Utility of QT interval corrected by Rautaharju method to predict drug-induced torsade de pointes

QTc interval measurement in patients with right bundle branch block: A practical method

BACKGROUND AND AIM

Prolonging the QT interval in the right bundle branch block (RBBB) can create challenges for electrophysiologists in estimating repolarization time and eliminating the effect of depolarization changes on QT interval. In this study, we aimed to develop a practice formula to eliminate the effect of depolarization changes on QT interval in patients with RBBB.

METHODS

This prospective study evaluated accidentally induced RBBB in patients undergoing electrophysiological study. Two expert electrophysiologists recorded the ECG parameters, including QRS duration, QT interval, and cycle length, in the patients. The formula was developed based on QT interval differences (with and without RBBB) and its proportion to QRS. Additionally, the Bazzet, Rautaharju, and Hodge formulas were used to evaluate QTc.

RESULTS

We evaluated 96 patients in this study. The mean QT interval without RBBB was 369.39 ± 37.38, reaching 404.22 ± 39.23 after inducing RBBB. ΔQT was calculated as 34.83 ± 17.61, and the ratio of ΔQT/QRS with RBBB was almost 23%. Our formula is: (QT_{with RBBB}  - 23% × QRS). Subtraction of 25% instead of 23% seems more straightforward and practical. Our formula could also predict the QTc interval in RBBB based on the Bazzet, Rautaharju, and Hodge formulas.

CONCLUSION

Previous formulas for QT correction were hard to apply in the clinical setting or were not specified for RBBB. Our new formula allows a rapid and practical method for QT correction in RBBB in clinical practice.

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.

Machine read frontal QRS-T angle and QTc is no substitute for manual measurement of QTc in pro-arrhythmic drug overdose

INTRODUCTION

To investigate whether there is an association between the blocking of cardiac potassium channels, which is characterised by a prolonged QTc interval and the frontal QRS-T angle after overdose by QT prolonging drugs.

METHODS

We obtained patient medical records associated with QT prolonging drugs from 3 different hospitals: the Calvary Mater Newcastle Hospital (CMNH), Royal Prince Alfred Hospital (RPAH) and Prince of Wales Hospital (POWH). RPAH and POWH admissions were taken between 4/01/2017 to 1/11/2019, and CMNH admissions were taken between 4/01/2013 to 24/06/2018. Demographic information and details of overdose were collected. All admission ECGs were manually measured. Linear regression was used to assess the relationship between various QTc formulas and the frontal QRS-T angle. A Bland-Altman plot was used to examine agreement between manual and machine QT intervals.

RESULTS

144 patients met the inclusion criteria for analysis. None of the patients developed torsades de pointes (TdP). There was no linear association between the QRS-T angle and the various QTc formulas (For QRS-T angle: QTcRTH: p = 0.76, QTcB: p = 0.83, QTcFri: p = 0.90, QTcFra: p = 0.13, QTcH: p = 0.97; For square root transformation of the QRS-T angle: QTcRTH: p = 0.18, QTcB: p = 0.33, QTcFri: p = 0.95, QTcFra: p = 0.47, QTcH: p = 0.33). Agreement between machine and manual QT measurements was low.

CONCLUSIONS

The frontal QRS-T angle cannot substitute the QTc in assessing the blockage of cardiac potassium channels in drug induced long QT syndrome. We also support the consensus that despite the availability of machine measurements of the QT interval, manual measurements should also be performed.

A Retrospective Analysis of Hospital Electrocardiogram Auto-Populated QT Interval Calculation

Background

The current electrocardiogram (ECG) standard for rate correction of the QT interval (QTc) is a power function known as the Bazett formula (QTcB). QTc formulae are either power functions or linear functions. QTcB is known to lack reliability, as heart rate (HR) rises from or falls below 60 beats per minute (bpm). The American Heart Association (AHA), the American College of Cardiology Foundation (ACCF), and the Heart Rhythm Society (HRS) have recommended using other formulae in place of QTcB since 2009. The Epic Electronic Health Record System (Epic Systems Corporation, Verona, WI) automatically populates the Fridericia formula (QTcFri) on hospital ECG reports without any provider calculation.

Methods

We aimed to retrospectively investigate the effect of QTcFri on one year of ECGs in the Epic Electronic Health Record (EHR) at a single tertiary care center. Inclusion criteria for ECG reports specified HR 60-120 bpm without QRS duration > 120 ms. Gathered data from Epic EHR ECG reports included patient age, sex, HR, QRS duration (QRSd), QT interval, QTcB, and QTcFri. EHR documented 61,946 ECG reports for the year, with 44,566 meeting criteria for inclusion. General statistical methods included range, median, mean, and standard deviation. Confidence intervals were assessed to maintain the fidelity of analysis. The normality of data distribution was assessed with Kolmogorov-Smirnov testing. The Wilcoxon rank-sum test was then performed to confirm a statistically significant difference between the Bazett and Fridericia formulae. The ∆QTc analysis was conducted on prolonged QTc (males > 450 ms; females > 460 ms) and severely prolonged QTc > 500 ms data subsets. A value of p<0.05 was interpreted as significant. Statistical analysis was performed using SPSS statistical software (IBM Statistics, v. 26; IBM Corp, Armonk, NY).

Results

The 44,566 ECG reports demonstrated 57% female gender and a mean age of 57 ± 17.5 years. The mean HR was 83 ± 14.7 bpm and the mean ∆QTc was 23 ± 12.9 ms shorter with QTcFri. Mean data showed minimal variation between sexes: age, heart rate, uncorrected QT, QTcB, QTcFri, and ∆QTc varied by less than 2%. Mean QRS varied by 4% between sexes. The Wilcoxon rank-sum test revealed 44,127 ranks with a negative difference, 0 ranks with a positive difference, and 439 ties, p <0.001 (99% CI: 22.5 ms, 23.0 ms). QTcB identified 37.4% (16665/44566) ECGs prolonged. Using QTcFri, 21% (9371/44566) of the total ECGs corrected to normal QTc (<450 ms (men) and 460 ms (women)). QTcFri use reduced the number of ECG reports with QTc > 500 ms by 57.3%. A total of 125 ECG reports, 117 females and eight males, corrected to normal gender-specific QTc with QTcFri. The mean decrease in QTc with the Fridericia formula when QTcB > 500 ms was 31 ± 14.5 ms (99% CI: 30.4 ms, 31.7 ms).

Conclusion

Our data from the Wilcoxon rank-sum analysis indicated that the EHR QTcFri analysis yields a statistically significant difference (p < 0.001) in QTc calculation of 22 ms over 44,566 ECG reports. The data showed a 21% reduction in inaccurately documented test results. The utilization of this resource will provide the most accurate and clinically relevant data to inform clinical decision-making. Accurate QT interval calculation will better inform downstream clinical decision-making through a wider scope of therapeutic intervention. This analysis is readily available to clinicians without calculation and its awareness will benefit patient care.

Drug-specific risk of severe QT prolongation following acute drug overdose

Background: Severe QT prolongation (SQTP) has been identified as a strong predictor of adverse cardiovascular events in acute drug overdose, but drug-specific causes of SQTP in the setting of acute drug overdose remain unclear. We aimed to perform the most definitive study to date describing drug-specific risk of SQTP following acute drug overdose.Methods: This was a prospective multicenter cohort study at >50 hospital sites across the US using the ToxIC Registry between 2015 and 2018. Inclusion criteria were adults (≥18 years) receiving medical toxicology consultation for acute drug overdose. The primary outcome was SQTP, which was defined using the computer automated Bazett QT correction (QTc) on the ECG with the previously validated cut point of 500 milliseconds. Mean difference in QTc was also calculated for specific drugs. Drugs associated with SQTP were analyzed using multivariable logistic regression to control for known confounders of QT risk (age, sex, race, cardiac disease).Results: From 25,303 patients screened, 6473 met inclusion criteria with SQTP occurring in 825 (13%). Drugs associated with increased adjusted odds of SQTP included Class III antidysrhythmics (sotalol), sodium channel blockers (amitriptyline, diphenhydramine, doxepin, imipramine, nortriptyline), antidepressants (bupropion, citalopram, escitalopram, trazodone), antipsychotics (haloperidol, quetiapine), and the antiemetic serotonin antagonist ondansetron.Conclusions: This large US cohort describes drug-specific risk of SQTP following acute drug overdose. Healthcare providers caring for acute drug overdoses from any of these implicated drugs should pay close attention to cardiac monitoring for occurrence of SQTP.