1
|
Levy ML, Kocks JWH, Bosnic-Anticevich S, Safioti G, Reich M, Depietro M, Castro M, Farooqui N, Lugogo NL, Brown R, Hill T, Li T, Chrystyn H. Uncovering patterns of inhaler technique and reliever use: the value of objective, personalized data from a digital inhaler. NPJ Prim Care Respir Med 2024; 34:23. [PMID: 39164292 PMCID: PMC11336086 DOI: 10.1038/s41533-024-00382-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 08/08/2024] [Indexed: 08/22/2024] Open
Abstract
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.
Collapse
Affiliation(s)
| | - Janwillem W H Kocks
- General Practitioners Research Institute, Groningen, Netherlands
- Observational and Pragmatic Research Institute, Midview City, Singapore
- Groningen Research Institute Asthma and COPD (GRIAC), University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Department of Pulmonology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | | | | | - Michael Reich
- Teva Pharmaceutical Industries Ltd, Tel Aviv, Israel
| | - Michael Depietro
- Teva Branded Pharmaceutical Products R&D Inc., West Chester, PA, USA
- Incyte Corporation, Newark, DE, USA
| | - Mario Castro
- Division of Pulmonary, Critical Care and Sleep Medicine, University of Kansas School of Medicine, Kansas City, KS, USA
| | | | - Njira L Lugogo
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Randall Brown
- Teva Branded Pharmaceutical Products R&D Inc., West Chester, PA, USA
| | - Tanisha Hill
- Teva Branded Pharmaceutical Products R&D Inc., West Chester, PA, USA
| | - Thomas Li
- Teva Branded Pharmaceutical Products R&D Inc., West Chester, PA, USA
| | | |
Collapse
|
2
|
Dierick BJH, Eikholt AA, van de Hei SJ, Muris JWM, Kerstjens HAM, van Boven JFM. Reshaping respiratory care: potential advances in inhaled pharmacotherapy in asthma. Expert Opin Pharmacother 2024; 25:1507-1516. [PMID: 39099418 DOI: 10.1080/14656566.2024.2389258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 07/30/2024] [Accepted: 08/02/2024] [Indexed: 08/06/2024]
Abstract
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.
Collapse
Affiliation(s)
- Boudewijn J H Dierick
- Department of Clinical Pharmacy & Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands
- Department of Primary and Long-term Care, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Amber A Eikholt
- Department of Clinical Pharmacy & Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands
| | - Susanne J van de Hei
- Department of Clinical Pharmacy & Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands
| | - Jean W M Muris
- Department of General Practice, Research Institute CAPHRI, Maastricht University, Maastricht, The Netherlands
| | - Huib A M Kerstjens
- Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands
- Department of Pulmonary Diseases and Tuberculosis, University of Groningen University Medical Center Groningen, Groningen, The Netherlands
| | - Job F M van Boven
- Department of Clinical Pharmacy & Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands
| |
Collapse
|
3
|
Gholap AD, Uddin MJ, Faiyazuddin M, Omri A, Gowri S, Khalid M. Advances in artificial intelligence for drug delivery and development: A comprehensive review. Comput Biol Med 2024; 178:108702. [PMID: 38878397 DOI: 10.1016/j.compbiomed.2024.108702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 05/12/2024] [Accepted: 06/01/2024] [Indexed: 07/24/2024]
Abstract
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.
Collapse
Affiliation(s)
- Amol D Gholap
- Department of Pharmaceutics, St. John Institute of Pharmacy and Research, Palghar, Maharashtra, 401404, India.
| | - Md Jasim Uddin
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
| | - Md Faiyazuddin
- School of Pharmacy, Al-Karim University, Katihar, Bihar, 854106, India; Centre for Global Health Research, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, India.
| | - Abdelwahab Omri
- Department of Chemistry and Biochemistry, The Novel Drug and Vaccine Delivery Systems Facility, Laurentian University, Sudbury, ON, P3E 2C6, Canada.
| | - S Gowri
- PG & Research, Department of Physics, Cauvery College for Women, Tiruchirapalli, Tamil Nadu, 620018, India
| | - Mohammad Khalid
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK; Sunway Centre for Electrochemical Energy and Sustainable Technology (SCEEST), School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500 Selangor Darul Ehsan, Malaysia; University Centre for Research and Development, Chandigarh University, Mohali, Punjab, 140413, India.
| |
Collapse
|
4
|
Oppenheimer J, Bender B, Sousa-Pinto B, Portnoy J. Use of Technology to Improve Adherence in Allergy/Immunology. THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY. IN PRACTICE 2024:S2213-2198(24)00749-9. [PMID: 39074604 DOI: 10.1016/j.jaip.2024.07.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 07/14/2024] [Accepted: 07/15/2024] [Indexed: 07/31/2024]
Abstract
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.
Collapse
Affiliation(s)
| | - Bruce Bender
- Center for Health Promotion, National Jewish Health, Denver, Colo
| | - Bernardo Sousa-Pinto
- MEDCIDS - Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal
| | | |
Collapse
|
5
|
Hakizimana A, Devani P, Gaillard EA. Current technological advancement in asthma care. Expert Rev Respir Med 2024; 18:499-512. [PMID: 38992946 DOI: 10.1080/17476348.2024.2380067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Accepted: 07/10/2024] [Indexed: 07/13/2024]
Abstract
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.
Collapse
Affiliation(s)
- Ali Hakizimana
- Department of Paediatric Respiratory Medicine. Leicester Children's Hospital, University Hospitals Leicester, Leicester, UK
| | - Pooja Devani
- Department of Paediatric Respiratory Medicine. Leicester Children's Hospital, University Hospitals Leicester, Leicester, UK
- Department of Respiratory Sciences, Leicester NIHR Biomedical Research Centre (Respiratory Theme), University of Leicester, Leicester, UK
| | - Erol A Gaillard
- Department of Paediatric Respiratory Medicine. Leicester Children's Hospital, University Hospitals Leicester, Leicester, UK
- Department of Respiratory Sciences, Leicester NIHR Biomedical Research Centre (Respiratory Theme), University of Leicester, Leicester, UK
| |
Collapse
|
6
|
Khan M, Banerjee S, Muskawad S, Maity R, Chowdhury SR, Ejaz R, Kuuzie E, Satnarine T. The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment. Curr Allergy Asthma Rep 2024; 24:361-372. [PMID: 38954325 DOI: 10.1007/s11882-024-01152-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/19/2024] [Indexed: 07/04/2024]
Abstract
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.
Collapse
Affiliation(s)
- Maham Khan
- Fatima Jinnah Medical University, Lahore, Pakistan.
| | | | | | - Rick Maity
- Institute of Post Graduate Medical Education and Research, Kolkata, West Bengal, India
| | | | - Rida Ejaz
- Shifa College of Medicine, Islamabad, Pakistan
| | | | | |
Collapse
|
7
|
Chan AHY, Te Ao B, Baggott C, Cavadino A, Eikholt AA, Harwood M, Hikaka J, Gibbs D, Hudson M, Mirza F, Naeem MA, Semprini R, Chang CL, Tsang KCH, Shah SA, Jeremiah A, Abeysinghe BN, Roy R, Wall C, Wood L, Dalziel S, Pinnock H, van Boven JFM, Roop P, Harrison J. DIGIPREDICT: physiological, behavioural and environmental predictors of asthma attacks-a prospective observational study using digital markers and artificial intelligence-study protocol. BMJ Open Respir Res 2024; 11:e002275. [PMID: 38777583 PMCID: PMC11116853 DOI: 10.1136/bmjresp-2023-002275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 04/11/2024] [Indexed: 05/25/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Amy Hai Yan Chan
- School of Pharmacy, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Region, New Zealand
| | - Braden Te Ao
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Christina Baggott
- Department of Respiratory Medicine and Respiratory research unit, Waikato Hospital, Hamilton, New Zealand
| | - Alana Cavadino
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Amber A Eikholt
- University Medical Centre Groningen, Groningen Research Institute for Asthma and COPD, Groningen, Netherlands
- Medication Adherence Expertise Center of the northern Netherlands (MAECON), Groningen, Netherlands
| | - Matire Harwood
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Joanna Hikaka
- Te Kupenga Hauora Māori, University of Auckland, Auckland, New Zealand
| | - Dianna Gibbs
- Pinnacle Midlands Health Network, Hamilton, New Zealand
| | - Mariana Hudson
- School of Pharmacy, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Region, New Zealand
| | - Farhaan Mirza
- Department of IT and Software Engineering, Auckland University of Technology, Auckland, New Zealand
| | - Muhammed Asif Naeem
- Department of IT and Software Engineering, Auckland University of Technology, Auckland, New Zealand
- National University of Computer and Emerging Sciences, Islamabad, Pakistan
| | - Ruth Semprini
- Medical Research Institute of New Zealand, Wellington, New Zealand
| | - Catherina L Chang
- Department of Respiratory Medicine and Respiratory research unit, Waikato Hospital, Hamilton, New Zealand
| | - Kevin C H Tsang
- University College London, London, UK
- The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, UK
| | - Syed Ahmar Shah
- The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, UK
| | - Aron Jeremiah
- Department of Electrical, Computer and Software Engineering, University of Auckland, Auckland, New Zealand
| | - Binu Nisal Abeysinghe
- Department of Electrical, Computer and Software Engineering, University of Auckland, Auckland, New Zealand
| | - Rajshri Roy
- Department of Nutrition and Dietetics, University of Auckland, Auckland, New Zealand
| | - Clare Wall
- Department of Nutrition and Dietetics, University of Auckland, Auckland, New Zealand
| | - Lisa Wood
- Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, New South Wales, Australia
| | - Stuart Dalziel
- Children's Emergency Department, Starship Children's Hospital, Auckland, New Zealand
| | - Hilary Pinnock
- The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, UK
| | - Job F M van Boven
- University Medical Centre Groningen, Groningen Research Institute for Asthma and COPD, Groningen, Netherlands
- Medication Adherence Expertise Center of the northern Netherlands (MAECON), Groningen, Netherlands
| | - Partha Roop
- Department of Electrical, Computer and Software Engineering, University of Auckland, Auckland, New Zealand
| | - Jeff Harrison
- School of Pharmacy, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Region, New Zealand
| |
Collapse
|
8
|
Darsha Jayamini WK, Mirza F, Asif Naeem M, Chan AHY. Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review. J Med Syst 2024; 48:49. [PMID: 38739297 PMCID: PMC11090925 DOI: 10.1007/s10916-024-02061-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 04/04/2024] [Indexed: 05/14/2024]
Abstract
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.
Collapse
Affiliation(s)
- Widana Kankanamge Darsha Jayamini
- School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, 1010, New Zealand.
- Department of Software Engineering, Faculty of Computing and Technology, University of Kelaniya, Kelaniya, 11300, Sri Lanka.
| | - Farhaan Mirza
- School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, 1010, New Zealand
| | - M Asif Naeem
- Department of Data Science & Artificial Intelligence, National University of Computer and Emerging Sciences (NUCES), Islamabad, 44000, Pakistan
| | - Amy Hai Yan Chan
- School of Pharmacy, Faculty of Medical and Health Sciences, University of Auckland, Auckland, 1142, New Zealand
| |
Collapse
|
9
|
Panettieri RA, Chipps BE, Skolnik N, George M, Murphy K, Lugogo N. The Use of Albuterol/Budesonide as Reliever Therapy to Reduce Asthma Exacerbations. THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY. IN PRACTICE 2024; 12:882-888. [PMID: 38316182 DOI: 10.1016/j.jaip.2024.01.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 12/18/2023] [Accepted: 01/17/2024] [Indexed: 02/07/2024]
Abstract
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.
Collapse
Affiliation(s)
- Reynold A Panettieri
- Rutgers Institute for Translational Medicine and Science, the State University of New Jersey, New Brunswick, NJ; Child Health Institute of New Jersey, Rutgers, the State University of New Jersey, New Brunswick, NJ
| | - Bradley E Chipps
- Capital Allergy & Respiratory Disease Center, Sacramento, Calif.
| | - Neil Skolnik
- Abington Family Medicine, Jenkintown, Pa; Department of Family and Community Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pa
| | - Maureen George
- Department of Nursing, Columbia University School of Nursing, New York, NY
| | - Kevin Murphy
- Boys Town National Research Hospital, Section of Adult and Pediatric Allergy and Pediatric Pulmonary, Boys Town, Neb
| | - Njira Lugogo
- Michigan Medicine, University of Michigan, Ann Arbor, Mich
| |
Collapse
|
10
|
van Dijkman SC, Yorgancıoğlu A, Pavord I, Brusselle G, Pitrez PM, Oosterholt S, Fumali S, Majumdar A, Della Pasqua O. Effect of Individual Patient Characteristics and Treatment Choices on Reliever Medication Use in Moderate-Severe Asthma: A Poisson Analysis of Randomised Clinical Trials. Adv Ther 2024; 41:1201-1225. [PMID: 38296921 PMCID: PMC10879282 DOI: 10.1007/s12325-023-02774-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 12/15/2023] [Indexed: 02/02/2024]
Abstract
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.
Collapse
Affiliation(s)
| | | | - Ian Pavord
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - Paulo M Pitrez
- Hospital Santa Casa de Porto Alegre, Porto Alegre, Brazil
| | - Sean Oosterholt
- Clinical Pharmacology Modelling and Simulation, GSK, London, UK
| | - Sourabh Fumali
- GSK, Global Classic and Established Medicines, Worli, India
| | - Anurita Majumdar
- GSK, Global Classic and Established Medicines, Singapore, Singapore
| | - Oscar Della Pasqua
- Clinical Pharmacology & Therapeutics Group, University College London, BMA House, Tavistock Square, London, WC1H 9JP, UK.
- GSK House, 980 Great West Rd, London, TW8 9GS, UK.
| |
Collapse
|
11
|
Koyuncu A, Ari A. Filling the gaps in the evaluation and selection of mobile health technologies in respiratory medicine. Expert Rev Respir Med 2024; 18:159-174. [PMID: 38795074 DOI: 10.1080/17476348.2024.2361048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 05/24/2024] [Indexed: 05/27/2024]
Abstract
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.
Collapse
Affiliation(s)
- Ayfer Koyuncu
- Graduate School of Science and Engineering, Bioengineering Department, Hacettepe University, Ankara, Turkey
| | - Arzu Ari
- College of Health Professions Department of Respiratory Care, Regent's Professor and Associate Dean for Research, Texas State University, Round Rock, TX, USA
| |
Collapse
|
12
|
van Boven JFM, Kocks JWH, Dierick BJH. Digital Inhaler Implementation in Daily Asthma Management: Who, When, and How? THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY. IN PRACTICE 2024; 12:396-398. [PMID: 38336396 DOI: 10.1016/j.jaip.2023.12.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 12/15/2023] [Indexed: 02/12/2024]
Affiliation(s)
- Job F M van Boven
- Department of Clinical Pharmacy & Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands; Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands; Medication Adherence Expertise Center of the northern Netherlands (MAECON), Groningen, The Netherlands.
| | - Janwillem W H Kocks
- Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands; Medication Adherence Expertise Center of the northern Netherlands (MAECON), Groningen, The Netherlands; General Practitioners Research Institute, Groningen, The Netherlands; Observational and Pragmatic Research Institute, Singapore, Singapore; Department of Pulmonary Diseases and Tuberculosis, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Boudewijn J H Dierick
- Department of Clinical Pharmacy & Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands; Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands
| |
Collapse
|
13
|
Molfino NA, Turcatel G, Riskin D. Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review. Adv Ther 2024; 41:534-552. [PMID: 38110652 PMCID: PMC10838858 DOI: 10.1007/s12325-023-02743-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 11/15/2023] [Indexed: 12/20/2023]
Abstract
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.
Collapse
Affiliation(s)
- Nestor A Molfino
- Global Development, Amgen Inc., One Amgen Center Dr, Thousand Oaks, CA, 91320, USA.
| | | | | |
Collapse
|
14
|
Kaplan A, Boivin M, Bouchard J, Kim J, Hayes S, Licskai C. The emerging role of digital health in the management of asthma. Ther Adv Chronic Dis 2023; 14:20406223231209329. [PMID: 38028951 PMCID: PMC10657529 DOI: 10.1177/20406223231209329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 09/25/2023] [Indexed: 12/01/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Alan Kaplan
- Department of Family and Community Medicine, University of Toronto, 14872 Yonge Street, Aurora, Toronto, ON L4G 1N2, Canada
- Family Physician Airways Group of Canada, Markham, ON, Canada
| | | | | | - James Kim
- Faculty of Medicine, University of Calgary, Calgary, AB, Canada
| | | | - Christopher Licskai
- Division of Respirology, Department of Medicine, Western University, London, ON, Canada
| |
Collapse
|
15
|
Lugogo N, O'Connor M, George M, Merchant R, Bensch G, Portnoy J, Oppenheimer J, Castro M. Expert Consensus on SABA Use for Asthma Clinical Decision-Making: A Delphi Approach. Curr Allergy Asthma Rep 2023; 23:621-634. [PMID: 37991672 PMCID: PMC10716188 DOI: 10.1007/s11882-023-01111-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/11/2023] [Indexed: 11/23/2023]
Abstract
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.
Collapse
Affiliation(s)
- Njira Lugogo
- Division of Pulmonary & Critical Care Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
| | - Maeve O'Connor
- Allergy Asthma and Immunology Relief, Charlotte, NC, USA
| | - Maureen George
- Columbia University School of Nursing, New York, NY, USA
| | - Rajan Merchant
- Woodland Clinic Medical Group, Allergy Department, Dignity Health, Woodland, CA, USA
| | - Greg Bensch
- Allergy Immunology and Asthma Medical Group, Stockton, CA, USA
| | - Jay Portnoy
- Section of Allergy, Asthma & Immunology, Children's Mercy Hospital, University of Missouri-Kansas City School of Medicine, Kansas City, MO, USA
| | - John Oppenheimer
- Department of Internal Medicine, New Jersey Medical School, Newark, NJ, USA
- Pulmonary and Allergy Associates, Morristown, NJ, USA
| | - Mario Castro
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, USA
| |
Collapse
|
16
|
Kallis C, Calvo RA, Schuller B, Quint JK. Development of an Asthma Exacerbation Risk Prediction Model for Conversational Use by Adults in England. Pragmat Obs Res 2023; 14:111-125. [PMID: 37817913 PMCID: PMC10560745 DOI: 10.2147/por.s424098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 09/19/2023] [Indexed: 10/12/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Constantinos Kallis
- National Heart and Lung Institute, and School of Public Health, Imperial College London, London, UK
| | - Rafael A Calvo
- Dyson School of Engineering, Faculty of Engineering, Imperial College London, London, UK
| | - Bjorn Schuller
- Department of Computing, Faculty of Engineering, Imperial College London, London, UK
| | - Jennifer K Quint
- National Heart and Lung Institute, and School of Public Health, Imperial College London, London, UK
| |
Collapse
|
17
|
van Breugel M, Fehrmann RSN, Bügel M, Rezwan FI, Holloway JW, Nawijn MC, Fontanella S, Custovic A, Koppelman GH. Current state and prospects of artificial intelligence in allergy. Allergy 2023; 78:2623-2643. [PMID: 37584170 DOI: 10.1111/all.15849] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 07/08/2023] [Accepted: 07/31/2023] [Indexed: 08/17/2023]
Abstract
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.
Collapse
Affiliation(s)
- Merlijn van Breugel
- Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- MIcompany, Amsterdam, the Netherlands
| | - Rudolf S N Fehrmann
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | | | - Faisal I Rezwan
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- Department of Computer Science, Aberystwyth University, Aberystwyth, UK
| | - John W Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospitals Southampton NHS Foundation Trust, Southampton, UK
| | - Martijn C Nawijn
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Department of Pathology and Medical Biology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Sara Fontanella
- National Heart and Lung Institute, Imperial College London, London, UK
- National Institute for Health and Care Research Imperial Biomedical Research Centre (BRC), London, UK
| | - Adnan Custovic
- National Heart and Lung Institute, Imperial College London, London, UK
- National Institute for Health and Care Research Imperial Biomedical Research Centre (BRC), London, UK
| | - Gerard H Koppelman
- Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| |
Collapse
|
18
|
MacMath D, Chen M, Khoury P. Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology. Curr Allergy Asthma Rep 2023:10.1007/s11882-023-01084-z. [PMID: 37160554 PMCID: PMC10169188 DOI: 10.1007/s11882-023-01084-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2023] [Indexed: 05/11/2023]
Abstract
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.
Collapse
Affiliation(s)
- Derek MacMath
- Department of Medicine, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Meng Chen
- Division of Pulmonary, Allergy & Critical Care Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Paneez Khoury
- National Institutes of Allergic and Infectious Diseases, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, USA.
| |
Collapse
|
19
|
Bosnic-Anticevich S, Bakerly ND, Chrystyn H, Hew M, van der Palen J. Advancing Digital Solutions to Overcome Longstanding Barriers in Asthma and COPD Management. Patient Prefer Adherence 2023; 17:259-272. [PMID: 36741814 PMCID: PMC9891071 DOI: 10.2147/ppa.s385857] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 12/09/2022] [Indexed: 01/30/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Sinthia Bosnic-Anticevich
- Woolcock Institute of Medical Research, University of Sydney, Sydney, NSW, Australia
- Correspondence: Sinthia Bosnic-Anticevich, Woolcock Institute of Medical Research, 431 Glebe Point Road, Glebe, 2037, NSW, Australia, Tel +61 414 015 614, Email
| | - Nawar Diar Bakerly
- Manchester Metropolitan University, Manchester, United Kingdom, Salford Royal NHS Foundation Trust, Manchester, UK
| | | | - Mark Hew
- Allergy, Asthma, and Clinical Immunology, Alfred Health, Melbourne, VIC, Australia
| | - Job van der Palen
- Medical School Twente, Medisch Spectrum Twente, Enschede, the Netherlands, and Section Cognition, Data and Education, University of Twente, Enschede, the Netherlands
| |
Collapse
|