Accuracy of portable spirometers in the diagnosis of chronic obstructive pulmonary disease A meta-analysis

An update on COPD prevention, diagnosis, and management: The 2024 GOLD Report

ABSTRACT

Chronic obstructive pulmonary disease (COPD) is one of the top three causes of death throughout the world. Because of the preventable and treatable nature of the disease along with its prevalence, COPD represents a major public health challenge. The Global Initiative for Chronic Obstructive Lung Disease (GOLD) Report provides a review of the most current evidence for prevention of COPD as well as the assessment, diagnosis, and treatment of people with the disease. The purpose of this article is to provide a summary of the 2024 revised GOLD Report and current best practices in accordance with the evidence.

Cardiovascular disease and risk in COPD: a state of the art review

INTRODUCTION

Chronic Obstructive Pulmonary Disease (COPD) and cardiovascular diseases (CVD) commonly co-exist. Outcomes of people living with both conditions are poor in terms of symptom burden, receiving evidence-based treatment and mortality. Increased understanding of the underlying mechanisms may help to identify treatments to relieve this disease burden. This narrative review covers the overlap of COPD and CVD with a focus on clinical presentation, mechanisms, and interventions. Literature up to December 2023 are cited.

AREAS COVERED

1. What is COPD 2. The co-existence of COPD and cardiovascular disease 3. Mechanisms of cardiovascular disease in COPD. 4. Populations with COPD are at risk of CVD 5. Complexity in the co-diagnosis of COPD in those with cardiovascular disease. 6. Therapy for COPD and implications for cardiovascular events and risk. 7. Cardiovascular risk and exacerbations of COPD. 8. Pro-active identification and management of CV risk in COPD.

EXPERT OPINION

The prospective identification of co-morbid COPD in CVD patients and of CVD and CV risk in people with COPD is crucial for optimizing clinical outcomes. This includes the identification of novel treatment targets and the design of clinical trials specifically designed to reduce the cardiovascular burden and mortality associated with COPD. Databases searched: Pubmed, 2006-2023.

How to Enhance the Diagnosis of Early Stages of Chronic Obstructive Pulmonary Disease (COPD)? The Role of Mobile Spirometry in COPD Screening and Diagnosis-A Systematic Review

COPD is the third leading cause of death worldwide. Its diagnosis can be made with spirometry, which is underused due to its limited accessibility. Portable spirometry holds promise for enhancing the efficacy of COPD diagnoses. The study aimed to estimate COPD prevalence diagnosed with a portable spirometer in high-risk patients and compare it with COPD prevalence based on data from conventional, on-site spirometry. We also evaluated the strategy of a proactive approach to identify COPD in high-risk individuals. We conducted a systematic review of original studies on COPD targeted screening and diagnosis with portable and conventional spirometers selected from 8496 publications initially found in three databases: Cochrane, PubMed, and Embase. The inclusion criteria were met by 28 studies. COPD prevalence evaluated with the use of portable spirometers reached 20.27% and was lower compared to that estimated with the use of conventional spirometers (24.67%). In 11 included studies, postbronchodilator tests were performed with portable spirometers, which enabled a bedside COPD diagnosis. Portable spirometers can be successfully used in COPD targeted screening and diagnosis and thus enhance the detection of COPD at early stages.

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.

Implementation of a screening and management pathway for chronic obstructive pulmonary disease in patients with atrial fibrillation

AIMS

Chronic obstructive pulmonary disease (COPD) negatively impacts the efficacy of heart rhythm control treatments in patients with atrial fibrillation (AF). Although COPD is recognized as a risk factor for AF, practical guidance about how and when to screen for COPD is not available. Herein, we describe the implementation of an integrated screening and management pathway for COPD into the existing pre-ablation work-up in an AF outpatient clinic infrastructure.

METHODS AND RESULTS

Consecutive unselected patients accepted for AF catheter ablation in the Maastricht University Medical Center+ were prospectively screened for airflow limitation using handheld (micro)spirometry at the pre-ablation outpatient clinic supervised by an AF nurse. Patients with results suggestive of airflow limitation were offered referral to the pulmonologist. Handheld (micro)spirometry was performed in 232 AF patients, which provided interpretable results in 206 (88.8%) patients. Airflow limitation was observed in 47 patients (20.3%). Out of these 47 patients, 29 (62%) opted for referral to the pulmonologist. The primary reason for non-referral was low perceived symptom burden. Using this screening strategy 17 (out of 232; 7.3%) ultimately received a diagnosis of chronic respiratory disease, either COPD or asthma.

CONCLUSION

A COPD care pathway can successfully be embedded in an existing AF outpatient clinic infrastructure, using (micro)spirometry and remote analysis of results. Although one out of five patients had results suggestive of an underlying chronic respiratory disease, only 62% of these patients opted for a referral. Pre-selection of patients as well as patient education might increase the diagnostic yield and requires further research.

Digital health in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) can be prevented and treated through effective care, reducing exacerbations and hospitalizations. Early identification of individuals at high risk of COPD exacerbation is an opportunity for preventive measures. However, many patients struggle to follow their treatment plans because of a lack of knowledge about the disease, limited access to resources, and insufficient clinical support. The growth of digital health-which encompasses advancements in health information technology, artificial intelligence, telehealth, the Internet of Things, mobile health, wearable technology, and digital therapeutics-offers opportunities for improving the early diagnosis and management of COPD. This study reviewed the field of digital health in terms of COPD. The findings showed that despite significant advances in digital health, there are still obstacles impeding its effectiveness. Finally, we highlighted some of the major challenges and possibilities for developing and integrating digital health in COPD management.