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Development and Validation of a QTc-Prolongation Risk Score to Optimize Interruptive Medication Alerts
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  • Emily Aboujaoude,
  • Jesni Mathew,
  • Stacey Sobocinski,
  • Mara Villanueva,
  • Chun Feng
Emily Aboujaoude
The University of Texas MD Anderson Cancer Center
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Jesni Mathew
The University of Texas MD Anderson Cancer Center
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Stacey Sobocinski
The University of Texas MD Anderson Cancer Center
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Mara Villanueva
The University of Texas MD Anderson Cancer Center
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Chun Feng
The University of Texas MD Anderson Cancer Center Division of Cancer Medicine
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Abstract

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.

Peer review status:UNDER REVIEW

18 Nov 2021Submitted to Journal of Cardiovascular Electrophysiology
22 Nov 2021Assigned to Editor
22 Nov 2021Submission Checks Completed
06 Dec 2021Reviewer(s) Assigned