Predictive Validity of a QTc Interval Prolongation Risk Score in the Intensive Care Unit

A Calculated Risk: Evaluation of QTc Drug-Drug Interaction (DDI) Clinical Decision Support (CDS) Alerts and Performance of the Tisdale Risk Score Calculator

INTRODUCTION

A risk factor for a potentially fatal ventricular arrhythmia Torsade de Pointes is a prolongation in the heart rate-corrected QT interval (QTc) ≥ 500 milliseconds (ms) or an increase of ≥ 60 ms from a patient's baseline value, which can cause sudden cardiac death. The Tisdale risk score calculator uses clinical variables to predict which hospitalized patients are at the highest risk for QTc prolongation.

OBJECTIVE

To determine the rate of overridden QTc drug-drug interaction (DDI)-related clinical decision support (CDS) alerts per patient admission and the prevalence by Tisdale risk score category of these overridden alerts. Secondary outcome was to determine the rate of drug-induced QTc prolongation (diQTP) associated with overrides.

METHODS

Our organization's enterprise data warehouse was used to retrospectively access QTc DDI alerts presented for patients aged ≥ 18 years who were admitted to Brigham and Women's Hospital during 2022. The QTc DDI CDS alerts were included if shown to a physician, fellow, resident, physician assistant, or nurse practitioner when entering the order in inpatient areas for patients with a length of stay of at least 2 days. Variables collected for the Tisdale calculator included age, sex, whether patient was on a loop diuretic, potassium level, admission QTc value, admitting diagnosis of acute myocardial infarction, sepsis, or heart failure, and number of QTc-prolonging drugs given to the patient.

RESULTS

A total of 2649 patients with 3033 patient admissions had 18,432 QTc DDI alerts presented that were overridden. An average of 3 unique QTc DDI alerts were presented per patient admission and the alerts were overridden an average of 6 times per patient admission. Overall, 6% of patient admissions were low risk (score ≤ 6), 64% moderate risk (score 7-10), and 30% high risk (score ≥ 11) of QTc prolongation. The most common QTc DDI alerts overridden resulting in an diQTP were quetiapine and propofol (11%) and amiodarone and haloperidol (7%). The diQTP occurred in 883 of patient admissions (29%) and was more frequent in those with higher risk score, with 46% of patient admissions with diQTP in high risk, 23% in moderate risk, and 8% in low risk.

CONCLUSION

Use of the Tisdale calculator to assess patient-specific risk of QT prolongation combined with CDS may improve overall alert quality and acceptance rate, which may decrease the diQTP rate.

Drug-drug interactions between COVID-19 therapeutics and psychotropic medications

INTRODUCTION

The coronavirus (COVID-19) pandemic has led to as well as exacerbated mental health disorders, leading to increased use of psychotropic medications. Co-administration of COVID-19 and psychotropic medications may result in drug-drug interactions (DDIs), that may compromise both the safety and efficacy of both medications.

AREAS COVERED

This review provides an update of the current evidence on DDIs between COVID-19 and psychotropic medications. The interactions are categorized into pharmacokinetic, pharmacodynamic, and other relevant types. A thorough literature search was conducted using electronic databases to identify relevant studies, and extract data to highlight potential DDIs, clinical implications, and management strategies.

EXPERT OPINION

Understanding and managing potential DDIs between COVID-19 and psychotropic medications is paramount to ensuring safe and effective treatment of patients with COVID-19 and mental illness. Awareness of the diverse spectrum of DDIs, vigilant monitoring, and judicious dose modifications, while choosing pharmacotherapeutic options with low risk of interaction whenever possible, are necessary. Ongoing and future investigations should continue to review the dynamic landscape of COVID-19 therapeutic modalities and their interactions with psychotropic medications.

Clinically Relevant Interactions with Anti-Infectives on Intensive Care Units-A Multicenter Delphi Study

Patients in intensive care units (ICUs) are at high risk of drug–drug interactions (DDIs) due to polypharmacy. Little is known about type and frequency of DDIs within German ICUs. Clinical pharmacists’ interventions (PI) recorded in a national database (ADKA-DokuPIK) were filtered for ICU patients. Binary DDIs involving ≥1 anti-infective agent with >1 database entry were selected. A modified two-step Delphi process with a group of senior hospital pharmacists was employed to evaluate selected DDIs for clinical relevance by using a five-point scale and to develop guidance for clinical practice. In total, 16,173 PI were recorded, including 1836 (11%) DDIs in the ICU setting. Of the latter, 41% (756/1836) included ≥1 anti-infective agent, 32% (590/1836) were binary DDIs, and 25% (455/1836) were listed at least twice. This translates into 88 different DDIs, 74% (65/88) of which were rated as being clinically relevant by our expert panel. The majority of DDIs (76% [67/88]) included macrolides, antifungals, or fluoroquinolones. This percentage was even higher in DDIs being rated as clinically relevant by the experts (85% [55/65]). It is noted that an inter-professional discussion and approach is needed in the individual patient management of DDIs. The guidance developed might be a tool for decision support.

Evaluating the risk of QTc prolongation associated with hydroxychloroquine use with antidepressants in lupus patients with fibromyalgia

OBJECTIVE

Hydroxychloroquine (HCQ) is widely used in patients with systemic lupus erythematosus (SLE) due to its immunomodulatory properties. Antidepressants are commonly used in patients with fibromyalgia syndrome (FMS). Both HCQ and antidepressants are reported to cause QTc prolongation, which potentially increases the risk for a lethal ventricular arrhythmia that can result in sudden death. The objective of the study is to investigate the risk of QTc prolongation associated with HCQ use concomitantly with antidepressants in lupus patients with FMS.

METHODS

Outpatient 12-lead electrocardiograms (ECGs) were extracted from an electronic medical record and QTc intervals were calculated using the Bazett's formula. QTc intervals in 135 lupus patients treated with HCQ with or without antidepressants were analyzed.

RESULTS

We found taking HCQ was associated with mild QTc prolongation, and the prolongation was not affected by the length of time of HCQ use or the accumulated dose of HCQ. Concurrent use of HCQ and antidepressants had not further increased QTc intervals in this cohort. However, four patients on HCQ alone and three patients on HCQ and antidepressants were found QTc interval more than 500 milliseconds and most of these patients had underlying cardiological conditions.

CONCLUSIONS

It is important to evaluate lupus patient with ECG before and after starting HCQ, though our study suggests that while HCQ use did prolong the QTc in some, but the overall prolongation was subclinical, with or without antidepressants. ECG monitoring therefore is essential to identify new changes potentially related to drug use.

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

Objective

Drug-induced QT prolongation can lead to life-threatening arrhythmia. In the intensive care unit (ICU), various drugs are administered concurrently, which can increase the risk of QT prolongation. However, no well-validated method to evaluate the risk of QT prolongation in real-world clinical practice has been established. We developed a risk scoring model to continuously evaluate the quantitative risk of QT prolongation in real-world clinical practice in the ICU.

Methods

Continuous electrocardiogram (ECG) signals measured by patient monitoring devices and Electronic Medical Records data were collected for ICU patients. QT and RR intervals were measured from raw ECG data, and a corrected QT interval (QTc) was calculated by Bazett's formula. A case-crossover study design was adopted. A case was defined as an occurrence of QT prolongation ≥12 hours after any previous QT prolongation. The patients served as their own controls. Conditional logistic regression was conducted to analyze prescription, surgical history, and laboratory test data. Based on the regression analysis, a QTc prolongation risk scoring model was established.

RESULTS

In total, 811 ICU patients who experienced QT prolongation were included in this study. Prescription information for 13 drugs was included in the risk scoring model. In the validation dataset, the high-risk group showed a higher rate of QT prolongation than the low-and low moderate-risk groups.

Conclusions

Our proposed model may facilitate risk stratification for QT prolongation during ICU care as well as the selection of appropriate drugs to prevent QT prolongation.