A portable prototype magnetometer to differentiate ischemic and non-ischemic heart disease in patients with chest pain

Background: Magnetocardiography (MCG) is a non-invasive technique used to measure and map cardiac magnetic fields. We describe the predictive performance of a portable prototype magnetometer designed for use in acute and routine clinical settings. We assessed the predictive ability of the measurements derived from the magnetometer for the ruling-out of healthy subjects and patients whose chest pain has a non-ischemic origin from those with ischemic heart disease (IHD). Methods: MCG data were analyzed from a technical performance study, a pilot clinical study, and a young healthy reference group. Participants were grouped to enable differentiation of those with IHD versus non-IHD versus controls: Group A (70 IHD patients); Group B (69 controls); Group C (37 young healthy volunteers). Scans were recorded in an unshielded room. Between-group differences were explored using analysis of variance. The ability of 10 candidate MCG predictors to predict normal/abnormal cases was analyzed using logistic regression. Predictive performance was internally validated using repeated five-fold cross-validation. Results: Three MCG predictors showed a significant difference between patients and age-matched controls (P<0.001); eight predictors showed a significant difference between patients and young healthy volunteers (P<0.001). Logistic regression comparing patients with controls yielded a specificity of 35.0%, sensitivity of 95.4%, and negative predictive value for the ruling-out of IHD of 97.8% (area under the curve 0.78). Conclusion: This analysis represents a preliminary indication that the portable magnetometer can help rule-out healthy subjects and patients whose chest pain has a non-ischemic origin from those with IHD

Entropic barriers for two-dimensional quantum memories

Comprehensive no-go theorems show that information encoded over local two-dimensional topologically ordered systems cannot support macroscopic energy barriers, and hence will not maintain stable quantum information at finite temperatures for macroscopic time scales. However, it is still well motivated to study low-dimensional quantum memories due to their experimental amenability. Here we introduce a grid of defect lines to Kitaev's quantum double model where different anyonic excitations carry different masses. This setting produces a complex energy landscape which entropically suppresses the diffusion of excitations that cause logical errors. We show numerically that entropically suppressed errors give rise to superexponential inverse temperature scaling and polynomial system size scaling for small system sizes over a low-temperature regime. Curiously, these entropic effects are not present below a certain low temperature. We show that we can vary the system to modify this bound and potentially extend the described effects to zero temperature

CONSORT Diagram: Pilot clinical study.

<p>Participant flow through the pilot clinical study. Data were analyzed for 15/21 patients and 18/21 healthy controls.</p

Apparent and cross-validation performance of the logistic regression models.

<p>Apparent and cross-validation performance of the logistic regression models.</p

Average value for each predictor for patient and control groups.

<p>Average value for each predictor for patient and control groups.</p

CONSORT Diagram: Technical performance study.

<p>Participant flow through the technical performance study. Data were analyzed for 55/63 patients and 51/60 healthy controls.</p

Histogram of the RS_peak predictors.

<p>A representative histogram of the RS_peak predictors for study participants enrolled in Group A, Group B, and Group C.</p

Confusion matrix for a binary classifier.

<p>Confusion matrix for a binary classifier.</p