Dataset Information

QT variability during initial exposure to sotalol: experience based on a large electronic medical record.

ABSTRACT: AIMS:A prolonged QT interval is associated with increased risk of Torsades de pointes (TdP) and may be fatal. We sought to investigate the extent to which clinical covariates affect the change in QT interval among 'real-world' patients treated with sotalol and followed in an electronic medical record (EMR) system. METHODS AND RESULTS:We used clinical alerts in our EMR system to identify all patients in whom a new prescription for sotalol was written (2001-11). Rate-corrected QT (QTc) was calculated by Bazett's formula. Correlates of sotalol-induced change in the QTc interval and sotalol discontinuation were examined using linear and logistic regression, respectively. Overall, 541 sotalol-exposed patients were identified (n = 200 women, 37%). The mean first sotalol dose was 86 ± 39 mg, age 64 ± 13 years, and BMI 30 ± 7 kg/m(2). Atrial fibrillation/flutter was the predominant indication (92.2%). After initial exposure, the change in the QTc interval from baseline was highly variable: ?QTc after 2 h = 3 ± 42 ms (P = 0.17) and 11 ± 37 ms after ?48 h (P < 0.001). Multivariable linear regression analysis identified female gender and age, reduced left ventricular ejection fraction, high sotalol dose, hypertrophic cardiomyopathy, and loop diuretic co-administration as correlates of increased ?QTc at ?48 h (P < 0.05 for all). Within 3 days of initiation, 12% discontinued sotalol of which 31% were because of exaggerated QTc prolongation. One percent developed TdP. CONCLUSION:In this EMR-based cohort, the increase in QTc with sotalol initiation was highly variable, and multiple clinical factors contributed. These data represent an important step in ongoing work to identify real-world patients likely to tolerate long-term therapy and reinforces the utility of EMR-based cohorts as research tools.

SUBMITTER: Weeke P

PROVIDER: S-EPMC3888125 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

QT variability during initial exposure to sotalol: experience based on a large electronic medical record.

Weeke Peter P Delaney Jessica J Mosley Jonathan D JD Wells Quinn Q Van Driest Sara S Norris Kris K Kucera Gayle G Stubblefield Tanya T Roden Dan M DM

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology 20130619 12

<h4>Aims</h4>A prolonged QT interval is associated with increased risk of Torsades de pointes (TdP) and may be fatal. We sought to investigate the extent to which clinical covariates affect the change in QT interval among 'real-world' patients treated with sotalol and followed in an electronic medical record (EMR) system.<h4>Methods and results</h4>We used clinical alerts in our EMR system to identify all patients in whom a new prescription for sotalol was written (2001-11). Rate-corrected QT (Q ...[more]

PMID: 23787903

Similar Datasets

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.

Project description:BackgroundElectronic medical records (EMRs) have been used for nearly three decades. Pharmacists use EMRs on a daily basis, but EMRs have only recently been incorporated into pharmacy education. Some pharmacy programs have implemented teaching electronic medical records (tEMRs), but best practices for incorporating tEMRs into pharmacy education remain unknown. The objectives of this study were to 1) assess pharmacy students' views and experiences with a tEMR; and 2) identify current learning activities and future priorities for tEMR use in pharmacy education.MethodsWe used a mixed-methods approach, including three, two-hour student focus groups and a 42-item web-based survey to examine student perspectives of the tEMR. All first, second, and third year professional pharmacy students were eligible to participate in the survey and a focus group. Web-based survey items were measured on a 7-point Likert scale, and quantitative analyses included descriptive statistics. Two researchers independently coded transcripts using both deductive and inductive approaches to identify emergent themes. These analysts met and resolved any coding discrepancies via consensus.ResultsFocus groups were conducted with 22 total students, with 6-8 students represented from each year of pharmacy training. The survey was completed by 156 students: 47 first year, 55 second year, and 54 third year. Overall, 48.7% of survey respondents altogether agreed or strongly agreed that using the tEMR enhanced their learning in pharmacy classes and laboratories. Qualitative data were organized into four major themes regarding tEMR adoption: current priorities for use within the pharmacy curriculum; tEMR benefits; tEMR barriers; and future priorities for tEMR use to prepare students for pharmacy practice.ConclusionsThis study reveals pharmacy students' perspectives and attitudes towards using a tEMR, the types of classroom activities that incorporate the tEMR, and students' future suggestions to enhance the design or application of the tEMR for their learning. Our research findings may aid other pharmacy programs and promote more effective use of tEMRs in pharmacy education. In the long-term, this study may strengthen pharmacy education on EMRs and thus increase the efficacy and safety of pharmacists' EMR use for patients' medication management.

Dataset Information

QT variability during initial exposure to sotalol: experience based on a large electronic medical record.

Publications

QT variability during initial exposure to sotalol: experience based on a large electronic medical record.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets