Prevalence of the QT interval prolongation and its risk factors in hospitalized geriatric patients: findings of a single center cross-sectional study in Pakistan
Notice bibliographique
Résumé
BACKGROUND AND OBJECTIVES: Geriatric inpatients are particularly susceptible to Torsades de Pointes (TdP) because they are usually polymorbid and often take QT-prolonging drugs. Since polypharmacy is common in hospitalized geriatrics, it may lead to QT-prolonging Drug-Drug interactions, resulting in adverse cardiac events. Additionally, a significant portion of geriatric patients likely have a combination of other risk factors such as heart failure, hypertension, left ventricular hypertrophy, myocardial infarction, ischemic heart disease, bradycardia, diabetes mellitus, and electrolyte imbalances. However, there is a lack of published data regarding the prevalence of risk factors for QT interval prolongation in this population. This study aimed to determine the prevalence of QT interval prolongation and its risk factors among hospitalized geriatric patients, shedding light on potential contributors to this life-threatening condition in a vulnerable population. METHODS: This cross-sectional study was conducted at Ayub Teaching Hospital, Abbottabad from December 17, 2023, to May 9, 2024. During this study, 384 patients aged 65 years and older were analyzed. Various QT-prolonging medications were assessed using the CredibleMeds® database, while drug-drug interactions were evaluated using the Lexicomp interactions database. Logistic regression was used to identify the predictors of QT interval prolongation. RESULTS: Our study found that QT prolongation was more common in females (50.8%) than in males (49.2%). Among these patients, 60.2% presented with six QT-prolonging risk factors. Overall, QT-prolonging drugs were prescribed to 99.5% of patients. A total of 970 QT-prolonging drugs were identified, with the majority (70.4%) carrying a conditional risk of Torsades de Pointes. The most frequently prescribed category of QT-prolonging drugs was diuretics, accounting for 228 instances. QT-prolonging Drug-Drug Interactions were identified in 23.2% of patients. Statistically significant differences were found between the two groups (Prolonged QT interval vs. normal QT interval) in various factors such as all DDIs (p = 0.008), triglycerides (p = 0.03), ischemic heart disease (p = 0.02), myocardial infarction (p = 0.01), antimicrobials (p = 0.004), anti-emetics (p = 0.01), and analgesics (p = 0.05). Univariate analysis showed a statistically significant association of QT interval prolongation with 6–10 DDIs (p = 0.03); 11–15 DDIs (p = 0.001), > 15 DDIs (p = 0.01), ischemic heart disease (p = 0.02), myocardial infarction (p = 0.01), antimicrobials (p = 0.04), and antiemetic’s (p = 0.01). In multivariate analysis, a statistically significant association of QT interval prolongation was found with 11–15 DDIs (p = 0.03). CONCLUSION: This study identified a high prevalence of various risk factors for QT interval prolongation. When prescribing medications to this patient population, clinicians should conduct comprehensive medication reviews, regularly monitor the QT interval, and consider alternative therapies. Educating patients on medication risks and adherence to monitoring is crucial for early detection and reporting of adverse effects.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».