The effect of clinically relevant changes in extracellular electrolyte concentrations on human atrial arrhythmias
Bibliographic record
Abstract
BACKGROUND: Patients with recent-onset atrial fibrillation (AF) frequently present with plasma electrolyte imbalances. Low plasma concentrations of potassium (hypokalaemia), and more recently low plasma concentrations of sodium (hyponatremia), have both been shown to contribute to a pro-arrhythmic substrate and may affect the success of restoring the normal rhythm (cardioversion). However, the mechanistic effects of these electrolyte alterations on atrial electrophysiology remain incompletely understood. This study aims to investigate how clinically relevant variations in extracellular electrolyte concentrations influence human atrial electrical activity and arrhythmia initiation. METHODS: We applied our cardiac digital twin methodology to a cohort of 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, 16 long-standing persistent). For each patient-specific model, we simulated sinus rhythm and AF induction under baseline and 30 distinct combinations of extracellular potassium, sodium and calcium concentrations. Global sensitivity analysis and machine learning were used to quantify how these electrolyte alterations affect key electrophysiological markers, including action potential duration, resting membrane potential (RMP), and conduction velocity (CV), and the induction and maintenance of AF. RESULTS: Here we show that our computational framework accurately replicates the experimentally observed sensitivity of human atrial electrophysiology to electrolyte variations. Hyponatremia significantly modifies the action potential waveform, thereby promoting AF sustainability, while hypokalaemia predominantly alters the RMP and thus CV, and only moderately increases AF inducibility. CONCLUSIONS: The combination of clinical data sets and multiscale computational analyses yields insights into cellular and tissue-level mechanisms for AF as well as suggesting personalised approaches for management and treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".