Central α2-adrenergic mechanisms regulate human sympathetic neuronal discharge strategies.

The present study investigated the impact of central α2-adrenergic mechanisms on sympathetic action potential (AP) discharge, recruitment and latency strategies. We used the microneurographic technique to record muscle sympathetic nerve activity and a continuous wavelet transform to investigate postganglionic sympathetic AP firing during a baseline condition and an infusion of a α2-adrenergic receptor agonist, dexmedetomidine (10 min loading infusion of 0.225 µg kg-1; maintenance infusion of 0.1-0.5 µg kg h-1) in eight healthy individuals (28 ± 7 years, five females). Dexmedetomidine reduced mean pressure (92 ± 7 to 80 ± 8 mmHg, P < 0.001) but did not alter heart rate (61 ± 13 to 60 ± 14 bpm; P = 0.748). Dexmedetomidine reduced sympathetic AP discharge (126 ± 73 to 27 ± 24 AP 100 beats-1, P = 0.003) most strongly for medium-sized APs (normalized cluster 2: 21 ± 10 to 5 ± 5 AP 100 beats-1; P < 0.001). Dexmedetomidine progressively de-recruited sympathetic APs beginning with the largest AP clusters (12 ± 3 to 7 ± 2 clusters, P = 0.002). Despite de-recruiting large AP clusters with shorter latencies, dexmedetomidine reduced AP latency across remaining clusters (1.18 ± 0.12 to 1.13 ± 0.13 s, P = 0.002). A subset of six participants performed a Valsalva manoeuvre (20 s, 40 mmHg) during baseline and the dexmedetomidine infusion. Compared to baseline, AP discharge (Δ 361 ± 292 to Δ 113 ± 155 AP 100 beats-1, P = 0.011) and AP cluster recruitment elicited by the Valsalva manoeuvre were lower during dexmedetomidine (Δ 2 ± 1 to Δ 0 ± 2 AP clusters, P = 0.041). The reduction in sympathetic AP latency elicited by the Valsalva manoeuvre was not affected by dexmedetomidine (Δ -0.09 ± 0.07 to Δ -0.07 ± 0.14 s, P = 0.606). Dexmedetomidine reduced baroreflex gain, most strongly for medium-sized APs (normalized cluster 2: -6.0 ± 5 to -1.6 ± 2 % mmHg-1; P = 0.008). These data suggest that α2-adrenergic mechanisms within the central nervous system modulate sympathetic postganglionic neuronal discharge, recruitment and latency strategies in humans. KEY POINTS: Sympathetic postganglionic neuronal subpopulations innervating the human circulation exhibit complex patterns of discharge, recruitment and latency. However, the central neural mechanisms governing sympathetic postganglionic discharge remain unclear. This microneurographic study investigated the impact of a dexmedetomidine infusion (α2-adrenergic receptor agonist) on muscle sympathetic postganglionic action potential (AP) discharge, recruitment and latency patterns. Dexmedetomidine infusion inhibited the recruitment of large and fast conducting sympathetic APs and attenuated the discharge of medium sized sympathetic APs that fired during resting conditions and the Valsalva manoeuvre. Dexmedetomidine infusion elicited shorter sympathetic AP latencies during resting conditions but did not affect the reductions in latency that occurred during the Valsalva manoeuvre. These data suggest that α2-adrenergic mechanisms within the central nervous system modulate sympathetic postganglionic neuronal discharge, recruitment and latency strategies in humans.

An empirical model for world record running speeds with distance, age, and sex: anaerobic and aerobic contributions to performance.

The objective of this study is to derive mathematical equations that closely describe published data on world record running speed as a function of distance, age, and sex. Running speed declines with increasing distance and age. Over long distances, where aerobic metabolism is dominant, speed declines in proportion to the logarithm of distance. Over short distances, anaerobic metabolism contributes significantly to performance, and speed is increased relative to the trend of the long-distance data. Equations are derived that explicitly represent these effects. The decline in speed with age is represented by an age-dependent multiplicative factor, which exhibits increasing sensitivity to age as age increases. Using these equations, data are analyzed separately for males and females, and close fits to published data are demonstrated, particularly for younger age groups. These equations provide insight into the contributions of aerobic and anaerobic components of metabolism to athletic performance and a framework for comparisons of performance across wide ranges of distance and age.NEW & NOTEWORTHY World record speeds at different distances for men and women in different age categories are used to develop a model to predict running performance as a function of race distance, age, and sex. This empirical model quantifies the decline in running speed with distance and age in a way that provides insight into the aerobic and anaerobic contributions to running speed and may help with developing training strategies for different age groups at various distances.

Comparing the compensatory reserve metric obtained from invasive arterial measurements and photoplethysmographic volume-clamp during simulated hemorrhage.

PURPOSE: The compensatory reserve metric (CRM) is a novel tool to predict cardiovascular decompensation during hemorrhage. The CRM is traditionally computed using waveforms obtained from photoplethysmographic volume-clamp (PPGVC), yet invasive arterial pressures may be uniquely available. We aimed to examine the level of agreement of CRM values computed from invasive arterial-derived waveforms and values computed from PPGVC-derived waveforms. METHODS: Sixty-nine participants underwent graded lower body negative pressure to simulate hemorrhage. Waveform measurements from a brachial arterial catheter and PPGVC finger-cuff were collected. A PPGVC brachial waveform was reconstructed from the PPGVC finger waveform. Thereafter, CRM values were computed using a deep one-dimensional convolutional neural network for each of the following source waveforms; (1) invasive arterial, (2) PPGVC brachial, and (3) PPGVC finger. Bland-Altman analyses were used to determine the level of agreement between invasive arterial CRM values and PPGVC CRM values, with results presented as the Mean Bias [95% Limits of Agreement]. RESULTS: The mean bias between invasive arterial- and PPGVC brachial CRM values at rest, an applied pressure of -45mmHg, and at tolerance was 6% [-17%, 29%], 1% [-28%, 30%], and 0% [-25%, 25%], respectively. Additionally, the mean bias between invasive arterial- and PPGVC finger CRM values at rest, applied pressure of -45mmHg, and tolerance was 2% [-22%, 26%], 8% [-19%, 35%], and 5% [-15%, 25%], respectively. CONCLUSION: There is generally good agreement between CRM values obtained from invasive arterial waveforms and values obtained from PPGVC waveforms. Invasive arterial waveforms may serve as an alternative for computation of the CRM.

Physiologic validation of the Compensatory Reserve Metric obtained from pulse oximetry: A step toward advanced medical monitoring on the battlefield.

BACKGROUND: The Compensatory Reserve Metric (CRM) provides a time sensitive indicator of hemodynamic decompensation. However, its in-field utility is limited because of the size and cost-intensive nature of standard vital sign monitors or photoplethysmographic volume-clamp (PPG VC ) devices used to measure arterial waveforms. In this regard, photoplethysmographic measurements obtained from pulse oximetry may serve as a useful, portable alternative. This study aimed to validate CRM values obtained using pulse oximeter (PPG PO ). METHODS: Forty-nine healthy adults (25 females) underwent a graded lower body negative pressure (LBNP) protocol to simulate hemorrhage. Arterial waveforms were sampled using PPG PO and PPG VC . The CRM was calculated using a one-dimensional convolutional neural network. Cardiac output and stroke volume were measured using PPG VC . A brachial artery catheter was used to measure intra-arterial pressure. A three-lead electrocardiogram was used to measure heart rate. Fixed-effect linear mixed models with repeated measures were used to examine the association between CRM values and physiologic variables. Log-rank analyses were used to examine differences in shock determination during LBNP between monitored hemodynamic parameters. RESULTS: The median LBNP stage reached was 70 mm Hg (range, 45-100 mm Hg). Relative to baseline, at tolerance, there was a 47% ± 12% reduction in stroke volume, 64% ± 27% increase in heart rate, and 21% ± 7% reduction in systolic blood pressure ( p < 0.001 for all). Compensatory Reserve Metric values obtained with both PPG PO and PPG VC were associated with changes in heart rate ( p < 0.001), stroke volume ( p < 0.001), and pulse pressure ( p < 0.001). Furthermore, they provided an earlier detection of hemodynamic shock relative to the traditional metrics of shock index ( p < 0.001 for both), systolic blood pressure ( p < 0.001 for both), and heart rate ( p = 0.001 for both). CONCLUSION: The CRM obtained from PPG PO provides a valid, time-sensitized prediction of hemodynamic decompensation, opening the door to provide military medical personnel noninvasive in-field advanced capability for early detection of hemorrhage and imminent onset of shock. LEVEL OF EVIDENCE: Diagnostic Tests or Criteria; Level III.

Non-invasive biomarkers for detecting progression toward hypovolemic cardiovascular instability in a lower body negative pressure model.

Occult hemorrhages after trauma can be present insidiously, and if not detected early enough can result in patient death. This study evaluated a hemorrhage model on 18 human subjects, comparing the performance of traditional vital signs to multiple off-the-shelf non-invasive biomarkers. A validated lower body negative pressure (LBNP) model was used to induce progression towards hypovolemic cardiovascular instability. Traditional vital signs included mean arterial pressure (MAP), electrocardiography (ECG), plethysmography (Pleth), and the test systems utilized electrical impedance via commercial electrical impedance tomography (EIT) and multifrequency electrical impedance spectroscopy (EIS) devices. Absolute and relative metrics were used to evaluate the performance in addition to machine learning-based modeling. Relative EIT-based metrics measured on the thorax outperformed vital sign metrics (MAP, ECG, and Pleth) achieving an area-under-the-curve (AUC) of 0.99 (CI 0.95-1.00, 100% sensitivity, 87.5% specificity) at the smallest LBNP change (0-15 mmHg). The best vital sign metric (MAP) at this LBNP change yielded an AUC of 0.6 (CI 0.38-0.79, 100% sensitivity, 25% specificity). Out-of-sample predictive performance from machine learning models were strong, especially when combining signals from multiple technologies simultaneously. EIT, alone or in machine learning-based combination, appears promising as a technology for early detection of progression toward hemodynamic instability.