Dataset Information

Torsade de pointes: A nested case-control study in an integrated healthcare delivery system.

ABSTRACT:

Background

TdP is a form of polymorphic ventricular tachycardia which develops in the setting of a prolonged QT interval. There are limited data describing risk factors, treatment, and outcomes of this potentially fatal arrhythmia.

Objective

Our goals were as follows: (1) to validate cases presenting with Torsade de Pointes (TdP), (2) to identify modifiable risk factors, and (3) to describe the management strategies used for TdP and its prognosis in a real-world healthcare setting.

Methods

Case-control study (with 2:1 matching on age, sex, and race/ethnicity) nested within the Genetic Epidemiology Research on Aging (GERA) cohort. Follow-up of the cohort for case ascertainment was between January 01, 2005 and December 31, 2018.

Results

A total of 56 cases of TdP were confirmed (incidence rate = 3.6 per 100,000 persons/years). The average (SD) age of the TdP cases was 74 (13) years, 55 percent were female, and 16 percent were non-white. The independent predictors of TdP were potassium concentration <3.6 mEq/L (OR = 10.6), prior history of atrial fibrillation/flutter (OR = 6.2), QTc >480 ms (OR = 4.4) and prior history of coronary artery disease (OR = 2.6). Exposure to furosemide and amiodarone was significantly greater in cases than in controls. The most common treatment for TdP was IV magnesium (78.6%) and IV potassium repletion (73.2%). The in-hospital and 1-year mortality rates for TdP cases were 10.7% and 25.0% percent, respectively.

Conclusions

These findings may inform quantitative multivariate risk indices for the prediction of TdP and could guide practitioners on which patients may qualify for continuous ECG monitoring and/or electrolyte replacement therapy.

SUBMITTER: Mantri N

PROVIDER: S-EPMC8739596 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Blockage of some ion channels and in particular, the hERG (human Ether-a'-go-go-Related Gene) cardiac potassium channel delays cardiac repolarization and can induce arrhythmia. In some cases it leads to a potentially life-threatening arrhythmia known as Torsade de Pointes (TdP). Therefore recognizing drugs with TdP risk is essential. Candidate drugs that are determined not to cause cardiac ion channel blockage are more likely to pass successfully through clinical phases II and III trials (and preclinical work) and not be withdrawn even later from the marketplace due to cardiotoxic effects. The objective of the present study is to develop an SAR (Structure-Activity Relationship) model that can be used as an early screen for torsadogenic (causing TdP arrhythmias) potential in drug candidates. The method is performed using descriptors comprised of atomic NMR chemical shifts (13C and 15N NMR) and corresponding interatomic distances which are combined into a 3D abstract space matrix. The method is called 3D-SDAR (3-dimensional spectral data-activity relationship) and can be interrogated to identify molecular features responsible for the activity, which can in turn yield simplified hERG toxicophores. A dataset of 55 hERG potassium channel inhibitors collected from Kramer et al. consisting of 32 drugs with TdP risk and 23 with no TdP risk was used for training the 3D-SDAR model. RESULTS:An artificial neural network (ANN) with multilayer perceptron was used to define collinearities among the independent 3D-SDAR features. A composite model from 200 random iterations with 25% of the molecules in each case yielded the following figures of merit: training, 99.2%; internal test sets, 66.7%; external (blind validation) test set, 68.4%. In the external test set, 70.3% of positive TdP drugs were correctly predicted. Moreover, toxicophores were generated from TdP drugs. CONCLUSION:A 3D-SDAR was successfully used to build a predictive model for drug-induced torsadogenic and non-torsadogenic drugs based on 55 compounds. The model was tested in 38 external drugs.