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Aims Mental stress-induced myocardial ischemia (MSIMI) in patients with coronary artery disease (CAD) is associated with adverse cardiovascular outcomes. We aim to assess hemodynamic, neuro-hormonal, endothelial, vasomotor and vascular predictors of MSIMI. Methods and Results We subjected 660 patients with stable CAD to 99mTc sestamibi myocardial perfusion imaging at rest, with mental (speech task) and with conventional (exercise/pharmacological) stress. Endothelium-dependent flow-mediated dilation (FMD), microvascular reactivity [reactive hyperemia index (RHI)] and arterial stiffness [pulse wave velocity (PWV)] were measured at rest and 30-min after mental stress. The digital microvascular vasomotor response during mental stress was assessed using peripheral arterial tonometry (PAT). A total of 106(16.1%) patients had MSIMI. Mental stress was accompanied by significant increases in rate-pressure-product (heart rate x systolic blood pressure; RPP), epinephrine levels and PWV, and significant decreases in FMD and PAT ratio denoting microvascular constriction. In comparison to those with no MSIMI, patients with MSIMI had higher hemodynamic and digital vasoconstrictive responses (p<0.05 for both), but did not differ in epinephrine, endothelial or macrovascular responses. Only presence of ischemia during conventional stress (OR of 7.1, 95%CI of 4.2, 11.9), high hemodynamic response (OR for RPP response ≥ vs < ROC cutoff of 1.8, 95%CI of 1.1, 2.8), and high digital vasoconstriction (OR for PAT ratio < vs ≥ ROC cutoff of 2.1, 95%CI of 1.3, 3.3) were independent predictors of MSIMI. Conclusion Ischemia during conventional stress testing and hemodynamic and vasoconstrictive responses to mental stress can help predict subjects with CAD at greater risk of developing MSIMI.

Atrial fibrillation (AFib) is diagnosed by analysis of the morphological and rhythmic properties of the electrocardiogram. It was recently shown that accurate detection of AFib is possible using beat-to-beat interval variations. This raises the question of whether AFib detection can be performed using a pulsatile waveform such as the Photoplethysmogram (PPG). The recent explosion in use of recreational and professional ambulatory wrist-based pulse monitoring devices means that an accurate pulse-based AFib screening algorithm would enable large scale screening for silent or undiagnosed AFib, a significant risk factor for multiple diseases. We propose a noise-resistant machine learning approach to detecting AFib from noisy ambulatory PPG recorded from the wrist using a modern research watch-based wearable device (the Samsung Simband). Ambulatory pulsatile and movement data were recorded from 46 subjects, 15 with AFib and 31 non symptomatic. Single channel electrocardiogram (ECG), multi-wavelength PPG and tri-axial accelerometry were recorded simultaneously at 128 Hz from the non-dominant wrist using the Simband. Recording lengths varied from 3.5 to 8.5 minutes. Pulse (beat) detection was performed on the PPG waveforms, and eleven features were extracted based on beat-to-beat variability and waveform signal quality. Using 10-fold cross validation, an accuracy of 95 % on out-of-sample data was achieved, with a sensitivity of 97%, specificity of 94%, and an area under the receiver operating curve (AUROC) of 0.99. The described approach provides a noise-resistant, accurate screening tool for AFib from PPG sensors located in an ambulatory wrist watch. To our knowledge this is the first study to demonstrate an algorithm with a high enough accuracy to be used in general population studies that does not require an ambulatory Holter electrocardiographic monitor.

Women and men have distinct cardiovascular reactivity mechanisms for MSIMI. For women, stress-induced peripheral vasoconstriction with mental stress, and not increased hemodynamic workload, is associated with MSIMI, whereas for men, it is the opposite. Future studies should examine these pathways on long-term outcomes.