1,721,065 research outputs found
Systemic drug-related intertriginous and flexural exanthema-like eruption after Oxford-AstraZeneca COVID-19 vaccine
No full textSystemic drug-related intertriginous and flexural exanthema (SDRIFE) is an adverse drug reaction which manifests as a symmetrical erythematous rash involving the skin folds after systemic drug exposure. A vast array of possible side effects associated with administration of different anti-SARS-CoV-2 vaccines have been reported in literature since the beginning of the COVID-19 pandemic, but only few times SDRIFE-like eruptions have been described in this context. We discuss here a case of SDRIFE-like eruption following the second dose of Oxford-Astrazeneca Vaxzevria vaccine
An interpretable cluster-based logistic regression model, with application to the characterization of response to therapy in severe eosinophilic asthma
No full textAsthma is a disease characterized by chronic airway hyperresponsiveness and inflammation, with signs of variable airflow limitation and impaired lung function leading to respiratory symptoms such as short- ness of breath, chest tightness and cough. Eosinophilic asthma is a distinct phenotype that affects more than half of patients diagnosed with severe asthma. It can be effectively treated with monoclonal antibodies target- ing specific immunological signaling pathways that fuel the inflammation underlying the disease, particularly Interleukin-5 (IL-5), a cytokine that plays a crucial role in asthma. In this study, we propose a data analysis pipeline aimed at identifying subphenotypes of severe eosinophilic asthma in relation to response to therapy at follow-up, which could have great potential for use in routine clinical practice. Once an optimal partition of patients into subphenotypes has been determined, the labels indicating the group to which each patient has been assigned are used in a novel way. For each input variable in a specialized logistic regression model, a clusterwise effect on response to therapy is determined by an appropriate interaction term between the input variable under consideration and the cluster label. We show that the clusterwise odds ratios can be meaning- fully interpreted conditional on the cluster label. In this way, we can define an effect measure for the response variable for each input variable in each of the groups identified by the clustering algorithm, which is not possi- ble in standard logistic regression because the effect of the reference class is aliased with the overall intercept. The interpretability of the model is enforced by promoting sparsity, a goal achieved by learning interactions in a hierarchical manner using a special group-Lasso technique. In addition, valid expressions are provided for computing odds ratios in the unusual parameterization used by the sparsity-promoting algorithm. We show how to apply the proposed data analysis pipeline to the problem of sub-phenotyping asthma patients also in terms of quality of response to therapy with monoclonal antibodies
A non-stationary Markov model for economic evaluation of grass pollen allergoid immunotherapy
Full text linkIntroduction
Allergic rhino-conjunctivitis (ARC) is an IgE-mediated disease that occurs after exposure to
indoor or outdoor allergens, or to non-specific triggers. Effective treatment options for seasonal
ARC are available, but the economic aspects and burden of these therapies are not of
secondary importance, also considered that the prevalence of ARC has been estimated at
23% in Europe. For these reasons, we propose a novel flexible cost-effectiveness analysis
(CEA) model, intended to provide healthcare professionals and policymakers with useful
information aimed at cost-effective interventions for grass-pollen induced allergic rhino-conjunctivitis
(ARC).
Methods
Treatments compared are: 1. no AIT, first-line symptomatic drug-therapy with no allergoid
immunotherapy (AIT). 2. SCIT, subcutaneous immunotherapy. 3. SLIT, sublingual immunotherapy.
The proposed model is a non-stationary Markovian model, that is flexible enough
to reflect those treatment-related problems often encountered in real-life and clinical practice,
but that cannot be adequately represented in randomized clinical trials (RCTs). At the
same time, we described in detail all the structural elements of the model as well as its input
parameters, in order to minimize any issue of transparency and facilitate the reproducibility
and circulation of the results among researchers.
Results
Using the no AIT strategy as a comparator, and the Incremental Cost Effectiveness Ratio
(ICER) as a statistic to summarize the cost-effectiveness of a health care intervention, we
could conclude that:
• SCIT systematically outperforms SLIT, except when a full societal perspective is considered.
For example, for T = 9 and a pollen season of 60 days, we have ICER = €16,729 for
SCIT vs. ICER = €15,116 for SLIT (in the full societal perspective).
• For longer pollen seasons or longer follow-up duration the ICER decreases, because each
patient experiences a greater clinical benefit over a larger time span, and Quality-adjusted
Life Year (QALYs) gained per cycle increase accordingly.
• Assuming that no clinical benefit is achieved after premature discontinuation, and that at
least three years of immunotherapy are required to improve clinical manifestations and perceiving
a better quality of life, ICERs become far greater than €30,000.
• If the immunotherapy is effective only at the peak of the pollen season, the relative ICERs
rise sharply. For example, in the scenario where no clinical benefit is present after premature
discontinuation of immunotherapy, we have ICER = €74,770 for SCIT vs. ICER = €152,110
for SLIT.
• The distance between SCIT and SLIT strongly depends on under which model the interventions
are meta-analyzed.
Conclusions
Even though there is a considerable evidence that SCIT outperforms SLIT, we could not
state that both SCIT and SLIT (or only one of these two) can be considered cost-effective
for ARC, as a reliable threshold value for cost-effectiveness set by national regulatory agencies
for pharmaceutical products is missing. Moreover, the impact of model input parameters
uncertainty on the reliability of our conclusions needs to be investigated further
Immunological and non-immunological mechanisms of allergic diseases in the elderly: Biological and clinical characteristics
Full text linkA better hygiene, a Westernized diet, air pollution, climate changes, and other factors that influence host microbiota, a key player in the induction and maintenance of immunoregulatory circuits and tolerance, are thought to be responsible for the increase of allergic diseases observed in the last years. The increase of allergic diseases in elderly is related to the presence of other factors as several comorbidities that should interfere with the development and the type of allergic reactions. A central role is played by immunosenescence responsible for modifying response to microbiota and triggering inflamm-ageing. In addition, in elderly there is a shift from Th1 responses vs. Th2, hence favouring allergic responses. Better understanding of the mechanisms of immunosenescence and its effects on allergic inflammation will most certainly lead to improved therapy
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