The PROMIZING trial enrollment algorithm for early identification of patients ready for unassisted breathing

Background: Liberating patients from mechanical ventilation (MV) requires a systematic approach. In the context of a clinical trial, we developed a simple algorithm to identify patients who tolerate assisted ventilation but still require ongoing MV to be randomized. We report on the use of this algorithm to screen potential trial participants for enrollment and subsequent randomization in the Proportional Assist Ventilation for Minimizing the Duration of MV (PROMIZING) study. Methods: The algorithm included five steps: enrollment criteria, pressure support ventilation (PSV) tolerance trial, weaning criteria, continuous positive airway pressure (CPAP) tolerance trial (0 cmHO during 2 min) and spontaneous breathing trial (SBT): on fraction of inspired oxygen (FO) 40% for 30-120 min. Patients who failed the weaning criteria, CPAP Zero trial, or SBT were randomized. We describe the characteristics of patients who were initially enrolled, but passed all steps in the algorithm and consequently were not randomized. Results: Among the 374 enrolled patients, 93 (25%) patients passed all five steps. At time of enrollment, most patients were on PSV (87%) with a mean (± standard deviation) FO of 34 (± 6) %, PSV of 8.7 (± 2.9) cmHO, and positive end-expiratory pressure of 6.1 (± 1.6) cmHO. Minute ventilation was 9.0 (± 3.1) L/min with a respiratory rate of 17.4 (± 4.4) breaths/min. Patients were liberated from MV with a median [interquartile range] delay between initial screening and extubation of 5 [1-49] hours. Only 7 (8%) patients required reintubation. Conclusion: The trial algorithm permitted identification of 93 (25%) patients who were ready to extubate, while their clinicians predicted a duration of ventilation higher than 24 h

Synchrony of hand-foot coupled movements: is it attained by mutual feedback entrainment or by independent linkage of each limb to a common rhythm generator?

BACKGROUND: Synchrony of coupled oscillations of ipsilateral hand and foot may be achieved by controlling the interlimb phase difference through a crossed kinaesthetic feedback between the two limbs, or by an independent linkage of each limb cycle to a common clock signal. These alternative models may be experimentally challenged by comparing the behaviour of the two limbs when they oscillate following an external time giver, either alone or coupled together. RESULTS: Ten subjects oscillated their right hand and foot both alone and coupled (iso- or antidirectionally), paced by a metronome. Wrist and ankle angular position and Electromyograms (EMG) from the respective flexor and extensor muscles were recorded. Three phase delays were measured: i) the clk-mov delay, between the clock (metronome beat) and the oscillation peak; ii) the neur (neural) delay, between the clock and the motoneurone excitatory input, as inferred from the EMG onset; and iii) the mech (mechanical) delay between the EMG onset and the corresponding point of the limb oscillation. During uncoupled oscillations (0.4 Hz to 3.0 Hz), the mech delay increased from -7° to -111° (hand) and from -4° to -83° (foot). In contrast, the clk-mov delay remained constant and close to zero in either limb since a progressive advance of the motoneurone activation on the pacing beat (neur advance) compensated for the increasing mech delay. Adding an inertial load to either extremity induced a frequency dependent increase of the limb mechanical delay that could not be completely compensated by the increase of the neural phase advance, resulting in a frequency dependent increment of clk-mov delay of the hampered limb. When limb oscillations were iso- or antidirectionally coupled, either in the loaded or unloaded condition, the three delays did not significantly change with respect to values measured when limbs were moved separately. CONCLUSION: The absence of any significant effect of limb coupling on the measured delays suggests that during hand-foot oscillations, both iso- and antidirectionally coupled, each limb is synchronised to the common rhythm generator by a "private" position control, with no need for a crossed feedback interaction between limbs

Neuromechanical Model of Rat Hind Limb Walking with Two Layer CPGs and Muscle Synergies

We present a synthetic nervous system modeling mammalian locomotion using separate central pattern generator and pattern formation layers. The central pattern generator defines the rhythm of locomotion and the timing of extensor and flexor phase. We also investigated the capability of the pattern formation network to operate using muscle synergies instead of single muscle pairs. The result is that this model is capable of adjusting rhythm and muscle forces independently, and stepping is successfully produced using two synergies, one with the hip, and the other with the knee and ankle combined. This work demonstrates that pattern formation networks can activate multiple muscles in a coordinated way to produce steady walking. It encourages the use of more complex synergies activating more muscles in the legs for 3D limb motion

Neuromechanical Model of Rat Hindlimb Walking with Two-Layer CPGs

This work demonstrates a neuromechanical model of rat hindlimb locomotionundergoing nominal walking with perturbations. In the animal, two types of responses toperturbations are observed: resetting and non-resetting deletions. This suggests that the animallocomotor system contains a memory-like organization. To model this phenomenon, we built asynthetic nervous system that uses separate rhythm generator and pattern formation layers toactivate antagonistic muscle pairs about each joint in the sagittal plane. Our model replicates theresetting and non-resetting deletions observed in the animal. In addition, in the intact (i.e., fullyafferented) rat walking simulation, we observe slower recovery after perturbation, which isdifferent from the deafferented animal experiment. These results demonstrate that our model is abiologically feasible description of some of the neural circuits in the mammalian spinal cord thatcontrol locomotion, and the difference between our simulation and fictive motion shows theimportance of sensory feedback on motor output. This model also demonstrates how the patternformation network can activate muscle synergies in a coordinated way to produce stable walking,which motivates the use of more complex synergies activating more muscles in the legs for threedimensionallimb motion

Proteomics characterization of abundant Golgi membrane proteins

NRC publication: Ye