foot syndrome and stomatitis/mucositis. Data on patient demographics, medical history, drug regimen, and AESI management were collected. Results: 220 patients from 27 centers were included. 63% of patients received TKI in 1st, 22% in 2nd and 15% in 3rd LoT. TKIs included axitinib (9%), pazopanib (27%), sorafenib (8%) and sunitinib (55%). 376 AESI occurred in 220 patients; 87% were non-serious. Fatigue (62%), hypertension (37%), diarrhea (31%), stomatitis/mucositis (29%), and hand-foot syndrome (12%) were reported. 55% of AESI resolved/were resolving within 3.5 months of onset. 8% of patients received AESI prophylaxis (Px) and 59% received treatment (Tx). Incidence of stomatitis/mucositis declined as LoT progressed (37%, 16%, 12%). More patients in 1st vs. ≥ 2nd LoT had AESI related TKI discontinuation (17% vs. 12%), interruptions (17% vs. 12%) and reductions (43% vs. 32%). Fewer patients missed doses as LoT progressed (38%, 25%, 18%). Fatigue was the commonest AESI across LoT, and fatigue as a reason for therapy delay/interruption increased across LoT (7%, 14%, 50%). The rates of Tx or Px for fatigue remained low across LoT. Rates of AESI Px were low across the LoT (10%, 0.4%, 0.3%). More patients in 1st (61%) received AESI Tx vs. 2nd (47%) LoT. Conclusions: Most AESI were non-serious, and over half of patients received some form of AESI Tx. Fatigue was the most common yet least frequently Tx AESI. Very few patients received Px across LoT. More patients in 1st versus 2nd LoT experienced dose modifications/discontinuation and received AESI Tx. More emphasis on Px/Tx options for non-serious bothersome AEs is warranted.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".