Development of a Risk Score for QT Prolongation in the Intensive Care Unit Using Time-Series Electrocardiogram Data and Electronic Medical Records

Determining sensitivity and specificity of risk scores for QTc interval prolongation in hemato-oncology patients prescribed systemic antifungal therapy: a retrospective cross-sectional study

BACKGROUND

QTc interval prolongation can result in potentially lethal arrhythmias. One risk factor is QTc-prolonging drugs, including some antifungals often used in hemato-oncology patients. Screening tools for patients at risk have not yet been investigated in this patient population.

AIM

Our aim was to evaluate the sensitivity and specificity of five QTc risk scores in hemato-oncology patients receiving systemic antifungal therapy.

METHOD

Data were retrieved from an internal study database including adult hemato-oncology patients prescribed systemic antifungal therapy. Data on QTc-prolonging medication, risk factors for QTc prolongation, and electrocardiograms (ECG) were collected retrospectively for a period of 12 months. The QTc risk scores according to Tisdale, Vandael, Berger, Bindraban, and Aboujaoude as well as their sensitivity and specificity were calculated.

RESULTS

During the evaluated period, 77 patients were prescribed systemic antifungals resulting in 187 therapy episodes. Regarding therapy episodes, median age was 56 years (IQR 44-68), 41% (77) were female, and a median of 3 QTc-prolonging drugs were prescribed (range 0-6). ECGs were available for 45 (24%) of the therapy episodes 3-11 days after initiation of the antifungal therapy, 22 of which showed QTc prolongation. Regarding these 45 therapy episodes, sensitivity and specificity of the risk scores were calculated as follows: Tisdale 86%/22%, Vandael 91%/35%, Berger 32%/83%, Bindraban 50%/78%, Aboujaoude 14%/87%.

CONCLUSION

The QTc risk scores according to Tisdale and Vandael showed sufficient sensitivity for risk stratification in the studied patient population. In contrast, risk scores according to Berger, Bindraban, and Aboujaoude cannot be considered suitable due to poor sensitivity.

Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review

OBJECTIVE

We conducted a systematic review to characterize and critically appraise developed prediction models based on structured electronic health record (EHR) data for adverse drug event (ADE) diagnosis and prognosis in adult hospitalized patients.

MATERIALS AND METHODS

We searched the Embase and Medline databases (from January 1, 1999, to July 4, 2022) for articles utilizing structured EHR data to develop ADE prediction models for adult inpatients. For our systematic evidence synthesis and critical appraisal, we applied the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS).

RESULTS

Twenty-five articles were included. Studies often did not report crucial information such as patient characteristics or the method for handling missing data. In addition, studies frequently applied inappropriate methods, such as univariable screening for predictor selection. Furthermore, the majority of the studies utilized ADE labels that only described an adverse symptom while not assessing causality or utilizing a causal model. None of the models were externally validated.

CONCLUSIONS

Several challenges should be addressed before the models can be widely implemented, including the adherence to reporting standards and the adoption of best practice methods for model development and validation. In addition, we propose a reorientation of the ADE prediction modeling domain to include causality as a fundamental challenge that needs to be addressed in future studies, either through acquiring ADE labels via formal causality assessments or the usage of adverse event labels in combination with causal prediction modeling.

Data-driven drug-induced QT prolongation surveillance using adverse reaction signals derived from 12-lead and continuous electrocardiogram data

Drug-induced QT prolongation is one of the most common side effects of drug use and can cause fatal outcomes such as sudden cardiac arrest. This study adopts the data-driven approach to assess the QT prolongation risk of all the frequently used drugs in a tertiary teaching hospital using both standard 12-lead ECGs and intensive care unit (ICU) continuous ECGs. We used the standard 12-lead ECG results (n = 1,040,752) measured in the hospital during 1994–2019 and the continuous ECG results (n = 4,835) extracted from the ICU’s patient-monitoring devices during 2016–2019. Based on the drug prescription frequency, 167 drugs were analyzed using 12-lead ECG data under the case-control study design and 60 using continuous ECG data under the retrospective cohort study design. Whereas the case-control study yielded the odds ratio, the cohort study generated the hazard ratio for each candidate drug. Further, we observed the possibility of inducing QT prolongation in 38 drugs in the 12-lead ECG analysis and 7 drugs in the continuous ECG analysis. The seven drugs (vasopressin, vecuronium, midazolam, levetiracetam, ipratropium bromide, nifedipine, and chlorpheniramine) that showed a significantly higher risk of QT prolongation in the continuous ECG analysis were also identified in the 12-lead ECG data analysis. The use of two different ECG sources enabled us to confidently assess drug-induced QT prolongation risk in clinical practice. In this study, seven drugs showed QT prolongation risk in both study designs.