Physiological origins of evoked magnetic fields and extracellular field potentials produced by guinea-pig CA3 hippocampal slices

This study examined whether evoked magnetic fields and intra- and extracellular potentials from longitudinal CA3 slices of guinea-pig can be interpreted within a single theoretical framework that incorporates ligand- and voltage-sensitive conductances in the dendrites and soma of the pyramidal cells. The 1991 CA3 mathematical model of R. D. Traub is modified to take into account the asymmetric branching patterns of the apical and basal dendrites of the pyramidal cells. The revised model accounts for the magnitude and waveform of the bi- and triphasic magnetic fields evoked by somatic and apical stimulations, respectively, in the slice in the absence of fast inhibition (blocked by 0.1 mm picrotoxin). The revised model also accounts for selective effects of 4-aminopyridine (4-AP) and tetraethylammonium (TEA), which block the potassium channels of A and C type, respectively, on the slow wave of the magnetic fields. Furthermore, the model correctly predicts the laminar profiles of field potential as well as intracellular potentials in the pyramidal cells produced by two classes of cells - those directly activated and those indirectly (synaptically) activated by the applied external stimulus. The intracellular potentials in this validated model reveal that the spikes and slow waves of the magnetic fields are generated in or near the soma and apical dendrites, respectively. These results demonstrate that a single theoretical framework couched within the modern concepts of cellular physiology provides a unified account of magnetic fields outside the slice, extracellular potentials within the slice and intracellular potentials of the pyramidal cells for CA3

Machine learning prediction of refractory ventricular fibrillation in out-of-hospital cardiac arrest using features available to EMS

Background: Shock-refractory ventricular fibrillation (VF) or ventricular tachycardia (VT) is a treatment challenge in out-of-hospital cardiac arrest (OHCA). This study aimed to develop and validate machine learning models that could be implemented by emergency medical services (EMS) to predict refractory VF/VT in OHCA patients. Methods: This was a retrospective study examining adult non-traumatic OHCA patients brought into the emergency department by Singapore EMS from the Pan-Asian Resuscitation Outcomes Study (PAROS) registry. Data from April 2010 to March 2020 were extracted for this study. Refractory VF/VT was defined as VF/VT persisting or recurring after at least one shock. Features were selected based on expert clinical opinion and availability to dispatch prior to arrival at scene. Multivariable logistic regression (MVR), LASSO and random forest (RF) models were investigated. Model performance was evaluated using receiver operator characteristic (ROC) area under curve (AUC) analysis and calibration plots. Results: 20,713 patients were included in this study, of which 860 (4.1%) fulfilled the criteria for refractory VF/VT. All models performed comparably and were moderately well-calibrated. ROC-AUC were 0.732 (95% CI, 0.695 – 0.769) for MVR, 0.738 (95% CI, 0.701 – 0.774) for LASSO, and 0.731 (95% CI, 0.690 – 0.773) for RF. The shared important predictors across all models included male gender and public location. Conclusion: The machine learning models developed have potential clinical utility to improve outcomes in cases of refractory VF/VT OHCA. Prediction of refractory VF/VT prior to arrival at patient’s side may allow for increased options for intervention both by EMS and tertiary care centres