Development and validation of a tool to assess the risk of QT drug-drug interactions in clinical practice

Evaluation of Triple Whammy Prescriptions After the Implementation of a Drug Safety Algorithm

BACKGROUND AND OBJECTIVE

The term triple whammy (TW) refers to the concomitant use of non-steroidal anti-inflammatory drugs, diuretics, and angiotensin system inhibitors; this combination significantly increases the risk of acute kidney injury (AKI). To prevent this serious complication, we developed an electronic algorithm that detects TW prescriptions in patients with additional risk factors such as old age and impaired kidney function. The algorithm alerts a clinical pharmacist who then evaluates and forwards the alert to the prescribing physician.

METHODS

We evaluated the performance of this algorithm in a retrospective observational study of clinical data from all adult patients admitted to the Cantonal Hospital of Aarau in Switzerland in 2021. We identified all patients who received a TW prescription, had a TW alert, or developed AKI during TW therapy. Algorithm performance was evaluated by calculating the sensitivity and specificity as a primary endpoint and determining the acceptance rate among clinical pharmacists and physicians as a secondary endpoint.

RESULTS

Among 21,332 hospitalized patients, 290 patients had a TW prescription, of which 12 patients experienced AKI. Overall, 216 patients were detected by the alert algorithm, including 11 of 12 patients with AKI; the algorithm sensitivity is 88.3% with a specificity of 99.7%. Physician acceptance was high (77.7%), but clinical pharmacists were reluctant to forward the alerts to prescribers in some cases.

CONCLUSION

The TW algorithm is highly sensitive and specific in identifying patients with TW therapy at risk for AKI. The algorithm may help to prevent AKI in TW patients in the future.

Impact of an inpatient pharmacist-driven renal dosing policy on order verification time and patient safety

Research regarding pharmacist-driven renal dosing policies has focused on cost savings or prevention of adverse drug events. However, little is known about how these policies influence time from order signature to order verification or how this efficiency may reduce the incidence of adverse outcomes. Objectives: The primary endpoint compared time from prescriber electronic order signature to pharmacist electronic order verification between pre- and post-renal dosing policy implementation. The secondary endpoint evaluated electrocardiogram QTc prolongation attributed to fluconazole accumulation in renal impairment. Methods: This retrospective analysis included adults with a creatine clearance ⩽50 mL/min who received at least two inpatient doses from a 34-medication renal dosing protocol between January-February 2020 and April-May 2020. Results: 502 patients met eligibility for the primary outcome. The pre- and post-policy cohorts shared similar baseline characteristics. Time from order signature to verification was 9 and 8 min in the pre- and post-policy groups, respectively (p = 0.0861). In all, 56 patients met inclusion criteria for the secondary outcome. The QTc interval during fluconazole increased relative to baseline in 3 of 7 (43%) pre-policy and 4 of 5 (80%) post-policy. The QTc interval exceeded 500 ms in two patients, both in the post-policy cohort. Conclusions: There was no difference in order signature to verification time. Post-policy fluconazole renal adjustment did not reduce QTc prolongation.

QTc prolongation risk among patients receiving oral targeted antineoplastic medications: A real-world community-based oncology analysis

Introduction

Thirty oral targeted antineoplastic agents are associated with prolongation of the QT interval. However, limited data exists regarding QTc prolongation and associated risk factors in the ambulatory oncology setting.

Methods

This retrospective study was completed to describe QTc prolongation incidence among patients receiving oral targeted tyrosine kinase inhibitors (TKI) and identify potential risk factors in the ambulatory community-based oncology clinic.

Results

Of the 341 patients identified as receiving oral TKI, 49 with a baseline and follow-up ECG were included. The incidence of QTc prolongation (QTc > 470 ms in males, QTc > 480 ms in females, or >20 ms increase in QTc from baseline) was 24%. Three patients developed significant QTc prolongation (QTc >500 ms or >60 ms increase in QTc from baseline). No patients discontinued therapy primarily due to QTc prolongation or experienced symptomatic torsades de pointes. Analysis of risk factors demonstrated that patients with QTc prolongation were more likely to receive concomitant therapy with a loop diuretic (41% vs 11%, respectively, p=0.029).

Discussion

The frequency of QTc prolongation may be higher in the real-world setting than that observed in clinical trials; however, continuation of therapy may be possible. Patients receiving concomitant loop diuretics should be monitored more closely for QTc prolongation and electrolyte abnormalities.

Interpretable Machine Learning Prediction of Drug-Induced QT Prolongation: Electronic Health Record Analysis

BACKGROUND

Drug-induced long-QT syndrome (diLQTS) is a major concern among patients who are hospitalized, for whom prediction models capable of identifying individualized risk could be useful to guide monitoring. We have previously demonstrated the feasibility of machine learning to predict the risk of diLQTS, in which deep learning models provided superior accuracy for risk prediction, although these models were limited by a lack of interpretability.

OBJECTIVE

In this investigation, we sought to examine the potential trade-off between interpretability and predictive accuracy with the use of more complex models to identify patients at risk for diLQTS. We planned to compare a deep learning algorithm to predict diLQTS with a more interpretable algorithm based on cluster analysis that would allow medication- and subpopulation-specific evaluation of risk.

METHODS

We examined the risk of diLQTS among 35,639 inpatients treated between 2003 and 2018 with at least 1 of 39 medications associated with risk of diLQTS and who had an electrocardiogram in the system performed within 24 hours of medication administration. Predictors included over 22,000 diagnoses and medications at the time of medication administration, with cases of diLQTS defined as a corrected QT interval over 500 milliseconds after treatment with a culprit medication. The interpretable model was developed using cluster analysis (K=4 clusters), and risk was assessed for specific medications and classes of medications. The deep learning model was created using all predictors within a 6-layer neural network, based on previously identified hyperparameters.

RESULTS

Among the medications, we found that class III antiarrhythmic medications were associated with increased risk across all clusters, and that in patients who are noncritically ill without cardiovascular disease, propofol was associated with increased risk, whereas ondansetron was associated with decreased risk. Compared with deep learning, the interpretable approach was less accurate (area under the receiver operating characteristic curve: 0.65 vs 0.78), with comparable calibration.

CONCLUSIONS

In summary, we found that an interpretable modeling approach was less accurate, but more clinically applicable, than deep learning for the prediction of diLQTS. Future investigations should consider this trade-off in the development of methods for clinical prediction.

Use of Electrocardiogram Monitoring in Adult Patients Taking High-Risk QT Interval Prolonging Medicines in Clinical Practice: Systematic Review and Meta-analysis

Introduction

Electrocardiogram (ECG) monitoring is an important tool to detect and mitigate the risk of potentially fatal drug-induced QT prolongation and remains fundamental in supporting the quality use of high-risk QT interval prolonging medicines.

Objective

The aim of this systematic review was to determine the prevalence of baseline and/or follow-up ECG use in adult patients taking high-risk QT interval prolonging medicines in clinical practice.

Methods

CINAHL, Cochrane Library, Embase, PubMed, EThOS, OpenGrey and Proquest were searched for studies in adults that reported ECG use at baseline and/or at follow-up in relation to the initiation of a high-risk QT interval prolonging medicine in any clinical setting; either hospital or non-hospital. Two reviewers independently assessed the methodological quality of included studies. Proportional meta-analysis was conducted with all studies reporting baseline ECG use, before medicine initiation, and follow-up ECG use, within 30 days of medicine initiation.

Results

There was variability in baseline ECG use according to the practice setting. The prevalence of baseline ECG use for high-risk QT interval prolonging medicines was moderate to high in the hospital setting at 75.1% (95% CI 64.3–84.5); however, the prevalence of baseline ECG use was low in the non-hospital setting at 33.7% (95% CI 25.8–42.2). The prevalence of follow-up ECG use was low to moderate in the hospital setting at 39.2% (95% CI 28.2–50.8) and could not be determined for the non-hospital setting.

Conclusions

The use of ECG monitoring for high-risk QT interval prolonging medicines is strongly influenced by the clinical practice setting. Baseline ECG use occurs more in the hospital setting in comparison to the non-hospital setting. There is lower use of follow-up ECG in comparison to baseline ECG.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40264-022-01215-x.

Clinician Responses to a Clinical Decision Support Advisory for High Risk of Torsades de Pointes

Background

Torsade de pointes (TdP) is a potentially fatal cardiac arrhythmia that is often drug induced. Clinical decision support (CDS) may help minimize TdP risk by guiding decision making in patients at risk. CDS has been shown to decrease prescribing of high‐risk medications in patients at risk of TdP, but alerts are often ignored. Other risk‐management options can potentially be incorporated in TdP risk CDS. Our goal was to evaluate actions clinicians take in response to a CDS advisory that uses a modified Tisdale QT risk score and presents management options that are easily selected (eg, single click).

Methods and Results

We implemented an inpatient TdP risk advisory systemwide across a large health care system comprising 30 hospitals. This CDS was programmed to appear when prescribers attempted ordering medications with a known risk of TdP in a patient with a QT risk score ≥12. The CDS displayed patient‐specific information and offered relevant management options including canceling offending medications and ordering electrolyte replacement protocols or ECGs. We retrospectively studied the actions clinicians took within the advisory and separated by drug class. During an 8‐month period, 7794 TdP risk advisories were issued. Antibiotics were the most frequent trigger of the advisory (n=2578, 33.1%). At least 1 action was taken within the advisory window for 2700 (34.6%) of the advisories. The most frequent action taken was ordering an ECG (n=1584, 20.3%). Incoming medication orders were canceled in 793 (10.2%) of the advisories. The frequency of each action taken varied by drug class (P<0.05 for all actions).

Conclusions

A modified Tisdale QT risk score–based CDS that offered relevant single‐click management options yielded a high action/response rate. Actions taken by clinicians varied depending on the class of the medication that evoked the TdP risk advisory, but the most frequent was ordering an ECG.

Overall performance of a drug-drug interaction clinical decision support system: quantitative evaluation and end-user survey

BACKGROUND

Clinical decision support systems are implemented in many hospitals to prevent medication errors and associated harm. They are however associated with a high burden of false positive alerts and alert fatigue. The aim of this study was to evaluate a drug-drug interaction (DDI) clinical decision support system in terms of its performance, uptake and user satisfaction and to identify barriers and opportunities for improvement.

METHODS

A quantitative evaluation and end-user survey were performed in a large teaching hospital. First, very severe DDI alerts generated between 2019 and 2021 were evaluated retrospectively. Data collection comprised alert burden, override rates, the number of alert overrides reviewed by pharmacists and the resulting pharmacist recommendations as well as their acceptance rate. Second, an e-survey was carried out among prescribers to assess satisfaction, usefulness and relevance of DDI alerts as well as reasons for overriding.

RESULTS

A total of 38,409 very severe DDI alerts were generated, of which 88.2% were overridden by the prescriber. In 3.2% of reviewed overrides, a recommendation by the pharmacist was provided, of which 79.2% was accepted. False positive alerts were caused by a too broad screening interval and lack of incorporation of patient-specific characteristics, such as QTc values. Co-prescribing of a non-vitamin K oral anticoagulant and a low molecular weight heparin accounted for 49.8% of alerts, of which 92.2% were overridden. In 88 (1.1%) of these overridden alerts, concurrent therapy was still present. Despite the high override rate, the e-survey revealed that the DDI clinical decision support system was found useful by prescribers.

CONCLUSIONS

Identified barriers were the lack of DDI-specific screening intervals and inclusion of patient-specific characteristics, both leading to a high number of false positive alerts and risk for alert fatigue. Despite these barriers, the added value of the DDI clinical decision support system was recognized by prescribers. Hence, integration of DDI-specific screening intervals and patient-specific characteristics is warranted to improve the performance of the DDI software.

Risk assessment tools for QT prolonging pharmacotherapy in older adults: a systematic review

Purpose

Many drugs are associated with the risk of QT prolongation and torsades de pointes (TdP), and different risk assessment tools (RATs) are developed to help clinicians to manage related risk. The aim of this systematic review was to summarize the evidence of different RATs for QT prolonging pharmacotherapy.

Methods

A systematic review was conducted using PubMed and Scopus databases. Studies concerning risk assessment tools for QT prolonging pharmacotherapy, including older adults, were included. Screening and selection of the studies, data extraction, and risk of bias assessment were undertaken.

Results

A total of 21 studies were included, involving different risk assessment tools. Most commonly used tools were risk scores (n = 9), computerized physician order entry systems (n = 3), and clinical decision support systems (n = 6). The tools were developed mainly for physicians and pharmacists. Risk scores included a high number of risk factors, both pharmacological and non-pharmacological, for QT prolongation and TdP. The inclusion of patients’ risk factors in computerized physician order entry and clinical decision support systems varied.

Conclusion

Most of the risk assessment tools for QT prolonging pharmacotherapy give a comprehensive overview of patient-specific risks of QT prolongation and TdP and reduce modifiable risk factors and actual events. The risk assessment tools could be better adapted to different health information systems to help in clinical decision-making. Further studies on clinical validation of risk assessment tools with randomized controlled trials are needed.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00228-022-03285-3.

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.

Acquired Long QT Syndrome and Torsades de Pointes after Mitral Valve Replacement Surgery

Acquired long QT syndrome (aLQTS) can occur in up to one third of patients undergoing cardiac surgery and is often undisclosed. We present a case of a 55-year-old male patient admitted to our center for mitral valve replacement surgery, and, during the postoperative period, a long QT greater than 600 ms was confirmed and in the Holter monitoring Torsade de Pointes (TdP) was evidenced. The patient received appropriate medical treatment and was discharge in stable clinical conditions.

The use of a clinical decision support tool to assess the risk of QT drug-drug interactions in community pharmacies

Introduction:

The handling of drug–drug interactions regarding QTc-prolongation (QT-DDIs) is not well defined. A clinical decision support (CDS) tool will support risk management of QT-DDIs. Therefore, we studied the effect of a CDS tool on the proportion of QT-DDIs for which an intervention was considered by pharmacists.

Methods:

An intervention study was performed using a pre- and post-design in 20 community pharmacies in The Netherlands. All QT-DDIs that occurred during a before- and after-period of three months were included. The impact of the use of a CDS tool to support the handling of QT-DDIs was studied. For each QT-DDI, handling of the QT-DDI and patient characteristics were extracted from the pharmacy information system. Primary outcome was the proportion of QT-DDIs with an intervention. Secondary outcomes were the type of interventions and the time associated with handling QT-DDIs. Logistic regression analysis was used to analyse the primary outcome.

Results:

Two hundred and forty-four QT-DDIs pre-CDS tool and 157 QT-DDIs post-CDS tool were included. Pharmacists intervened in 43.0% and 35.7% of the QT-DDIs pre- and post-CDS tool respectively (odds ratio 0.74; 95% confidence interval 0.49–1.11). Substitution of interacting agents was the most frequent intervention. Pharmacists spent 20.8 ± 3.5 min (mean ± SD) on handling QT-DDIs pre-CDS tool, which was reduced to 14.9 ± 2.4 min (mean ± SD) post-CDS tool. Of these, 4.5 ± 0.7 min (mean ± SD) were spent on the CDS tool.

Conclusion:

The CDS tool might be a first step to developing a tool to manage QT-DDIs via a structured approach. Improvement of the tool is needed in order to increase its diagnostic value and reduce redundant QT-DDI alerts.

Plain Language Summary

The use of a tool to support the handling of QTc-prolonging drug interactions in community pharmacies

Introduction: Several drugs have the ability to cause heart rhythm disturbances as a rare side effect. This rhythm disturbance is called QTc-interval prolongation. It may result in cardiac arrest. For health care professionals, such as physicians and pharmacists, it is difficult to decide whether or not it is safe to proceed treating a patient with combinations of two or more of these QT-prolonging drugs. Recently, a tool was developed that supports the risk management of these QT drug–drug interactions (QT-DDIs).

Methods: In this study, we studied the effect of this tool on the proportion of QT-DDIs for which an intervention was considered by pharmacists. An intervention study was performed using a pre- and post-design in 20 community pharmacies in The Netherlands. All QT-DDIs that occurred during a before- and after-period of 3 months were included.

Results: Two hundred and forty-four QT-DDIs pre-implementation of the tool and 157 QT-DDIs post-implementation of the tool were included. Pharmacists intervened in 43.0% of the QT-DDIs before the tool was implemented and in 35.7% after implementation of the tool. Substitution of one of the interacting agents was the most frequent intervention. Pharmacists spent less time on handling QT-DDIs when the tool was used.

Conclusion: The clinical decision support tool might be a first step to developing a tool to manage QT-DDIs via a structured approach.

Comparison of two algorithms to support medication surveillance for drug-drug interactions between QTc-prolonging drugs

BACKGROUND

QTc-prolongation is an independent risk factor for developing life-threatening arrhythmias. Risk management of drug-induced QTc-prolongation is complex and digital support tools could be of assistance. Bindraban et al. and Berger et al. developed two algorithms to identify patients at risk for QTc-prolongation.

OBJECTIVE

The main aim of this study was to compare the performances of these algorithms for managing QTc-prolonging drug-drug interactions (QT-DDIs).

MATERIALS AND METHODS

A retrospective data analysis was performed. A dataset was created from QT-DDI alerts generated for in- and outpatients at a general teaching hospital between November 2016 and March 2018. ECGs recorded within 7 days of the QT-DDI alert were collected. Main outcomes were the performance characteristics of both algorithms. QTc-intervals of > 500 ms on the first ECG after the alert were taken as outcome parameter, to which the performances were compared. Secondary outcome was the distribution of risk scores in the study cohort.

RESULTS

In total, 10,870 QT-DDI alerts of 4987 patients were included. ECGs were recorded in 26.2 % of the QT-DDI alerts. Application of the algorithms resulted in area under the ROC-curves of 0.81 (95 % CI 0.79-0.84) for Bindraban et al. and 0.73 (0.70-0.75) for Berger et al. Cut-off values of ≥ 3 and ≥ 6 led to sensitivities of 85.7 % and 89.1 %, and specificities of 60.8 % and 44.3 % respectively.

CONCLUSIONS

Both algorithms showed good discriminative abilities to identify patients at risk for QTc-prolongation when using ≥ 2 QTc-prolonging drugs. Implementation of digital algorithms in clinical decision support systems could support the risk management of QT-DDIs.

Managing drug-induced QT prolongation in clinical practice

Many drug therapies are associated with prolongation of the QT interval. This may increase the risk of Torsades de Pointes (TdP), a potentially life-threatening cardiac arrhythmia. As the QT interval varies with a change in heart rate, various formulae can adjust for this, producing a ‘corrected QT’ (QTc) value. Normal QTc intervals are typically <450 ms for men and <460 ms for women. For every 10 ms increase, there is a ~5% increase in the risk of arrhythmic events. When prescribing drugs associated with QT prolongation, three key factors should be considered: patient-related risk factors (eg, female sex, age >65 years, uncorrected electrolyte disturbances); the potential risk and degree of QT prolongation associated with the proposed drug; and co-prescribed medicines that could increase the risk of QT prolongation. To support clinicians, who are likely to prescribe such medicines in their daily practice, we developed a simple algorithm to help guide clinical management in patients who are at risk of QT prolongation/TdP, those exposed to QT-prolonging medication or have QT prolongation.