The use of autologous hematopoietic SCT (auto-HSCT) has expanded to include older patients. Increasing age is a well-appreciated risk factor for the development of atrial fibrillation and/or atrial flutter (AF/AFL) in the general population. As more elderly patients undergo auto-HSCT, the risk of developing AF/AFL post transplant may also increase. However, few data evaluating other risk factors for the development of AF/AFL following auto-HSCT exist. Therefore, we performed a retrospective study to determine the incidence of AF/AFL following auto-HSCT and to determine the risk factors associated with the development of AF/ AFL. Patients who developed AF/AFL were compared with a group of patients who received auto-HSCT within the same time period (April 1999 to May 2005 and were within 5 years of age. Of the 516 patients who underwent auto-HSCT at the University of Nebraska Medical Center 44 (8.5%) developed AF/AFL at a median time of 4 days (range, days 1-9) following auto-HSCT. In multivariate analysis, risk factors for developing AF/AFL were older age, odds ratio and 95% CI of 1.14 (1.07-1.21), elevated serum creatinine level, 2.69 (1.00-7.22), history of previous arrhythmia, 9.33 (3.01-28.99), and history of previous mediastinal irradiation, 11.12 (1.33-92.96).

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Introduction: Drug-drug interaction (DDI) warnings are employed in many institutions when more than one QTc-prolonging medication is prescribed; however, this leads to alert fatigue where alerts are frequently overridden by clinicians due to patient non-specificity or low risk. This study aimed at reducing alert fatigue through developing a custom alert triggered by a patient-specific QTc-prolongation risk score, and validating it against database-driven DDI warnings for QTc prolongation. Methods and Results: Between November 23, 2019 and January 31, 2020, inpatients with a baseline and a follow-up 12-lead ECG reading within 14 days were identified. Each time a QTc-prolonging medication order was signed or verified, the QTc-prolongation risk score was calculated in the electronic health record (EHR), triggering a custom alert in the background. Follow-up 12-lead ECG readings were used to calculate sensitivity and specificity for both the custom alert and the DDI warning. A total of 100 patients had a risk score calculation and were included in our analysis, representing 521 custom alerts and 449 DDI warnings. The preliminary QTc-prolongation risk score did not achieve a reduction in false positive alerts with a cutoff of 10 points. A multiple logistic regression was performed to re-arrange the components and optimize the risk score. Conclusion: Our adjusted QTc-prolongation risk score, with a cutoff of 5 points, achieved a specificity of 66% and a negative predictive value of 83%. These results will allow us to integrate the risk score into the EHR as a guidance tool to predict QTc-prolongation.