Prevalence and Risk Factors for QTc Prolongation in Acute Psychiatric Hospitalization
Bibliographic record
Abstract
Objective: Prolongation of corrected QT (QTc) interval increases the risk of severe ventricular arrhythmias, in particular torsades de pointes. Patients with severe mental illness (SMI) represent a vulnerable population. This study aimed to measure the prevalence of QTc prolongation in inpatients with SMI and to identify risk factors for QTc prolongation. Methods: Demographic, clinical, anthropometric, laboratory, and electrocardiographic information was extracted from the electronic records of a cohort of patients hospitalized in a psychiatry inpatient unit between July 1, 2017, and July 22, 2019. The primary outcome was the estimation of prevalence of QTc prolongation. The secondary outcome was the identification of risk factors for QTc prolongation. Results: A total of 597 admissions were included. Only 1.4% had a QTc > 500 msec, while 11.6% had a QTc > 460 msec. The proportion of women with a QTc > 470 msec was 3.6% and men with a QTc > 450 msec was 7.3%. Several risk factors were individually associated with QTc prolongation. In the multivariate model explaining almost one-third of QTc variance, female sex (P = .04), older age (P = .011), heart rate (P < .001), systolic blood pressure (P = .042), potassium (P = .012), hemoglobin (P = .006), number of antipsychotics (P = .026), and treatment with clotiapine (P = .012) and clozapine (P = .003) were associated with QTc length. Several factors beyond pharmacologic treatment identify subjects at risk for QTc prolongation, and polypharmacotherapy does not seem to increase the risk of QTc prolongation. Conclusions: QTc prolongation was rare in this cohort of SMI inpatients. Most of the risk factors involved in QTc prolongation are unchangeable elements or linked to general medical conditions, and only a few are modifiable factors, including psychotropic 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".