What could health technology assessment learn from living clinical practice guidelines?

A "living" approach to clinical practice guidelines is when the identification, appraisal and synthesis of evidence is maintained and repeated at an agreed frequency, with a clear process for when and how new evidence is to be incorporated. The value of a living approach to guidelines was emphasised during the COVID-19 pandemic when health professionals and policymakers needed to make decisions regarding patient care in the context of a nascent but rapidly evolving evidence base. In this perspective, we draw on our recent experience developing Australian and international living guidelines and reflect on the feasibility of applying living guideline methods and processes to a lifecycle approach to health technology assessment (HTA). We believe the opportunities and challenges of adopting a living approach in HTA fall into five key themes: identification, appraisal and synthesis of evidence; optimising the frequency of updates; embedding ongoing multi-stakeholder engagement; linking the emergence of new evidence to reimbursement; and system capacity to support a living approach. We acknowledge that the suitability of specific living approaches to HTA will be heavily influenced by the type of health technology, its intended use in the health system, local reimbursement pathways, and other policy settings. But we believe that the methods and processes applied successfully to guideline development to manage evidentiary uncertainty could be applied in the context of HTA and reimbursement decision-making to help manage similar sources of uncertainty.

A Systematic Review of Pharmacist-Led Antimicrobial Stewardship Programs in Sub-Saharan Africa.

Background: The misuse of antibiotics contributes significantly to antimicrobial resistance (AMR). Higher treatment costs, longer hospital stays, and clinical failure can all result from AMR. According to projections, Africa and Asia will bear the heaviest burden of AMR-related mortalities in the coming years. Antimicrobial stewardship (AMS) programmes are therefore critical in mitigating the effects of AMR. Pharmacists may play an important role in such programmes, as seen in Europe and North America, but the impact, challenges, and opportunities of pharmacist-led antimicrobial stewardship interventions in Sub-Saharan African hospitals are unknown. The purpose of this systematic review was to assess the impact, challenges, and opportunities of pharmacist-led antimicrobial stewardship interventions in Sub-Saharan African hospitals. Methods: The Joanna Briggs Institute (JBI) guidelines were used to search for peer-reviewed pharmacist-led studies based in hospitals in Sub-Saharan Africa that were published in English between January 2015 and January 2021. The PubMed, Embase, and Ovid databases were used. Results: Education and training, audits and feedback, protocol development, and ward rounds were identified as primary components of pharmacist-led antimicrobial stewardship interventions in Sub-Saharan Africa. The pharmacist-led antimicrobial interventions improved adherence to guidelines and reduced inappropriate prescribing, but were hampered by a lack of laboratory and technological support, limited stewardship time, poor documentation, and a lack of guidelines and policies. Funding, mentorship, guidelines, accountability, continuous monitoring, feedback, multidisciplinary engagements, and collaborations were identified as critical in the implementation of pharmacist-led antimicrobial stewardship programmes. Conclusions: These findings suggest that pharmacists in Sub-Saharan African hospitals can successfully lead antimicrobial stewardship programmes but their implementation is limited by lack of mentorship, accountability, continuous monitoring, feedback, collaborations, and poor funding.

Patterns of synchronization in 2D networks of inhibitory neurons.

Neural firing in many inhibitory networks displays synchronous assembly or clustered firing, in which subsets of neurons fire synchronously, and these subsets may vary with different inputs to, or states of, the network. Most prior analytical and computational modeling of such networks has focused on 1D networks or 2D networks with symmetry (often circular symmetry). Here, we consider a 2D discrete network model on a general torus, where neurons are coupled to two or more nearest neighbors in three directions (horizontal, vertical, and diagonal), and allow different coupling strengths in all directions. Using phase model analysis, we establish conditions for the stability of different patterns of clustered firing behavior in the network. We then apply our results to study how variation of network connectivity and the presence of heterogeneous coupling strengths influence which patterns are stable. We confirm and supplement our results with numerical simulations of biophysical inhibitory neural network models. Our work shows that 2D networks may exhibit clustered firing behavior that cannot be predicted as a simple generalization of a 1D network, and that heterogeneity of coupling can be an important factor in determining which patterns are stable.

Exact mean-field models for spiking neural networks with adaptation.

Networks of spiking neurons with adaption have been shown to be able to reproduce a wide range of neural activities, including the emergent population bursting and spike synchrony that underpin brain disorders and normal function. Exact mean-field models derived from spiking neural networks are extremely valuable, as such models can be used to determine how individual neurons and the network they reside within interact to produce macroscopic network behaviours. In the paper, we derive and analyze a set of exact mean-field equations for the neural network with spike frequency adaptation. Specifically, our model is a network of Izhikevich neurons, where each neuron is modeled by a two dimensional system consisting of a quadratic integrate and fire equation plus an equation which implements spike frequency adaptation. Previous work deriving a mean-field model for this type of network, relied on the assumption of sufficiently slow dynamics of the adaptation variable. However, this approximation did not succeed in establishing an exact correspondence between the macroscopic description and the realistic neural network, especially when the adaptation time constant was not large. The challenge lies in how to achieve a closed set of mean-field equations with the inclusion of the mean-field dynamics of the adaptation variable. We address this problem by using a Lorentzian ansatz combined with the moment closure approach to arrive at a mean-field system in the thermodynamic limit. The resulting macroscopic description is capable of qualitatively and quantitatively describing the collective dynamics of the neural network, including transition between states where the individual neurons exhibit asynchronous tonic firing and synchronous bursting. We extend the approach to a network of two populations of neurons and discuss the accuracy and efficacy of our mean-field approximations by examining all assumptions that are imposed during the derivation. Numerical bifurcation analysis of our mean-field models reveals bifurcations not previously observed in the models, including a novel mechanism for emergence of bursting in the network. We anticipate our results will provide a tractable and reliable tool to investigate the underlying mechanism of brain function and dysfunction from the perspective of computational neuroscience.

The Impact of Small Time Delays on the Onset of Oscillations and Synchrony in Brain Networks.

The human brain constitutes one of the most advanced networks produced by nature, consisting of billions of neurons communicating with each other. However, this communication is not in real-time, with different communication or time-delays occurring between neurons in different brain areas. Here, we investigate the impacts of these delays by modeling large interacting neural circuits as neural-field systems which model the bulk activity of populations of neurons. By using a Master Stability Function analysis combined with numerical simulations, we find that delays (1) may actually stabilize brain dynamics by temporarily preventing the onset to oscillatory and pathologically synchronized dynamics and (2) may enhance or diminish synchronization depending on the underlying eigenvalue spectrum of the connectivity matrix. Real eigenvalues with large magnitudes result in increased synchronizability while complex eigenvalues with large magnitudes and positive real parts yield a decrease in synchronizability in the delay vs. instantaneously coupled case. This result applies to networks with fixed, constant delays, and was robust to networks with heterogeneous delays. In the case of real brain networks, where the eigenvalues are predominantly real, owing to the nearly symmetric nature of these weight matrices, biologically plausible, small delays, are likely to increase synchronization, rather than decreasing it.

Spatially localized cluster solutions in inhibitory neural networks.

Neurons in the inhibitory network of the striatum display cell assembly firing patterns which recent results suggest may consist of spatially compact neural clusters. Previous computational modeling of striatal neural networks has indicated that non-monotonic, distance-dependent coupling may promote spatially localized cluster firing. Here, we identify conditions for the existence and stability of cluster firing solutions in which clusters consist of spatially adjacent neurons in inhibitory neural networks. We consider simple non-monotonic, distance-dependent connectivity schemes in weakly coupled 1-D networks where cells make stronger connections with their kth nearest neighbors on each side and weaker connections with closer neighbors. Using the phase model reduction of the network system, we prove the existence of cluster solutions where neurons that are spatially close together are also synchronized in the same cluster, and find stability conditions for these solutions. Our analysis predicts the long-term behavior for networks of neurons, and we confirm our results by numerical simulations of biophysical neuron network models. Our results demonstrate that an inhibitory network with non-monotonic, distance-dependent connectivity can exhibit cluster solutions where adjacent cells fire together.

M-current induced Bogdanov-Takens bifurcation and switching of neuron excitability class.

In this work, we consider a general conductance-based neuron model with the inclusion of the acetycholine sensitive, M-current. We study bifurcations in the parameter space consisting of the applied current [Formula: see text], the maximal conductance of the M-current [Formula: see text] and the conductance of the leak current [Formula: see text]. We give precise conditions for the model that ensure the existence of a Bogdanov-Takens (BT) point and show that such a point can occur by varying [Formula: see text] and [Formula: see text]. We discuss the case when the BT point becomes a Bogdanov-Takens-cusp (BTC) point and show that such a point can occur in the three-dimensional parameter space. The results of the bifurcation analysis are applied to different neuronal models and are verified and supplemented by numerical bifurcation diagrams generated using the package MATCONT. We conclude that there is a transition in the neuronal excitability type organised by the BT point and the neuron switches from Class-I to Class-II as conductance of the M-current increases.

Patient and Public Involvement Refines the Design of ProtOeus: A Proposed Phase II Trial of Proton Beam Therapy in Oesophageal Cancer.

BACKGROUND: Neoadjuvant chemoradiotherapy for oesophageal cancer significantly improves overall survival but is associated with severe post-operative complications. Proton beam therapy may reduce these toxicities by sparing normal tissues compared with standard radiotherapy. ProtOeus is a proposed randomised phase II study of neoadjuvant chemoradiotherapy in oesophageal cancer that compares proton beam therapy to standard radiotherapy techniques. As proton beam therapy services are often centralised in academic centres in major cities, proton beam therapy trials raise distinct challenges including patient acceptance of travelling for proton beam therapy, coordination of treatments with local centres and ensuring equity of access for patients. METHODS: Focus groups were held early in the trial development process to establish patients' views on the trial proposal. Topics discussed include perception of proton beam therapy, patient acceptability of the trial pathway and design, patient-facing materials, and common clinical scenarios. Focus groups were led by the investigators and facilitated by patient involvement teams from the institutions who are involved in this research. Responses for each topic were analysed, and fed back to the trial's development group. RESULTS: Three focus groups were held in separate locations in the UK (Manchester, Cardiff, Wigan). Proton beam therapy was perceived as superior to standard radiotherapy making the trial attractive. Patients felt strongly that travel costs should be reimbursed to ensure equity of access to proton beam therapy. They were very supportive of a shorter treatment schedule and felt that toxicity reduction was the most important endpoint. DISCUSSION AND CONCLUSIONS: Incorporating patient views early in the trial development process resulted in significant trial design refinements including travel/accommodation provisions, choice of primary endpoint, randomisation ratio and fractionation schedule. Focus groups are a reproducible and efficient method of incorporating the patient and public voice into research.

Stability, bifurcation and phase-locking of time-delayed excitatory-inhibitory neural networks.

We study a model for a network of synaptically coupled, excitable neurons to identify the role of coupling delays in generating different network behaviors. The network consists of two distinct populations, each of which contains one excitatory-inhibitory neuron pair. The two pairs are coupled via delayed synaptic coupling between the excitatory neurons, while each inhibitory neuron is connected only to the corresponding excitatory neuron in the same population. We show that multiple equilibria can exist depending on the strength of the excitatory coupling between the populations. We conduct linear stability analysis of the equilibria and derive necessary conditions for delay-induced Hopf bifurcation. We show that these can induce two qualitatively different phase-locked behaviors, with the type of behavior determined by the sizes of the coupling delays. Numerical bifurcation analysis and simulations supplement and confirm our analytical results. Our work shows that the resting equilibrium point is unaffected by the coupling, thus the network exhibits bistability between a rest state and an oscillatory state. This may help understand how rhythms spontaneously arise in neuronal networks.

Evaluating the application of Pareto navigation guided automated radiotherapy treatment planning to prostate cancer.

BACKGROUND AND PURPOSE: Current automated planning methods do not allow for the intuitive exploration of clinical trade-offs during calibration. Recently a novel automated planning solution, which is calibrated using Pareto navigation principles, has been developed to address this issue. The purpose of this work was to clinically validate the solution for prostate cancer patients with and without elective nodal irradiation. MATERIALS AND METHODS: For 40 randomly selected patients (20 prostate and seminal vesicles (PSV) and 20 prostate and pelvic nodes (PPN)) automatically generated volumetric modulated arc therapy plans (VMATAuto) were compared against plans created by expert dosimetrists under clinical conditions (VMATClinical) and no time pressures (VMATIdeal). Plans were compared through quantitative comparison of dosimetric parameters and blind review by an oncologist. RESULTS: Upon blind review 39/40 and 33/40 VMATAuto plans were considered preferable or equal to VMATClinical and VMATIdeal respectively, with all deemed clinically acceptable. Dosimetrically, VMATAuto, VMATClinical and VMATIdeal were similar, with observed differences generally of low clinical significance. Compared to VMATClinical, VMATAuto reduced hands-on planning time by 94% and 79% for PSV and PPN respectively. Total planning time was significantly reduced from 22.2 mins to 14.0 mins for PSV, with no significant reduction observed for PPN. CONCLUSIONS: A novel automated planning solution has been evaluated, whose Pareto navigation based calibration enabled clinical decision-making on trade-off balancing to be intuitively incorporated into automated protocols. It was successfully applied to two sites of differing complexity and robustly generated high quality plans in an efficient manner.

Geometric analysis of synchronization in neuronal networks with global inhibition and coupling delays.

We study synaptically coupled neuronal networks to identify the role of coupling delays in network synchronized behaviour. We consider a network of excitable, relaxation oscillator neurons where two distinct populations, one excitatory and one inhibitory, are coupled with time-delayed synapses. The excitatory population is uncoupled, while the inhibitory population is tightly coupled without time delay. A geometric singular perturbation analysis yields existence and stability conditions for periodic solutions where the excitatory cells are synchronized and different phase relationships between the excitatory and inhibitory populations can occur, along with formulae for the periods of such solutions. In particular, we show that if there are no delays in the coupling, oscillations where the excitatory population is synchronized cannot occur. Numerical simulations are conducted to supplement and validate the analytical results. The analysis helps to explain how coupling delays in either excitatory or inhibitory synapses contribute to producing synchronized rhythms. This article is part of the theme issue 'Nonlinear dynamics of delay systems'.

Outgrowing seizures in Childhood Absence Epilepsy: time delays and bistability.

We formulate a conductance-based model for a 3-neuron motif associated with Childhood Absence Epilepsy (CAE). The motif consists of neurons from the thalamic relay (TC) and reticular nuclei (RT) and the cortex (CT). We focus on a genetic defect common to the mouse homolog of CAE which is associated with loss of GABAA receptors on the TC neuron, and the fact that myelination of axons as children age can increase the conduction velocity between neurons. We show the combination of low GABAA mediated inhibition of TC neurons and the long corticothalamic loop delay gives rise to a variety of complex dynamics in the motif, including bistability. This bistability disappears as the corticothalamic conduction delay shortens even though GABAA activity remains impaired. Thus the combination of deficient GABAA activity and changing axonal myelination in the corticothalamic loop may be sufficient to account for the clinical course of CAE.

Chaos in homeostatically regulated neural systems.

Low-dimensional yet rich dynamics often emerge in the brain. Examples include oscillations and chaotic dynamics during sleep, epilepsy, and voluntary movement. However, a general mechanism for the emergence of low dimensional dynamics remains elusive. Here, we consider Wilson-Cowan networks and demonstrate through numerical and analytical work that homeostatic regulation of the network firing rates can paradoxically lead to a rich dynamical repertoire. The dynamics include mixed-mode oscillations, mixed-mode chaos, and chaotic synchronization when the homeostatic plasticity operates on a moderately slower time scale than the firing rates. This is true for a single recurrently coupled node, pairs of reciprocally coupled nodes without self-coupling, and networks coupled through experimentally determined weights derived from functional magnetic resonance imaging data. In all cases, the stability of the homeostatic set point is analytically determined or approximated. The dynamics at the network level are directly determined by the behavior of a single node system through synchronization in both oscillatory and non-oscillatory states. Our results demonstrate that rich dynamics can be preserved under homeostatic regulation or even be caused by homeostatic regulation.

Symmetry, Hopf bifurcation, and the emergence of cluster solutions in time delayed neural networks.

We consider the networks of N identical oscillators with time delayed, global circulant coupling, modeled by a system of delay differential equations with ZN symmetry. We first study the existence of Hopf bifurcations induced by the coupling time delay and then use symmetric Hopf bifurcation theory to determine how these bifurcations lead to different patterns of symmetric cluster oscillations. We apply our results to a case study: a network of FitzHugh-Nagumo neurons with diffusive coupling. For this model, we derive the asymptotic stability, global asymptotic stability, absolute instability, and stability switches of the equilibrium point in the plane of coupling time delay (τ) and excitability parameter (a). We investigate the patterns of cluster oscillations induced by the time delay and determine the direction and stability of the bifurcating periodic orbits by employing the multiple timescales method and normal form theory. We find that in the region where stability switching occurs, the dynamics of the system can be switched from the equilibrium point to any symmetric cluster oscillation, and back to equilibrium point as the time delay is increased.

A closed NPZ model with delayed nutrient recycling.

We consider a closed Nutrient-Phytoplankton-Zooplankton (NPZ) model that allows for a delay in the nutrient recycling. A delay-dependent conservation law allows us to quantify the total biomass in the system. With this, we can investigate how a planktonic ecosystem is affected by the quantity of biomass it contains and by the properties of the delay distribution. The quantity of biomass and the length of the delay play a significant role in determining the existence of equilibrium solutions, since a sufficiently small amount of biomass or a long enough delay can lead to the extinction of a species. Furthermore, the quantity of biomass and length of delay are important since a small change in either can change the stability of an equilibrium solution. We explore these effects for a variety of delay distributions using both analytical and numerical techniques, and verify these results with simulations.

Sexual health needs and the LGBT community.

Lesbian, gay, bisexual and trans (LGBT) individuals have particular vulnerabilities to sexually transmitted infections and HIV infection. Globally, reasons for this include physiological factors, discrimination and poor understanding of their sexual health needs. In many countries LGBT individuals are not able to exercise fully their rights to health care. This raises public health concerns for the LGBT community and the wider population. This article explores these issues, and makes recommendations for the healthcare profession to address health inequalities and promote improved health outcomes for LGBT populations. This article aims to promote an evidence-based approach that focuses on rights and public health issues.

Bifurcations of large networks of two-dimensional integrate and fire neurons.

Recently, a class of two-dimensional integrate and fire models has been used to faithfully model spiking neurons. This class includes the Izhikevich model, the adaptive exponential integrate and fire model, and the quartic integrate and fire model. The bifurcation types for the individual neurons have been thoroughly analyzed by Touboul (SIAM J Appl Math 68(4):1045-1079, 2008). However, when the models are coupled together to form networks, the networks can display bifurcations that an uncoupled oscillator cannot. For example, the networks can transition from firing with a constant rate to burst firing. This paper introduces a technique to reduce a full network of this class of neurons to a mean field model, in the form of a system of switching ordinary differential equations. The reduction uses population density methods and a quasi-steady state approximation to arrive at the mean field system. Reduced models are derived for networks with different topologies and different model neurons with biologically derived parameters. The mean field equations are able to qualitatively and quantitatively describe the bifurcations that the full networks display. Extensions and higher order approximations are discussed.

Mean-field models for heterogeneous networks of two-dimensional integrate and fire neurons.

We analytically derive mean-field models for all-to-all coupled networks of heterogeneous, adapting, two-dimensional integrate and fire neurons. The class of models we consider includes the Izhikevich, adaptive exponential and quartic integrate and fire models. The heterogeneity in the parameters leads to different moment closure assumptions that can be made in the derivation of the mean-field model from the population density equation for the large network. Three different moment closure assumptions lead to three different mean-field systems. These systems can be used for distinct purposes such as bifurcation analysis of the large networks, prediction of steady state firing rate distributions, parameter estimation for actual neurons and faster exploration of the parameter space. We use the mean-field systems to analyze adaptation induced bursting under realistic sources of heterogeneity in multiple parameters. Our analysis demonstrates that the presence of heterogeneity causes the Hopf bifurcation associated with the emergence of bursting to change from sub-critical to super-critical. This is confirmed with numerical simulations of the full network for biologically reasonable parameter values. This change decreases the plausibility of adaptation being the cause of bursting in hippocampal area CA3, an area with a sizable population of heavily coupled, strongly adapting neurons.

Network bursting using experimentally constrained single compartment CA3 hippocampal neuron models with adaptation.

The hippocampus is a brain structure critical for memory functioning. Its network dynamics include several patterns such as sharp waves that are generated in the CA3 region. To understand how population outputs are generated, models need to consider aspects of network size, cellular and synaptic characteristics and context, which are necessarily 'balanced' in appropriate ways to produce particular outputs. Thick slice hippocampal preparations spontaneously produce sharp waves that are initiated in CA3 regions and depend on the right balance of glutamatergic activities. As a step toward developing network models that can explain important balances in the generation of hippocampal output, we develop models of CA3 pyramidal cells. Our models are single compartment in nature, use an Izhikevich-type structure and involve parameter values that are specifically designed to encompass CA3 intrinsic properties. Importantly, they incorporate spike frequency adaptation characteristics that are directly comparable to those measured experimentally. Excitatory networks using these model cells are able to produce bursting suggesting that the amount of spike frequency adaptation expressed in the biological cells is an essential contributor to network bursting, and as such, may be important for sharp wave generation. The network bursting mechanism is numerically dissected showing the critical balance between adaptation and excitatory drive. The compact nature of our models allows large network simulations to be efficiently computed. This, together with the linkage of our models to cellular characteristics, will allow us to develop an understanding of population output of CA3 hippocampus with direct biological comparisons.

A phase 2 study of SP1049C, doxorubicin in P-glycoprotein-targeting pluronics, in patients with advanced adenocarcinoma of the esophagus and gastroesophageal junction.

PURPOSE: To evaluate the antitumor activity of SP1049C, a novel P-glycoprotein targeting micellar formulation of doxorubicin, consisting of doxorubicin and two non-ionic block copolymers (pluronics), in patients with advanced adenocarcinoma of the esophagus and gastroesophageal junction (GEJ). PATIENTS AND METHODS: Patients with metastatic or locally advanced unresectable adenocarcinoma of the esophagus or GEJ who had not previously received systemic chemotherapy and had measurable disease were treated with SP1049C 75 mg/m(2) (doxorubicin equivalents) as a brief intravenous infusion every 3 weeks until disease progression or unacceptable toxicity. The primary endpoint was the objective response rate in patients who had received a least one course of SP1049C and had undergone tumor assessment, whereas, secondary endpoints included the objective response rate, progression-free survival (PFS), overall survival, and safety in the intent-to-treat (ITT) population. A review of scans was also conducted post-hoc by a blinded panel of radiologists. RESULTS: Twenty-one patients, of which 19 were evaluable for response, were treated with at least one dose of SP1049C. Nine patients had a partial response (PR) and eight patients had either a minor response or stable disease as their best response. The objective response rate was 47% (95% CI: 24.4-71) in the evaluable patient population, whereas the objective response rate was 43% (95% CI: 21.8-65.9) in the ITT population. The post-hoc radiological review confirmed that all nine responders had a PR; seven of the nine had a PR that was confirmed by a subsequent scan, whilst two patients had unconfirmed PRs. The median overall survival and PFS were 10.0 months (95%CI: 4.8-11.2) and 6.6 months (95% CI: 4.5-7.6), respectively. Neutropenia was the principal toxicity of SP1049C. Four patients developed an absolute percentage decrement of at least 15% in their left ventricular ejection fraction, none of which decreased to below 45% nor were symptomatic. CONCLUSION: SP1049C has a notable single-agent activity in patients with adenocarcinoma of the esophagus and GEJ, as well as an acceptable safety profile. These results, in addition to the results of preclinical studies demonstrating superior antitumor activity of SP1049C compared with doxorubicin in a standard formulation, indicate that further evaluations of SP1049C alone or combined with other relevant therapeutics in this disease setting are warranted.