Predictive Analysis of Non-Cardiac Drug-Induced QTc Interval Prolongation: A Cross-Sectional Study

This study aimed to assess the real-world impacts of non-cardiac drug-induced QTc interval prolongation and identify associated risk factors in acute care settings.

A cross-sectional study reviewed medical charts of 7,778 patients admitted to tertiary teaching hospitals from January 2016 to December 2022. Patients on CredibleMeds-listed QTc-prolonging non-cardiac drugs were identified, excluding those with congenital long QTc syndrome or on QTc-prolonging cardiac medications. Data collection involved reviewing medication charts and recording demographic and clinical data, including comorbidities and laboratory values. A logistic regression analysis was performed to address confounders, and known risk factors, calculating Odds Ratios (OR) and 95% confidence intervals (CI). Statistical analysis used SPSS Version 21.0, with p < 0.05 indicating significance.

Out of 7,778 screened patients, 151 met the inclusion criteria. Among these, 75.5% demonstrated prolonged QTc values. The study identified 42 distinct medications associated with QT interval prolongation, categorized into six therapeutic groups. Proton pump inhibitors (PPIs) were the most common cause of non-cardiac drug-induced QTc interval prolongation, with esomeprazole representing 46.5% of the cases. Antimicrobial medications followed, with azithromycin at 9.6% and piperacillin-tazobactam at 6.1%. The multivariate analysis revealed that heart failure was significantly associated with QTc prolongation odd ratio (OR) 4.98 with 95% confidence interval CI [1.58 to 17.35], while other factors such as age, BMI, and certain comorbidities did not show a statistically significant impact.

The findings highlight the significant risk associated with the in-hospital administration of QTc-prolonging non-cardiac medications, particularly among patients with heart failure. Future research should aim to include a larger patient population and employ comprehensive data collection methods across multiple centers to enhance the robustness and generalizability of the findings.