A literature-based algorithm for the assessment, management, and monitoring of drug-induced QTc prolongation in the psychiatric population
Bibliographic record
Abstract
BACKGROUND: Certain psychotropics and a number of other medications used to treat medical conditions in psychiatric patients can increase the risk of prolonging the corrected QT (QTc) interval on the electrocardiogram, which puts patients at risk of life-threatening ventricular arrhythmias such as torsades de pointes. Pharmacists are often consulted about medications which are known to prolong the QTc interval. Although this information is often accessible, advising how to identify, assess, manage, and refer psychiatric patients at risk for drug-induced QTc prolongation is more challenging. OBJECTIVES: The objective of this project was first to review the literature, which describes guidelines and recommendations for the assessment and management of drug-induced QTc prolongation, and then to design an algorithm to be used by pharmacists working closely with mental health professionals or who provide care to psychiatric patients. METHODS: A review of the literature was undertaken. Predefined keywords were used to perform the database search in MEDLINE, EMBASE, and International Pharmaceutical Abstracts to identify reviews, reports and guidelines on the assessment, prevention and monitoring of drug-induced QTc prolongation with an emphasis on psychotropic medications and management in the psychiatric population. RESULTS: The electronic database search retrieved 637 relevant citations. These were initially screened by title and all duplicates were removed. The abstracts were then reviewed for relevancy based on the inclusion/exclusion criteria. Additional citations were retrieved from the bibliography of the articles identified in the initial search. A total of 79 articles describing QTc prolongation in the psychiatric population were thoroughly examined, but only 31 articles were selected to guide the development of the algorithm. CONCLUSION: The literature-based algorithm developed provides a stepped-based approach for the assessment, monitoring, and management of drug-induced QTc prolongation in the psychiatric population. The algorithm may assist mental health clinicians in the decision-making process when psychiatric patients are prescribed medications known to increase the QTc interval.
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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 | 0.022 | 0.063 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.062 | 0.029 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".