ArticleJournal of cardiovascular pharmacology and therapeutics2021
Prediction of Drug-Induced Long QT Syndrome Using Machine Learning Applied to Harmonized Electronic Health Record Data.
Article in Journal of cardiovascular pharmacology and therapeutics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis.Frontiers in pharmacology · 2024Pooled it
- Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature.Pharmaceutical medicine · 2022Pooled it
- Artificial Intelligence for Precision Antiarrhythmic Drug Therapy in Atrial Fibrillation: From Recurrence Prediction to Comparative Treatment Selection.Medicina (Kaunas, Lithuania) · 2026Review
- Using Artificial Intelligence to Identify Patterns and Predictors of Adverse Drug Reactions in Cardiovascular Patients: A Comprehensive Narrative Review.American journal of cardiovascular drugs : drugs, devices, and other interventions · 2026Review
- Clinical impact of a multidisciplinary remote-based hybrid antibiotic stewardship program in critically ill patients during COVID-19 pandemic in Korea: a prospective pilot implementation study.Antimicrobial resistance and infection control · 2026Article
- Drug-Induced QT Prolongation: Associations Between Risk Classifications in a Swedish Clinical Decision Support System and Clinical Outcomes.Clinical pharmacology and therapeutics · 2026Article
- Risk prediction of QTc prolongation occurrence in cancer patients treated with commonly used oral tyrosine kinase inhibitors: machine learning modeling or conventional statistical analysis better?BMC medical informatics and decision making · 2025Article
- Towards a prescribing monitoring system for medication safety evaluation within electronic health records: a scoping review.BMC medical informatics and decision making · 2025Article
- Impact of an Alert-Based Inpatient Clinical Decision Support Tool to Prevent Drug-Induced Long QT Syndrome: Large-Scale, System-Wide Observational Study.Journal of medical Internet research · 2025Observational
- Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review.Current cardiology reports · 2025Review
- A Calculated Risk: Evaluation of QTc Drug-Drug Interaction (DDI) Clinical Decision Support (CDS) Alerts and Performance of the Tisdale Risk Score Calculator.Drug safety · 2024Article
- Determining sensitivity and specificity of risk scores for QTc interval prolongation in hemato-oncology patients prescribed systemic antifungal therapy: a retrospective cross-sectional study.International journal of clinical pharmacy · 2024Article
- Artificial Intelligence in Diagnosis of Long QT Syndrome: A Review of Current State, Challenges, and Future Perspectives.Mayo Clinic proceedings. Digital health · 2024Review
- A polygenic risk score for the QT interval is an independent predictor of drug-induced QT prolongation.PloS one · 2024Article
- Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline.Reviews in cardiovascular medicine · 2023Review
- A machine learning evaluation of patient characteristics associated with prescribing of guideline-directed medical therapy for heart failure.Frontiers in cardiovascular medicine · 2023Article
- Interpretable Machine Learning Prediction of Drug-Induced QT Prolongation: Electronic Health Record Analysis.Journal of medical Internet research · 2022Article
- Intelligent Telehealth in Pharmacovigilance: A Future Perspective.Drug safety · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundDrug-induced QT prolongation is a potentially preventable cause of morbidity and mortality, however there are no widespread clinical tools utilized to predict which individuals are at greatest risk. Machine learning (ML) algorithms may provide a method for identifying these individuals, and could be automated to directly alert providers in real time.
objectiveThis study applies ML techniques to electronic health record (EHR) data to identify an integrated risk-prediction model that can be deployed to predict risk of drug-induced QT prolongation.
methodsWe examined harmonized data from the UCHealth EHR and identified inpatients who had received a medication known to prolong the QT interval. Using a binary outcome of the development of a QTc interval >500 ms within 24 hours of medication initiation or no ECG with a QTc interval >500 ms, we compared multiple machine learning methods by classification accuracy and performed calibration and rescaling of the final model.
resultsWe identified 35,639 inpatients who received a known QT-prolonging medication and an ECG performed within 24 hours of administration. Of those, 4,558 patients developed a QTc > 500 ms and 31,081 patients did not. A deep neural network with random oversampling of controls was found to provide superior classification accuracy (F1 score 0.404; AUC 0.71) for the development of a long QT interval compared with other methods. The optimal cutpoint for prediction was determined and was reasonably accurate (sensitivity 71%; specificity 73%).
conclusionsWe found that deep neural networks applied to EHR data provide reasonable prediction of which individuals are most susceptible to drug-induced QT prolongation. Future studies are needed to validate this model in novel EHRs and within the physician order entry system to assess the ability to improve patient safety.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.