A Predictive Machine Learning Tool for Asthma Exacerbations: Results from a 12-Week, Open-Label Study Using an Electronic Multi-Dose Dry Powder Inhaler with Integrated Sensors

Uncovering patterns of inhaler technique and reliever use: the value of objective, personalized data from a digital inhaler

Electronic inhalers provide information about patterns of routine inhaler use. During a 12-week study, 360 asthma patients using albuterol Digihaler generated 53,083 inhaler events that were retrospectively analyzed. A total of 41,528 (78%) of the recorded inhalation events were suitable for flow analysis (having a PIF ≥ 18 L/min and <120 L/min). Median PIF, inhalation volume, inhalation duration, and time to PIF for these events steadily decreased between the first and last 10 days of the study, by 5.1%, 12.6%, 15.9%, and 6.4%, respectively. Continuous short-acting beta2-agonist (SABA) overuse, defined as ≥2 SABA inhalations/week throughout the study period, was seen in 29% (n = 104) of patients. Of 260 patients with ≥1 instance of acute short-term SABA overuse, 55 (21%) had a confirmed exacerbation. Electronic recording of real-life inhaler use can capture valuable, objective information that could inform disease management and clinical decision-making.

Reshaping respiratory care: potential advances in inhaled pharmacotherapy in asthma

INTRODUCTION

Asthma is a common disease with a global burden of 358 million patients. Despite improvements in pharmacological and non-pharmacological treatments, many patients still do not achieve complete asthma control. Therefore, innovative pharmacotherapy is important.

AREAS COVERED

Following a semi-structured search in Pubmed, an overview of advances in inhaled asthma therapy is provided, looking at innovations in digital inhalers, eco-friendly inhalers and novel inhaled biologic therapies, antibiotics and vaccines, as well as other potential novel asthma therapy targets.

EXPERT OPINION

Digital inhalers, sending reminders and monitoring inhalation technique electronically, can support medication adherence and improve asthma control. To reduce the global warming potential of traditional aerosols used in pressurized metered-dose inhalers (HFA-134a, HFA-227ea), greener alternatives are under development (HFA-152a, HFO-1234ze) that are expected to be available by 2025. Current pharmacological advances in asthma therapy are mainly achieved by novel biologicals (anti-IgE, anti-IL5, anti-IL4/13, and anti-TSLP) targeting specific severe asthma phenotypes. While injection is the usual administration route for biologics and vaccines used in asthma, inhalation is an option being explored, although several (mainly formulation) challenges need to be overcome. Other potential novel future inhaled asthma therapies include anti-IL-33/ST2 biologicals and JAK inhibitors, all still requiring more clinical evidence.

Advances in artificial intelligence for drug delivery and development: A comprehensive review

Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.

Use of Technology to Improve Adherence in Allergy/Immunology

The integration of technology into health care has shown significant promise in enhancing patient adherence, particularly in the field of allergy/immunology. This article explores the multifaceted approaches through which digital health interventions can be used to improve adherence rates among patients with allergic diseases and immunologic disorders. By reviewing recent advancements in telemedicine, mobile health applications, wearable devices, and digital reminders, as well as smart inhalers, we aim to provide a comprehensive overview of how these technologies can support patients in managing their conditions. The analysis highlights the role of personalized digital health plans, which, through the use of artificial intelligence and machine learning algorithms, can offer tailored advice, monitor symptoms, and adjust treatment protocols in real time. Moreover, the article discusses the impact of electronic health records and patient portals in fostering a collaborative patient-provider relationship, thereby enhancing communication and adherence. The integration of these technologies has been shown to not only improve clinical outcomes but also increase patient satisfaction and engagement.

Current technological advancement in asthma care

INTRODUCTION

Asthma is a common chronic respiratory disease affecting 262 million people globally, causing half a million deaths each year. Poor asthma outcomes are frequently due to non-adherence to medication, poor engagement with asthma services, and a lack of objective diagnostic tests. In recent years, technologies have been developed to improve diagnosis, monitoring, and care.

AREAS COVERED

Technology has impacted asthma care with the potential to improve patient outcomes, reduce healthcare costs, and provide personalized management. We focus on current evidence on home diagnostics and monitoring, remote asthma reviews, and digital smart inhalers. PubMed, Ovid/Embase, Cochrane Library, Scopus and Google Scholar were searched in November 2023 with no limit by year of publication.

EXPERT OPINION

Advanced diagnostic technologies have enabled early asthma detection and personalized treatment plans. Mobile applications and digital therapeutics empower patients to manage their condition and improve adherence to treatments. Telemedicine platforms and remote monitoring devices have the potential to streamline asthma care. AI algorithms can analyze patient data and predict exacerbations in proof-of-concept studies. Technology can potentially provide precision medicine to a wider patient group in the future, but further development is essential for implementation into routine care which in itself will be a major challenge.

The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment

PURPOSE OF REVIEW

Artificial intelligence (AI), be it neuronal networks, machine learning or deep learning, has numerous beneficial effects on healthcare systems; however, its potential applications and diagnostic capabilities for immunologic diseases have yet to be explored. Understanding AI systems can help healthcare workers better assimilate artificial intelligence into their practice and unravel its potential in diagnostics, clinical research, and disease management.

RECENT FINDINGS

We reviewed recent advancements in AI systems and their integration in healthcare systems, along with their potential benefits in the diagnosis and management of diseases. We explored machine learning as employed in allergy diagnosis and its learning patterns from patient datasets, as well as the possible advantages of using AI in the field of research related to allergic reactions and even remote monitoring. Considering the ethical challenges and privacy concerns raised by clinicians and patients with regard to integrating AI in healthcare, we explored the new guidelines adapted by regulatory bodies. Despite these challenges, AI appears to have been successfully incorporated into various healthcare systems and is providing patient-centered solutions while simultaneously assisting healthcare workers. Artificial intelligence offers new hope in the field of immunologic disease diagnosis, monitoring, and management and thus has the potential to revolutionize healthcare systems.

DIGIPREDICT: physiological, behavioural and environmental predictors of asthma attacks-a prospective observational study using digital markers and artificial intelligence-study protocol

INTRODUCTION

Asthma attacks are a leading cause of morbidity and mortality but are preventable in most if detected and treated promptly. However, the changes that occur physiologically and behaviourally in the days and weeks preceding an attack are not always recognised, highlighting a potential role for technology. The aim of this study 'DIGIPREDICT' is to identify early digital markers of asthma attacks using sensors embedded in smart devices including watches and inhalers, and leverage health and environmental datasets and artificial intelligence, to develop a risk prediction model to provide an early, personalised warning of asthma attacks.

METHODS AND ANALYSIS

A prospective sample of 300 people, 12 years or older, with a history of a moderate or severe asthma attack in the last 12 months will be recruited in New Zealand. Each participant will be given a smart watch (to assess physiological measures such as heart and respiratory rate), peak flow meter, smart inhaler (to assess adherence and inhalation) and a cough monitoring application to use regularly over 6 months with fortnightly questionnaires on asthma control and well-being. Data on sociodemographics, asthma control, lung function, dietary intake, medical history and technology acceptance will be collected at baseline and at 6 months. Asthma attacks will be measured by self-report and confirmed with clinical records. The collected data, along with environmental data on weather and air quality, will be analysed using machine learning to develop a risk prediction model for asthma attacks.

ETHICS AND DISSEMINATION

Ethical approval has been obtained from the New Zealand Health and Disability Ethics Committee (2023 FULL 13541). Enrolment began in August 2023. Results will be presented at local, national and international meetings, including dissemination via community groups, and submission for publication to peer-reviewed journals.

TRIAL REGISTRATION NUMBER

Australian New Zealand Clinical Trials Registry ACTRN12623000764639; Australian New Zealand Clinical Trials Registry.

Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review

Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in decision-making. In asthma management, machine learning excels in performing well-defined tasks, such as diagnosis, prediction, medication, and management. However, there remain uncertainties about how machine learning can be applied to predict asthma exacerbation. This study aimed to systematically review recent applications of machine learning techniques in predicting the risk of asthma attacks to assist asthma control and management. A total of 860 studies were initially identified from five databases. After the screening and full-text review, 20 studies were selected for inclusion in this review. The review considered recent studies published from January 2010 to February 2023. The 20 studies used machine learning techniques to support future asthma risk prediction by using various data sources such as clinical, medical, biological, and socio-demographic data sources, as well as environmental and meteorological data. While some studies considered prediction as a category, other studies predicted the probability of exacerbation. Only a group of studies applied prediction windows. The paper proposes a conceptual model to summarise how machine learning and available data sources can be leveraged to produce effective models for the early detection of asthma attacks. The review also generated a list of data sources that other researchers may use in similar work. Furthermore, we present opportunities for further research and the limitations of the preceding studies.

The Use of Albuterol/Budesonide as Reliever Therapy to Reduce Asthma Exacerbations

Prevention of asthma exacerbations and reduction of systemic corticosteroid burden remain unmet needs in asthma. US asthma guidelines recommend concomitant short-acting β2-agonist (SABA) and inhaled corticosteroid (ICS) as an alternative reliever at step 2. The Food and Drug Administration approved a pressurized metered-dose inhaler containing albuterol and budesonide for as-needed treatment or prevention of bronchoconstriction and for reducing exacerbation risk in patients with asthma aged ≥18 years. This combination is approved for use as a reliever with or without maintenance therapy, but it is not indicated for maintenance therapy (or for single maintenance and reliever therapy). Intervening with as-needed SABA-ICS during the window of opportunity to reduce inflammation during loss of asthma control can reduce exacerbation risk, by exerting both genomic and nongenomic anti-inflammatory effects. We propose that the use of albuterol-budesonide rather than albuterol as a reliever to manage episodic symptoms driven by acute bronchoconstriction and airway inflammation can improve outcomes. This combination approach, shown to decrease asthma exacerbations and oral corticosteroid burden in patients with moderate-to-severe asthma, represents a paradigm shift for asthma treatment in the United States. Further safety and efficacy studies should provide evidence that this type of reliever should be standard of care.

Effect of Individual Patient Characteristics and Treatment Choices on Reliever Medication Use in Moderate-Severe Asthma: A Poisson Analysis of Randomised Clinical Trials

INTRODUCTION

Even though increased use of reliever medication, including short-acting beta agonists (SABA), provides an indirect measure of symptom worsening, there have been limited efforts to assess how different patterns of reliever use correlate with symptom control and future risk of exacerbations. Here, we evaluate the effect of individual baseline characteristics on reliever use in patients with moderate-severe asthma on regular maintenance therapy with fluticasone propionate (FP) or combination therapy with fluticasone propionate/salmeterol (FP/SAL) or budesonide/formoterol (BUD/FOR).

METHODS

A drug-disease model describing the number of 24-h puffs and overnight occasions was developed with data from five clinical studies (N = 6212). The model was implemented using a nonlinear mixed effects approach and a Poisson function, considering clinical and demographic baseline characteristics. Goodness of fit and model predictive performance were assessed. Heatmaps were created to summarise the effect of concurrent baseline factors on reliever utilisation.

RESULTS

The final model accurately described individual patterns of reliever use, which is significantly increased with time since diagnosis, smoking, higher Asthma Control Questionnaire (ACQ-5) score and higher body mass index (BMI) at baseline. Whilst the number of puffs decreases slowly after an initial drop relative to the start of treatment, exacerbating patients utilise significantly more reliever than those who do not exacerbate. The mean effect of FP/SAL (median dose: 250/50 μg BID) on reliever use was slightly higher than that of BUD/FOR (median dose: 160/4.5 μg BID), i.e. a 75.3% vs 69.3% reduction in reliever use, respectively.

CONCLUSIONS

The availability of individual-level patient data in conjunction with a parametric approach enabled the characterisation of interindividual differences in the patterns of reliever use in patients with moderate-severe asthma. Taken together, individual demographic and clinical characteristics, as well as exacerbation history, can be considered an indicator of the degree of asthma control. High SABA reliever use suggests suboptimal clinical management of patients on maintenance therapy.

Filling the gaps in the evaluation and selection of mobile health technologies in respiratory medicine

INTRODUCTION

Mobile health (mHealth) technology in respiratory medicine is a fast-growing and promising digital technology that is popular among patients and healthcare providers (HCPs). They provide reminders and step-by-step instructions for the correct inhalation technique, monitor patients' adherence to treatment, and facilitate communication between patients and HCPs.

AREAS COVERED

While numerous mHealth apps have been developed over the years, most applications do not have supporting evidence. Selecting the best mHealth app in respiratory medicine is challenging due to limited studies carrying out mHealth app selection. Although mHealth technologies play an important part in the future of respiratory medicine, there is no single guide on the evaluation and selection of mHealth technologies for patients with pulmonary diseases. This paper aims to provide an overview of mHealth technologies, particularly emphasizing digital inhalers and standalone applications used in asthma. Additionally, it offers insights into the evaluation, selection, and pertinent considerations surrounding mHealth applications in respiratory medicine.

EXPERT OPINION

Evaluating mHealth apps will take time, resources, and collaboration between stakeholders such as governmental regulatory bodies, subject-matter experts, and industry representatives. Filling the gaps in the evaluation and selection of the mHealth app will improve clinical decision-making, personalized treatments, self-management and disease monitoring in respiratory medicine.

Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review

The implementation of artificial intelligence (AI) and machine learning (ML) techniques in healthcare has garnered significant attention in recent years, especially as a result of their potential to revolutionize personalized medicine. Despite advances in the treatment and management of asthma, a significant proportion of patients continue to suffer acute exacerbations, irrespective of disease severity and therapeutic regimen. The situation is further complicated by the constellation of factors that influence disease activity in a patient with asthma, such as medical history, biomarker phenotype, pulmonary function, level of healthcare access, treatment compliance, comorbidities, personal habits, and environmental conditions. A growing body of work has demonstrated the potential for AI and ML to accurately predict asthma exacerbations while also capturing the entirety of the patient experience. However, application in the clinical setting remains mostly unexplored, and important questions on the strengths and limitations of this technology remain. This review presents an overview of the rapidly evolving landscape of AI and ML integration into asthma management by providing a snapshot of the existing scientific evidence and proposing potential avenues for future applications.

The emerging role of digital health in the management of asthma

The most common reasons seen for lack of asthma control include misconceptions about disease control, low controller treatment adherence, poor inhaler technique, and the resulting underuse of controllers and overuse of short-acting beta2 agonists (SABAs). Narrowing these care gaps may be achieved through well-designed patient education that considers the patient's motivation, beliefs, and capabilities regarding their asthma and its management and empowers the patient to become an active participant in treatment decisions. Digital health technologies (DHTs) and digital therapeutic (DT) devices provide new opportunities to monitor treatment behaviors, improve communication between healthcare providers and patients, and generate data that inform educational interactions. DHT and DT have been proven effective in enhancing patient self-management in other chronic conditions, particularly diabetes. Accelerated integration of DHT and DT into the management of asthma patients is facilitated by the use of digital inhalers that employ sensor technology ("smart" inhalers). These devices efficiently provide real-time feedback on controller adherence, SABA use, and inhaler technique that have the strong potential to optimize asthma control.

Expert Consensus on SABA Use for Asthma Clinical Decision-Making: A Delphi Approach

PURPOSE OF REVIEW

A modified Delphi process was undertaken to provide a US expert-led consensus to guide clinical action on short-acting beta2-agonist (SABA) use. This comprised an online survey (Phase 1), forum discussion and statement development (Phase 2), and statement adjudication (Phase 3).

RECENT FINDINGS

In Phase 1 (n = 100 clinicians), 12% routinely provided patients with ≥4 SABA prescriptions/year, 73% solicited SABA use frequency at every patient visit, and 21% did not consult asthma guidelines/expert reports. Phase 3 experts (n = 8) reached consensus (median Likert score, interquartile range) that use of ≥3 SABA canisters/year is associated with increased risk of exacerbation and asthma-related death (5, 4.75-5); SABA use history should be solicited at every patient visit (5, 4.75-5); usage patterns over time, not absolute thresholds, should guide response to SABA overuse (5, 4.5-5). Future asthma guidelines should include clear recommendations regarding SABA usage, using expert-led thresholds for action.

Development of an Asthma Exacerbation Risk Prediction Model for Conversational Use by Adults in England

Background

Improving accurate risk assessment of asthma exacerbations, and reduction via relevant behaviour change among people with asthma could save lives and reduce health care costs. We developed a simple personalised risk prediction model for asthma exacerbations using factors collected in routine healthcare data for use in a risk modelling feature for automated conversational systems.

Methods

We used pseudonymised primary care electronic healthcare records from the Clinical Practice Research Datalink (CPRD) Aurum database in England. We combined variables for prediction of asthma exacerbations using logistic regression including age, gender, ethnicity, Index of Multiple Deprivation, geographical region and clinical variables related to asthma events.

Results

We included 1,203,741 patients divided into three cohorts to implement temporal validation: 898,763 (74.7%) in the training sample, 226,754 (18.8%) in the testing sample and 78,224 (6.5%) in the validation sample. The Area under the ROC curve (AUC) for the full model was 0.72 and for the restricted model was 0.71. Using a cut-off point of 0.1, approximately 27 asthma reviews by clinicians per 100 patients would be prevented compared with a strategy that all patients are regarded as high risk. Compared with patients without an exacerbation, patients who exacerbated were older, more likely to be female, prescribed more SABA and ICS in the preceding 12 months, have history of GORD, COPD, anxiety, depression, live in very deprived areas and have more severe disease.

Conclusion

Using information available from routinely collected electronic healthcare record data, we developed a model that has moderate ability to separate patients who had an asthma exacerbation within 3 months from their index date from patients who did not. When comparing this model with a simplified model with variables that can easily be self-reported through a WhatsApp chatbot, we have shown that the predictive performance of the model is not substantially different.

Current state and prospects of artificial intelligence in allergy

The field of medicine is witnessing an exponential growth of interest in artificial intelligence (AI), which enables new research questions and the analysis of larger and new types of data. Nevertheless, applications that go beyond proof of concepts and deliver clinical value remain rare, especially in the field of allergy. This narrative review provides a fundamental understanding of the core concepts of AI and critically discusses its limitations and open challenges, such as data availability and bias, along with potential directions to surmount them. We provide a conceptual framework to structure AI applications within this field and discuss forefront case examples. Most of these applications of AI and machine learning in allergy concern supervised learning and unsupervised clustering, with a strong emphasis on diagnosis and subtyping. A perspective is shared on guidelines for good AI practice to guide readers in applying it effectively and safely, along with prospects of field advancement and initiatives to increase clinical impact. We anticipate that AI can further deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.

Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology

PURPOSE OF REVIEW

Artificial intelligence (AI) has increasingly been used in healthcare. Given the capacity of AI to handle large data and complex relationships between variables, AI is well suited for applications in healthcare. Recently, AI has been applied to allergy research.

RECENT FINDINGS

In this article, we review how AI technologies have been utilized in basic science and clinical allergy research for asthma, atopic dermatitis, rhinology, adverse reactions to drugs and vaccines, food allergy, anaphylaxis, urticaria, and eosinophilic gastrointestinal disorders. We discuss barriers for AI adoption to improve the care of patients with atopic diseases. These studies demonstrate the utility of applying AI to the field of allergy to help investigators expand their understanding of disease pathogenesis, improve diagnostic accuracy, enable prediction for treatments and outcomes, and for drug discovery.

Advancing Digital Solutions to Overcome Longstanding Barriers in Asthma and COPD Management

Maintenance therapy delivered via inhaler is central to asthma and chronic obstructive pulmonary disease (COPD) management. Poor adherence to inhaled medication and errors in inhalation technique have long represented major barriers to the optimal management of these chronic conditions. Technological innovations may provide a means of overcoming these barriers. This narrative review examines ongoing advances in digital technologies relevant to asthma and COPD with the potential to inform clinical decision-making and improve patient care. Digital inhaler devices linked to mobile apps can help bring about changes in patients' behaviors and attitudes towards disease management, particularly when they build in elements of interactivity and gamification. They can also support ongoing technique education, empowering patients and helping providers maximize the value of consultations and develop effective action plans informed by insights into the patient's inhaler use patterns and their respiratory health. When combined with innovative techniques such as machine learning, digital devices have the potential to predict exacerbations and prompt pre-emptive intervention. Finally, digital devices may support an advanced precision medicine approach to respiratory disease management and help support shared decision-making. Further work is needed to increase uptake of digital devices and integrate their use into care pathways before their full potential in personalized asthma and COPD management can be realized.