COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda

Nikolaou, Vasilis; Massaro, Sebastiano; Fakhimi, Masoud; Stergioulas, Lampros K.; Price, David

doi:10.1016/j.rmed.2020.106093

Cited by 23 publications

(26 citation statements)

References 113 publications

(40 reference statements)

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“…According to the Global Obstructive Lung Disease Initiative, COPD patients are classified into four phenotypes based on their symptomatic assessment, exacerbation and hospitalization history [83]. However, the discriminatory ability of this method is insufficient, leading to the AI/ML-based integration of additional information, including physiological features, lung function test results, comorbidities, genome, and biomarkers, for precise phenotype classification, severity assessment, and therapeutic guidance [84][85][86][87][88][89].…”

Section: Application Of Ai/ml To the Classification And Assessment Of Copdmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Feng¹,

Wang²,

Zeng³

et al. 2021

Int. J. Med. Sci.

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Chronic airway diseases are characterized by airway inflammation, obstruction, and remodeling and show high prevalence, especially in developing countries. Among them, asthma and chronic obstructive pulmonary disease (COPD) show the highest morbidity and socioeconomic burden worldwide. Although there are extensive guidelines for the prevention, early diagnosis, and rational treatment of these lifelong diseases, their value in precision medicine is very limited. Artificial intelligence (AI) and machine learning (ML) techniques have emerged as effective methods for mining and integrating large-scale, heterogeneous medical data for clinical practice, and several AI and ML methods have recently been applied to asthma and COPD. However, very few methods have significantly contributed to clinical practice. Here, we review four aspects of AI and ML implementation in asthma and COPD to summarize existing knowledge and indicate future steps required for the safe and effective application of AI and ML tools by clinicians.

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Section: Application Of Ai/ml To the Classification And Assessment Of Copdmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Feng¹,

Wang²,

Zeng³

et al. 2021

Int. J. Med. Sci.

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“…In recent years, attempts to subtyping of obstructive pulmonary diseases have been shifted to more data-driven methods. 13 , 14 A system of classification of obstructive lung diseases that integrate the multidimensionality of asthma and COPD on clinical, cellular and molecular levels may be a tool for identifying numerous distinct phenotypes, with specific pathobiological components that respond to particular therapy. Phenotyping of obstructive pulmonary diseases has usually been studied in severe stage of the diseases.…”

Section: Introductionmentioning

confidence: 99%

Phenotypic Variations of Mild-to-Moderate Obstructive Pulmonary Diseases According to Airway Inflammation and Clinical Features

Proboszcz¹,

Goryca²,

Nejman-Gryz³

et al. 2021

JIR

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Purpose Asthma and chronic obstructive pulmonary disease (COPD) are complex and heterogeneous inflammatory diseases. We sought to investigate distinct disease profiles based on clinical, cellular and molecular data from patients with mild-to-moderate obstructive pulmonary diseases. Patients and Methods Patients with mild-to-moderate allergic asthma (n=30) and COPD (n=30) were prospectively recruited. Clinical characteristics and induced sputum were collected. In total, 35 mediators were assessed in induced sputum. Logistic regression analysis was conducted to identify the optimal factors that were able to discriminate between asthma and COPD. Further, the data were explored using hierarchical clustering in order to discover and compare clusters of combined samples of asthma and COPD patients. Clinical parameters, cellular composition, and sputum mediators of asthma and COPD were assessed between and within obtained clusters. Results We found five clinical and biochemical variables, namely IL-6, IL-8, CCL4, FEV 1 /VC ratio pre-bronchodilator (%), and sputum neutrophils (%) that differentiated asthma and COPD and were suitable for discrimination purposes. A combination of those variables yielded high sensitivity and specificity in the differentiation between asthma and COPD, although only FEV 1 /VC ratio pre-bronchodilator (%) proven significant in the combined model. In cluster analysis, two main clusters were identified: cluster 1, asthma predominant with evidence of eosinophilic airway inflammation and low level of Th1 and Th2 cytokines; and cluster 2, COPD predominant with elevated levels of Th1 and Th2 mediators. Conclusion The inflammatory profile of sputum samples from patients with stable mild-to-moderate asthma and COPD is not disease specific, varies within the disease and might be similar between these diseases. This study highlights the need for phenotyping the mild-to-moderate stages according to their clinical and molecular features.

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“…Furthermore, a future goal could be that the models in this study provide a framework for the integration of this information into electronic healthcare records to ultimately inform decision making in the management of patients with COPD. Further research into machine learning algorithms and artificial intelligence applications is ongoing [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Method to Estimate COPD Severity in Multiple Datasets: A Retrospective Study

et al. 2020

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Introduction Outcomes in chronic obstructive pulmonary disease (COPD) such as symptoms, hospitalisations and mortality rise with increasing disease severity. However, the heterogeneity of electronic medical records presents a significant challenge in measuring severity across geographies. We aimed to develop and validate a method to approximate COPD severity using the Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2011 classification scheme, which categorises patients based on forced expiratory volume in 1 s, hospitalisations and the modified Medical Research Council dyspnoea scale or COPD Assessment Test. Methods This analysis was part of a comprehensive retrospective study, including patients sourced from the IQVIA Medical Research Data [IMRD; incorporating data from The Health Improvement Network (THIN), a Cegedim database] and the Clinical Practice Research Datalink (CPRD) in the UK, the Disease Analyzer in Germany and the Longitudinal Patient Data in Italy, France and Australia. Patients in the CPRD with the complete set of information required to calculate GOLD 2011 groups were used to develop the method. Ordinal logistic models at COPD diagnosis and at index (first episode of triple therapy) were then used to validate the method to estimate COPD severity, and this was applied to the full study population to estimate GOLD 2011 categories. Results Overall, 4579 and 12,539 patients were included in the model at COPD diagnosis and at index, respectively. Models correctly classified 74.4% and 75.9% of patients into severe and non-severe categories at COPD diagnosis and at index, respectively. Age, gender, time between diagnosis and start of triple therapy, healthcare resource use, comorbid conditions and prescriptions were included as covariates. Conclusion This study developed and validated a method to approximate disease severity based on GOLD 2011 categories that can potentially be used in patients without all the key parameters needed for this calculation.

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COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda

Cited by 23 publications

References 113 publications

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Phenotypic Variations of Mild-to-Moderate Obstructive Pulmonary Diseases According to Airway Inflammation and Clinical Features

Development and Validation of a Method to Estimate COPD Severity in Multiple Datasets: A Retrospective Study

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