Sudden Death of Cardiac Origin and Psychotropic Drugs
Bibliographic record
Abstract
Mortality rate is high in psychiatric patients versus general population. An important cause of this increased mortality is sudden cardiac death (SCD) as a major side-effect of psychotropic drugs. These SCDs generally result from arrhythmias occurring when the posology is high and may attain a toxic threshold but also at dosages within therapeutic range, in the presence of risk factors. There are three kinds of risk factors: physiological (e.g., low cardiac rate of sportsmen), physiopathological (e.g., hepatic insufficiency, hypothyroidism) and "therapeutic" (due to interactions between psychotropic drugs and other medicines). Association of pharmacological agents may increase the likelihood of SCDs either by (i) a pharmacokinetic mechanism (e.g., increased torsadogenic potential of a psychotropic drug when its destruction and/or elimination are compromised) or (ii) a pharmacodynamical mechanism (e.g., mutual potentiation of proarrhythmic properties of two drugs). In addition, some psychotropic drugs may induce sudden death in cases of pre-existing congenital cardiopathies such as (i) congenital long QT syndrome, predisposing to torsade de pointes that eventually cause syncope and sudden death. (ii) A Brugada syndrome, that may directly cause ventricular fibrillation due to reduced sodium current through Nav1.5 channels. Moreover, psychotropic drugs may be a direct cause of cardiac lesions also leading to SCD. This is the case, for example, of phenothiazines responsible for ischemic coronaropathies and of clozapine that is involved in the occurrence of myocarditis. The aims of this work are to delineate: (i) the risk of SCD related to the use of psychotropic drugs; (ii) mechanisms involved in the occurrence of such SCD; (iii) preventive actions of psychotropic drugs side effects, on the basis of the knowledge of patient-specific risk factors, documented from clinical history, ionic balance, and ECG investigation by the psychiatrist.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".