Detection of volume loss using the Nexfin device in blood donors

Clinical evidence of autonomic dysfunction due to atrial fibrillation: implications for rhythm control strategy

PURPOSE

The role of the autonomic nervous system in the genesis of atrial fibrillation (AF) has been well studied; however, the converse remains poorly understood. Pulmonary veins (PV) contain receptors important in cardiac reflexes. Here, we evaluated reflex responses in patients with paroxysmal AF (PAF) to lower body negative pressure (LBNP).

METHODS

Thirty-four PAF patients (including 14 PAF patients post successful PV Isolation; PVI) were compared to 14 age and sex-matched controls. Mean arterial pressure (MAP), heart rate (HR), systemic vascular resistance index (SVRI), cardiac index (CI), and stroke volume index (SVI) were measured continuously during - 0, - 20, and - 40 mmHg LBNP. LBNP reduces venous return, deactivating atrial receptors, thereby eliciting a reflex increase in SVRI to maintain MAP.

RESULTS

AF patients have higher BMI than the controls (p = 0.02). In control subjects, LBNP did not alter MAP as SVRI increased. In PAF patients, LBNP resulted in a reduction in MAP (- 4.8%) with attenuated SVRI response (+ 4.2%) compared to controls (p < 0.05). However, in the post-PVI group, SVRI increase was similar to controls (p = 0.12) although that was insufficient to maintain MAP. In all patients, both reduction in SVI and CI and increase in HR were similar in response to LBNP.

CONCLUSIONS

This study provides novel clinical evidence of autonomic dysfunction in PAF patients. Successful PVI results in partial recovery of the cardiac reflex. Therefore, not only does autonomic disturbance predispose to AF but it is also a consequence of AF; potentially contributing to disease progression. This could help explain the dictum "AF begets AF."

Using support vector machines on photoplethysmographic signals to discriminate between hypovolemia and euvolemia

Identifying trauma patients at risk of imminent hemorrhagic shock is a challenging task in intraoperative and battlefield settings given the variability of traditional vital signs, such as heart rate and blood pressure, and their inability to detect blood loss at an early stage. To this end, we acquired N = 58 photoplethysmographic (PPG) recordings from both trauma patients with suspected hemorrhage admitted to the hospital, and healthy volunteers subjected to blood withdrawal of 0.9 L. We propose four features to characterize each recording: goodness of fit (r²), the slope of the trend line, percentage change, and the absolute change between amplitude estimates in the heart rate frequency range at the first and last time points. Also, we propose a machine learning algorithm to distinguish between blood loss and no blood loss. The optimal overall accuracy of discriminating between hypovolemia and euvolemia was 88.38%, while sensitivity and specificity were 88.86% and 87.90%, respectively. In addition, the proposed features and algorithm performed well even when moderate blood volume was withdrawn. The results suggest that the proposed features and algorithm are suitable for the automatic discrimination between hypovolemia and euvolemia, and can be beneficial and applicable in both intraoperative/emergency and combat casualty care.