Serious arrhythmia in initiators of citalopram, escitalopram, and other selective serotonin reuptake inhibitors: A population‐based cohort study in older adults

Aakjær, Mia; Werther, Sarah Kimmer; Bruin, Marie L De; Andersen, Morten

doi:10.1111/cts.13319

Cited by 7 publications

(16 citation statements)

References 36 publications

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“…This finding points to the multifactorial nature of diLQTS, highlighting the need to consider other relevant contextual factors in assessing risk. However, it may also suggest that in the inpatient setting, there might be more benefit than risk with using these medications, which is also consistent with prior studies [9][10][11][12], including one where a clinical decision support tool to prevent diLQTS had a paradoxical decrease in mortality for patients in whom the treating provider ignored the alert and prescribed the known QT-prolonging medication despite risk [4]. Particularly in subjects who were not critically ill (not in cluster 0) and without a history of cardiovascular disease (not in cluster 1), there appeared to be more benefit to using ondansetron, balanced against more risk with using propofol.…”

Section: Principal Findingssupporting

confidence: 84%

“…Drug-induced long-QT syndrome (diLQTS) [1,2] is a major concern for inpatients worldwide and has been identified as a key target for clinical decision support tools [3][4][5][6][7]. Importantly, although certain medications have been implicated as having significant clinical risk [8,9], for others, despite a known risk of diLQTS, clinical validation has been lacking [10][11][12]. In the past few years, several groups have sought to apply prediction models using electronic health record (EHR) data to model risk [13][14][15][16][17] toward the goal of developing an automated approach that leverages innovations in data science and machine learning.…”

Section: Introductionmentioning

confidence: 99%

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Interpretable Machine Learning Prediction of Drug-Induced QT Prolongation: Electronic Health Record Analysis

Simon¹,

Trinkley²,

Malone³

et al. 2022

J Med Internet Res

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Background Drug-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. Objective In 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. Methods We 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. Results Among 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. Conclusions In 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.

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Section: Principal Findingssupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning Prediction of Drug-Induced QT Prolongation: Electronic Health Record Analysis

Simon¹,

Trinkley²,

Malone³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

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“…We retrieved 3133 unique records, of which 200 were included in the review (Figure 1). All reasons for excluding records after full-text review are given in eAppendix 6 in Supplement 1.…”

Section: Resultsmentioning

confidence: 99%

“… The included studies spanned 26 fields of medicine, predominately infectious disease (43 [22%]; 27 [14%] on COVID-19), cardiology (30 [15%]), and oncology (30 [15%]) . One hundred and thirty-one studies (66%)…”

Section: Resultsmentioning

confidence: 99%

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Reporting of Observational Studies Explicitly Aiming to Emulate Randomized Trials

Hansford,

Cashin,

Jones

et al. 2023

JAMA Netw Open

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ImportanceObservational (nonexperimental) studies that aim to emulate a randomized trial (ie, the target trial) are increasingly informing medical and policy decision-making, but it is unclear how these studies are reported in the literature. Consistent reporting is essential for quality appraisal, evidence synthesis, and translation of evidence to policy and practice.ObjectiveTo assess the reporting of observational studies that explicitly aimed to emulate a target trial.Evidence ReviewWe searched Medline, Embase, PsycINFO, and Web of Science for observational studies published between March 2012 and October 2022 that explicitly aimed to emulate a target trial of a health or medical intervention. Two reviewers double-screened and -extracted data on study characteristics, key predefined components of the target trial protocol and its emulation (eligibility criteria, treatment strategies, treatment assignment, outcome[s], follow-up, causal contrast[s], and analysis plan), and other items related to the target trial emulation.FindingsA total of 200 studies that explicitly aimed to emulate a target trial were included. These studies included 26 subfields of medicine, and 168 (84%) were published from January 2020 to October 2022. The aim to emulate a target trial was explicit in 70 study titles (35%). Forty-three studies (22%) reported use of a published reporting guideline (eg, Strengthening the Reporting of Observational Studies in Epidemiology). Eighty-five studies (43%) did not describe all key items of how the target trial was emulated and 113 (57%) did not describe the protocol of the target trial and its emulation.Conclusion and RelevanceIn this systematic review of 200 studies that explicitly aimed to emulate a target trial, reporting of how the target trial was emulated was inconsistent. A reporting guideline for studies explicitly aiming to emulate a target trial may improve the reporting of the target trial protocols and other aspects of these emulation attempts.

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Promising new pharmacological targets for depression: The search for efficacy

Tsugiyama,

Moraes,

Moraes

et al. 2023

Drug Discovery Today

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Serious arrhythmia in initiators of citalopram, escitalopram, and other selective serotonin reuptake inhibitors: A population‐based cohort study in older adults

Cited by 7 publications

References 36 publications

Interpretable Machine Learning Prediction of Drug-Induced QT Prolongation: Electronic Health Record Analysis

Interpretable Machine Learning Prediction of Drug-Induced QT Prolongation: Electronic Health Record Analysis

Reporting of Observational Studies Explicitly Aiming to Emulate Randomized Trials

Promising new pharmacological targets for depression: The search for efficacy

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