Institution-wide QT alert system identifies patients with a high risk of mortality

The Impact of QT-Prolonging Medications and Drug-Drug Interactions on QTc Interval Prolongation in Hospitalized Patients: A Case-Crossover Study

Researchers have studied potential corrected QT interval (QTc) prolongation from drug-drug interactions (DDIs), raising unresolved questions about their real-world impact. This retrospective case-crossover study investigated the effects of QT-prolonging drugs and DDIs on QTc prolongation in hospitalized patients aged 45 years and above. The cohort comprised patients who had multiple hospitalizations and developed QTc prolongation (QTc > 500 ms or an increase of >60 ms from baseline) at least 24 hours after admission between 2011 and 2019. Conditional logistic regression compared drug exposure between hospitalizations with QTc prolongation (case window) and those without (reference window). Among 2,276 patients (mean age 71; 43.8% female), the use of QT-prolonging drugs significantly increased the risk of QTc prolongation (odds ratio: 2.42 (95% confidence interval: 1.95-3.02)). The risk was higher with drugs of "known risks" (OR: 3.78 (2.91-4.90)) and "conditional risk" (OR: 2.08 (1.65-2.62)). DDIs, particularly involving multiple "known risk" drugs (OR: 7.86 (4.96-12.45)), strong cytochrome P450 enzyme inhibitors (OR: 5.57 (2.75-11.30)), or the concurrent use of ≥4 QT-prolonging drugs with any risk (OR: 5.28 (3.96-7.03)) substantially increased the risk. Cautious prescribing for patients with multiple risk factors is important to minimize the likelihood of QTc prolongation. However, when considering enhanced monitoring or drug choices, it is crucial to carefully evaluate the overall risk of QT prolongation against the benefits of treatment to ensure optimal patient care.

An Updated Safety Review of the Relationship Between Atypical Antipsychotic Drugs, the QTc Interval and Torsades de Pointe As: Implications for Clinical Use

INTRODUCTION

The rising prevalence of psychiatric disorders has resulted in a significant increase in the use of antipsychotic medications. These agents may prolong the corrected QT interval (QTc), running the risk of precipitating ventricular arrhythmias, notably Torsades de Pointes (TdP). Current recommendations vary regarding the optimal approach to safe prescribing practices and QTc surveillance for antipsychotics. This review summarizes the current literature addressing these clinical concerns.

AREAS COVERED

The physiologic basis of the QTc interval, mechanisms underlying its susceptibility to pharmacological influence, specific risks associated with atypical antipsychotic agents, and recommendations for safe prescription practices. We performed a literature review using Pubmed and Embase databases, searching for 'antipsychotics' and 'torsades de pointes.'

EXPERT OPINION

Finding a safe and universally accepted protocol for prescribing antipsychotics remains a persistent challenge in medicine. Predictive models that integrate clinical history with demographic and ECG characteristics can help estimate an individual's susceptibility to therapy-associated risks, including QTc prolongation. Agents such as ziprasidone and iloperidone are significantly more likely to prolong the QTc interval compared to others such as brexpiprazole, cariprazine, olanzapine, and clozapine. A personalized approach using low-risk medications when clinically feasible, and at the lowest efficacious dose, offers a promising path toward safer antipsychotic prescribing.

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.

Proarrhythmic major adverse cardiac events with donepezil: A systematic review with meta-analysis

BACKGROUND

Cholinesterase inhibitors (ChEIs) are regularly used in Alzheimer's disease. Of the three ChEIs approved for dementia, donepezil is among the most prescribed drugs in the United States with nearly 6 million prescriptions in 2020; however, it is classified as a "known risk" QT interval-prolonging medication (QTPmed). Given this claim is derived from observational data including single case reports, we aimed to evaluate high-quality literature on the frequency and nature of proarrhythmic major adverse cardiac events (MACE) associated with donepezil.

METHODS

We searched Medline, Embase, International Pharmaceutical Abstracts, and Cochrane Central from 1996 onwards for randomized controlled trials (RCTs) involving patients age ≥18 years comparing donepezil to placebo. The MACE composite included mortality, sudden cardiac death, non-fatal cardiac arrest, Torsades de pointes, ventricular tachyarrhythmia, seizure or syncope. Random-effects meta-analyses were performed with a treatment-arm continuity correction for single and double zero event studies.

RESULTS

Sixty RCTs (n = 12,463) were included. Twenty-five of 60 trials (n = 5886) investigated participants with Alzheimer's disease and 33 trials monitored electrocardiogram data. The mean follow-up duration was 31 weeks (SD = 36). Mortality was the most commonly reported MACE (252/331, 75.8% events), the remainder were syncope or seizures, with no arrhythmia events. There was no increased risk of MACE with exposure to donepezil compared to placebo (risk ratio [RR] 1.08, 95% CI 0.88-1.33, I2 = 0%) and this was consistent in the subgroup analysis of trials including participants with cardiovascular morbidities (RR 1.14, 95% CI 0.88-1.47). Subgroup analysis suggested a trend toward more events with donepezil with follow-up ≥52 weeks (RR: 1.32, 0.98-1.79).

CONCLUSIONS

This systematic review with meta-analysis found donepezil may not be arrhythmogenic. Donepezil was not associated with mortality, ventricular arrhythmias, seizure or syncope, although longer durations of therapy need more study. Further research to clarify actual clinical outcomes related to QTPmed is important to inform prescribing practices.

QTc Interval: A frequently unrecognized electrocardiographic interval

The QT interval, an electrocardiographic temporal representation of the ventricular depolarization and repolarization, is an integral parameter that must be carefully evaluated to gather critical information regarding electrical instability that may cause malignant ventricular dysrhythmias or sudden cardiac death. The QT interval is affected by several inheritable and acquired factors, such as genetic mutations, electrolyte disturbances, and medication interactions. We strongly believe that prompt and accurate recognition of any QT interval abnormalities is critical in many clinical settings. This concise review article highlights the importance of accurate measurement of the QT interval, enhances understanding of the most prevalent factors yielding abnormalities within the QT interval and the prognostic value of the QT interval, as well as provides several key practical reminders for healthcare professionals to strengthen our clinical practice.

QTNet: Predicting Drug-Induced QT Prolongation With Artificial Intelligence-Enabled Electrocardiograms

BACKGROUND

Prediction of drug-induced long QT syndrome (diLQTS) is of critical importance given its association with torsades de pointes. There is no reliable method for the outpatient prediction of diLQTS.

OBJECTIVES

This study sought to evaluate the use of a convolutional neural network (CNN) applied to electrocardiograms (ECGs) to predict diLQTS in an outpatient population.

METHODS

We identified all adult outpatients newly prescribed a QT-prolonging medication between January 1, 2003, and March 31, 2022, who had a 12-lead sinus ECG in the preceding 6 months. Using risk factor data and the ECG signal as inputs, the CNN QTNet was implemented in TensorFlow to predict diLQTS.

RESULTS

Models were evaluated in a held-out test dataset of 44,386 patients (57% female) with a median age of 62 years. Compared with 3 other models relying on risk factors or ECG signal or baseline QTc alone, QTNet achieved the best (P < 0.001) performance with a mean area under the curve of 0.802 (95% CI: 0.786-0.818). In a survival analysis, QTNet also had the highest inverse probability of censorship-weighted area under the receiver-operating characteristic curve at day 2 (0.875; 95% CI: 0.848-0.904) and up to 6 months. In a subgroup analysis, QTNet performed best among males and patients ≤50 years or with baseline QTc <450 ms. In an external validation cohort of solely suburban outpatient practices, QTNet similarly maintained the highest predictive performance.

CONCLUSIONS

An ECG-based CNN can accurately predict diLQTS in the outpatient setting while maintaining its predictive performance over time. In the outpatient setting, our model could identify higher-risk individuals who would benefit from closer monitoring.

Effect on QTc interval by switching from methadone to equipotent R-methadone dose in methadone maintenance treatment patients

Methadone (R,S-methadone) can prolong the QT interval. R-methadone inhibits cardiac potassium channel function less than S-methadone. We tested if switching from methadone to R-methadone would reduce corrected QT (QTc) intervals in methadone maintenance treatment (MMT) patients. Nine patients, with automatically read QTc intervals ≥450 ms, were required to detect a 20 ms (clinically relevant) reduction in QTc intervals with 15 ms standard deviation (SD) and 90% power. Nine stabilized MMT patients, using median (range) 70 (40-120) mg methadone, were included. Data (ECG recordings, serum samples, and withdrawal symptoms) were collected both before drug intake (Cmin ) and at 3 h after drug intake (Cmax ), and were collected on the day before the switch from methadone to equipotent R-methadone dose and at 14 and 28 days after the switch. A cardiologist calculated QTc intervals retrospectively. Serum electrolytes and methadone concentrations were measured. Mean QTc intervals at Cmin were 472 ms and 422 ms on methadone (automatically and manually read) and 414 ms on R-methadone (manually read). Mean (SD) change in QTc intervals was -8 (10) ms (p = 0.047) at Cmin but non-significant at Cmax . R-methadone showed a concentration-dependent relationship with QTc intervals. Switching to R-methadone reduced QTc intervals, but far less than the 20 ms considered clinically relevant.

Evaluating the prescribing and monitoring of medications associated with QTc-prolongation in the ambulatory care setting

RATIONALE

Little is known about the prescribing of medications with potential to cause QTc-prolongation in the ambulatory care settings. Understanding real-world prescribing of QTc-prolonging medications and actions taken to mitigate this risk will help guide strategies to optimize safety and appropriate prescribing among ambulatory patients.

OBJECTIVE

To evaluate the frequency of clinician action taken to monitor and mitigate modifiable risk factors for QTc-prolongation when indicated.

METHODS

This retrospective, cross-sectional study evaluated clinician action at the time of prescribing prespecified medications with potential to prolong QTc in adult patients in primary care. The index date was defined as the date the medication was ordered. Electronic health record (EHR) data were evaluated to assess patient, clinician and visit characteristics. Clinician action was determined if baseline or follow-up monitoring was ordered or if action was taken to mitigate modifiable risk factors (laboratory abnormalities or electrocardiogram [ECG] monitoring) within 48 h of prescribing a medication with QTc-prolonging risk. Descriptive statistics were used to describe current practice.

RESULTS

A total of 399 prescriptions were prescribed to 386 patients, with a mean age of 51 ± 18 years, during March 2021 from a single-centre, multisite health system. Of these, 17 (4%) patients had a known history of QTc-prolongation, 170 (44%) did not have a documented history of QTc-prolongation and 199 (52%) had an unknown history (no ECG documented). Thirty-nine patients (10%) had at least one laboratory-related risk factor at the time of prescribing, specifically hypokalemia (16 patients), hypomagnesemia (8 patients) or hypocalcemia (19 patients). Of these 39 patients with laboratory risk factors, only 6 patients (15%) had their risk acknowledged or addressed by a clinician. Additionally, eight patients' most recent QTc was ≥500 ms and none had an ECG checked at the time the prescription was ordered.

CONCLUSION

Despite national recommendations, medication monitoring and risk mitigation is infrequent when prescribing QTc-prolonging medications in the ambulatory care setting. These findings call for additional research to better understand this gap, including reasons for the gap and consequences on patient outcomes.

Pharmacist Reported Protocols for QTc Monitoring of Psychiatric Medications

Background Psychiatric medications, such as antipsychotics and antidepressants, are associated with QTc interval prolongation. There is currently no consensus best practice on how to mitigate this risk. This study aimed to collect and analyze information about methods used for QTc monitoring in patients taking psychiatric medications to better understand current practice. Methods An anonymous electronic survey was distributed on September 22, 2022, using a national psychiatric pharmacist organization email list. The survey closed on December 15, 2022. Descriptive statistics were used to analyze the multiple-choice questions. Qualitative analysis applying grounded theory for thematic analysis was performed for free response questions. Results A total of 48 initiated the survey. Of the respondents, 11.4% (5/44) reported that their institution had a formal protocol for monitoring QTc intervals in patients receiving psychiatric medications, while 32.4% (12/37) reported that their institution had an informal process. Out of those with a protocol or process, approximately half reported that it was drug-specific. Among the respondents, 88.6% (31/35) reported that there was a psychiatric clinical pharmacy specialist at their institution and 34.3% (12/35) reported that pharmacists could order an electrocardiogram (ECG). Major themes that emerged from the qualitative analysis included pharmacist-driven QTc monitoring, referring the patient to another provider for monitoring, and encountering significant barriers to monitoring. Conclusion A variety of methods are currently being employed to monitor QTc prolongation risk in patients taking psychiatric medications. Pharmacist authorization to order ECGs may be an opportunity to advance practice and improve care for this population. Further research is needed to more clearly understand best practices for QTc prolongation risk mitigation in patients receiving psychiatric medications.

QT prolongation predicts all-cause mortality above and beyond a validated risk score

INTRODUCTION

QT prolongation is a risk factor for life-threatening arrhythmias and sudden cardiac death. In large cohorts, QT interval was associated with all-cause mortality, but these analyses may contain residual confounding. Whether the QT interval provides prognostic information above and beyond a validated mortality risk score is unknown. We hypothesized that QT interval on ECG will independently predict mortality after adjustment for the Care Assessment Needs (CAN) score, which was validated to predict mortality nationwide at the Veterans Administration (VA) (c-index 0.86).

METHODS

Outpatients with an ECG at the Minneapolis VA from 2012 to 2016 were included in this retrospective cohort study. ECGs with ventricular rate < 50 or > 100 beats/min and those with QRS > 120 ms were excluded. QT intervals were corrected (QTc) using the Bazett's formula. CAN score, calculated within 1-week of the ECG, was obtained from the VA Corporate Data Warehouse.

RESULTS

Of the 31,201 patients, 427 (1.4%) had QTc ≥ 500 ms, 1799 (5.8%) had QTc 470-500 ms and 28,975 (92.9%) had QTc < 470 ms. Compared to those with QTc < 470 ms, CAN-adjusted odds ratios (OR) for 1-year mortality (1.76 for QTc 470-500 and 2.70 for QTc > 500 ms; p < 0.0001 for both) and for 5-year mortality (1.75 for QTc 470-500 and 2.48 for QTc > 500 ms; p < 0.0001 for both) were significantly higher in those with longer QTc. C-index for CAN score and QTc predicting 1-year mortality was 0.837.

CONCLUSIONS

QT prolongation predicts all-cause mortality independently of a validated mortality risk prediction score.

Developing a Model for Quantifying QTc-Prolongation Risk to Enhance Medication Safety Assessment: A Retrospective Analysis

There are currently no established methods to predict quantitatively whether the start of a drug with the potential to prolong the QTc interval poses patients at risk for relevant QTc prolongation. Therefore, this retrospective study aimed to pave the way for the development of models for estimating QTc prolongation in patients newly exposed to medications with QTc-prolonging potential. Data of patients with a documented QTc prolongation after initiation of a QTc-prolonging drug were extracted from hospital charts. Using a standard model-building approach, general linear mixed models were identified as the best models for predicting both the extent of QTc prolongation and its absolute value after the start of a QTc-time-prolonging drug. The cohort consisted of 107 adults with a mean age of 64.2 years. Patients were taking an average of 2.4 drugs associated with QTc prolongation, with amiodarone, propofol, pipamperone, ondansetron, and mirtazapine being the most frequently involved. There was a significant but weak correlation between measured and predicted absolute QTc values under medication (r2 = 0.262, p < 0.05), as well as for QTc prolongation (r2 = 0.238, p < 0.05). As the developed models are based on a relatively small number of subjects, further research is necessary to ensure their applicability and reliability in real-world scenarios. Overall, this research contributes to the understanding of QTc prolongation and its association with medications, providing insight into the development of predictive models. With improvements, these models could potentially aid healthcare professionals in assessing the risk of QTc prolongation before adding a new drug and in making informed decisions in clinical settings.

Genetic QT Score and Sleep Apnea as Predictors of Sudden Cardiac Death in the UK Biobank

Introduction

The goal of this study was to evaluate the association between a polygenic risk score (PRS) for QT prolongation (QTc-PRS), QTc intervals and mortality in patients enrolled in the UK Biobank with and without sleep apnea.

Methods

The QTc-PRS was calculated using allele copy number and previously reported effect estimates for each single nuclear polymorphism SNP. Competing-risk regression models adjusting for age, sex, BMI, QT prolonging medication, race, and comorbid cardiovascular conditions were used for sudden cardiac death (SCD) analyses.

Results

500,584 participants were evaluated (56.5 ±8 years, 54% women, 1.4% diagnosed with sleep apnea). A higher QTc-PRS was independently associated with the increased QTc interval duration (p<0.0001). The mean QTc for the top QTc-PRS quintile was 15 msec longer than the bottom quintile (p<0.001). Sleep apnea was found to be an effect modifier in the relationship between QTc-PRS and SCD. The adjusted HR per 5-unit change in QTc-PRS for SCD was 1.64 (95% CI 1.16 - 2.31, p=0.005) among those with sleep apnea and 1.04 (95% CI 0.95 - 1.14, p=0.44) among those without sleep apnea (p for interaction =0.01). Black participants with sleep apnea had significantly elevated adjusted risk of SCD compared to White participants (HR=9.6, 95% CI 1.24 - 74, p=0.03).

Conclusion

In the UK Biobank population, the QTc-PRS was associated with SCD among participants with sleep apnea but not among those without sleep apnea, indicating that sleep apnea is a significant modifier of the genetic risk. Black participants with sleep apnea had a particularly high risk of SCD.

Navigating the pharmacologic complexities of QTc prolongation: assessing the cumulative burden in individuals with serious mental illness

The scientific progress made in the pharmacologic management of serious mental illness (SMI) has been significant. However, the benefits of medication management must continually be weighed against the risks of adverse effects of the agents prescribed. While many medications pose increased danger of QTc prolongation, which may result in malignant arrhythmia and sudden cardiac death, the combination of medications contributing QTc risk can result in an impact of unpredictable pharmacodynamic magnitude. Pharmacists play a key role in communicating QTc risks to prescribers, however little if any clinical guidance informs clinicians of specific actions to take when initiating or continuing a clinically necessary combination that poses risk. This study is a cross-sectional view of Med Safety Scan (MSS) QT prolongation risk scores, generated from the ranking tool available from the CredibleMeds website, intended to provide greater understanding of overall risk of QT burden to assist in the prescribing of medications to patients with SMI in a psychiatric hospital.

Possible Delay in Symptomatology of a Methadone Overdose in a Patient Ingesting an Energy Drink and Dextroamphetamine/Amphetamine

Introduction: Methadone is a mu-opioid agonist with a delayed time to peak concentration that requires an extended period of monitoring following an overdose. Available data suggests that the combination of psychostimulants with methadone may augment antinociception and tolerance to opioids. Case Report: A 26-year-old male (83.9 kg) presented to the ED approximately 1 hour after unintentionally ingesting 200-250 mg of liquid methadone and an energy drink, along with 20 mg of dextroamphetamine/amphetamine prior to the event. Vital signs were: blood pressure (BP), 143/91 mmHg; heart rate (HR), 74; respirations (RR), 16; oxygen saturation 95% on room air. His urine drug screen was positive for amphetamines and methadone. Patient was monitored for 4 hours with no change in status and was discharged home. Approximately 26 hours later patient was found cyanotic and apneic. Patient was given 2 mg of naloxone and awoke with normal mental status. On presentation the patient's vital signs were: BP, 114/70 mmHg; HR, 114; RR 16; oxygen saturation 94% on 3 liters nasal cannula. During his ED stay, he required 2 doses of naloxone secondary to oxygen desaturation and was admitted to the hospital. His repeat urine drug screen was positive for amphetamines and methadone. The patient was discharged the following day. Discussion: This case illustrates an unintentional methadone overdose combined with the intentional ingestion of an energy drink and dextroamphetamine/amphetamine that possibly masked the toxicity for over 24 hrs. Monitoring parameters for methadone overdoses may need re-evaluation in the setting of the co-ingestion of a stimulant.

A QTc risk score in patients with obstructive sleep apnea

INTRODUCTION

Patients with obstructive sleep apnea (OSA) are at risk for QTc prolongation, a known risk factor for increased mortality. The pro-QTc score can help identify individuals at increased risk for mortality associated with increased QTc however, it has not been evaluated in patients with OSA. The goal of this study was to evaluate the pro-QTc score in patients with OSA.

METHODS

Medical records of patients undergoing a sleep study at our sleep center from February 2012 to August 2020 were analyzed. Presence or absence of OSA was determined by polysomnography. The pro-QTc score was calculated with 1 point assigned for each of the following: female sex, QT-prolonging diagnoses and conditions, QT-prolonging electrolyte abnormalities, and medications with known risk for QT-prolongation. Mortality was determined from the electronic medical record of an integrated healthcare system.

RESULTS

There were 2246 patients (age 58 ± 15 years, 54% male, 82 dead) with OSA and 421 patients (age 54 ± 18 years, 43% male, 18 dead) without OSA. Of those with OSA, 1628 (72.5%) had at least one risk factor for QTc prolongation. A higher pro-QTc score was associated with greater mortality in patients with OSA (HR 1.48 per pro-QTc score, p < 0.001, 95% CI 1.3-1.7) but not in patients without OSA (HR 1.25 per pro-QTc score, p = 0.30, 95% CI 0.82-1.9), after adjusting for age, body mass index (BMI), and smoking status.

CONCLUSION

In patients with OSA, a higher pro-QTc score was associated with greater mortality.

Relationship between a risk score for QT interval prolongation and mortality across rural and urban inpatient facilities

OBJECTIVES

To evaluate the relationship between a modified Tisdale QTc-risk score (QTc-RS) and inpatient mortality and length of stay in a broad inpatient population with an order for a medication with a known risk of torsades de pointes (TdP).

BACKGROUND

Managing the risk of TdP is challenging due to the number of medications with known risk of TdP and the complexity of precipitating factors. A model to predict risk of mortality may be useful to guide treatment decisions.

METHODS

This was a retrospective observational study using inpatient data from 28 healthcare facilities in the western United States. This risk score ranges from zero to 23 with weights applied to each risk factor based on a previous validation study. Logistic regression and a generalized linear model were performed to assess the relationship between QTc-RS and mortality and length of stay.

RESULTS

Between April and December 2020, a QTc-RS was calculated for 92,383 hospitalized patients. Common risk factors were female (55.0%); age > 67 years (32.1%); and receiving a medication with known risk of TdP (24.5%). A total of 2770 (3%) patients died during their hospitalization. Relative to patients with QTc-RS < 7, the odds ratio for mortality was 4.80 (95%CI:4.42-5.21) for patients with QTc-RS = 7-10 and 11.51 (95%CI:10.23-12.94) for those with QTc-RS ≥ 11. Length of hospital stay increased by 0.7 day for every unit increase in the risk score (p < 0.0001).

CONCLUSION

There is a strong relationship between increased mortality as well as longer duration of hospitalization with an increasing QTc-RS.

High-Throughput Assessment of Real-World Medication Effects on QT Interval Prolongation: Observational Study

BACKGROUND

Drug-induced prolongation of the corrected QT interval (QTc) increases the risk for Torsades de Pointes (TdP) and sudden cardiac death. Medication effects on the QTc have been studied in controlled settings but may not be well evaluated in real-world settings where medication effects may be modulated by patient demographics and comorbidities as well as the usage of other concomitant medications.

OBJECTIVE

We demonstrate a new, high-throughput method leveraging electronic health records (EHRs) and the Surescripts pharmacy database to monitor real-world QTc-prolonging medication and potential interacting effects from demographics and comorbidities.

METHODS

We included all outpatient electrocardiograms (ECGs) from September 2008 to December 2019 at a large academic medical system, which were in sinus rhythm with a heart rate of 40-100 beats per minute, QRS duration of <120 milliseconds, and QTc of 300-700 milliseconds, determined using the Bazett formula. We used prescription information from the Surescripts pharmacy database and EHR medication lists to classify whether a patient was on a medication during an ECG. Negative control ECGs were obtained from patients not currently on the medication but who had been or would be on that medication within 1 year. We calculated the difference in mean QTc between ECGs of patients who are on and those who are off a medication and made comparisons to known medication TdP risks per the CredibleMeds.org database. Using linear regression analysis, we studied the interaction of patient-level demographics or comorbidities on medication-related QTc prolongation.

RESULTS

We analyzed the effects of 272 medications on 310,335 ECGs from 159,397 individuals. Medications associated with the greatest QTc prolongation were dofetilide (mean QTc difference 21.52, 95% CI 10.58-32.70 milliseconds), mexiletine (mean QTc difference 18.56, 95% CI 7.70-29.27 milliseconds), amiodarone (mean QTc difference 14.96, 95% CI 13.52-16.33 milliseconds), rifaximin (mean QTc difference 14.50, 95% CI 12.12-17.13 milliseconds), and sotalol (mean QTc difference 10.73, 95% CI 7.09-14.37 milliseconds). Several top QT prolonging medications such as rifaximin, lactulose, cinacalcet, and lenalidomide were not previously known but have plausible mechanistic explanations. Significant interactions were observed between demographics or comorbidities and QTc prolongation with many medications, such as coronary disease and amiodarone.

CONCLUSIONS

We demonstrate a new, high-throughput technique for monitoring real-world effects of QTc-prolonging medications from readily accessible clinical data. Using this approach, we confirmed known medications for QTc prolongation and identified potential new associations and demographic or comorbidity interactions that could supplement findings in curated databases. Our single-center results would benefit from additional verification in future multisite studies that incorporate larger numbers of patients and ECGs along with more precise medication adherence and comorbidity data.

Prevalence and risk factors for acquired long QT syndrome in the emergency department: a retrospective observational study

BACKGROUND

Long QT syndrome (LQTS) is a heterogeneous syndrome that may be congenital or, more frequently, acquired. The real-world prevalence of acquired LQTS (aLQTS) in the emergency department (ED) remains to be determined. The aim of this study was to determine prevalence of aLQTS and its impact on symptoms on ED admissions.

METHODS

Electrocardiograms (ECG) of 5,056 consecutively patients admitted in the ED of a tertiary hospital between January 28th and March 17th of 2020 were reviewed. All patients with aLQTS were included. Clinical data with a focus on QT prolonging drugs and clinical factors were recorded. Statistical comparison was made between the groups with and without corrected QT (QTc) interval greater than 500 ms (value that is considered severely increased).

RESULTS

A total of 383 ECGs with prolonged QTc were recognized, corresponding to a prevalence of aLQTS at admission of 7.82%. Patients with aLQTS were more commonly men (53.3%) with an age of (73.49±14.79) years old and QTc interval of (505.3±32.4) ms. Only 20.4% of these patients with aLQTS were symptomatic. No ventricular arrhythmias were recorded. Patients with QT interval greater than 500 ms were more frequently female (59.5%; P<0.001) and were more frequently on QT prolonging drugs (77.3%; P=0.025). Main contributing factor was intake of antibiotics (odds ratio [OR] 4.680) followed by female gender (OR 2.473) and intake of antipsychotics (OR 1.925).

CONCLUSION

aLQTS is particularly prevalent in the ED. Female patients on antibiotics and antipsychotics are at particularly high risk. Efforts must be made to avoid, detect and treat aLQTS as early as possible.

The diagnostic role of T wave morphology biomarkers in congenital and acquired long QT syndrome: A systematic review

INTRODUCTION

QTc prolongation is key in diagnosing long QT syndrome (LQTS), however 25%-50% with congenital LQTS (cLQTS) demonstrate a normal resting QTc. T wave morphology (TWM) can distinguish cLQTS subtypes but its role in acquired LQTS (aLQTS) is unclear.

METHODS

Electronic databases were searched using the terms "LQTS," "long QT syndrome," "QTc prolongation," "prolonged QT," and "T wave," "T wave morphology," "T wave pattern," "T wave biomarkers." Whole text articles assessing TWM, independent of QTc, were included.

RESULTS

Seventeen studies met criteria. TWM measurements included T-wave amplitude, duration, magnitude, Tpeak-Tend, QTpeak, left and right slope, center of gravity (COG), sigmoidal and polynomial classifiers, repolarizing integral, morphology combination score (MCS) and principal component analysis (PCA); and vectorcardiographic biomarkers. cLQTS were distinguished from controls by sigmoidal and polynomial classifiers, MCS, QTpeak, Tpeak-Tend, left slope; and COG x axis. MCS detected aLQTS more significantly than QTc. Flatness, asymmetry and notching, J-Tpeak; and Tpeak-Tend correlated with QTc in aLQTS. Multichannel block in aLQTS was identified by early repolarization (ERD30% ) and late repolarization (LRD30% ), with ERD reflecting hERG-specific blockade. Cardiac events were predicted in cLQTS by T wave flatness, notching, and inversion in leads II and V5 , left slope in lead V6 ; and COG last 25% in lead I. T wave right slope in lead I and T-roundness achieved this in aLQTS.

CONCLUSION

Numerous TWM biomarkers which supplement QTc assessment were identified. Their diagnostic capabilities include differentiation of genotypes, identification of concealed LQTS, differentiating aLQTS from cLQTS; and determining multichannel versus hERG channel blockade.

Moderately Prolonged QTc in Computer-Assessed ECG, Random Variation or Significant Risk Factor? A Literature Review

Most ECGs in European hospitals are recorded with equipment giving computer measured intervals and interpretation of the recording. In addition to measurements of interval and QRS axis, this interpretation frequently provides the Bazett’s-corrected QTc time. The introduction of computer-corrected QTc revealed QTc prolongation to be a frequent condition among medical patients. Nevertheless, the finding is frequently overlooked by the treating physician. The authors combine experience from a local hospital with a review of the current literature in this field in order to elucidate the importance of this risk factor both as congenital long QT syndrome and as acquired QT prolongation.

Practical Approaches to Antipsychotic-Associated Corrected QT Interval Prolongation in Patients With Serious Mental Illness: A Review of Cases

BACKGROUND

There is no consensus for assessment and management of patients with serious mental illness (SMI) who are at risk for cardiac morbidity and mortality due to antipsychotic-associated QTc prolongation.

OBJECTIVE

The objective of this review was to assess methods for risk scoring, QT correction calculation, and clinical management in SMI patients with antipsychotic-associated QTc prolongation.

METHODS

A search was performed in PubMed for case reports that described QTc prolongation in adult patients with schizophrenia or bipolar disorder prescribed an antipsychotic. Reports published in North America between 2000 and 2020 were eligible. The Mayo, Tisdale, and RISQ-PATH scoring tools were applied to cases to categorize risk level.

RESULTS

Seventeen cases were included. Most patients were prescribed a second-generation antipsychotic for schizophrenia, with baseline and maximum QTc values of 429 milliseconds and 545 milliseconds, respectively. The Mayo scoring tool identified 17 (100%) cases as "high risk," Tisdale identified 9 (53%) cases as "moderate risk" and 7 (41%) cases as "low risk," while RISQ-PATH identified 9 (53%) cases as "not low risk" and 8 (47%) cases as "low risk." Three cases reported the QT correction formula utilized (18%). The most common intervention to address antipsychotic-associated QTc prolongation was switching to a different antipsychotic (35%). Approximately one third of patients experienced Torsades de Pointes.

CONCLUSION

There is a lack of standardization for antipsychotic-associated QTc prolongation risk assessment and management in patients with SMI. This review provides real-world data representing actual clinical practice.

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.

QT prolongation in patients with index evaluation for seizure or epilepsy is predictive of all-cause mortality

BACKGROUND

Refractory epilepsy confers a considerable lifetime risk of sudden unexplained death in epilepsy (SUDEP). Mechanisms may overlap with sudden cardiac death (SCD), particularly regarding QTc prolongation. Guidelines in the United States do not mandate the use of electrocardiography (ECG) in diagnostic evaluation of seizures or epilepsy.

OBJECTIVE

The purpose of this study was to determine the frequency of ECG use and of QT prolongation, and whether QT prolongation predicts mortality in patients with seizures.

METHODS

We performed a retrospective cohort study including all patients seen at Mayo Clinic in Rochester, Minnesota, from January 1, 2000, to July 31, 2015, with index evaluation for seizure or epilepsy. Patients with an ECG were categorized by the presence of a prolonged QT interval with a primary endpoint of all-cause mortality after the 15-year observation period.

RESULTS

Optimal cutoff QT intervals most predictive of mortality were identified. Median age was 40.0 years. An ECG was obtained in 18,222 patients (57.4%). After patients with confounding ECG findings were excluded, primary prolonged QT intervals were seen in 223 cases (1.4%), similar to the general population. Kaplan-Meier analysis demonstrated a significant increase in mortality (Cox hazard ratio [HR] 1.90; 95% confidence interval [CI] 1.76-2.05) for prolonged optimal cutoff QT, maintained after adjustments for age, Charlson comorbidity index, and sex (HR 1.48; 95% CI 1.37-1.59).

CONCLUSION

Use of ECG in diagnostic workup of patients with seizures is poor. A prolonged optimal cutoff QTc interval predicts all-cause mortality in patients evaluated for seizure and those diagnosed with epilepsy. We advocate the routine use of a 12-lead ECG at index evaluation in patients with seizure or epilepsy.

Out-of-hospital cardiac arrest and differential risk of cardiac and non-cardiac QT-prolonging drugs in 37 000 cases

Aims

Drugs that prolong the QT interval, either by design (cardiac QT‐prolonging drugs: anti‐arrhythmics) or as off‐target effect (non‐cardiac QT‐prolonging drugs), may increase the risk of ventricular arrhythmias and out‐of‐hospital cardiac arrest (OHCA). Risk mitigation measures were instituted, in particular, surrounding prescription of cardiac QT‐prolonging drugs. We studied OHCA risk of both drug types in current clinical practice.

Methods

Using data from large population‐based OHCA registries in the Netherlands and Denmark, we conducted two independent case–control studies. OHCA cases with presumed cardiac causes were matched on age/sex/index date with up to five non‐OHCA controls. We calculated odds ratios (ORs) for the association of cardiac or non‐cardiac QT‐prolonging drugs with OHCA risk using conditional logistic regression analyses.

Results

We identified 2503 OHCA cases and 10 543 non‐OHCA controls in the Netherlands, and 35 017 OHCA cases and 175 085 non‐OHCA controls in Denmark. Compared to no use of QT‐prolonging drugs, use of non‐cardiac QT‐prolonging drugs (Netherlands: cases: 3.0%, controls: 1.9%; Denmark: cases: 14.9%, controls: 7.5%) was associated with increased OHCA risk (Netherlands: OR 1.37 [95% CI: 1.03–1.81]; Denmark: OR 1.63 [95% CI: 1.57–1.70]). The association between cardiac QT‐prolonging drugs (Netherlands: cases: 4.0%, controls: 2.5%; Denmark: cases: 2.1%, controls: 0.9%) and OHCA was weaker (Netherlands: OR 1.17 [95% CI: 0.92–1.50]; Denmark: OR 1.21 [95% CI: 1.09–1.33]), although users of cardiac QT‐prolonging drugs had more medication use and comorbidities associated with OHCA risk than users of non‐cardiac QT‐prolonging drugs.

Conclusion

In clinical practice, cardiac QT‐prolonging drugs confer lower OHCA risk than non‐cardiac QT‐prolonging drugs, although users of the former have higher a priori risk. This is likely due to risk mitigation measures surrounding prescription of cardiac QT‐prolonging drugs.

QT Interval Monitoring with Handheld Heart Rhythm ECG Device in COVID-19 Patients

Background

QTc prolongation is an adverse effect of COVID-19 therapies. The use of a handheld device in this scenario has not been addressed.

Objectives

To evaluate the feasibility of QTc monitoring with a smart device in COVID-19 patients receiving QTc-interfering therapies.

Methods

Prospective study of consecutive COVID-19 patients treated with hydroxychloroquine ± azithromycin ± lopinavir-ritonavir. ECG monitoring was performed with 12-lead ECG or with KardiaMobile-6L. Both registries were also sequentially obtained in a cohort of healthy patients. We evaluated differences in QTc in COVID-19 patients between three different monitoring strategies: 12-lead ECG at baseline and follow-up (A), 12-lead ECG at baseline and follow-up with the smart device (B), and fully monitored with handheld 6-lead ECG (group C). Time needed to obtain an ECG registry was also documented.

Results

One hundred and eighty-two COVID-19 patients were included (A: 119(65.4%); B: 50(27.5%); C: 13(7.1%). QTc peak during hospitalization did significantly increase in all groups. No differences were observed between the three monitoring strategies in QTc prolongation (p = 0.864). In the control group, all but one ECG registry with the smart device allowed QTc measurement and mean QTc did not differ between both techniques (p = 0.612), displaying a moderate reliability (ICC 0.56 [0.19-0.76]). Time of ECG registry was significantly longer for the 12-lead ECG than for handheld device in both cohorts (p < 0.001).

Conclusion

QTc monitoring with KardiaMobile-6L in COVID-19 patients was feasible. Time of ECG registration was significantly lower with the smart device, which may offer an important advantage for prevention of virus dissemination among healthcare providers.

Development of a Patient-Specific p.D85N-Potassium Voltage-Gated Channel Subfamily E Member 1-Induced Pluripotent Stem Cell-Derived Cardiomyocyte Model for Drug-Induced Long QT Syndrome

BACKGROUND

Prior epidemiological studies demonstrated that the p.D85N-Potassium voltage-gated channel subfamily E member 1 (KCNE1) common variant reduces repolarization reserve and predisposes to drug-induced QT prolongation/torsades de pointes. We sought to develop a cellular model for drug-induced long QT syndrome using a patient-specific induced pluripotent stem cell-derived cardiomyocyte (iPSC-CM).

METHODS

p.D85N-KCNE1 iPSCs were generated from a 23-year-old female with an exaggerated heart rate-corrected QT interval response to metoclopramide (ΔQTc of 160 ms). Clustered regularly interspaced short palindromic repeats-associated 9 technology was used to generate gene-corrected isogenic iPSCs. Field potential duration and action potential duration (APD) were measured from iPSC-CMs.

RESULTS

At baseline, p.D85N-KCNE1 iPSC-CMs displayed significantly longer field potential duration (281±15 ms, n=13 versus 223±8.6 ms, n=14, P<0.01) and action potential duration at 90% repolarization (APD90; 579±22 ms, n=24 versus 465±33 ms, n=26, P<0.01) than isogenic-control iPSC-CMs. Dofetilide at a concentration of 2 nM increased significantly field potential duration (379±20 ms, n=13, P<0.01) and APD90 (666±11 ms, n=46, P<0.01) in p.D85N-KCNE1 iPSC-CMs but not in isogenic-control. The effect of dofetilide on APD90 (616±54 ms, n=7 versus 526±54 ms, n=10, P<0.05) was confirmed by Patch-clamp. Interestingly, treatment of p.D85N-KCNE1 iPSC-CMs with estrogen at a concentration of 1 nM exaggerated further dofetilide-induced APD90 prolongation (696±9 ms, n=81, P<0.01) and caused more early afterdepolarizations (11.7%) compared with isogenic control (APD90: 618±8 ms, n=115 and early afterdepolarizations: 2.6%, P<0.05).

CONCLUSIONS

This iPSC-CM study provides further evidence that the p.D85N-KCNE1 common variant in combination with environmental factors such as QT prolonging drugs and female sex is proarrhythmic.

Use of Artificial Intelligence and Deep Neural Networks in Evaluation of Patients With Electrocardiographically Concealed Long QT Syndrome From the Surface 12-Lead Electrocardiogram

Importance

Long QT syndrome (LQTS) is characterized by prolongation of the QT interval and is associated with an increased risk of sudden cardiac death. However, although QT interval prolongation is the hallmark feature of LQTS, approximately 40% of patients with genetically confirmed LQTS have a normal corrected QT (QTc) at rest. Distinguishing patients with LQTS from those with a normal QTc is important to correctly diagnose disease, implement simple LQTS preventive measures, and initiate prophylactic therapy if necessary.

Objective

To determine whether artificial intelligence (AI) using deep neural networks is better than the QTc alone in distinguishing patients with concealed LQTS from those with a normal QTc using a 12-lead electrocardiogram (ECG).

Design, Setting, and Participants

A diagnostic case-control study was performed using all available 12-lead ECGs from 2059 patients presenting to a specialized genetic heart rhythm clinic. Patients were included if they had a definitive clinical and/or genetic diagnosis of type 1, 2, or 3 LQTS (LQT1, 2, or 3) or were seen because of an initial suspicion for LQTS but were discharged without this diagnosis. A multilayer convolutional neural network was used to classify patients based on a 10-second, 12-lead ECG, AI-enhanced ECG (AI-ECG). The convolutional neural network was trained using 60% of the patients, validated in 10% of the patients, and tested on the remaining patients (30%). The study was conducted from January 1, 1999, to December 31, 2018.

Main Outcomes and Measures

The goal of the study was to test the ability of the convolutional neural network to distinguish patients with LQTS from those who were evaluated for LQTS but discharged without this diagnosis, especially among patients with genetically confirmed LQTS but a normal QTc value at rest (referred to as genotype positive/phenotype negative LQTS, normal QT interval LQTS, or concealed LQTS).

Results

Of the 2059 patients included, 1180 were men (57%); mean (SD) age at first ECG was 21.6 (15.6) years. All 12-lead ECGs from 967 patients with LQTS and 1092 who were evaluated for LQTS but discharged without this diagnosis were included for AI-ECG analysis. Based on the ECG-derived QTc alone, patients were classified with an area under the curve (AUC) value of 0.824 (95% CI, 0.79-0.858); using AI-ECG, the AUC was 0.900 (95% CI, 0.876-0.925). Furthermore, in the subset of patients who had a normal resting QTc (<450 milliseconds), the QTc alone distinguished those with LQTS from those without LQTS with an AUC of 0.741 (95% CI, 0.689-0.794), whereas the AI-ECG increased this discrimination to an AUC of 0.863 (95% CI, 0.824-0.903). In addition, the AI-ECG was able to distinguish the 3 main genotypic subgroups (LQT1, LQT2, and LQT3) with an AUC of 0.921 (95% CI, 0.890-0.951) for LQT1 compared with LQT2 and 3, 0.944 (95% CI, 0.918-0.970) for LQT2 compared with LQT1 and 3, and 0.863 (95% CI, 0.792-0.934) for LQT3 compared with LQT1 and 2.

Conclusions and Relevance

In this study, the AI-ECG was found to distinguish patients with electrocardiographically concealed LQTS from those discharged without a diagnosis of LQTS and provide a nearly 80% accurate pregenetic test anticipation of LQTS genotype status. This model may aid in the detection of LQTS in patients presenting to an arrhythmia clinic and, with validation, may be the stepping stone to similar tools to be developed for use in the general population.

Assessing Prescriber Behavior with a Clinical Decision Support Tool to Prevent Drug-Induced Long QT Syndrome

OBJECTIVE

Clinical decision support (CDS) alerts built into the electronic health record (EHR) have the potential to reduce the risk of drug-induced long QT syndrome (diLQTS) in susceptible patients. However, the degree to which providers incorporate this information into prescription behavior and the impact on patient outcomes is often unknown.

METHODS

We examined provider response data over a period from October 8, 2016 until November 8, 2018 for a CDS alert deployed within the EHR from a 13-hospital integrated health care system that fires when a patient with a QTc ≥ 500 ms within the past 14 days is prescribed a known QT-prolonging medication. We used multivariate generalized estimating equations to analyze the impact of therapeutic alternatives, relative risk of diLQTS for specific medications, and patient characteristics on provider response to the CDS and overall patient mortality.

RESULTS

The CDS alert fired 15,002 times for 7,510 patients for which the most common response (51.0%) was to override the alert and order the culprit medication. In multivariate models, we found that patient age, relative risk of diLQTS, and presence of alternative agents were significant predictors of adherence to the CDS alerts and that nonadherence itself was a predictor of mortality. Risk of diLQTS and presence of an alternative agent are major factors in provider adherence to a CDS to prevent diLQTS; however, provider nonadherence was associated with a decreased risk of mortality.

CONCLUSION

Surrogate endpoints, such as provider adherence, can be useful measures of CDS value but attention to hard outcomes, such as mortality, is likely needed.

Prediction of Drug-Induced Long QT Syndrome Using Machine Learning Applied to Harmonized Electronic Health Record Data

BACKGROUND

Drug-induced QT prolongation is a potentially preventable cause of morbidity and mortality, however there are no widespread clinical tools utilized to predict which individuals are at greatest risk. Machine learning (ML) algorithms may provide a method for identifying these individuals, and could be automated to directly alert providers in real time.

OBJECTIVE

This study applies ML techniques to electronic health record (EHR) data to identify an integrated risk-prediction model that can be deployed to predict risk of drug-induced QT prolongation.

METHODS

We examined harmonized data from the UCHealth EHR and identified inpatients who had received a medication known to prolong the QT interval. Using a binary outcome of the development of a QTc interval >500 ms within 24 hours of medication initiation or no ECG with a QTc interval >500 ms, we compared multiple machine learning methods by classification accuracy and performed calibration and rescaling of the final model.

RESULTS

We identified 35,639 inpatients who received a known QT-prolonging medication and an ECG performed within 24 hours of administration. Of those, 4,558 patients developed a QTc > 500 ms and 31,081 patients did not. A deep neural network with random oversampling of controls was found to provide superior classification accuracy (F1 score 0.404; AUC 0.71) for the development of a long QT interval compared with other methods. The optimal cutpoint for prediction was determined and was reasonably accurate (sensitivity 71%; specificity 73%).

CONCLUSIONS

We found that deep neural networks applied to EHR data provide reasonable prediction of which individuals are most susceptible to drug-induced QT prolongation. Future studies are needed to validate this model in novel EHRs and within the physician order entry system to assess the ability to improve patient safety.

Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device

BACKGROUND

Heart rate-corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome, and/or systemic diseases including SARS-CoV-2-mediated coronavirus disease 2019 (COVID-19), can predispose to ventricular arrhythmias and sudden cardiac death. Currently, QTc assessment and monitoring relies largely on 12-lead electrocardiography. As such, we sought to train and validate an artificial intelligence (AI)-enabled 12-lead ECG algorithm to determine the QTc, and then prospectively test this algorithm on tracings acquired from a mobile ECG (mECG) device in a population enriched for repolarization abnormalities.

METHODS

Using >1.6 million 12-lead ECGs from 538 200 patients, a deep neural network (DNN) was derived (patients for training, n = 250 767; patients for testing, n = 107 920) and validated (n = 179 513 patients) to predict the QTc using cardiologist-overread QTc values as the "gold standard". The ability of this DNN to detect clinically-relevant QTc prolongation (eg, QTc ≥500 ms) was then tested prospectively on 686 patients with genetic heart disease (50% with long QT syndrome) with QTc values obtained from both a 12-lead ECG and a prototype mECG device equivalent to the commercially-available AliveCor KardiaMobile 6L.

RESULTS

In the validation sample, strong agreement was observed between human over-read and DNN-predicted QTc values (-1.76±23.14 ms). Similarly, within the prospective, genetic heart disease-enriched dataset, the difference between DNN-predicted QTc values derived from mECG tracings and those annotated from 12-lead ECGs by a QT expert (-0.45±24.73 ms) and a commercial core ECG laboratory [10.52±25.64 ms] was nominal. When applied to mECG tracings, the DNN's ability to detect a QTc value ≥500 ms yielded an area under the curve, sensitivity, and specificity of 0.97, 80.0%, and 94.4%, respectively.

CONCLUSIONS

Using smartphone-enabled electrodes, an AI DNN can predict accurately the QTc of a standard 12-lead ECG. QTc estimation from an AI-enabled mECG device may provide a cost-effective means of screening for both acquired and congenital long QT syndrome in a variety of clinical settings where standard 12-lead electrocardiography is not accessible or cost-effective.

Validation of Corrected and Dispersed QT as Predictors of Adverse Outcomes in Acute Cardiotoxicities

Acute cardiovascular poisoning is a major cause of adverse outcomes in poisoning emergencies. The prognostic validity of corrected QT (QTc) and dispersed QT (QTd) in these outcomes is still limited. The present study aimed to determine the risk factors of mortality, adverse cardiovascular events (ACVE), and intensive care unit (ICU) admission in patients with acute cardiovascular toxicities and assess the validity of QTc and QTd intervals in predicting these outcomes. This study was conducted on adult patients admitted to Tanta University Poison Control Center with a history of acute cardiotoxic drugs or toxins exposure. The demographic and toxicological data of patients were recorded. Clinical examination, routine laboratory investigations, ECG grading, and measurement of QTc and QTd were performed. The patients were grouped according to their adverse outcomes. Among the included patients, 51 (31.48%) patients died, 61 (37.65%) patients had ACVE, and 68 (41.98%) patients required ICU admission. The most common cause of poisoning is aluminum phosphide, followed by cholinesterase inhibitors. QTd and QTdc showed no significant difference among outcome groups. The best cut-off values of QTc to predict mortality, ACVE, and ICU admission were > 491.1 ms, > 497.9 ms, and ≥ 491.9 ms, respectively. The derived cut-off QTc values were independent predictors for all adverse outcomes after adjusting for poison type, serum HCO3, and pulse. The highest odds ratios for all adverse outcomes were observed in aluminum phosphide poisoning and low HCO3 < 18 mmol/L. Thus, serum HCO3 and QTc interval should be monitored for acute cardiotoxicities, especially in aluminum phosphide and cholinesterase inhibitors poisoning.

Risk of QTc prolongation among cancer patients treated with tyrosine kinase inhibitors

QTc interval prolongation can lead to life-threatening complications such as Torsade de Pointes (TdP), ventricular tachycardia (VT) and sudden cardiac death (SCD). It can occur with tyrosine kinase inhibitors (TKIs) but comparative real-world analyses on the incidence and complication rates are scarce. We retrospectively reviewed all cancer patients treated with TKI therapy at Mayo Clinic between January 2005 and December 2018 and had at least two ECGs (before and after TKI). For each TKI type, we determined the administration rate and incidence of QTc prolongation. QTc prolongation was defined as a corrected QT interval (by Fridericia formula) ≥450 ms in men and ≥470 ms in women. A total of 618 cancer patients were included with 902 TKI administrations, of which 654 (72.5%) were accounted for by pazopanib, sunitinib, imatinib, nilotinib and dasatinib. QTc prolongation (any grade) was reported in 28.8%, most commonly with nilotinib (38.7%) and dasatinib (41.7%). A QTc interval ≥500 ms and a QTc increase ≥60 ms was documented in 46 and 63 administrations, respectively. Life-threatening toxicity was seen in 14 cases (5.4% of QTc prolongation cases) including VT in 9, SCD in 3 and TdP in two administrations. The response to QTc prolongation was: discontinuation in 68%, dose reduction in 13.5%, temporary hold in 8.1% and no action in 10.4%. In conclusion, QTc prolongation with TKI therapy is very common (∼1/3 of cases) and in 5% (1.7% overall) associated with life-threatening complications. These data support recommendations for careful ECG monitoring in cancer patients undergoing TKI therapy.

Effect of hydroxychloroquine, azithromycin and lopinavir/ritonavir on the QT corrected interval in patients with COVID-19

Background

Administration of Hydroxychloroquine and Azithromycin in patients with coronavirus disease 2019 (COVID-19) prolongs QTc corrected interval (QTc). The effect and safety of Lopinavir/Ritonavir in combination with these therapies have seldom been studied.

Objectives

Our aim was to evaluate changes in QTc in patients receiving double (Hydroxychloroquine + Azithromycin) and triple therapy (Hydroxychloroquine + Azithromycin + Lopinavir/Ritonavir) to treat COVID-19. Secondary outcome was the incidence of in-hospital all-cause mortality.

Methods

Patients under treatment with double (DT) and triple therapy (TT) for COVID-19 were consecutively included in this prospective observational study. Serial in-hospital electrocardiograms were performed to measure QTc at baseline and during therapy.

Results

168 patients (±66.2 years old) were included: 32.1% received DT and 67.9% received TT. The mean baseline QTc was 410.33 ms. Patients under DT and TT prolonged QTc interval respect baseline values (p < 0.001), without significant differences between both therapy groups (p = 0.748). Overall, 33 patients (19.6%) had a peak QTc and/or an increase QTc 60 ms from baseline, with a higher prevalence among those with hypokalemia (p = 0.003). All-cause mortality was similar between both strategy groups (p = 0.093) and high risk QTc prolongation was no related to clinical events in this series.

Conclusions

DT and TT prolong the QTc in patients with COVID-19. Addition of Lopinavir/Ritonavir on top of Hydroxychloroquine and Azithromycin did not increase QTc compared to DT.

Impact of a clinical decision support tool targeting QT-prolonging medications

PURPOSE

To evaluate the impact of a newly implemented clinical decision support (CDS) tool targeting QT interval-prolonging medications on order verification and provider interventions.

METHODS

A multicenter, retrospective quasi-experimental study was conducted to evaluate provider response to CDS alerts triggered during ordering of QT-prolonging medications for adult patients. The primary outcome was the proportion of orders triggering QTc alerts that were continued without intervention during a specified preimplementation phase (n = 49) and during a postimplementation phase (n = 100). Patient risk factors for QTc prolongation, provider alert response, and interventions to reduce the risk of QTc-associated adverse events were evaluated.

RESULTS

The rate of order continuation without intervention was 82% in the preimplementation phase and 37% in the postimplementation phase, representing an 55% reduction in continued verified orders following implementation of the QT-focused CDS tool. Most alerts were initially responded to by the prescriber, with pharmacist intervention needed in only 33% of cases. There were no significant differences in patient QTc-related risk factors between the 2 study groups (P = 0.11); the postimplementation group had a higher proportion of patients using at least 2 QTc-prolonging medications (48%, compared to 26% in the preimplementation group; P = 0.02).

CONCLUSION

Implementation of the CDS tool was associated with a reduction in the proportion of orders continued without intervention in patients at high risk for QTc-related adverse events.

Assisted prescribing: Clinical decision support with MedSafety Scan now available

Too often, adverse events due to prescription medications are a cause of death and disability. Many of these events could be prevented, but most efforts to do so have had limited success, mainly due to the challenges of having the information that is necessary for safe prescribing available at the time when prescriptions are being written. Hospital-based Clinical Decision Support (CDS) systems are being developed to manage this information, identify at- risk patients, and help mitigate their risk of medication-induced harm. AZCERT, a non-profit created in 1999 with federal funding has helped hospitals develop these systems and has released an internet-based CDS program to assist in the safe prescribing of medications. This CDS program, MedSafety Scan, can be customized for any clinical venue and is available as an open-source program for all healthcare providers at www.medsafetyscan.org.

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.

Cardiac Toxicity of Chloroquine or Hydroxychloroquine in Patients With COVID-19: A Systematic Review and Meta-regression Analysis

Objective

To systematically review the literature and to estimate the risk of chloroquine (CQ) and hydroxychloroquine (HCQ) cardiac toxicity in patients with coronavirus disease 2019 (COVID-19).

Methods

We searched multiple data sources including PubMed/MEDLINE, Ovid Embase, Ovid EBM Reviews, Scopus, and Web of Science and medrxiv.org from November 2019 through May 27, 2020. We included studies that enrolled patients with COVID-19 treated with CQ or HCQ, with or without azithromycin, and reported on cardiac toxic effects. We performed a meta-analysis using the arcsine transformation of the different incidences.

Results

A total of 19 studies with a total of 5652 patients were included. The pooled incidence of torsades de pointes arrhythmia, ventricular tachycardia, or cardiac arrest was 3 per 1000 (95% CI, 0-21; I ² =96%) in 18 studies with 3725 patients. Among 13 studies of 4334 patients, the pooled incidence of discontinuation of CQ or HCQ due to prolonged QTc or arrhythmias was 5% (95% CI, 1-11; I ² =98%). The pooled incidence of change in QTc from baseline of 60 milliseconds or more or QTc of 500 milliseconds or more was 9% (95% CI, 3-17; I ² =97%). Mean or median age, coronary artery disease, hypertension, diabetes, concomitant QT-prolonging medications, intensive care unit admission, and severity of illness in the study populations explained between-studies heterogeneity.

Conclusion

Treatment of patients with COVID-19 with CQ or HCQ is associated with an important risk of drug-induced QT prolongation and relatively higher incidence of torsades de pointes, ventricular tachycardia, or cardiac arrest. Therefore, these agents should not be used routinely in the management of COVID-19 disease. Patients with COVID-19 who are treated with antimalarials for other indications should be adequately monitored.

QT prolongation in a diverse, urban population of COVID-19 patients treated with hydroxychloroquine, chloroquine, or azithromycin

PURPOSE

Hydroxychloroquine, chloroquine, and azithromycin have been used for treatment of COVID-19, but may cause QT prolongation. Minority populations are disproportionately impacted by COVID-19. This study evaluates the risk of QT prolongation and subsequent outcomes after administration of these medications in largely underrepresented minority COVID-19 patients.

METHODS

We conducted an observational study on hospitalized COVID-19 patients in the Montefiore Health System (Bronx, NY). We examined electrocardiograms (ECG) pre/post-medication initiation to evaluate QTc, HR, QRS duration, and presence of other arrhythmias.

RESULTS

One hundred five patients (mean age 67 years; 44.8% F) were analyzed. The median time from the first dose of any treatment to post-medication ECG was 2 days (IQR: 1-3). QTc in men increased from baseline (440 vs 455 ms, p < 0.001), as well as in women (438 vs 463 ms, p < 0.001). The proportion of patients with QT prolongation increased significantly (14.3% vs 34.3%, p < 0.001) even when adjusted for electrolyte abnormalities. The number of patients whose QTc > 500 ms was significantly increased after treatment (16.2% vs. 4.8%, p < 0.01). Patients with either QTc > 500 ms or an increase of 60 ms had a higher frequency of death (47.6% vs. 22.6%, p = 0.02) with an odds ratio of 3.1 (95% CI: 1.1-8.7). Adjusting for race/ethnicity yielded no significant associations.

CONCLUSIONS

Hydroxychloroquine, chloroquine, and/or azithromycin were associated with QTc prolongation but did not result in fatal arrhythmias. Our findings suggest that any harm is unlikely to outweigh potential benefits of treatment. Careful risk-benefit analyses for individual patients should guide the use of these medications. Randomized control trials are necessary to evaluate their efficacies.

It is time to drop hydroxychloroquine from our COVID-19 armamentarium

Chloroquine (CQ) and hydroxychloroquine (HCQ) were among the first drugs repurposed for the treatment of SARS-CoV-2 infection. A few in vitro studies confirmed that both drugs exhibited dose dependent anti-SARS-CoV-2 activities. These observations and the encouraging results from early poorly conducted observational studies created a major hype about the therapeutic potential of these drugs in the treatment of COVID-19 disease. This was further catalyzed by media and political influences leading to a widespread use of these agents. Subsequent randomized trials revealed lack of efficacy of these agents in improving the outcomes of COVID-19 or in preventing infection in post-exposure prophylaxis studies. Nevertheless, many ongoing trials continue to actively recruit tens of thousands of patients to receive HCQ worldwide. In this perspective, we address the possible mechanisms behind the lack of efficacy and the increased risk of cardiac toxicity of HCQ in COVID-19 disease. For the lack of efficacy, we discuss the fundamental differences of treatment initiation between in vitro and in vivo studies, the pitfalls of the pharmacological calculations of effective blood drug concentrations and related dosing regimens, and the possible negative effect of HCQ on the antiviral type-I interferon response. Although it has been repeatedly claimed that HCQ has a longstanding safety track record for many decades in use, we present counterarguments for this contention due to disease-drug and drug-drug interactions. We discuss the molecular mechanisms and the cumulative epidemiological evidence of HCQ cardiac toxicity.

Adverse Drug Events in Patients with Chronic Kidney Disease Associated with Multiple Drug Interactions and Polypharmacy

BACKGROUND AND PURPOSE

Chronic kidney disease (CKD) is associated with adverse drug events due to medication errors and the risks of polypharmacy. The aim of this study was to investigate whether multiple pharmacodynamic interactions are a significant problem in CKD patients to improve medication safety.

METHODS

The discharge medication of 200 elderly patients with stage 3, 4 and 5/5D CKD was analysed in a retrospective observational study with respect to kidney-related medication errors and multiple pharmacodynamic interactions. The clinical relevance of the most common and hazardous multiple interactions was assessed by evaluating adverse events at the primary or the subsequent hospital stay.

RESULTS

Findings showed that 29.5% of the study cohort were at risk of QTc-interval prolongation in association with their medication combinations and half of them exhibited QTc-interval prolongation. The QTc interval was extended among all patients receiving a combination of two or more drugs with 'known' risk of Torsades de pointes. Amiodarone, citalopram and ciprofloxacin turned out to be the most hazardous drugs in this context. Eight percent of the patient population received a regimen of 4-6 potassium-enhancing drugs during their hospital stay, which was not de-escalated in 75.0% in the ambulatory setting. Despite close monitoring in the clinical setting, 37.5% of these patients developed hyperkalaemic episodes during their primary stay and 66.7% during rehospitalization. Of the study cohort, 8.5% received a combination of three drugs with antithrombotic or antiplatelet effects. Of these, 64.7% developed haemorrhagic events with two of them proving fatal.

CONCLUSION

Multiple pharmacodynamic interactions related to QTc prolongation, hyperkalaemia and haemorrhage are frequently associated with a negative outcome in older adults with CKD and often require recurrent medical treatment or rehospitalization.

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.

Drug-Induced Arrhythmias: A Scientific Statement From the American Heart Association

Many widely used medications may cause or exacerbate a variety of arrhythmias. Numerous antiarrhythmic agents, antimicrobial drugs, psychotropic medications, and methadone, as well as a growing list of drugs from other therapeutic classes (neurological drugs, anticancer agents, and many others), can prolong the QT interval and provoke torsades de pointes. Perhaps less familiar to clinicians is the fact that drugs can also trigger other arrhythmias, including bradyarrhythmias, atrial fibrillation/atrial flutter, atrial tachycardia, atrioventricular nodal reentrant tachycardia, monomorphic ventricular tachycardia, and Brugada syndrome. Some drug-induced arrhythmias (bradyarrhythmias, atrial tachycardia, atrioventricular node reentrant tachycardia) are significant predominantly because of their symptoms; others (monomorphic ventricular tachycardia, Brugada syndrome, torsades de pointes) may result in serious consequences, including sudden cardiac death. Mechanisms of arrhythmias are well known for some medications but, in other instances, remain poorly understood. For some drug-induced arrhythmias, particularly torsades de pointes, risk factors are well defined. Modification of risk factors, when possible, is important for prevention and risk reduction. In patients with nonmodifiable risk factors who require a potentially arrhythmia-inducing drug, enhanced electrocardiographic and other monitoring strategies may be beneficial for early detection and treatment. Management of drug-induced arrhythmias includes discontinuation of the offending medication and following treatment guidelines for the specific arrhythmia. In overdose situations, targeted detoxification strategies may be needed. Awareness of drugs that may cause arrhythmias and knowledge of distinct arrhythmias that may be drug-induced are essential for clinicians. Consideration of the possibility that a patient's arrythmia could be drug-induced is important.

Saudi Heart Rhythm Society Task Force on Management of Potential Arrhythmogenicity Associated with Pharmacotherapy for COVID-19

Evidence of cardiovascular complications associated with the COVID-19 global pandemic continues to evolve. These include direct and indirect myocardial injury with subsequent acute myocardial ischemia, and cardiac arrhythmia. Some results from a limited number of trials of antiviral medications, along with chloroquine/hydroxychloroquine and azithromycin, have been beneficial. However, these pharmacotherapies may cause drug-induced QT prolongation leading to ventricular arrhythmias and sudden cardiac death. Mitigation of the potential risk in these susceptible patients may prove exceptionally challenging. The Saudi Heart Rhythm Society established a task force to perform a review of this subject based on has recently published reports, and studies and recommendations from major medical organizations. The objective of this review is to identify high-risk patients, and to set clear guidelines for management of patients receiving these pharmacotherapies.

Acute QT Interval Modifications During Hydroxychloroquine-Azithromycin Treatment in the Context of COVID-19 Infection

Among candidate drugs to treat coronavirus disease 2019 (COVID-19), the combination of hydroxychloroquine (HCQ) and azithromycin (AZ) has received intense worldwide attention. Even as the efficacy of this combination is under evaluation, clinicians have begun to use it largely. As these medications are known to prolong the QT interval, we analyzed serial electrocardiograms recorded in patients hospitalized for COVID-19 pneumonia and treated with HCQ + AZ. Fifty consecutive patients received the combination of HCQ (600 mg/d for 10 days) and AZ (500 mg/d on day 1 and 250 mg/d from day 2 to day 5). Twelve-lead electrocardiograms were recorded before treatment, at day 3, at day 5, and at discharge. The median age of patients was 68 years (interquartile range, 53-81 years); 28 (56%) were men. The main comorbidities were hypertension (36%; n=18) and diabetes (16%; n=8). The mean corrected QT (QTc) interval was 408 ms at baseline and increased up to 437 ms at day 3 and to 456 ms at day 5. Thirty-eight patients (76%) presented short-term modifications of the QTc duration (>30 ms). Treatment discontinuation was decided in 6 patients (12%), leading to QTc normalization in 5 of them. No deaths and no cardiac arrhythmic events were observed in this cohort. Our report confirms that a short duration treatment with HCQ + AZ modifies the QTc interval. The treatment had to be discontinued for QTc modifications in 12% of patients. Nevertheless, in inpatients hospitalized for COVID-19, we did not observe any clinically relevant consequences of these transitory modifications. In conclusion, when patients are treated with HCQ + AZ, cardiac monitoring should be regularly performed and hospital settings allow monitoring under in safe conditions.

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

BACKGROUND

The exact risk of developing QTc-prolongation when using a combination of QTc-prolonging drugs is still unknown, making it difficult to interpret these QT drug-drug interactions (QT-DDIs). A tool to identify high-risk patients is needed to support healthcare providers in handling automatically generated alerts in clinical practice. The main aim of this study was to develop and validate a tool to assess the risk of QT-DDIs in clinical practice.

METHODS

A model was developed based on risk factors associated with QTc-prolongation determined in a prospective study on QT-DDIs in a university medical center inthe Netherlands. The main outcome measure was QTc-prolongation defined as a QTc interval > 450 ms for males and > 470 ms for females. Risk points were assigned to risk factors based on their odds ratios. Additional risk factors were added based on a literature review. The ability of the model to predict QTc-prolongation was validated in an independent dataset obtained from a general teaching hospital against QTc-prolongation as measured by an ECG as the gold standard. Sensitivities, specificities, false omission rates, accuracy and Youden's index were calculated.

RESULTS

The model included age, gender, cardiac comorbidities, hypertension, diabetes mellitus, renal function, potassium levels, loop diuretics, and QTc-prolonging drugs as risk factors. Application of the model to the independent dataset resulted in an area under the ROC-curve of 0.54 (95% CI 0.51-0.56) when QTc-prolongation was defined as > 450/470 ms, and 0.59 (0.54-0.63) when QTc-prolongation was defined as > 500 ms. A cut-off value of 6 led to a sensitivity of 76.6 and 83.9% and a specificity of 28.5 and 27.5% respectively.

CONCLUSIONS

A clinical decision support tool with fair performance characteristics was developed. Optimization of this tool may aid in assessing the risk associated with QT-DDIs.

TRIAL REGISTRATION

No trial registration, MEC-2015-368.

Cardiovascular Complications of COVID-19: Pharmacotherapy Perspective

Coronavirus disease of 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is spreading rapidly the world over. The disease was declared “pandemic” by the World Health Organization. An approved therapy for patients with COVID-19 has yet to emerge; however, there are some medications used in the treatment of SARS-CoV-2 infection globally including hydroxychloroquine, remdesivir, dexamethasone, protease inhibitors, and anti-inflammatory agents. Patients with underlying cardiovascular disease are at increased risk of mortality and morbidity from COVID-19. Moreover, patients with chronic stable states and even otherwise healthy individuals might sustain acute cardiovascular problems due to COVID-19 infection. This article seeks to review the latest evidence with a view to explaining possible pharmacotherapies for the cardiovascular complications of COVID-19 including acute coronary syndrome, heart failure, myocarditis, arrhythmias, and venous thromboembolism, as well as possible interactions between these medications and those currently administered (or under evaluation) in the treatment of COVID-19.