Cardiac Stroke Volume Index Is Associated With Early Neurological Improvement in Acute Ischemic Stroke Patients

Early neurological improvement as assessed with the NIH stroke scale (NIHSS) at 24 h has been associated with improved long-term functional outcomes following acute ischemic stroke (AIS). Cardiac dysfunction is often present in AIS, but its association with outcomes is incompletely defined. We performed a pilot study to evaluate the association between non-invasively measured cardiac parameters and 24-h neurological improvement in prospectively enrolled patients with suspected AIS who presented within 12 h of symptom-onset and had an initial systolic blood pressure>140 mm Hg. Patients receiving thrombolytic therapy or mechanical thrombectomy were excluded. Non-invasive pulse contour analysis was used to measure mean arterial blood pressure (MAP), cardiac stroke volume index (cSVI), cardiac output (CO) and cardiac index (CI). Transcranial Doppler recorded mean middle cerebral artery flow velocity (MFV). We defined a decrease of 4 NIHSS points or NIHSS ≤ 1 at 24-h as neurological improvement. Of 75 suspected, 38 had confirmed AIS and did not receive reperfusion therapy. Of these, 7/38 (18.4%) had neurological improvement over 24 h. MAP was greater in those without improvement (108, IQR 96–123 mm Hg) vs. those with (89, IQR 73–104 mm Hg). cSVI, CO, and MFV were similar between those without and with improvement: 37.4 (IQR 30.9–47.7) vs. 44.7 (IQR 42.3–55.3) ml/m2; 5.2 (IQR 4.2–6.6) vs. 5.3 (IQR 4.7–6.7) mL/min; and 39.9 (IQR 32.1–45.7) vs. 34.4 (IQR 27.1–49.2) cm/s, respectively. Multivariate analysis found MAP and cSVI as predictors for improvement (OR 0.93, 95%CI 0.85–0.98 and 1.14, 95%CI 1.03–1.31). In this pilot study, cSVI and MAP were associated with 24-h neurological improvement in AIS.

Remote exposure to secondhand tobacco smoke is associated with lower exercise capacity through effects on oxygen pulse- a proxy of cardiac stroke volume

Background: Prolonged past exposure to secondhand tobacco smoke (SHS) is associated with exercise limitation. Pulmonary factors including air trapping contribute to this limitation but the contribution of cardiovascular factors is unclear. Methods: To determine contribution of cardiovascular mechanisms to SHS-associated exercise limitation, we examined the cardiovascular responses to maximum effort exercise testing in 166 never-smokers with remote but prolonged occupational exposure to SHS and no known history of cardiovascular disease except nine with medically-controlled hypertension. We estimated the contribution of oxygen-pulse (proxy for cardiac stroke volume) and changes in systolic (SBP) and diastolic blood pressures (DBP) and heart rate (HR) over workload towards exercise capacity, and examined whether the association of SHS with exercise capacity was mediated through these variables. Results: Oxygen consumption (VO2Peak) and oxygen-pulse (O2-PulsePeak) at peak exercise were 1,516±431mL/min (100±23 %predicted) and 10.6±2.8mL/beat (117±25 %predicted), respectively, with 91 (55%) and 43 (26%) of subjects not being able to achieve their maximum predicted values. Sixty-two percent showed hypertensive response to exercise by at least one established criterion. In adjusted models, VO2Peak was associated directly with O2-Pulse and inversely with rise of SBP and DBP over workload (all P<0.05). Moreover, SHS exposure association with VO2Peak was mainly (84%) mediated through its effect on oxygen-pulse (P=0.034). Notably, although not statistically significant, a large proportion (60%) of air trapping effect on VO2Peak seemed to be mediated through oxygen-pulse (P=0.066). Discussion: In a never-smoker population with remote prolonged exposure to SHS, abnormal escalation of afterload and an SHS-associated reduction in cardiac output contributed to lower exercise capacity.

Classification of Photoplethysmographic Signal Quality with Deep Convolution Neural Networks for Accurate Measurement of Cardiac Stroke Volume

As photoplethysmographic (PPG) signals are comprised of numerous pieces of important physiological information, they have been widely employed to measure many physiological parameters. However, only a high-quality PPG signal can provide a reliable physiological assessment. Unfortunately, PPG signals are easily corrupted by motion artifacts and baseline drift during recording. Although several rule-based algorithms have been developed for evaluating the quality of PPG signals, few artificial intelligence-based algorithms have been presented. Thus, this study aims to classify the quality of PPG signals by using two two-dimensional deep convolution neural networks (DCNN) when the PPG pulse is used to measure cardiac stroke volume (SV) by impedance cardiography. An image derived from a PPG pulse and its differential pulse is used as the input to the two DCNN models. To quantify the quality of individual PPG pulses, the error percentage of the beat-to-beat SV measured by our device and medis® CS 2000 synchronously is used to determine whether the pulse quality is high, middle, or low. Fourteen subjects were recruited, and a total of 3135 PPG pulses (1342 high quality, 73 middle quality, and 1720 low quality) were obtained. We used a traditional DCNN, VGG-19, and a residual DCNN, ResNet-50, to determine the quality levels of the PPG pulses. Their results were all better than the previous rule-based methods. The accuracies of VGG-19 and ResNet-50 were 0.895 and 0.925, respectively. Thus, the proposed DCNN may be applied for the classification of PPG quality and be helpful for improving the SV measurement in impedance cardiography.