Acquired Long QT Interval: A Case Series of Multifactorial QT Prolongation
Bibliographic record
Abstract
BACKGROUND: Acquired long QT (LQT) interval is thought to be a consequence of drug therapy and electrolyte disturbances. HYPOTHESIS: We characterize the potential effects of polypharmacy in a case series of acquired LQT and torsades de pointes (TdP) in order to determine whether multiple risk factors play a role in the development of LQT. METHODS: The case series consisted of 11 patients presenting to 4 tertiary care hospitals with LQT and ≥ 2 risk factors for developing LQT. Clinical characteristics, medications, electrolyte disturbances, and course in hospital were analyzed. RESULTS: Mean age was 49.1 ± 5.8 years. Eight patients were female. Four had hypertension, 1 had a history of dilated cardiomyopathy, and 1 patient demonstrated complete atrioventricular block. Average QTc interval at presentation was 633.8 ± 29.2 ms. Nine patients developed TdP. In 3, LQT was not initially detected and amiodarone was administered, followed by development of TdP. Patients were taking an average of 2.8 ± 0.3 QT-prolonging medications-an antidepressant in 6 cases and a diuretic in 8 cases. All patients had an electrolyte abnormality; 8 patients presented with severe hypokalemia (<3.0 mmol/L). Average serum potassium and magnesium were 2.82 ± 0.10 mmol/L and 0.75 ± 0.03 mmol/L, respectively. There were no deaths. CONCLUSIONS: This case series highlights the risks of polypharmacy in the development of LQT and TdP. It illustrates the importance of early detection of LQT in patients with multiple risk factors in ensuring appropriate treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".