Diagnostic accuracy of the magnetocardiograph for patients with suspected acute coronary syndrome

Abstract

BackgroundWe aimed to estimate the diagnostic accuracy of the VitalScan magnetocardiograph (MCG) for suspected acute coronary syndrome (ACS).MethodsWe undertook a prospective cohort study evaluating the diagnostic accuracy of the MCG in adults with suspected ACS. The reference standard of ACS was determined by an independent adjudication committee based on 30-day investigations and events. The cohort was split into a training sample, to derive the MCG algorithm and an algorithm combining MCG with a modified Manchester Acute Coronary Syndrome (MACS) clinical probability score, and a validation sample, to estimate diagnostic accuracy.ResultsWe recruited 756 participants and analysed data from 680 (293 training, 387 validation), of whom 96 (14%) had ACS. In the training sample, the respective area under the receiver operating characteristic (AUROC) curves were the following: MCG 0.66 (95% CI 0.58 to 0.74), MACS 0.64 (95% CI 0.54 to 0.73) and MCG+MACS 0.70 (95% CI 0.63 to 0.77). MCG specificity was 0.16 (95% CI 0.12 to 0.21) at the threshold achieving acceptable sensitivity for rule-out (>0.98). In the validation sample (n=387), the respective AUROCs were the following: MCG 0.56 (95% CI 0.48 to 0.64), MACS 0.69 (95% CI 0.61 to 0.77) and MCG+MACS 0.64 (95% CI 0.56 to 0.72). MCG sensitivity was 0.89 (95% CI 0.77 to 0.95) and specificity 0.15 (95% CI 0.12 to 0.20) at the rule-out threshold. MCG+MACS sensitivity was 0.85 (95% CI 0.73 to 0.92) and specificity 0.30 (95% CI 0.25 to 0.35).ConclusionThe VitalScan MCG is currently unable to accurately rule out ACS and is not yet ready for use in clinical practice. Further developmental research is required.

Assessing heart disease using a novel magnetocardiography device

“…Before generating the principal components, MCG predictors are passed through a regularisation proceedure to produce a set of values with consistent gaussian distributions. The parameters were derived using the methods outlined in [24]. Principal component analysis is often used in machine learning, however, in the current work this was a device to determine which of the MCG parameters were uncorrelated indicators.…”

“…Two risks that can adversely influence this choice are (1) that the coefficients are proportional to the mean of the variable set and (2) that the correlation present in the data are purely coincidental. To circumvent these risks the Ps were created using the data set presented in [24], and therefore the association between the prominance of these elements with high levels of correlation in the current experiment indicate that this is real.…”

“…With such a small sample of patients we are faced with the difficulty of having insufficient numbers of patients to allow the development and testing of a model, hence we restricted our search to identifying indicators that were correlated to the conditions of interest. The analysis of the data presented in this paper uses tools and principal components developed during a recent 700 patient multicentre clinical trial [24].…”

Clinical magnetocardiography: the unshielded bet—past, present, and future

“…A first pilot clinical study (protocol NCT02359773, on ClinicalTrials.gov) had shown that the device provided high sensitivity (95.4%) and a negative predictive value (NPV) (97.7%) for the rule-out of healthy subjects and of patients whose chest pain was non-ischemic from those with ischemic heart disease ( 262 ). However, a subsequent multicenter prospective cohort study evaluating the diagnostic accuracy of the MCG in adults with a suspected acute coronary syndrome (ACS) was conducted with their first generation of the derived industrial product (VitalScan/Corsens©, Creavo Medical Technologies, Coventry, United Kingdom) ( Figure 7B ) and concluded that, at least in 2020, the VitalScan did not yet meet the level of accuracy required to confidently rule out ACS in the ED clinical practice ( 263 ). More recently, another study carried out with a new 37-channel (19 active sensors and 18 for noise reduction) prototype by Leeds University suggests that, with appropriate modeling, 5 of 38 magnetic QRS parameters could be used to provide a MCG estimate of left ventricular ejection fraction ( 264 ).…”