Underuse of ECG monitoring in oncology patients receiving QT-interval prolonging drugs
Bibliographic record
Abstract
OBJECTIVE: We examined use of ECG monitoring in oncology patients prescribed QT-prolonging drugs. METHODS: Patients ≥66 years diagnosed with cancer between 2005 and 2011 were identified through the Ontario Cancer Registry and linked to multiple population-based administrative databases to ascertain demographics, comorbidities, prescription drug use, systemic therapy and ECG. QT-prolonging drugs were identified as per drug lists developed by the Arizona Center for Education and Research on Therapeutics. Univariable and multivariable analyses were used to examine factors associated with ECG use in patients on first-line systemic therapy. RESULTS: A total of 48 236 patients (median age 74; 49% women) received one or more drugs associated with a risk of QT-interval prolongation but only 27% of patients had an ECG performed. Factors associated with more ECG use on multivariable analysis included recent cancer diagnosis (p for trend <0.001 between 2005 and 2011), use of concurrent QT-prolonging drugs (OR=1.15 per each additional QT-prolonging drug, 95% CI 1.12 to 1.17) and the presence of coronary artery disease (OR 1.31; 95% CI 1.25 to 1.38) and heart failure (OR 1.25; 95% CI 1.17 to 1.35). Use of anticancer (OR 0.74; 95% CI 0.70 to 0.79) and antiemetic (OR 0.93; 95% CI 0.88 to 0.99) QT-prolonging drugs was paradoxically associated with less ECG use. CONCLUSIONS: Our study highlights common use of QT-prolonging drugs and underuse of ECG in oncology patients. Since ECG is an inexpensive, non-invasive and widely available test, it may be readily incorporated in the monitoring of patients for toxicities in routine clinical practice.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".