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Record W4399114353 · doi:10.1016/j.hrthm.2024.02.007

Top stories: Drug-induced long QT syndrome

2024· review· en· W4399114353 on OpenAlexaboutno aff
Raymond L. Woosley, Craig Heise

Bibliographic record

VenueHeart Rhythm · 2024
Typereview
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLong QT syndromeDrugCardiologyInternal medicineQT intervalPharmacology

Abstract

fetched live from OpenAlex

Drug-induced long QT syndrome and torsades de pointes (TdP) have gained the attention of drug-safety researchers and government regulators, resulting in 14 drugs being removed from the market and many others discarded during development. Yet this increased scrutiny has not prevented >100 QT-prolonging drugs from reaching the market. Clearly, the >200 QT-prolonging drugs now on the market have medical value. If they are to remain available, clinicians must manage their risk of TdP. As shown below, scientists with expertise in clinical informatics and health outcomes research are developing the evidence and clinical decision support tools necessary for the prevention and management of drug-induced long QT syndrome and TdP. The manual measurement of the corrected QT (QTc) interval to monitor drug safety can be challenging because of its limited accuracy and high cost. Diaw et al1Diaw M.D. Papelier S. Durand-Salmon A. Felblinger J. Oster J. AI-assisted QT measurements for highly automated drug safety studies.IEEE Trans Biomed Eng. 2023; 70: 1504-1515Crossref Scopus (4) Google Scholar built a convolutional neural network, validated it on data sets, and showed that it outperformed the automated measurement of the QTc interval. One limitation of the current iteration of this neural network is that it tended to underestimate drug-induced QTc changes and falls prey to some of the same QT morphology issues that can obfuscate the manual measurement. With its anticipated evolution, artificial intelligence may soon be able to facilitate or perhaps replace the manual QTc measurement, especially for continuous monitoring of drug safety. More than 100,000 Americans die each year from drug overdoses. Medications for opioid use disorder (MOUD) are highly effective but not without risk. Wang et al2Wang L. Volkow N.D. Berger N.A. Davis P.B. Kaelber D.C. Xu R. Cardiac and mortality outcome differences between methadone, buprenorphine and naltrexone prescriptions in patients with an opioid use disorder.J Clin Psychol. 2023; 79: 2869-2883Crossref Scopus (4) Google Scholar evaluated a nationwide electronic database to examine the risk of arrhythmias and mortality associated with the use of methadone, buprenorphine, and naltrexone for MOUD. Methadone, well known to increase the QTc interval, was found to have a higher risk of arrhythmia (hazard ratio 1.31; confidence interval [CI] 1.23–1.38) and death (hazard ratio 1.48; CI 1.21–1.81) as compared with buprenorphine or naltrexone. These findings are critical when considering MOUD. To better understand the epidemiology of TdP, Mantri et al3Mantri N. Lu M. Zaroff J.G. et al.Torsade de pointes: a nested case-control study in an integrated healthcare delivery system.Ann Noninvasive Electrocardiol. 2022; 27e12888Crossref Scopus (2) Google Scholar conducted a case-control study of confirmed cases of TdP (matched 2:1) drawn from a managed care population of 110,000 in California. They identified and validated 56 TdP cases that had an incidence of 3.6 per 100,000 persons per year. The independent predictors of TdP were low serum potassium (odds ratio [OR] 10.6), history of atrial fibrillation/flutter (OR 6.25), QTc interval > 480 ms (OR 4.4), and coronary artery disease (OR 2.59). TdP cases were significantly more likely to have been prescribed furosemide, amiodarone, or another QT-prolonging drug. In-hospital mortality was 10.7%, and 1-year mortality was 25%. High TdP mortality and multivariate risk indices observed in this real-world study should better inform the planning of research to prevent drug-induced TdP. Because of the many medications and clinical states that increase the QTc interval and their associated risk of TdP, QT risk scores have been developed to screen electronic medical records. Tan et al4Tan M.S. Heise C.W. Gallo T. et al.Relationship between a risk score for QT interval prolongation and mortality across rural and urban inpatient facilities.J Electrocardiol. 2023; 77: 4-9Crossref Scopus (2) Google Scholar evaluated a clinical decision support system that, when triggered by prescription of a drug with the risk of TdP, calculates a QT risk score using patient-specific data in the electronic medical record. They found that patients with high QT risk scores had higher mortality (OR 11.51; CI 10.23–12.94) and longer hospitalization. This study sets the stage for prospective trials designed to prevent TdP. The consensus paper by Davies et al5Davies R.A. Ladouceur V.B. Green M.S. et al.The 2023 Canadian Cardiovascular Society Clinical Practice Update on Management of the Patient With a Prolonged QT Interval.Can J Cardiol. 2023; 39: 1285-1301Abstract Full Text Full Text PDF Google Scholar incorporates the principles of safe medication use when prescribing drugs that prolong the QTc interval. The authors provide practical recommendations for measuring the QTc interval, distinguishing between congenital and acquired long QT syndromes and managing patients with drug-induced long QT syndrome and/or TdP. This article is recommended for all clinicians. The authors have no conflicts of interest to disclose. The authors have no funding sources for this article to disclose.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.689
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.005

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.

Opus teacher head0.033
GPT teacher head0.346
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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