ArticleSensors (Basel, Switzerland)2015
Computerised Analysis of Telemonitored Respiratory Sounds for Predicting Acute Exacerbations of COPD.
Article in Sensors (Basel, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02919033 (A Coordinated PCP-Cardiologist Telemedicine Model), which is not on this map. Cited by 36 papers, 8 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Coordinated PCP-Cardiologist Telemedicine Model (PCTM) in China's Community Hypertension Care: Study Protocol for a Randomized Controlled Trial
Who cites it
36 citing papers in PubMed, 8 syntheses or guidelines pooled it, 77 citations in OpenAlex.
- AI and Internet of Things for Chronic Obstructive Pulmonary Disease Remote Monitoring: Systematic Review of Exacerbation Prediction and Key Physiological Variables.JMIR medical informatics · 2026Pooled it
- Predicting disease outcomes from remote monitoring using machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Predictors of Acute Exacerbations in COPD: A Systematic Review.International journal of chronic obstructive pulmonary disease · 2026Pooled it
- Audio-based digital biomarkers in diagnosing and managing respiratory diseases: a systematic review and bibliometric analysis.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review.BMC medical informatics and decision making · 2025Pooled it
- Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis.Journal of medical Internet research · 2024Pooled it
- Effects of Home-Based Training with Internet Telehealth Guidance in COPD Patients Entering Pulmonary Rehabilitation: A Systematic Review.International journal of chronic obstructive pulmonary disease · 2023Pooled it
- Recruiting Rural Healthcare Providers Today: a Systematic Review of Training Program Success and Determinants of Geographic Choices.Journal of general internal medicine · 2018Pooled it
- Trial
- Multimodal machine learning predicts type 2 respiratory failure in COPD exacerbations: a multicenter XGBoost model with clinical nomogram.Frontiers in medicine · 2026Article
- Short-Term Prognosis in Acute Asthma Exacerbations: A Comparative Evaluation of Machine Learning Models Using Spirometry and Tracheal Respiratory Sound Analysis.International journal of general medicine · 2026Article
- Acoustic Intensity as a Potential Indicator for Congestive Heart Failure Exacerbation: An Exploratory Pilot Study.Cardiology research and practice · 2025Article
- Height prediction of individuals with osteogenesis imperfecta by machine learning.Orphanet journal of rare diseases · 2024Article
- Industry 4.0-Compliant Occupational Chronic Obstructive Pulmonary Disease Prevention: Literature Review and Future Directions.Sensors (Basel, Switzerland) · 2024Review
- Feasibility and Acceptability of Pediatric Smartphone Lung Auscultation by Parents: Cross-Sectional Study.JMIR pediatrics and parenting · 2024Article
- Phenotypes, Etiotypes, and Endotypes of Exacerbations of Chronic Obstructive Pulmonary Disease.American journal of respiratory and critical care medicine · 2023Review
- Evolution of the Stethoscope: Advances with the Adoption of Machine Learning and Development of Wearable Devices.Tuberculosis and respiratory diseases · 2023Article
- Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging.Sensors (Basel, Switzerland) · 2023Review
- A deep learning algorithm to quantify AVF stenosis and predict 6-month primary patency: a pilot study.Clinical kidney journal · 2023Article
- The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review.Archives of academic emergency medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic obstructive pulmonary disease (COPD) is one of the commonest causes of death in the world and poses a substantial burden on healthcare systems and patients' quality of life. The largest component of the related healthcare costs is attributable to admissions due to acute exacerbation (AECOPD). The evidence that might support the effectiveness of the telemonitoring interventions in COPD is limited partially due to the lack of useful predictors for the early detection of AECOPD. Electronic stethoscopes and computerised analyses of respiratory sounds (CARS) techniques provide an opportunity for substantial improvement in the management of respiratory diseases. This exploratory study aimed to evaluate the feasibility of using: (a) a respiratory sensor embedded in a self-tailored housing for ageing users; (b) a telehealth framework; (c) CARS and (d) machine learning techniques for the remote early detection of the AECOPD. In a 6-month pilot study, 16 patients with COPD were equipped with a home base-station and a sensor to daily record their respiratory sounds. Principal component analysis (PCA) and a support vector machine (SVM) classifier was designed to predict AECOPD. 75.8% exacerbations were early detected with an average of 5 ± 1.9 days in advance at medical attention. The proposed method could provide support to patients, physicians and healthcare systems.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.