EFFECTS OF MULTIPLE MEDICATIONS ASSOCIATED WITH QTC PROLONGATION: PREVALENCE AND OUTCOMES ASSOCIATED WITH USE AMONG OLDER ADULTS
Bibliographic record
Abstract
Medications are commonly used by older adults and can provide many benefits. However, harmful side effects may occur, particularly when multiple medications are prescribed. Prolongation of the corrected QT (QTc) interval is a disturbance in heart rhythm that can precede ventricular arrhythmia and sudden cardiac death (VA/SCD). More than one hundred medications have been linked to QTc prolongation; however, how often they are prescribed to older adults in Ontario is unknown. Effects of multiple QTc prolonging medications on adverse cardiac outcomes are also not well understood. This thesis aimed to identify how frequently QTc prolonging medications are prescribed to older adults in Ontario, identify factors associated with their use, and investigate associations between these medications and VA/SCD. A cross-sectional design was used to determine frequency of QTc medication prescriptions. Factors associated with such prescriptions were identified using logistic regression. A nested case-control design and conditional logistic regression were used to investigate exposure to known QTc risk medications and VA/SCD. Over half of the 1,912,061 older adults identified were prescribed at least 1 QTc prolonging medication; 22.9% were prescribed 2 or more. 15.8% were prescribed at least 1 known risk medication. Antidepressants and antibiotics were the most commonly prescribed known risk medications. Factors associated with these prescriptions included long-term care residence and conditions these medications are used to treat, such as infection. Exposure to prescription of QTc prolonging medications of known risk was associated with increased risk of VA/SCD (unadjusted odds ratio [OR]: 1.52, 95% confidence interval [CI]: 1.43-1.61), with higher risks among new users (unadjusted OR: 1.86, 95% CI: 1.69-2.04). This thesis increases knowledge of prescription patterns and adverse effects of QTc prolonging medications and helps identify situations in which they may be more likely to occur. Awareness of these risks will allow for appropriate prevention and intervention. Prescription strategies minimizing risks could be developed that protect those at higher risk and permit those at lower risk to take additional medications that could prove beneficial.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".