PAGE Study: Summary of a Study Protocol to Estimate the Prevalence of Severe Asthma in Spain Using Big Data Methods

Cost-effectiveness and resource use analysis of patients with asthma before and after treatment with mepolizumab in a real-life setting

OBJECTIVE

To define the cost-effectiveness and health resource use of mepolizumab in a cohort of patients with severe eosinophilic asthma in real-life conditions in Spain.

METHODS

This was an observational, retrospective, single-center study. Patients included were diagnosed with severe eosinophilic asthma and treated with mepolizumab 100 mg subcutaneous (SC) 4-weekly for 12 months. Outcomes evaluated: incremental cost-effectiveness ratio (ICER), number of exacerbations, disease control with the Asthma Control Test (ACT), Asthma Quality of Life Questionnaire (AQLQ), and direct and indirect cost per patient.

RESULTS

12 months after mepolizumab initiation, a significant decrease in exacerbations was shown, from a mean (standard deviation [SD]) of 3.1 (2.6) to 0.7 (1.5), an increase from 4.9 (0.4) to 6.1 (0.5) in AQLQ, and from 14.9 (5.7) to 21.5 (3.9) in ACT scores. The number of cortico-dependent patients significantly decreased from 53.3% to 13.3% during this period. There was a significant decrease of 94% in the cost of hospitalization, from a mean (SD) of €4063.9 (5423.9) pretreatment to €238.6 (1306.9) post-treatment (p = 0.0003). Total costs decreased significantly from a median of €2,423.1 (1,512.8; 9,320.9) pretreatment to €1,177.5 (965.0; 1,737.8) post-treatment if mepolizumab was excluded. ICER per exacerbation avoided was €3606.9, per 3-point ACT score increase €3934.8, and per 0.5-point AQLQ score increase €3606.9.

CONCLUSIONS

Mepolizumab improves control of asthma and quality of life in patients with severe diseases in a cost-effectiveness range. The number of exacerbations decreased, and there was a clear reduction in primary care visits and hospitalizations. Further economic analyses of biological therapies for asthma are required.

Multimodal, open-source big data analysis in asthma: A novel approach to inform public health programming

Asthma is a chronic respiratory disease affecting over 358 million people worldwide; for this reason analysing big data on asthma from different countries could give a more detailed picture of current disease burden. We aim to investigate the correlations between asthma and key socio-demographic parameters from different world databases. We found a direct correlation with the gross domestic product (GDP) per capita and its nominal counterpart, with wealthiest countries seen to have the highest prevalence of asthma, as also confirmed by a similar correlation with the human development index (HDI). A positive correlation was also seen between asthma prevalence and a number of socio-cultural data being representative of a good life quality index and prevalent in more developed and wealthier countries. Concerning medical data, an inverse relationship was seen between asthma prevalence and helminthiasis. Those data indicate a higher prevalence for asthma in more developed countries, where socio-economic status is higher and also the access to medical care is more ubiquitous. The approach used in our study highlighted the role of medical literacy and access to healthcare facilities in the correct diagnosis of asthma and vice versa. Our data appear to be suitable in terms of a health programming approach because of the high burden of disease worldwide.

A Framework for Augmented Intelligence in Allergy and Immunology Practice and Research—A Work Group Report of the AAAAI Health Informatics, Technology, and Education Committee

Artificial and augmented intelligence (AI) and machine learning (ML) methods are expanding into the health care space. Big data are increasingly used in patient care applications, diagnostics, and treatment decisions in allergy and immunology. How these technologies will be evaluated, approved, and assessed for their impact is an important consideration for researchers and practitioners alike. With the potential of ML, deep learning, natural language processing, and other assistive methods to redefine health care usage, a scaffold for the impact of AI technology on research and patient care in allergy and immunology is needed. An American Academy of Asthma Allergy and Immunology Health Information Technology and Education subcommittee workgroup was convened to perform a scoping review of AI within health care as well as the specialty of allergy and immunology to address impacts on allergy and immunology practice and research as well as potential challenges including education, AI governance, ethical and equity considerations, and potential opportunities for the specialty. There are numerous potential clinical applications of AI in allergy and immunology that range from disease diagnosis to multidimensional data reduction in electronic health records or immunologic datasets. For appropriate application and interpretation of AI, specialists should be involved in the design, validation, and implementation of AI in allergy and immunology. Challenges include incorporation of data science and bioinformatics into training of future allergists-immunologists.

The applications of eHealth technologies in the management of asthma and allergic diseases

Portable devices, such as smartphones and mobile Internet access have become ubiquitous in the last decades. The term 'eHealth' stands for electronic health. The tools included in the eHealth concept utilize phones, computers and the Internet and related applications to improve the health care industry. Implementation of eHealth technologies has been documented for the management of different chronic diseases, including asthma and allergic conditions. Clinicians and patients have gained opportunity to communicate in new ways, which could be used cost-effectively to improve disease control and quality of life of those affected. Additionally, these innovations bring new opportunities to academic researchers. For example, eHealth has allowed researchers to compile data points that were previously unavailable or difficult to access, and analyse them using novel tools, collectively described as 'big data'. The role of eHealth become more important since early 2020, due to the physical distancing rules and the restrictions on mobility that have been applied worldwide as a response to the coronavirus disease 2019 pandemic. In this review, we summarize the most recent developments in various eHealth platforms and their relevance to the speciality of allergy and immunology, from the point of view of three major stakeholders: clinicians, patients and researchers.

Assessing the Performance of Clinical Natural Language Processing Systems: Development of an Evaluation Methodology

Background

Clinical natural language processing (cNLP) systems are of crucial importance due to their increasing capability in extracting clinically important information from free text contained in electronic health records (EHRs). The conversion of a nonstructured representation of a patient’s clinical history into a structured format enables medical doctors to generate clinical knowledge at a level that was not possible before. Finally, the interpretation of the insights gained provided by cNLP systems has a great potential in driving decisions about clinical practice. However, carrying out robust evaluations of those cNLP systems is a complex task that is hindered by a lack of standard guidance on how to systematically approach them.

Objective

Our objective was to offer natural language processing (NLP) experts a methodology for the evaluation of cNLP systems to assist them in carrying out this task. By following the proposed phases, the robustness and representativeness of the performance metrics of their own cNLP systems can be assured.

Methods

The proposed evaluation methodology comprised five phases: (1) the definition of the target population, (2) the statistical document collection, (3) the design of the annotation guidelines and annotation project, (4) the external annotations, and (5) the cNLP system performance evaluation. We presented the application of all phases to evaluate the performance of a cNLP system called “EHRead Technology” (developed by Savana, an international medical company), applied in a study on patients with asthma. As part of the evaluation methodology, we introduced the Sample Size Calculator for Evaluations (SLiCE), a software tool that calculates the number of documents needed to achieve a statistically useful and resourceful gold standard.

Results

The application of the proposed evaluation methodology on a real use-case study of patients with asthma revealed the benefit of the different phases for cNLP system evaluations. By using SLiCE to adjust the number of documents needed, a meaningful and resourceful gold standard was created. In the presented use-case, using as little as 519 EHRs, it was possible to evaluate the performance of the cNLP system and obtain performance metrics for the primary variable within the expected CIs.

Conclusions

We showed that our evaluation methodology can offer guidance to NLP experts on how to approach the evaluation of their cNLP systems. By following the five phases, NLP experts can assure the robustness of their evaluation and avoid unnecessary investment of human and financial resources. Besides the theoretical guidance, we offer SLiCE as an easy-to-use, open-source Python library.

The impact of COVID-19 on patients with asthma

Background

An association between the severity of coronavirus disease 2019 (COVID-19) and the presence of certain chronic conditions has been suggested. However, unlike influenza and other viruses, the disease burden of COVID-19 in patients with asthma has been less evident.

Objective

To understand the impact of COVID-19 in patients with asthma.

Methods

Using big-data analytics and artificial intelligence through the SAVANA Manager clinical platform, we analysed clinical data from patients with asthma from January 1 to May 10, 2020.

Results

Out of 71 182 patients with asthma, 1006 (1.41%) suffered from COVID-19. Compared to asthmatic individuals without COVID-19, patients with asthma and COVID-19 were significantly older (55 versus 42 years), predominantly female (66% versus 59%), smoked more frequently and had higher prevalence of hypertension, dyslipidaemias, diabetes and obesity. Allergy-related factors such as rhinitis and eczema were less common in asthmatic patients with COVID-19 (p<0.001). In addition, higher prevalence of these comorbidities was observed in patients with COVID-19 who required hospital admission. The use of inhaled corticosteroids (ICS) was lower in patients who required hospitalisation due to COVID-19, as compared to non-hospitalised patients (48.3% versus 61.5%; OR 0.58, 95% CI 0.44–0.77). Although patients treated with biologics (n=865; 1.21%) showed increased severity and more comorbidities at the ear, nose and throat level, COVID-19-related hospitalisations in these patients were relatively low (0.23%).

Conclusion

Patients with asthma and COVID-19 were older and at increased risk due to comorbidity-related factors. ICS and biologics are generally safe and may be associated with a protective effect against severe COVID-19 infection.

The increased risk of hospitalisation due to COVID-19 in patients with asthma is largely associated with age and related comorbidities. ICS and biologics may be associated with a protective effect against the most severe manifestations of COVID-19. https://bit.ly/37yhr5b