Does this patient really have chronic obstructive pulmonary disease?

Li, Hui; How, Choon How; Tee, Augustine

doi:10.11622/smedj.2015058

Cited by 6 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 3 , 32 Taken together, these data suggest that more proactive evaluation of patients early in the course of their illness including spirometry might improve identification of patients at risk, earlier initiation of pharmacotherapies, and improved outcomes. 29 , 33 , 34 Additionally, although exacerbations are described as acute events, evidence indicates that significant risks following an exacerbation persist beyond the acute phase of the event. Therefore, the approach to managing COPD patients following an exacerbation should consider both immediate and longer-term risks.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…Further research is needed to improve early COPD diagnosis prior to the patient experiencing moderate or severe exacerbations and to optimize treatment and prevention of exacerbations and downstream risks in these patients. 10,29,[34][35][36] Patients who lived in rural and lower SES neighborhoods had, on average, higher associations between AECOPD and both acute CV events and all-cause mortality. Although all patients in this study population had health insurance, this…”

Section: Key Findingsmentioning

confidence: 99%

See 2 more Smart Citations

Risk of Death and Cardiovascular Events Following an Exacerbation of COPD: The EXACOS-CV US Study

Daniels,

Lanes,

Tave

et al. 2024

COPD

View full text Add to dashboard Cite

Purpose This study estimated the magnitude and duration of risk of cardiovascular events and mortality following acute exacerbations of chronic obstructive pulmonary disease (AECOPD), and whether risks varied by number and severity of exacerbation in a commercially insured population in the United States. Methods This was a retrospective cohort study of newly diagnosed COPD patients ≥40 years old in the Healthcare Integrated Research Database from 2012 to 2019. Patients experiencing exacerbations comprised the “exacerbation cohort”. Moderate exacerbations were outpatient visits with contemporaneous antibiotic or glucocorticoid administration; severe exacerbations were emergency department visits or hospitalizations for AECOPD. Follow-up started on the exacerbation date. Distribution of time between diagnosis and first exacerbation was used to assign index dates to the “unexposed” cohort. Cox proportional hazards models estimated risks of a cardiovascular event or death following an exacerbation adjusted for medical and prescription history and stratified by follow-up time, type of cardiovascular event, exacerbation severity, and rank of exacerbation (first, second, or third). Results Among 435,925 patients, 170,236 experienced ≥1 exacerbation. Risk of death was increased for 2 years following an exacerbation and was highest during the first 30 days (any exacerbation hazard ratio (HR)=1.79, 95% CI=1.58—2.04; moderate HR=1.22, 95% CI=1.04—1.43; severe HR=5.09, 95% CI=4.30—6.03). Risks of cardiovascular events were increased for 1 year following an AECOPD and highest in the first 30-days (any exacerbation HR=1.34, 95% CI=1.23—1.46; moderate HR=1.23 (95% CI 1.12—1.35); severe HR=1.93 (95% CI=1.67—2.22)). Each subsequent AECOPD was associated with incrementally higher rates of both death and cardiovascular events. Conclusion Risk of death and cardiovascular events was greatest in the first 30 days and rose with subsequent exacerbations. Risks were elevated for 1–2 years following moderate and severe exacerbations, highlighting a sustained increased cardiopulmonary risk associated with exacerbations.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Key Findingsmentioning

confidence: 99%

See 1 more Smart Citation

Risk of Death and Cardiovascular Events Following an Exacerbation of COPD: The EXACOS-CV US Study

Daniels,

Lanes,

Tave

et al. 2024

COPD

View full text Add to dashboard Cite

show abstract

“…The most common symptoms of COPD are wheezing, shortness of breath and coughing. Coronary heart disease, weight loss, obesity, cognitive dysfunction, anorexia, and lung cancer indicate COPD [ 2 ]. According to a recent report by the world health organization (WHO), COPD is the third leading cause of death worldwide and has caused around 3.23 million deaths in 2019 alone [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features

et al. 2023

View full text Add to dashboard Cite

Chronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the disease frequently goes undiagnosed until it is too late, leaving patients without the care they desperately need. So, COPD detection at an early stage is crucial to prevent further damage to the lungs and improve quality of life. Traditional COPD detection methods often rely on physical examinations and tests such as spirometry, chest radiography, blood gas tests, and genetic tests. However, these methods may not always be accurate or accessible. One of the key vital signs for detecting COPD is the patient’s respiration rate. However, it is crucial to consider a patient’s medical and demographic characteristics simultaneously for better detection results. To address this issue, this study aims to detect COPD patients using artificial intelligence techniques. To achieve this goal, a novel framework is proposed that utilizes ultra-wideband (UWB) radar-based temporal and spectral features to build machine learning and deep learning models. This new set of temporal and spectral features is extracted from respiration data collected non-invasively from 1.5 m distance using UWB radar. Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection. This means that the proposed framework could potentially save lives by identifying COPD patients at an early stage. The k-fold cross-validation technique and performance comparison with the state-of-the-art studies are applied to validate its performance, ensuring that the results are robust and reliable. The high accuracy score achieved in the study implies that the proposed framework has the potential for the efficient detection of COPD at an early stage.

show abstract

“…Cough, shortness of breath, and wheezing are common symptoms of COPD. Symptoms of very severe COPD include weight loss, coronary heart disease, obesity, anorexia, cognitive dysfunction, and lung cancer [6]. Airbags and airways are elastic by nature, and the lungs have stretching capacities.…”

Section: Introductionmentioning

confidence: 99%

Respiration-Based COPD Detection Using UWB Radar Incorporation with Machine Learning

et al. 2022

View full text Add to dashboard Cite

COPD is a progressive disease that may lead to death if not diagnosed and treated at an early stage. The examination of vital signs such as respiration rate is a promising approach for the detection of COPD. However, simultaneous consideration of the demographic and medical characteristics of patients is very important for better results. The objective of this research is to investigate the capability of UWB radar as a non-invasive approach to discriminate COPD patients from healthy subjects. The non-invasive approach is beneficial in pandemics such as the ongoing COVID-19 pandemic, where a safe distance between people needs to be maintained. The raw data are collected in a real environment (a hospital) non-invasively from a distance of 1.5 m. Respiration data are then extracted from the collected raw data using signal processing techniques. It was observed that the respiration rate of COPD patients alone is not enough for COPD patient detection. However, incorporating additional features such as age, gender, and smoking history with the respiration rate lead to robust performance. Different machine-learning classifiers, including Naïve Bayes, support vector machine, random forest, k nearest neighbor (KNN), Adaboost, and two deep-learning models—a convolutional neural network and a long short-term memory (LSTM) network—were utilized for COPD detection. Experimental results indicate that LSTM outperforms all employed models and obtained 93% accuracy. Performance comparison with existing studies corroborates the superior performance of the proposed approach.

show abstract

Does this patient really have chronic obstructive pulmonary disease?

Cited by 6 publications

References 17 publications

Risk of Death and Cardiovascular Events Following an Exacerbation of COPD: The EXACOS-CV US Study

Risk of Death and Cardiovascular Events Following an Exacerbation of COPD: The EXACOS-CV US Study

An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features

Respiration-Based COPD Detection Using UWB Radar Incorporation with Machine Learning

Contact Info

Product

Resources

About