Prevalence of the QT interval prolongation and its risk factors in hospitalized geriatric patients: findings of a single center cross-sectional study in Pakistan
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".