Low-dose Initiation of Buprenorphine in Hospitalized Patients on Full Agonist Opioid Therapy: A Retrospective Observational Study.

OBJECTIVE: Buprenorphine can be challenging to initiate in hospitalized patients with opioid dependence because of difficulty tolerating an opioid-free period for buprenorphine induction. The objective of this study was to evaluate efficacy and safety of low-dose initiation of buprenorphine in hospitalized patients receiving full agonist opioids. METHODS: This is a retrospective observational study between January 1, 2019, and December 31, 2020, at an academic tertiary care center and affiliated community hospital. Participants included adult patients at least 18 years old receiving scheduled full agonist opioids who were given sublingual buprenorphine 0.5 mg or less with the intent of increasing to at least 4 mg daily. The primary endpoint was the proportion of patients reaching a target dose of at least 4 mg total per day. The secondary endpoints included the incidence of precipitated opioid withdrawal based on documentation of symptoms and change in morphine milligram equivalents before and after low-dose buprenorphine initiation. RESULTS: A total of 76 low-dose initiation attempts were performed in 71 predominantly male (68%) patients (some patients had multiple attempts). Most patients received low-dose initiation because of history of opioid use disorder (83%). Low-dose initiation was completed in 54 of 71 patients (76%) after 76 attempts. Precipitated withdrawal was identified in 2 patients (2.8%). Median morphine milligram equivalents excluding buprenorphine 24 hours before low-dose initiation was 1000 mg (interquartile range, 303.5-1720.5 mg) compared with 37.5 mg (interquartile range, 0-254 mg) after reaching target dose ( P < 0.001). CONCLUSIONS: Buprenorphine was safely initiated using low-dose initiation in hospitalized patients. This was associated with significant reduction in full agonist opioids.

A spiking neuron model: applications and learning.

This paper presents a biologically inspired, hardware-realisable spiking neuron model, which we call the Temporal Noisy-Leaky Integrator (TNLI). The dynamic applications of the model as well as its applications in Computational Neuroscience are demonstrated and a learning algorithm based on postsynaptic delays is proposed. The TNLI incorporates temporal dynamics at the neuron level by modelling both the temporal summation of dendritic postsynaptic currents which have controlled delay and duration and the decay of the somatic potential due to its membrane leak. Moreover, the TNLI models the stochastic neurotransmitter release by real neuron synapses (with probabilistic RAMs at each input) and the firing times including the refractory period and action potential repolarisation. The temporal features of the TNLI make it suitable for use in dynamic time-dependent tasks like its application as a motion and velocity detector system presented in this paper. This is done by modelling the experimental velocity selectivity curve of the motion sensitive H1 neuron of the visual system of the fly. This application of the TNLI indicates its potential applications in artificial vision systems for robots. It is also demonstrated that Hebbian-based learning can be applied in the TNLI for postsynaptic delay training based on coincidence detection, in such a way that an arbitrary temporal pattern can be detected and recognised. The paper also demonstrates that the TNLI can be used to control the firing variability through inhibition; with 80% inhibition to concurrent excitation, firing at high rates is nearly consistent with a Poisson-type firing variability observed in cortical neurons. It is also shown with the TNLI, that the gain of the neuron (slope of its transfer function) can be controlled by the balance between inhibition and excitation, the gain being a decreasing function of the proportion of inhibitory inputs. Finally, in the case of perfect balance between inhibition and excitation, i.e. where the average input current is zero, the neuron can still fire as a result of membrane potential fluctuations. The firing rate is then determined by the average input firing rate. Overall this work illustrates how a hardware-realisable neuron model can capitalise on the unique computational capabilities of biological neurons.