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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.
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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
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He P, Moraes TJ, Dai D, Reyna-Vargas ME, Dai R, Mandhane P, Simons E, Azad MB, Hoskinson C, Petersen C, Del Bel KL, Turvey SE, Subbarao P, Goldenberg A, Erdman L. Early prediction of pediatric asthma in the Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort using machine learning. Pediatr Res 2024; 95:1818-1825. [PMID: 38212387 PMCID: PMC11245385 DOI: 10.1038/s41390-023-02988-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 11/29/2023] [Accepted: 12/15/2023] [Indexed: 01/13/2024]
Abstract
BACKGROUND Early identification of children at risk of asthma can have significant clinical implications for effective intervention and treatment. This study aims to disentangle the relative timing and importance of early markers of asthma. METHODS Using the CHILD Cohort Study, 132 variables measured in 1754 multi-ethnic children were included in the analysis for asthma prediction. Data up to 4 years of age was used in multiple machine learning models to predict physician-diagnosed asthma at age 5 years. Both predictive performance and variable importance was assessed in these models. RESULTS Early-life data (≤1 year) has limited predictive ability for physician-diagnosed asthma at age 5 years (area under the precision-recall curve (AUPRC) < 0.35). The earliest reliable prediction of asthma is achieved at age 3 years, (area under the receiver-operator curve (AUROC) > 0.90) and (AUPRC > 0.80). Maternal asthma, antibiotic exposure, and lower respiratory tract infections remained highly predictive throughout childhood. Wheezing status and atopy are the most important predictors of early childhood asthma from among the factors included in this study. CONCLUSIONS Childhood asthma is predictable from non-biological measurements from the age of 3 years, primarily using parental asthma and patient history of wheezing, atopy, antibiotic exposure, and lower respiratory tract infections. IMPACT Machine learning models can predict physician-diagnosed asthma in early childhood (AUROC > 0.90 and AUPRC > 0.80) using ≥3 years of non-biological and non-genetic information, whereas prediction with the same patient information available before 1 year of age is challenging. Wheezing, atopy, antibiotic exposure, lower respiratory tract infections, and the child's mother having asthma were the strongest early markers of 5-year asthma diagnosis, suggesting an opportunity for earlier diagnosis and intervention and focused assessment of patients at risk for asthma, with an evolving risk stratification over time.
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Affiliation(s)
- Ping He
- Center for Computational Medicine, The Hospital for Sick Children, Toronto, ON, Canada
| | - Theo J Moraes
- Translational Medicine Program, The Hospital for Sick Children, Toronto, ON, Canada
| | - Darlene Dai
- Department of Pediatrics, BC Children's Hospital, University of British Columbia, Vancouver, BC, Canada
| | | | - Ruixue Dai
- Translational Medicine Program, The Hospital for Sick Children, Toronto, ON, Canada
| | | | - Elinor Simons
- Department of Pediatrics & Child Health, University of Manitoba, Winnipeg, MB, Canada
| | - Meghan B Azad
- Department of Pediatrics & Child Health, University of Manitoba, Winnipeg, MB, Canada
| | - Courtney Hoskinson
- Department of Pediatrics, BC Children's Hospital, University of British Columbia, Vancouver, BC, Canada
- Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, Canada
| | - Charisse Petersen
- Department of Pediatrics, BC Children's Hospital, University of British Columbia, Vancouver, BC, Canada
| | - Kate L Del Bel
- Department of Pediatrics, BC Children's Hospital, University of British Columbia, Vancouver, BC, Canada
| | - Stuart E Turvey
- Department of Pediatrics, BC Children's Hospital, University of British Columbia, Vancouver, BC, Canada
| | - Padmaja Subbarao
- Translational Medicine Program, The Hospital for Sick Children, Toronto, ON, Canada
| | - Anna Goldenberg
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Department of Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
- CIFAR, Toronto, ON, Canada
| | - Lauren Erdman
- Center for Computational Medicine, The Hospital for Sick Children, Toronto, ON, Canada.
- Department of Computer Science, University of Toronto, Toronto, ON, Canada.
- Department of Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada.
- Vector Institute, Toronto, ON, Canada.
- James M. Anderson Center for Health Centers Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
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Scadding GK, McDonald M, Backer V, Scadding G, Bernal-Sprekelsen M, Conti DM, De Corso E, Diamant Z, Gray C, Hopkins C, Jesenak M, Johansen P, Kappen J, Mullol J, Price D, Quirce S, Reitsma S, Salmi S, Senior B, Thyssen JP, Wahn U, Hellings PW. Pre-asthma: a useful concept for prevention and disease-modification? A EUFOREA paper. Part 1-allergic asthma. FRONTIERS IN ALLERGY 2024; 4:1291185. [PMID: 38352244 PMCID: PMC10863454 DOI: 10.3389/falgy.2023.1291185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 12/26/2023] [Indexed: 02/16/2024] Open
Abstract
Asthma, which affects some 300 million people worldwide and caused 455,000 deaths in 2019, is a significant burden to suffers and to society. It is the most common chronic disease in children and represents one of the major causes for years lived with disability. Significant efforts are made by organizations such as WHO in improving the diagnosis, treatment and monitoring of asthma. However asthma prevention has been less studied. Currently there is a concept of pre- diabetes which allows a reduction in full blown diabetes if diet and exercise are undertaken. Similar predictive states are found in Alzheimer's and Parkinson's diseases. In this paper we explore the possibilities for asthma prevention, both at population level and also investigate the possibility of defining a state of pre-asthma, in which intensive treatment could reduce progression to asthma. Since asthma is a heterogeneous condition, this paper is concerned with allergic asthma. A subsequent one will deal with late onset eosinophilic asthma.
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Affiliation(s)
- G. K. Scadding
- Department of Allergy & Rhinology, Royal National ENT Hospital, London, United Kingdom
- Division of Immunity and Infection, University College, London, United Kingdom
| | - M. McDonald
- The Allergy Clinic, Blairgowrie, Randburg, South Africa
| | - V. Backer
- Department of Otorhinolaryngology, Head & Neck Surgery, and Audiology, Rigshospitalet, Copenhagen University, Copenhagen, Denmark
| | - G. Scadding
- Allergy, Royal Brompton Hospital, London, United Kingdom
| | - M. Bernal-Sprekelsen
- Head of ORL-Deptartment, Clinic Barcelona, Barcelona, Spain
- Chair of ORL, University of Barcelona, Barcelona, Spain
| | - D. M. Conti
- The European Forum for Research and Education in Allergy and Airway Diseases Scientific Expert Team Members, Brussels, Belgium
| | - E. De Corso
- Otolaryngology Head and Neck Surgery, A. Gemelli University Hospital Foundation IRCCS, Rome, Italy
| | - Z. Diamant
- Department of Respiratory Medicine & Allergology, Institute for Clinical Science, Skane University Hospital, Lund University, Lund, Sweden
- Department of Respiratory Medicine, First Faculty of Medicine, Charles University and Thomayer Hospital, Prague, Czech Republic
- Department Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Deptarment of Microbiology Immunology & Transplantation, KU Leuven, Catholic University of Leuven, Leuven, Belgium
| | - C. Gray
- Paediatric Allergist, Red Cross Children’s Hospital and University of Cape Town, Cape Town, South Africa
- Kidsallergy Centre, Cape Town, South Africa
| | - C. Hopkins
- Department of Rhinology and Skull Base Surgery, Guy’s and St Thomas’ Hospital NHS Foundation Trust, London, United Kingdom
| | - M. Jesenak
- Department of Clinical Immunology and Allergology, University Teaching Hospital in Martin, Martin, Slovakia
- Department of Paediatrics, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, University Teaching Hospital in Martin, Martin, Slovakia
- Department of Pulmonology and Phthisiology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, University Teaching Hospital in Martin, Martin, Slovakia
| | - P. Johansen
- Department of Dermatology, University of Zurich, Zurich, Switzerland
- Department of Dermatology, University Hospital of Zurich, Zurich, Switzerland
| | - J. Kappen
- Department of Pulmonology, STZ Centre of Excellence for Asthma, COPD and Respiratory Allergy, Franciscus Gasthuis & Vlietland, Rotterdam, Netherlands
| | - J. Mullol
- Rhinology Unit and Smell Clinic, ENT Department, Hospital Clínic, FRCB-IDIBAPS, Universitat de Barcelona, CIBERES, Barcelona, Spain
| | - D. Price
- Observational and Pragmatic Research Institute, Singapore, Singapore
- Division of Applied Health Sciences, Centre of Academic Primary Care, University of Aberdeen, Aberdeen, United Kingdom
| | - S. Quirce
- Department of Allergy, La Paz University Hospital, IdiPAZ, Madrid, Spain
| | - S. Reitsma
- Department of Otorhinolarynogology and Head/Neck Surgery, Amsterdam University Medical Centres, Location AMC, University of Amsterdam, Amsterdam, Netherlands
| | - S. Salmi
- Department of Otorhinolaryngology, Kuopio University Hospital and University of Eastern Finland, Kuopio, Finland
- Department of Allergy, Inflammation Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - B. Senior
- Department of Otolaryngology/Head and Neck Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - J. P. Thyssen
- Department of Dermatology, Bispebjerg Hospital, University of Copenhagen, Copenhagen, Denmark
| | - U. Wahn
- Former Head of the Department for Pediatric Pneumology and Immunology, Charite University Medicine, Berlin, Germany
| | - P. W. Hellings
- Department of Otorhinolaryngology-Head and Neck Surgery, University Hospitals, Leuven, Belgium
- Laboratory of Allergy and Clinical Immunology, University Hospitals Leuven, Leuven, Belgium
- Upper Airways Research Laboratory, Department of Head and Skin, Ghent University, Ghent, Belgium
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Li D, Abhadiomhen SE, Zhou D, Shen XJ, Shi L, Cui Y. Asthma prediction via affinity graph enhanced classifier: a machine learning approach based on routine blood biomarkers. J Transl Med 2024; 22:100. [PMID: 38268004 PMCID: PMC10809685 DOI: 10.1186/s12967-024-04866-9] [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: 10/14/2023] [Accepted: 01/06/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND Asthma is a chronic respiratory disease affecting millions of people worldwide, but early detection can be challenging due to the time-consuming nature of the traditional technique. Machine learning has shown great potential in the prompt prediction of asthma. However, because of the inherent complexity of asthma-related patterns, current models often fail to capture the correlation between data samples, limiting their accuracy. Our objective was to use our novel model to address the above problem via an Affinity Graph Enhanced Classifier (AGEC) to improve predictive accuracy. METHODS The clinical dataset used in this study consisted of 152 samples, where 24 routine blood markers were extracted as features to participate in the classification due to their ease of sourcing and relevance to asthma. Specifically, our model begins by constructing a projection matrix to reduce the dimensionality of the feature space while preserving the most discriminative features. Simultaneously, an affinity graph is learned through the resulting subspace to capture the internal relationship between samples better. Leveraging domain knowledge from the affinity graph, a new classifier (AGEC) is introduced for asthma prediction. AGEC's performance was compared with five state-of-the-art predictive models. RESULTS Experimental findings reveal the superior predictive capabilities of AGEC in asthma prediction. AGEC achieved an accuracy of 72.50%, surpassing FWAdaBoost (61.02%), MLFE (60.98%), SVR (64.01%), SVM (69.80%) and ERM (68.40%). These results provide evidence that capturing the correlation between samples can enhance the accuracy of asthma prediction. Moreover, the obtained [Formula: see text] values also suggest that the differences between our model and other models are statistically significant, and the effect of our model does not exist by chance. CONCLUSION As observed from the experimental results, advanced statistical machine learning approaches such as AGEC can enable accurate diagnosis of asthma. This finding holds promising implications for improving asthma management.
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Affiliation(s)
- Dejing Li
- Department of Respiratory, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, 214023, China
| | - Stanley Ebhohimhen Abhadiomhen
- School of Computer Science and Communication Engineering, JiangSu University, Zhenjiang, JiangSu, 212013, China
- Department of Computer Science, University of Nigeria, Nsukka, Nigeria
| | - Dongmei Zhou
- Clinical Research Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, 214023, China
| | - Xiang-Jun Shen
- School of Computer Science and Communication Engineering, JiangSu University, Zhenjiang, JiangSu, 212013, China
| | - Lei Shi
- Department of Clinical Laboratory, Shuguang Hospital Affiliated to Shanghai University of Chinese Traditional Medicine, Shanghai, 201203, China.
| | - Yubao Cui
- Clinical Research Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, 214023, China.
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Yoon SJ, Kim D, Park SH, Han JH, Lim J, Shin JE, Eun HS, Lee SM, Park MS. Prediction of Postnatal Growth Failure in Very Low Birth Weight Infants Using a Machine Learning Model. Diagnostics (Basel) 2023; 13:3627. [PMID: 38132211 PMCID: PMC10743090 DOI: 10.3390/diagnostics13243627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/04/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023] Open
Abstract
Accurate prediction of postnatal growth failure (PGF) can be beneficial for early intervention and prevention. We aimed to develop a machine learning model to predict PGF at discharge among very low birth weight (VLBW) infants using extreme gradient boosting. A total of 729 VLBW infants, born between 2013 and 2017 in four hospitals, were included. PGF was defined as a decrease in z-score between birth and discharge that was greater than 1.28. Feature selection and addition were performed to improve the accuracy of prediction at four different time points, including 0, 7, 14, and 28 days after birth. A total of 12 features with high contribution at all time points by feature importance were decided upon, and good performance was shown as an area under the receiver operating characteristic curve (AUROC) of 0.78 at 7 days. After adding weight change to the 12 features-which included sex, gestational age, birth weight, small for gestational age, maternal hypertension, respiratory distress syndrome, duration of invasive ventilation, duration of non-invasive ventilation, patent ductus arteriosus, sepsis, use of parenteral nutrition, and reach at full enteral nutrition-the AUROC at 7 days after birth was shown as 0.84. Our prediction model for PGF performed well at early detection. Its potential clinical application as a supplemental tool could be helpful for reducing PGF and improving child health.
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Affiliation(s)
- So Jin Yoon
- Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.J.Y.)
| | - Donghyun Kim
- Department of Advanced General Dentistry, Yonsei University College of Dentistry, Seoul 03722, Republic of Korea
- InVisionLab Inc., Seoul 05854, Republic of Korea
| | - Sook Hyun Park
- Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.J.Y.)
| | - Jung Ho Han
- Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.J.Y.)
| | - Joohee Lim
- Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.J.Y.)
| | - Jeong Eun Shin
- Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.J.Y.)
| | - Ho Seon Eun
- Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.J.Y.)
| | - Soon Min Lee
- Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.J.Y.)
| | - Min Soo Park
- Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.J.Y.)
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Custovic D, Fontanella S, Custovic A. Understanding progression from pre-school wheezing to school-age asthma: Can modern data approaches help? Pediatr Allergy Immunol 2023; 34:e14062. [PMID: 38146116 DOI: 10.1111/pai.14062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 12/01/2023] [Indexed: 12/27/2023]
Abstract
Preschool wheezing and childhood asthma create a heavy disease burden which is only exacerbated by the complexity of the conditions. Preschool wheezing exhibits both "curricular" and "aetiological" heterogeneity: that is, heterogeneity across patients both in the time-course of its development and in its underpinning pathological mechanisms. Since these are not fully understood, but clinical presentations across patients may nonetheless be similar, current diagnostic labels are imprecise-not mapping cleanly onto underlying disease mechanisms-and prognoses uncertain. These uncertainties also make a identifying new targets for therapeutic intervention difficult. In the past few decades, carefully designed birth cohort studies have collected "big data" on a large scale, incorporating not only a wealth of longitudinal clinical data, but also detailed information from modalities as varied as imaging, multiomics, and blood biomarkers. The profusion of big data has seen the proliferation of what we term "modern data approaches" (MDAs)-grouping together machine learning, artificial intelligence, and data science-to make sense and make use of this data. In this review, we survey applications of MDAs (with an emphasis on machine learning) in childhood wheeze and asthma, highlighting the extent of their successes in providing tools for prognosis, unpicking the curricular heterogeneity of these conditions, clarifying the limitations of current diagnostic criteria, and indicating directions of research for uncovering the etiology of the diseases underlying these conditions. Specifically, we focus on the trajectories of childhood wheeze phenotypes. Further, we provide an explainer of the nature and potential use of MDAs and emphasize the scope of what we can hope to achieve with them.
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Affiliation(s)
- Darije Custovic
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Sara Fontanella
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Adnan Custovic
- National Heart and Lung Institute, Imperial College London, London, UK
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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: 0] [Impact Index Per Article: 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.
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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
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Li RJ, Wen YX. Association of body mass index with asthma occurrence and persistence in adolescents: A retrospective study of NHANES (2011-2018). Heliyon 2023; 9:e20092. [PMID: 37809502 PMCID: PMC10559872 DOI: 10.1016/j.heliyon.2023.e20092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 09/10/2023] [Accepted: 09/11/2023] [Indexed: 10/10/2023] Open
Abstract
Objective To investigate the association of body mass index (BMI) with asthma and analyze the risk factors of asthma persistence among overweight/obese adolescents and those with a high risk for obesity. Methods In this cross-sectional study, adolescents aged 11-17 years with complete general information and asthma diagnoses were selected from the National Health and Nutrition Examination Survey database. For adolescents without self-reported asthma, we performed matching according to age and sex at a case-to-control ratio of 1:3. Logistic regression analysis was employed to identify independent predictors of asthma occurrence followed by constructing a nomogram and comparing its efficacy to independent factors in predicting asthma occurrence. Besides, associations of BMI with asthma occurrence and persistence were evaluated. Finally, we obtained risk factors for asthma persistence in overweight/obese individuals and those at a high risk for obesity. Results Totally 753 adolescents with asthma and 2259 adolescents without asthma were included to analyze the occurrence of asthma. BMI and Hispanic Ethnicity were independent predictors of asthma occurrence and were included in nomogram construction. BMI had an efficiency comparable to that of the nomogram model in predicting asthma occurrence, which is superior to that of Hispanic Ethnicity. Of the 753 adolescents diagnosed with asthma, 464 were still diagnosed with asthma of at least a year's duration. Interestingly, BMI may have the ability to predict asthma persistence. Further, Hispanic Ethnicity and household income were significantly related to asthma occurrence among overweight/obese and high-risk obese individuals. Conclusions High BMI could independently predict increased asthma occurrence. Additionally, BMI may play an essential role in predicting asthma persistence. This study may help improve the diagnosis and reduce the occurrence of asthma.
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Affiliation(s)
- Ren-jie Li
- Emergency Department, Affiliated Hospital of Putian University, Putian, 351100, Fujian, China
| | - Ying-xu Wen
- Emergency Department, The Second Affiliated Hospital of Hainan Medical University, Haikou, 570000, Hainan, China
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Salehian S, Fleming L, Saglani S, Custovic A. Phenotype and endotype based treatment of preschool wheeze. Expert Rev Respir Med 2023; 17:853-864. [PMID: 37873657 DOI: 10.1080/17476348.2023.2271832] [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: 03/17/2023] [Accepted: 10/13/2023] [Indexed: 10/25/2023]
Abstract
INTRODUCTION Preschool wheeze (PSW) is a significant public health issue, with a high presentation rate to emergency departments, recurrent symptoms, and severe exacerbations. A heterogenous condition, PSW comprises several phenotypes that may relate to a range of pathobiological mechanisms. However, treating PSW remains largely generalized to inhaled corticosteroids and a short acting beta agonist, guided by symptom-based labels that often do not reflect underlying pathways of disease. AREAS COVERED We review the observable features and characteristics used to ascribe phenotypes in children with PSW and available pathobiological evidence to identify possible endotypes. These are considered in the context of treatment options and future research directions. The role of machine learning (ML) and modern analytical techniques to identify patterns of disease that distinguish phenotypes is also explored. EXPERT OPINION Distinct clusters (phenotypes) of severe PSW are characterized by different underlying mechanisms, some shared and some unique. ML-based methodologies applied to clinical, biomarker, and environmental data can help design tools to differentiate children with PSW that continues into adulthood, from those in whom wheezing resolves, identifying mechanisms underpinning persistence and resolution. This may help identify novel therapeutic targets, inform mechanistic studies, and serve as a foundation for stratification in future interventional therapeutic trials.
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Affiliation(s)
- Sormeh Salehian
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Respiratory Paediatrics, Royal Brompton Hospital, London, UK
| | - Louise Fleming
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Respiratory Paediatrics, Royal Brompton Hospital, London, UK
| | - Sejal Saglani
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Respiratory Paediatrics, Royal Brompton Hospital, London, UK
| | - Adnan Custovic
- NIHR Imperial Biomedical Research Centre (BRC), London, UK
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Ekpo RH, Osamor VC, Azeta AA, Ikeakanam E, Amos BO. Machine learning classification approach for asthma prediction models in children. HEALTH AND TECHNOLOGY 2023. [DOI: 10.1007/s12553-023-00732-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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Ambrożej D, Makrinioti H, Whitehouse A, Papadopoulos N, Ruszczyński M, Adamiec A, Castro-Rodriguez JA, Alansari K, Jartti T, Feleszko W. Respiratory virus type to guide predictive enrichment approaches in the management of the first episode of bronchiolitis: A systematic review. Front Immunol 2022; 13:1017325. [PMID: 36389820 PMCID: PMC9647543 DOI: 10.3389/fimmu.2022.1017325] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 10/17/2022] [Indexed: 11/23/2022] Open
Abstract
It has become clear that severe bronchiolitis is a heterogeneous disease; even so, current bronchiolitis management guidelines rely on the one-size-fits-all approach regarding achieving both short-term and chronic outcomes. It has been speculated that the use of molecular markers could guide more effective pharmacological management and achieve the prevention of chronic respiratory sequelae. Existing data suggest that asthma-like treatment (systemic corticosteroids and beta2-agonists) in infants with rhinovirus-induced bronchiolitis is associated with improved short-term and chronic outcomes, but robust data is still lacking. We performed a systematic search of PubMed, Embase, Web of Science, and the Cochrane’s Library to identify eligible randomized controlled trials to determine the efficacy of a personalized, virus-dependent application of systemic corticosteroids in children with severe bronchiolitis. Twelve studies with heterogeneous methodology were included. The analysis of the available results comparing the respiratory syncytial virus (RSV)-positive and RSV-negative children did not reveal significant differences in the associatons between systemic corticosteroid use in acute episode and duration of hospitalization (short-term outcome). However, this systematic review identified a trend of the positive association between the use of systematic corticosteroids and duration of hospitalization in RSV-negative infants hospitalized with the first episode of bronchiolitis (two studies). This evidence is not conclusive. Taken together, we suggest the design for future studies to assess the respiratory virus type in guiding predictive enrichment approaches in infants presenting with the first episode of bronchiolitis.
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Affiliation(s)
- Dominika Ambrożej
- Department of Pediatric Pneumonology and Allergy, Medical University of Warsaw, Warsaw, Poland
- Doctoral School, Medical University of Warsaw, Warsaw, Poland
| | - Heidi Makrinioti
- Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Abigail Whitehouse
- Centre for Genomics and Child Health, Queen Mary University of London, London, United Kingdom
| | - Nikolas Papadopoulos
- Division of Infection, Immunity and Respiratory Medicine, University of Manchester, Manchester, United Kingdom
- Allergy Department, 2nd Pediatric Clinic, University of Athens, Athens, Greece
| | - Marek Ruszczyński
- Department of Pediatrics, Medical University of Warsaw, Warsaw, Poland
| | - Aleksander Adamiec
- Department of Pediatric Pneumonology and Allergy, Medical University of Warsaw, Warsaw, Poland
- Doctoral School, Medical University of Warsaw, Warsaw, Poland
| | - Jose A. Castro-Rodriguez
- Department of Pediatric Pulmonology and Cardiology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Khalid Alansari
- Department of Pediatric Emergency Medicine, Sidra Medicine, Doha, Qatar
- Clinical Pediatrics, Qatar University College of Medicine, Doha, Qatar
- Clinical Pediatrics, Weill Cornell Medical College- Qatar, Doha, Qatar
| | - Tuomas Jartti
- Department of Pediatrics, Turku University Hospital and University of Turku, Turku, Finland
- PEDEGO Research Unit, Medical Research Center, University of Oulu, Oulu, Finland
- Department of Pediatrics and Adolescent Medicine, Oulu University Hospital, Oulu, Finland
| | - Wojciech Feleszko
- Department of Pediatric Pneumonology and Allergy, Medical University of Warsaw, Warsaw, Poland
- *Correspondence: Wojciech Feleszko,
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A Methylation Diagnostic Model Based on Random Forests and Neural Networks for Asthma Identification. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:2679050. [PMID: 36213574 PMCID: PMC9534672 DOI: 10.1155/2022/2679050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 09/11/2022] [Accepted: 09/12/2022] [Indexed: 11/17/2022]
Abstract
Background Asthma significantly impacts human life and health as a chronic disease. Traditional treatments for asthma have several limitations. Artificial intelligence aids in cancer treatment and may also accelerate our understanding of asthma mechanisms. We aimed to develop a new clinical diagnosis model for asthma using artificial neural networks (ANN). Methods Datasets (GSE85566, GSE40576, and GSE13716) were downloaded from Gene Expression Omnibus (GEO) and identified differentially expressed CpGs (DECs) enriched by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Random forest (RF) and ANN algorithms further identified gene characteristics and built clinical models. In addition, two external validation datasets (GSE40576 and GSE137716) were used to validate the diagnostic ability of the model. Results The methylation analysis tool (ChAMP) considered DECs that were up-regulated (n =121) and down-regulated (n =20). GO results showed enrichment of actin cytoskeleton organization and cell-substrate adhesion, shigellosis, and serotonergic synapses. RF (random forest) analysis identified 10 crucial DECs (cg05075579, cg20434422, cg03907390, cg00712106, cg05696969, cg22862094, cg11733958, cg00328720, and cg13570822). ANN constructed the clinical model according to 10 DECs. In two external validation datasets (GSE40576 and GSE137716), the Area Under Curve (AUC) for GSE137716 was 1.000, and AUC for GSE40576 was 0.950, confirming the reliability of the model. Conclusion Our findings provide new methylation markers and clinical diagnostic models for asthma diagnosis and treatment.
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Kothalawala DM, Kadalayil L, Curtin JA, Murray CS, Simpson A, Custovic A, Tapper WJ, Arshad SH, Rezwan FI, Holloway JW. Integration of Genomic Risk Scores to Improve the Prediction of Childhood Asthma Diagnosis. J Pers Med 2022; 12:75. [PMID: 35055391 PMCID: PMC8777841 DOI: 10.3390/jpm12010075] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 12/18/2021] [Accepted: 12/31/2021] [Indexed: 01/24/2023] Open
Abstract
Genome-wide and epigenome-wide association studies have identified genetic variants and differentially methylated nucleotides associated with childhood asthma. Incorporation of such genomic data may improve performance of childhood asthma prediction models which use phenotypic and environmental data. Using genome-wide genotype and methylation data at birth from the Isle of Wight Birth Cohort (n = 1456), a polygenic risk score (PRS), and newborn (nMRS) and childhood (cMRS) methylation risk scores, were developed to predict childhood asthma diagnosis. Each risk score was integrated with two previously published childhood asthma prediction models (CAPE and CAPP) and were validated in the Manchester Asthma and Allergy Study. Individually, the genomic risk scores demonstrated modest-to-moderate discriminative performance (area under the receiver operating characteristic curve, AUC: PRS = 0.64, nMRS = 0.55, cMRS = 0.54), and their integration only marginally improved the performance of the CAPE (AUC: 0.75 vs. 0.71) and CAPP models (AUC: 0.84 vs. 0.82). The limited predictive performance of each genomic risk score individually and their inability to substantially improve upon the performance of the CAPE and CAPP models suggests that genetic and epigenetic predictors of the broad phenotype of asthma are unlikely to have clinical utility. Hence, further studies predicting specific asthma endotypes are warranted.
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Affiliation(s)
- Dilini M. Kothalawala
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK; (D.M.K.); (L.K.); (W.J.T.); (F.I.R.)
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton SO16 6YD, UK;
| | - Latha Kadalayil
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK; (D.M.K.); (L.K.); (W.J.T.); (F.I.R.)
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK
| | - John A. Curtin
- Division of Infection, Immunity, and Respiratory Medicine, School of Biological Sciences, Manchester University Hospital NHS Foundation Trust, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9PL, UK; (J.A.C.); (C.S.M.); (A.S.)
| | - Clare S. Murray
- Division of Infection, Immunity, and Respiratory Medicine, School of Biological Sciences, Manchester University Hospital NHS Foundation Trust, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9PL, UK; (J.A.C.); (C.S.M.); (A.S.)
| | - Angela Simpson
- Division of Infection, Immunity, and Respiratory Medicine, School of Biological Sciences, Manchester University Hospital NHS Foundation Trust, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9PL, UK; (J.A.C.); (C.S.M.); (A.S.)
| | - Adnan Custovic
- National Heart and Lung Institute, Imperial College of Science, Technology, and Medicine, London SW3 6LY, UK;
| | - William J. Tapper
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK; (D.M.K.); (L.K.); (W.J.T.); (F.I.R.)
| | - S. Hasan Arshad
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton SO16 6YD, UK;
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK
- The David Hide Asthma and Allergy Research Centre, St. Mary’s Hospital, Isle of Wight PO30 5TG, UK
| | - Faisal I. Rezwan
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK; (D.M.K.); (L.K.); (W.J.T.); (F.I.R.)
- Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK
| | - John W. Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK; (D.M.K.); (L.K.); (W.J.T.); (F.I.R.)
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton SO16 6YD, UK;
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Alharbi ET, Nadeem F, Cherif A. Predictive models for personalized asthma attacks based on patient's biosignals and environmental factors: a systematic review. BMC Med Inform Decis Mak 2021; 21:345. [PMID: 34886852 PMCID: PMC8656014 DOI: 10.1186/s12911-021-01704-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 11/21/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Asthma is a chronic disease that exacerbates due to various risk factors, including the patient's biosignals and environmental conditions. It is affecting on average 7% of the world population. Preventing an asthma attack is the main challenge for asthma patients, which requires keeping track of any risk factor that can cause a seizure. Many researchers developed asthma attacks prediction models that used various asthma biosignals and environmental factors. These predictive models can help asthmatic patients predict asthma attacks in advance, and thus preventive measures can be taken. This paper introduces a review of these models to evaluate the used methods, model's performance, and determine the need to improve research in this field. METHOD A systematic review was conducted for the research articles introducing asthma attack prediction models for children and adults. We searched the PubMed, ScienceDirect, Springer, and IEEE databases from January 2000 to December 2020. The search includes the prediction models that used biosignal, environmental, and both risk factors. The research article's quality was assessed and scored based on two checklists, the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) and the Critical Appraisal Skills Programme clinical prediction rule checklist (CASP). The highest scored articles were selected to review. RESULT From 1068 research articles we reviewed, we found that most of the studies used asthma biosignal factors only for prediction, few of the studies used environmental factors, and limited studies used both of these factors. Fifteen different asthma attack predictive models were selected for this review. we found that most of the studies used traditional prediction methods, like Support Vector Machine and regression. We have identified the pros and cons of the reviewed asthma attack prediction models and propose solutions to advance the studies in this field. CONCLUSION Asthma attack predictive models become more significant when using both patient's biosignal and environmental factors. There is a lack of utilizing advanced machine learning methods, like deep learning techniques. Besides, there is a need to build smart healthcare systems that provide patients with decision-making systems to identify risk and visualize high-risk regions.
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Affiliation(s)
- Eman T. Alharbi
- Department of Information Systems, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Farrukh Nadeem
- Department of Information Systems, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Asma Cherif
- Department of Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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