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

Top stories: Drug-induced long QT syndrome

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

Notice bibliographique

RevueHeart Rhythm · 2024
Typereview
Langueen
DomaineMedicine
ThématiqueCardiac electrophysiology and arrhythmias
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineLong QT syndromeDrugCardiologyInternal medicineQT intervalPharmacology

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,689
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0030,002
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0000,005

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,033
Tête enseignante GPT0,346
Écart entre enseignants0,313 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2024
Routes d'admission1
Résumé présentoui

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