Dataset Information

Delayed QT Prolongation: Derivation of a Novel Risk Factor for Adverse Cardiovascular Events from Acute Drug Overdose.

ABSTRACT:

Introduction

In ED patients with acute drug overdose involving prescription medication and/or substances of abuse, severe QTc prolongation (> 500 ms) is predictive of adverse cardiovascular events (ACVE), defined as myocardial injury, ventricular dysrhythmia, shock, or cardiac arrest. However, it is unclear whether delayed severe QTc prolongation (dsQTp) is a risk factor for ACVE and if specific clinical factors are associated with occurrence of dsQTp.

Methods

A secondary analysis of a prospective cohort of consecutive adult ED patients with acute drug overdose was performed on patients with initial QTc < 500 ms. The predictor variable, dsQTp, was defined as initial QTc < 500 ms followed by repeat QTc ≥ 500 ms. The primary outcome was occurrence of ACVE. Multivariable logistic regression was performed to test whether dsQTp was an independent predictor of ACVE and to derive clinical factors associated with dsQTp.

Results

Of 2311 patients screened, 1648 patients were included. The dsQTp group (N = 27) was older than the control group (N = 1621) (51.6 vs 40.2, p < 0.001) and had a higher number of drug exposures (2.92 vs 2.16, p = 0.003). Following adjustment for age, sex, race/ethnicity, number of exposures, serum potassium, and opioid exposure, dsQTp remained an independent predictor of ACVE (aOR: 12.44, p < 0.0001). Clinical factors associated with dsQTp were age > 45 years and polydrug (≥ 3) overdoses.

Conclusion

In this large secondary analysis of ED patients with acute drug overdose, dsQTp was an independent risk factor for in-hospital occurrence of ACVE.

SUBMITTER: Shastry S

PROVIDER: S-EPMC8455785 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Publications

Delayed QT Prolongation: Derivation of a Novel Risk Factor for Adverse Cardiovascular Events from Acute Drug Overdose.

Shastry Siri S Aluise Eleanor R ER Richardson Lynne D LD Vedanthan Rajesh R Manini Alex F AF

Journal of medical toxicology : official journal of the American College of Medical Toxicology 20210827 4

<h4>Introduction</h4>In ED patients with acute drug overdose involving prescription medication and/or substances of abuse, severe QTc prolongation (> 500 ms) is predictive of adverse cardiovascular events (ACVE), defined as myocardial injury, ventricular dysrhythmia, shock, or cardiac arrest. However, it is unclear whether delayed severe QTc prolongation (dsQTp) is a risk factor for ACVE and if specific clinical factors are associated with occurrence of dsQTp.<h4>Methods</h4>A secondary analysis ...[more]

PMID: 34449039

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Project description:Context: Risk factors for adverse cardiovascular events (ACVE) from drug exposures have been well-characterized in adults but not studied in children. The objective of the present study is to describe the incidence, characteristics, and risk factors for in-hospital ACVEs among pediatric emergency department (ED) patients with acute drug exposures.Methods: This is a prospective cohort design evaluating patients in the Toxicology Investigators Consortium (ToxIC) Registry. Pediatric patients (age <18 years) who were evaluated at the bedside by a medical toxicologist for a suspected acute drug exposure were included. The primary outcome was in-hospital ACVE (myocardial injury, shock, ventricular dysrhythmia, or cardiac arrest). The secondary outcome was in-hospital death. Multiple logistic regression analyses were performed to examine novel clinical risk factors and extrapolate adult risk factors (bicarbonate <20?mEq/L; QTc ?500?ms), for the primary/secondary outcomes.Results: Among the 13,097 patients (58.5% female), there were 278 in-hospital ACVEs (2.1%) and 39 in-hospital deaths (0.3%). Age and drug class of exposure (specifically opioids and cardiovascular drugs) were independently associated with ACVE. Compared with adolescents, children under 2 years old (OR: 0.41, 95% CI: 0.21-0.80), ages 2-6 (OR: 0.37, 95% CI: 0.21-0.80), and ages 7-12 (OR: 0.51, 95% CI: 0.27-0.95) were significantly less likely to experience an ACVE. Serum bicarbonate concentration <20?mEq/L (OR: 2.31, 95% CI: 1.48-3.60) and QTc ? 500?ms (OR: 2.83, 95% CI: 1.67-4.79) were independently associated with ACVE.Conclusion: Previously derived clinical predictors of ACVE from an adult drug overdose population were successfully extrapolated to this pediatric population. Novel associations with ACVE and death included adolescent age and opioid drug exposures. In the midst of the opioid crisis, these findings urgently warrant further investigation to combat adolescent opioid overdose morbidity and mortality.

Project description:BACKGROUND:QT interval-prolonging drug-drug interactions (QT-DDIs) may increase the risk of life-threatening arrhythmia. Despite guidelines for testing from regulatory agencies, these interactions are usually discovered after drugs are marketed and may go undiscovered for years. OBJECTIVES:Using a combination of adverse event reports, electronic health records (EHR), and laboratory experiments, the goal of this study was to develop a data-driven pipeline for discovering QT-DDIs. METHODS:1.8 million adverse event reports were mined for signals indicating a QT-DDI. Using 1.6 million electrocardiogram results from 380,000 patients in our institutional EHR, these putative interactions were either refuted or corroborated. In the laboratory, we used patch-clamp electrophysiology to measure the human ether-à-go-go-related gene (hERG) channel block (the primary mechanism by which drugs prolong the QT interval) to evaluate our top candidate. RESULTS:Both direct and indirect signals in the adverse event reports provided evidence that the combination of ceftriaxone (a cephalosporin antibiotic) and lansoprazole (a proton-pump inhibitor) will prolong the QT interval. In the EHR, we found that patients taking both ceftriaxone and lansoprazole had significantly longer QTc intervals (up to 12 ms in white men) and were 1.4 times more likely to have a QTc interval above 500 ms. In the laboratory, we found that, in combination and at clinically relevant concentrations, these drugs blocked the hERG channel. As a negative control, we evaluated the combination of lansoprazole and cefuroxime (another cephalosporin), which lacked evidence of an interaction in the adverse event reports. We found no significant effect of this pair in either the EHR or in the electrophysiology experiments. Class effect analyses suggested this interaction was specific to lansoprazole combined with ceftriaxone but not with other cephalosporins. CONCLUSIONS:Coupling data mining and laboratory experiments is an efficient method for identifying QT-DDIs. Combination therapy of ceftriaxone and lansoprazole is associated with increased risk of acquired long QT syndrome.

Project description:BackgroundDrug-induced long-QT syndrome (diLQTS) is a major concern among patients who are hospitalized, for whom prediction models capable of identifying individualized risk could be useful to guide monitoring. We have previously demonstrated the feasibility of machine learning to predict the risk of diLQTS, in which deep learning models provided superior accuracy for risk prediction, although these models were limited by a lack of interpretability.ObjectiveIn this investigation, we sought to examine the potential trade-off between interpretability and predictive accuracy with the use of more complex models to identify patients at risk for diLQTS. We planned to compare a deep learning algorithm to predict diLQTS with a more interpretable algorithm based on cluster analysis that would allow medication- and subpopulation-specific evaluation of risk.MethodsWe examined the risk of diLQTS among 35,639 inpatients treated between 2003 and 2018 with at least 1 of 39 medications associated with risk of diLQTS and who had an electrocardiogram in the system performed within 24 hours of medication administration. Predictors included over 22,000 diagnoses and medications at the time of medication administration, with cases of diLQTS defined as a corrected QT interval over 500 milliseconds after treatment with a culprit medication. The interpretable model was developed using cluster analysis (K=4 clusters), and risk was assessed for specific medications and classes of medications. The deep learning model was created using all predictors within a 6-layer neural network, based on previously identified hyperparameters.ResultsAmong the medications, we found that class III antiarrhythmic medications were associated with increased risk across all clusters, and that in patients who are noncritically ill without cardiovascular disease, propofol was associated with increased risk, whereas ondansetron was associated with decreased risk. Compared with deep learning, the interpretable approach was less accurate (area under the receiver operating characteristic curve: 0.65 vs 0.78), with comparable calibration.ConclusionsIn summary, we found that an interpretable modeling approach was less accurate, but more clinically applicable, than deep learning for the prediction of diLQTS. Future investigations should consider this trade-off in the development of methods for clinical prediction.

Dataset Information

Delayed QT Prolongation: Derivation of a Novel Risk Factor for Adverse Cardiovascular Events from Acute Drug Overdose.

Introduction

Methods

Results

Conclusion

Publications

Delayed QT Prolongation: Derivation of a Novel Risk Factor for Adverse Cardiovascular Events from Acute Drug Overdose.

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