Title |
Automated Decision Support for Drug-Induced Long QT.
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Published in |
South Dakota Medicine, March 2020
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Pubmed ID | |
Authors |
Roxana A Lupu, Heidi Twedt, Sreekanth Chavour, Eric A Larson |
Abstract |
Advancements in clinical informatics and translational genomics are changing the way we practice medicine. Automated decision support currently helps providers adjust prescribing patterns to reduce the likelihood of QT prolongation based upon drug-drug interaction. A similar approach is being explored for drug-gene interaction. Like many adverse drug reactions, QT prolongation can be influenced by variability in genetic factors. However, drug-induced QT prolongation can occur in the absence of any known ion channel gene abnormalities. We therefore review differences between congenital long QT syndrome and drug-induced long QT syndrome, and we underscore the need for decision support that integrates EKG data. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 33% |
Unknown | 2 | 67% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 67% |
Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 4 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Bachelor | 2 | 50% |
Student > Doctoral Student | 1 | 25% |
Unknown | 1 | 25% |
Readers by discipline | Count | As % |
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Nursing and Health Professions | 1 | 25% |
Business, Management and Accounting | 1 | 25% |
Computer Science | 1 | 25% |
Unknown | 1 | 25% |