Early therapy improves outcomes of exacerbations of chronic obstructive pulmonary disease

Albuterol Digihaler in COPD Disease Management: A Real-World Study to Assess Digihaler Inhalation Parameters, Thresholds and Their Use to Identify Deterioration in Clinical Practice

Purpose

Despite increasing awareness, chronic obstructive pulmonary disease (COPD) exacerbations are often unrecognized, not reported or not treated. Assisting patients and caregivers to better identify deteriorations in COPD can help improve care. This study was designed to collect usage and inhalation parameters from albuterol Digihaler devices and its associated Digihaler dashboard, to identify potential inhalation parameters and alerts that might predict worsening COPD.

Patients and Methods

Real-time rescue albuterol Digihaler (albuterol sulfate) results for peak inspiratory flow (PIF), rescue inhaler usage and inhalation volume (InV) were assessed in 20 COPD patients over 6 months. Alert thresholds from device measurements were analyzed for 14 days prior to all COPD deteriorations defined by a COPD exacerbation or an acute worsening in COPD assessment test (CAT) score.

Results

Eleven subjects experienced 22 COPD exacerbations, and 16 subjects experienced 40 CAT score worsening over 6 months' time. No demographic or physiologic differences were identified comparing patients with or without exacerbations or CAT score worsening. Falls in PIF and increases in rescue inhaler usage were weak predictors of impending exacerbations, while a higher percentage (36%) of subjects had a fall in InV prior to an exacerbation. No notable changes in inhaler parameters were associated with deteriorating CAT scores, and no changes in lung function were observed over the study. A combination of 3 alert thresholds was present in 59% of patients within the 2 weeks prior to a COPD exacerbation.

Conclusion

Our study suggests that alert thresholds based on Digihaler device-measured physiologic parameters may have value in a predictive model for clinical deterioration in COPD.

Predictive machine learning algorithm for COPD exacerbations using a digital inhaler with integrated sensors

PURPOSE

By using data obtained with digital inhalers, machine learning models have the potential to detect early signs of deterioration and predict impending exacerbations of chronic obstructive pulmonary disease (COPD) for individual patients. This analysis aimed to determine if a machine learning algorithm capable of predicting impending exacerbations could be developed using data from an integrated digital inhaler.

PATIENTS AND METHODS

A 12-week, open-label clinical study enrolled patients (≥40 years old) with COPD to use ProAir Digihaler, a digital dry powder inhaler with integrated sensors, to deliver their reliever medication (albuterol, 90 µg/dose; 1-2 inhalations every 4 hours, as needed). The Digihaler recorded inhaler use through timestamps, peak inspiratory flow (PIF), inhalation volume, inhalation duration, and time to PIF throughout the study. By applying machine learning methodology to data downloaded from the inhalers after study completion, along with clinical and demographic information, a model predictive of impending exacerbations was generated.

RESULTS

The predictive analysis included 336 patients, 98 of whom experienced a total of 111 exacerbations. PIF and inhalation volume were observed to decline in the days preceding an exacerbation. Using gradient-boosting trees with data from the Digihaler and baseline patient characteristics, the machine learning model was able to predict an exacerbation over the following 5 days with a receiver operating characteristic area under curve of 0.77 (95% CI: 0.71-0.83). Features of the model with the highest weight were baseline inhalation parameters and changes in inhalation parameters before an exacerbation compared with baseline.

CONCLUSION

We demonstrated the development of a proof-of-concept machine learning model predictive of impending COPD exacerbations using data from the integrated digital reliever inhaler. This approach may potentially support patient monitoring, help improve disease management, and enable pre-emptive interventions to minimise exacerbations.

CLINICAL TRIAL REGISTRATION NUMBER

NCT03256695.

At-home validation of a non-contact, radar-based breathing monitor for long-term care of patients with respiratory diseases: A proof-of-concept study

Long-term monitoring of respiratory rate (RR) is an important component in the management of chronic respiratory diseases (CRDs). Specifically, predicting acute exacerbations of chronic obstructive pulmonary disease (AECOPD) is of significant scientific and clinical interest. This study aimed to evaluate the long-term validity of a novel contactless sleep monitor (CSM) in the home environment of CRD patients receiving ventilatory support. Additionally, we assessed patient acceptance, device usability, and RR fluctuations associated with AECOPD to establish a robust foundation for future research.In this prospective proof-of-concept study, nineteen patients requiring non-invasive ventilation (NIV) were provided with the CSM in their home environment for six months and seven patients requiring invasive mechanical ventilation (IMV) for one month. The primary indication for NIV therapy was chronic obstructive pulmonary disease (COPD).The CSM was validated under real-life conditions by comparing its nocturnal RR values with software data from both types of ventilators. Acceptability and usability of the sensor were assessed using a questionnaire. Additionally, COPD exacerbations occurring during the study period were analyzed for potential RR fluctuations preceding these events.Mean absolute error (MAE) of median RR between the NIV device and the CSM, based on 2326 nights, was 0.78 (SD: 1.96) breaths per minute (brpm). MAE between the IMV device and the CSM was 0.12 brpm (SD: 0.52) for 215 nights. The non-contact device was accepted by the patients and proved to be easy in use. In some of the overall only 13 cases of AECOPD, RR time courses showed variations of increased nocturnal respiratory activity a few days before the occurrence of such events.The present CSM is suitable for valid long-term monitoring of nocturnal RR in patients' home environment and is well accepted by the patients. The exploratory findings related to AECOPD events may serve as a starting point for larger studies aimed at developing robust prediction rules.

Respiratory Muscle Strength as a Predictor of Exacerbations in Patients With Chronic Obstructive Pulmonary Disease

BACKGROUND AND OBJECTIVE

Chronic obstructive pulmonary disease (COPD) is closely related to skeletal muscle dysfunction, and the evaluation of respiratory muscle function has recently been recommended. We aimed to investigate the effects of respiratory muscle dysfunction on clinical outcomes.

METHODS

We retrospectively reviewed the medical records of patients with COPD whose respiratory muscle strength was measured between June 2015 and December 2021. We then analysed the effects of respiratory muscle strength on moderate-to-severe exacerbations after adjusting for confounding factors, including sex, age, forced expiratory volume in 1-s percent predicted, hand grip strength, and skeletal muscle mass index. We also compared the temporal relationship between respiratory and systemic skeletal muscle dysfunctions.

RESULTS

Respiratory muscle weakness (RMW) was observed in 48.1% (100) of the 208 patients. Low percent predicted maximal inspiratory pressure was an independent risk factor for moderate-to-severe exacerbations within 1 year in the Cox regression analysis (adjusted hazard ratio per 1 standard deviation increase, 0.521; 95% confidence interval, 0.317-0.856). Approximately half of the patients already exhibited RMW at the mild systemic skeletal muscle dysfunction, while those with sarcopenia had higher RMW rates. More patients with RMW experienced progressive systemic skeletal muscle dysfunction within 1 year compared to those without RMW.

CONCLUSION

Lower respiratory muscle strength is associated with an increased risk of exacerbation. Respiratory muscle function could serve as a marker of disease status and early prognosis in COPD.

Clinical Significance of Various Pathogens Identified in Patients Experiencing Acute Exacerbations of COPD: A Multi-center Study in South Korea

BACKGROUND

Respiratory infections play a major role in acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study assessed the prevalence of bacterial and viral pathogens and their clinical impact on patients with AECOPD.

METHODS

This retrospective study included 1,186 patients diagnosed with AECOPD at 28 hospitals in South Korea between 2015 and 2018. We evaluated the identification rates of pathogens, basic patient characteristics, clinical features, and the factors associated with infections by potentially drug-resistant (PDR) pathogens using various microbiological tests.

RESULTS

Bacteria, viruses, and both were detected in 262 (22.1%), 265 (22.5%), and 129 (10.9%) of patients, respectively. The most common pathogens included Pseudomonas aeruginosa (17.8%), Mycoplasma pneumoniae (11.2%), Streptococcus pneumoniae (9.0%), influenza A virus (19.0%), rhinovirus (15.8%), and respiratory syncytial virus (6.4%). Notably, a history of pulmonary tuberculosis (odds ratio [OR], 1.66; p=0.046), bronchiectasis (OR, 1.99; p=0.032), and the use of a triple inhaler regimen within the past 6 months (OR, 2.04; p=0.005) were identified as significant factors associated with infection by PDR pathogens. Moreover, patients infected with PDR pathogens exhibited extended hospital stays (15.9 days vs. 12.4 days, p=0.018) and higher intensive care unit admission rates (15.9% vs. 9.5%, p=0.030).

CONCLUSION

This study demonstrates that a variety of pathogens are involved in episodes of AECOPD. Nevertheless, additional research is required to confirm their role in the onset and progression of AECOPD.

A MEMS seismometer respiratory monitor for work of breathing assessment and adventitious lung sounds detection via deep learning

Physicians evaluate a patient's respiratory health during a physical examination by visual assessment of the work of breathing (WoB) to determine respiratory stability, and by detecting abnormal lung sounds via lung auscultation using a stethoscope to identify common pathological lung diseases, such as chronic obstructive pulmonary disease (COPD) and pneumonia. Since these assessment methods are subjective, a low-profile device used for an accurate and quantitative monitoring approach could provide valuable preemptive insights into respiratory health, proving to be clinically beneficial. To achieve this goal, we have developed a miniature patch consisting of a sensitive wideband multi-axis seismometer that can be placed on the anatomical areas of a patient's lungs to enable an effective quantification of a patient's WoB and lung sounds. When used on a patch, the seismometer captures chest wall vibrations due to respiratory muscle effort, known as high-frequency mechanomyogram (MMG), during tidal breathing as well as seismic pulmonary-induced vibrations (PIVs) during deep breathing due to normal and/or adventitious lung sounds like crackles, while simultaneously recording respiration rate and phase. A system comprised of multiple patches was evaluated on 124 patients in the hospital setting and shown to accurately assess and quantify a patent's physical signs of WoB by measuring the average respiratory effort extracted from high-frequency MMG signals, demonstrating statistical significance of this method in comparison to clinical bedside observation of WoB and respiration rate. A data fusion deep learning model was developed which combined the inputs of PIVs lung sounds and the corresponding respiration phase to detect crackle, wheeze and normal breath sound features. The model exhibited high accuracy, sensitivity, specificity, precision and F1 score of 93%, 93%, 97%, 93% and 93% respectively, with area under the curve (AUC) of precision recall (PR) of 0.97 on the test set. Additionally, the PIVs with corresponding respiration phase captured from each auscultation point generated an acoustic map of the patient's lung, which correlated with traditional lung radiographic findings.

Contactless Sleep Monitoring for the Detection of Exacerbations in People With Chronic Obstructive Pulmonary Disease: Protocol for a Longitudinal Observational Study

BACKGROUND

Exacerbations of chronic obstructive pulmonary disease (COPD) are one of the main causes of mortality, and early detection of exacerbations is thus essential. Telemedicine solutions have shown promising results for the detection of exacerbations in COPD and have increasingly been used. However, the effect of telemedicine is divergent. According to several studies, respiration rate (RR) increases before, during, and after an exacerbation and the change is measurable with several contactless devices. Despite this, RR is rarely measured, and telemedicine solutions only use wearable devices for measuring RR, even though wearable respiratory monitoring devices have been associated with certain drawbacks. Contactless devices are often used during sleep, as measurements conducted during sleep minimize the risk of disturbance from physical activities. However, the potential of measuring RR and heart rate (HR) during sleep for the detection of exacerbations in COPD remains unclear.

OBJECTIVE

The aim of this observational study is to investigate whether contactless measurement of RR, HR, and sleep stages can be used to detect exacerbations in people with COPD.

METHODS

An observational study including 50 participants with COPD will be conducted. The participants reside in Aalborg municipality, located in the North Denmark Region. Participants will use a contactless monitor (Sleepiz One+) near their bed during sleep for a period of 4 months. After data collection, descriptive statistics will be used to identify any extremes or variations in RR, HR, or sleep stages in the nights preceding an exacerbation. Correlation analysis will be performed to evaluate the relationship between the number of exacerbations and extremes or variations in RR, HR, or sleep stages. Finally, qualitative interviews will be conducted with 12 participants to explore their experiences of sleeping with the monitor nearby.

RESULTS

Recruitment started at the end of April 2024. A total of 12 participants have been recruited, and the remaining participants are expected to be recruited during March and April 2025. Six out of 12 participants have completed the data collection and qualitative interview stages. Overall data collection is expected to be completed by September 2025. The results are expected to provide insight into the potential for identifying extremes or variations in RR, HR, or sleep stages in the days preceding an exacerbation. Additionally, the results are expected to assess the correlation between the number of exacerbations and extremes or variations in RR, HR, and sleep stages.

CONCLUSIONS

The findings from this study may clarify the possibility of using a contactless monitor to detect exacerbations in COPD. Furthermore, the results may have the potential to improve the ability to predict exacerbations in the future.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/63230.

Detection of COPD exacerbations with continuous monitoring of breathing rate and inspiratory amplitude under oxygen therapy

BACKGROUND

Chronic Obstructive Pulmonary Disease (COPD) is one of the main causes of morbidity and mortality worldwide. Its management represents real economic and public health burdens, accentuated by periods of acute disease deterioration, called exacerbations. Some researchers have studied the interest of monitoring patients' breathing rate as an indicator of exacerbation, although achieving limited sensitivity and/or specificity. In this study, we look to improve the previously described method, by combining breathing variables, using multiple daily measures, and using an artificial intelligence-based novelty detection approach.

METHODS

Patients with COPD were monitored with a telemedicine device during their stay in a rehabilitation care center. Daily measures are compared to individually trained reference models based on: i. oxygen therapy duration ii. mean breathing rate, iii. mean inspiratory amplitude, iv. mean breathing rate and mean inspiratory amplitude, v. average distribution of breathing rate and inspiratory amplitude, vi. hidden Markov model (HMM) from a time series of breathing rate and inspiratory amplitude.

RESULTS

A set of 16 recordings with exacerbation and 23 recordings without exacerbation was obtained. When using a daily measure of breathing rate, pre-exacerbation periods were identified with a specificity of 50% and a sensitivity of 55.6%. The method based on daily oxygen therapy usage and the method based on time series obtain a sensitivity of 76.8% and 73.2%, respectively, for a fixed specificity of 50%.

CONCLUSION

A single daily measure of breathing rate alone is not sufficient for the detection of pre-exacerbation periods. More complete models also achieve limited performance, equivalent to models based on changes in the duration of therapy usage.

Early Identification of Exacerbations in Patients with Chronic Obstructive Pulmonary Disease (COPD)

Exacerbations of Chronic Obstructive Pulmonary Disease (COPD) have a substantial effect on overall disease management, health system costs, and patient outcomes. However, exacerbations are often underdiagnosed or recognized with great delay due to several factors such as patients' inability to differentiate between acute episodes and symptom fluctuations, delays in seeking medical assistance, and disparities in dyspnea perception. Self-management intervention plans, telehealth and smartphone-based programs provide educational material, counseling, virtual hospitals and telerehabilitation, and help COPD patients to identify exacerbations early. Moreover, biomarkers such as blood eosinophil count, fibrinogen, CRP, Serum amyloid A(SAA),together with imaging parameters such as the pulmonary artery-to-aorta diameter ratio, have emerged as potential predictors of exacerbations, yet their clinical utility is limited by variability and lack of specificity. In this review, we provide information regarding the importance of the early identification of exacerbation events in COPD patients and the available methods which can be used for this purpose.

Efficacy and Acceptability of a Mobile App for Monitoring the Clinical Status of Patients With Chronic Obstructive Pulmonary Disease Receiving Home Oxygen Therapy: Randomized Controlled Trial

BACKGROUND

Chronic obstructive pulmonary disease (COPD) primarily originates from exposure to tobacco smoke, although factors, such as air pollution and exposure to chemicals, also play a role. One of the primary treatments for COPD is oxygen therapy, which helps manage dyspnea and improve survival rates. Mobile health (mHealth) technologies have demonstrated significant potential in monitoring patients with chronic diseases, offering new avenues for enhancing patient care and disease management.

OBJECTIVE

The purpose of this study was to evaluate the efficacy and acceptability of a mobile app designed for the clinical monitoring of patients with COPD and home oxygen (HO) therapy, compared with conventional monitoring in real-world community settings.

METHODS

A parallel-group, nonblinded, multicenter randomized controlled trial was conducted with 45 participants; the intervention group (IG), which used the mobile app in addition to conventional monitoring (n=23) and the control group, which received only conventional monitoring (n=22), administered by therapists over a duration of 3 months. The primary outcomes included the chronic obstructive pulmonary disease assessment test (CAT) score, the level of dyspnea measured by the Borg scale, and oxygen saturation percentage, assessed at both the beginning and end of the trial. Secondary outcomes included the frequency of app use, the number of hospitalizations, and survival rates. In addition, a satisfaction survey and an interview were conducted with the IG.

RESULTS

The median use of the mobile app was 21 (IQR 16-28) days. At the end of the follow-up, the Borg dyspnea scale was significantly lower in patients who used the mobile app for HO therapy monitoring (mean 0.6, SD 0.8 vs mean 4.1, SD 1.4; P=.001). Regarding the impact of COPD on quality of life, as measured by the CAT, no differences were found in the scores between baseline and end-of-follow-up within the control group. However, a significant decrease was observed in the IG (baseline median CAT 27, IQR 23-31 vs final median CAT 22, IQR 14-28; P<.001). In addition, the CAT score was significantly higher in patients receiving conventional monitoring compared with those monitored with the mobile app (median 30, IQR 23-32 vs median 22, IQR 14-28; P=.02).

CONCLUSIONS

The use of the mobile app, AppO2 (SINCO), designed for the clinical monitoring of patients with COPD and HO therapy, is associated with improved quality of life. In addition, the app is highly accepted by users, promotes self-care, and fosters patient confidence in managing their own condition.

TRIAL REGISTRATION

ClinicalTrials NCT04820790; https://clinicaltrials.gov/study/NCT04820790.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-https://doi.org/10.1186/s12875-021-01450-8.

Facilitators and barriers affecting the implementation of e-health for chronic respiratory diseases in remote settings: a qualitative evidence synthesis

BACKGROUND

Chronic respiratory diseases are important causes of disability and mortality globally. Their incidence may be higher in remote locations where healthcare is limited and risk factors, such as smoking and indoor air pollution, are more prevalent. E-health could overcome some healthcare access obstacles in remote locations, but its utilisation has been limited. An improved understanding of barriers and facilitators to the implementation of e-health in remote locations could aid enhanced application of these approaches.

METHODS

We performed a qualitative evidence synthesis to explore factors affecting the successful implementation of e-health interventions in remote locations for patients with chronic respiratory diseases. We searched PubMed, CINAHL, Embase, Web of Science and PsycINFO databases for qualitative and mixed-methods studies. Studies were assessed by two researchers, and 41 studies were included in the synthesis. Quality was assessed via the CASP-tool. Findings were coded with Atlas.ti software and categorised based on an adapted Digital Health Equity Framework.

RESULTS

Nineteen themes were identified across five levels (individual, interpersonal, community, society and technology), with associated facilitators and barriers for implementation. An important facilitator of e-health was its role as a tool to overcome obstacles of distance and to increase access to care and patients' self-efficacy. Potential barriers included the reduction of in-person interactions and an increased burden of work for healthcare providers. Good quality, usability, adaptability and efficacy of e-health interventions were important for implementation to be successful, as were adaptation to the local setting - including culture and language -and involvement of relevant stakeholders throughout the process.

CONCLUSIONS

Several factors affecting the implementation of e-health in remote and rural locations for patients with chronic respiratory disease were identified. Intervention objectives, target population, geographical location, local culture, and available resources should be carefully considered when designing an e-health intervention. These findings can be used to inform the successful design and implementation of future e-health interventions.

Risk of severe cardiovascular events following COPD exacerbations: results from the EXACOS-CV study in Spain

INTRODUCTION AND OBJECTIVES

This real-world study-the first of its kind in a Spanish population-aimed to explore severe risk for cardiovascular events and all-cause death following exacerbations in a large cohort of patients with chronic obstructive pulmonary disease (COPD).

METHODS

We included individuals with a COPD diagnosis code between 2014 and 2018 from the BIG-PAC health care claims database. The primary outcome was a composite of a first severe cardiovascular event (acute coronary syndrome, heart failure decompensation, cerebral ischemia, arrhythmia) or all-cause death following inclusion in the cohort. Time-dependent Cox proportional hazards models estimated HRs for associations between exposed time periods (1-7, 8-14, 15-30, 31-180, 181-365, and >365 days) following an exacerbation of any severity, and following moderate or severe exacerbations separately (vs unexposed time before a first exacerbation following cohort inclusion).

RESULTS

During a median follow-up of 3.03 years, 18 901 of 24 393 patients (77.5%) experienced ≥ 1 moderate/severe exacerbation, and 8741 (35.8%) experienced the primary outcome. The risk of a severe cardiovascular event increased following moderate/severe COPD exacerbation onset vs the unexposed period, with rates being most increased during the first 1 to 7 days following exacerbation onset (HR, 10.10; 95%CI, 9.29-10.97) and remaining increased >365 days after exacerbation onset (HR, 1.65; 95%CI, 1.49-1.82).

CONCLUSIONS

The risk of severe cardiovascular events or death increased following moderate/severe exacerbation onset, illustrating the need for proactive multidisciplinary care of patients with COPD to prevent exacerbations and address other cardiovascular risk factors.

Intelligent wearable devices with audio collection capabilities to assess chronic obstructive pulmonary disease severity

Background

Intelligent wearable devices have potential for chronic obstructive pulmonary disease (COPD) monitoring, but the effectiveness of combining cough and blowing sounds for disease assessment is unclear.

Objective

The objective was to assess COPD severity via physiological parameters and audio data collected by a smartwatch.

Methods

COPD patients underwent lung function tests, electrocardiograms, blood gas analysis, and 6-min walk tests. The patients' peripheral arterial oxygen saturation (SpO2), heart rate variability (HRV), heart rate (HR), and respiratory rate (RR) were continuously monitored via a smartwatch for 7-14 days, and voluntary cough and forceful blowing sounds were recorded twice daily. The HR, SpO2, and RR were categorized into all-day, sleep, and wake periods and summarized using the mean, standard deviation, median, 25th percentile, 75th percentile and percent variation. The correlations among lung function, physiological parameters, and audio data were analyzed to develop a model for predicting COPD severity.

Results

Twenty-nine stable patients, with a mean age of 67.0 ± 5.8 years, were enrolled, and 89.7% were male. HR, HRV, RR, cough sounds, and blowing sounds were significantly correlated with the Global Initiative for Chronic Obstructive Lung Disease (GOLD) grade, with cough sounds showing the highest correlation (r = 0.7617, p < .001). Cough sounds also had the strongest correlation with the mean 6-minute walking distance (r = 0.6847, p < .001), whereas blowing sounds had the strongest correlation with the Body mass index, airflow Obstruction, Dyspnea, and Exercise capacity index (r = -0.6749, p < .001). A logistic regression model using the RR and blowing sounds as key predictors achieved accuracies of 0.77-0.89 in determining the GOLD grade, with a Cohen's kappa coefficient of 0.6757.

Conclusions

Audio data were more strongly correlated with lung function in COPD patients than were physiological parameters. A smartwatch with audio collection capabilities effectively assessed COPD severity.

Trial Registration

ClinicalTrials.gov NCT05551169.

Implementation of an algorithm for predicting exacerbations in telemonitoring: A multimethod study of patients' and clinicians' experiences

Background

Prediction algorithms may improve the ability of telehealth solutions to assess the risk of future exacerbations in patients with chronic obstructive pulmonary disease. Learning from patients' and clinicians' evaluations and experiences about the use of such algorithms is essential to evaluate its potential and examine factors that could potentially influence the implementation and sustained use.

Objective

To investigate the patients' and clinicians' perceptions and satisfaction with an algorithm for predicting exacerbations in patients with chronic obstructive pulmonary disease.

Design

Multimethod study.

Setting

Three community nursing sites in Aalborg Municipality, Denmark.

Participants

One hundred and eleven adults with chronic obstructive pulmonary disease and four clinicians (three nurses and one physiotherapist) specialized in telehealth monitoring of the disease.

Methods

The study was performed from November 2021 to November 2022 alongside a clinical trial in which a prediction algorithm was integrated into an existing telehealth system. The patients' perspectives were investigated using a self-constructed questionnaire. The clinicians' perspective was explored using semistructured individual interviews.

Results

Most patients (84.0 %-90.8 %) were satisfied with the algorithm and the additional measurements required by the algorithm. Approximately 71.7 %-75.9 % found that the algorithm could be a useful tool for disease assessment. Patients elaborated that they could see an exacerbation prevention potential in the algorithm. Patients trusted the algorithm and found an increased sense of security. The clinicians showed a positive response toward the algorithm and its user-friendliness. However, they were concerned that the additional measurements could be too demanding for some patients and questioned the accuracy of the measurements. Some felt that the algorithm could risk being time-consuming and harm the overall assessment of the individual patient. They expressed a need for continuous information about the algorithm to understand its functions and alarms.

Conclusions

Optimal use of the algorithm would require that patients perform additional pulse and oxygen saturation measurements. Furthermore, it will require in-depth insight among clinicians regarding the algorithm's functions and alarms.

Registration

The study was performed alongside a clinical trial, which was first registered September 9, 2021, at clinicaltrials.gov (registration number NCT05218525). Date of first recruitment was September 28, 2021.

Understanding the Gaps in the Reporting of COPD Exacerbations by Patients: A Review

Exacerbations of chronic obstructive pulmonary disease (COPD) are associated with loss of lung function, poor quality of life, loss of exercise capacity, risk of serious cardiovascular events, hospitalization, and death. However, patients underreport exacerbations, and evidence suggests that unreported exacerbations have similar negative health implications for patients as those that are reported. Whilst there is guidance for physicians to identify patients who are at risk of exacerbations, they do not help patients recognise and report them. Newly developed tools, such as the COPD Exacerbation Recognition Tool (CERT) have been designed to achieve this objective. This review focuses on the underreporting of COPD exacerbations by patients, the factors associated with this, the consequences of underreporting, and potential solutions.

Do Antidepressants Worsen COPD Outcomes in Depressed Patients with COPD?

The coexistence of depression with chronic obstructive pulmonary disease (COPD) has been associated with poorer outcomes. Studies have questioned the safety of antidepressants in patients with COPD. This review shows the potential relationships and the possible mechanisms and gives us good warnings on how to approach this problem. Treatment should be both non-pharmacological and pharmacological, but importantly tailored to the individual patient.

Respiratory Specialist Visits Before Admissions with COPD Exacerbation are Linked to Improved Management and Outcomes

Purpose

Exacerbations of COPD (ECOPD) significantly impact disease progression and mortality. Visiting a respiratory specialist (RS) in proximity to the exacerbation may lead to prompt treatment and improved outcomes. We aimed to evaluate the association between an RS visit 30-days before admission and exacerbation outcomes.

Patients and methods

The prospective study included subjects that were hospitalized with ECOPD between 2017 and 2019 in 13 medical centers. Pre-admission, in-hospital, and 30-day outcomes were assessed and compared between patients with and without a 30-day RS visit, using propensity score matching. A sub-group analysis was performed based on the reason for the RS visit (emergent vs regular follow-up).

Results

Three hundred and forty-four subjects were included, and 105 (31%) had pre-admission RS visit (RS group). Before matching, indicators of severe COPD were prevalent in the RS group, while after matching there were no differences. RS visits were associated with pre-hospital initiation of short acting bronchodilators (50% vs 36%), antibiotics (30% vs 17%), and systemic steroids (38% vs 22%). The RS group had longer duration between first symptoms to hospital arrival (median 5 vs 3 days, p < 0.01) and shorter hospital length-of-stay (median 4 vs 5 days, p = 0.04). In-hospital and 30-days outcomes were similar between the groups. However, a non-emergent pre-hospital RS visit was associated with improved in-hospital and 30-day outcomes.

Conclusion

Routine RS visits could lead to correct and early treatment for ECOPD with a potential for improved outcomes. These findings highlight the need for available specialists and higher awareness.

Management of acute exacerbations of COPD in the emergency department and its associations with clinical variables

Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a common cause for emergency department (ED) visits. Still, large scale studies that assess the management of AECOPD in the ED are limited. Our aim was to evaluate treatment characteristics of AE-COPD in the ED on a national scale. A prospective study as part of the COPD Israeli survey, conducted between 2017 and 2019, in 13 medical centers. Patients hospitalized with AECOPD were included and interviewed. Clinical data related to their ED and hospital stay were collected. 344 patients were included, 38% females, mean age of 70 ± 11 years. Median (IQR) time to first ED treatment was 59 (23-125) minutes and to admission 293 (173-490) minutes. Delayed ED treatment (> 1 h) was associated with older age (p = 0.01) and lack of a coded diagnosis of COPD in hospital records (p = 0.01). Long ED length-of-stay (> 5 h) was linked with longer hospitalizations (p = 0.01). Routine ED care included inhalations of short-acting bronchodilators (246 patients, 72%) and systemic steroids (188 patients, 55%). Receiving routine ED care was associated with its continuation during hospitalization (p < 0.001). In multivariate analysis, predictors for patients not receiving routine care were obesity (adjusted odds ratio 0.5, 95% CI 0.3-0.8, p = 0.01) and fever (AOR 0.3, 95% CI 0.1-0.6, p < 0.01), while oxygen saturation < 91% was an independent predictor for ED routine treatment (AOR 3.6, 95% CI 2.1-6.3, p < 0.01). Our findings highlight gaps in the treatment of AECOPD in the ED on a national scale, with specific predictors for their occurrence.

Does adherence to exacerbation action plans matter? Insights from two COPD self-management studies

Introduction

Patients' adherence is essential for COPD self-management, as beneficial effects can only be expected in adherent patients. We explored associations between patients' adherence to COPD exacerbation action plans and health outcomes.

Materials and methods

Pooled COPD self-treatment intervention group data from two RCTs were analysed, only including patients who had ≥1 COPD exacerbation or started ≥1 course of oral corticosteroids over one-year follow-up. Optimal adherence was defined as 'self-treatment initiated ≤1 day before or after exacerbation start', suboptimal adherence as 'self-treatment initiated 2 days before or after exacerbation start or no self-treatment initiated for a short (1-3 days) exacerbation', and significant delay or no treatment as 'self-treatment initiated >2 days after exacerbation start or no self-treatment initiated for a longer (>3 days) exacerbation'. Regression models were built for several health outcomes, with the number of COPD exacerbation days/patient/year being the primary outcome.

Results

Patients with significant delay or no treatment (n = 46) had more exacerbation days/patient/year (33.3 (95 % CI 10.9; 55.6)) than optimal adherent patients (n = 38) (23.7 (95 % CI 1.7; 45.7)). The duration per COPD exacerbation was longer for patients with significant delay or no treatment (15.5 days) compared to optimal adherent patients (7.8 days). No differences in health outcomes were observed between optimal and suboptimal adherent patients.

Conclusions

Being adherent to action plans is associated with better health outcomes than significant delayed treatment or no treatment at all. Interestingly, suboptimal adherence demonstrated health benefits comparable to optimal adherence. COPD self-management interventions should prioritise strategies to optimise patients' adherence to action plans.

Rationale and design of healthy at home for COPD: an integrated remote patient monitoring and virtual pulmonary rehabilitation pilot study

Chronic obstructive pulmonary disease (COPD) is a common, costly, and morbid condition. Pulmonary rehabilitation, close monitoring, and early intervention during acute exacerbations of symptoms represent a comprehensive approach to improve outcomes, but the optimal means of delivering these services is uncertain. Logistical, financial, and social barriers to providing healthcare through face-to-face encounters, paired with recent developments in technology, have stimulated interest in exploring alternative models of care. The Healthy at Home study seeks to determine the feasibility of a multimodal, digitally enhanced intervention provided to participants with COPD longitudinally over 6 months. This paper details the recruitment, methods, and analysis plan for the study, which is recruiting 100 participants in its pilot phase. Participants were provided with several integrated services including a smartwatch to track physiological data, a study app to track symptoms and study instruments, access to a mobile integrated health program for acute clinical needs, and a virtual comprehensive pulmonary support service. Participants shared physiologic, demographic, and symptom reports, electronic health records, and claims data with the study team, facilitating a better understanding of their symptoms and potential care needs longitudinally. The Healthy at Home study seeks to develop a comprehensive digital phenotype of COPD by tracking and responding to multiple indices of disease behavior and facilitating early and nuanced responses to changes in participants' health status. This study is registered at Clinicaltrials.gov (NCT06000696).

Predictive Value of Physiological Values and Symptom Scores for Exacerbations in Bronchiectasis and Chronic Obstructive Pulmonary Disease With Frequent Exacerbations: Longitudinal Observational Cohort Study

BACKGROUND

COPD (chronic obstructive pulmonary disease) and bronchiectasis are common, and exacerbations contribute to their morbidity and mortality. Predictive factors for the frequency of future exacerbations include previous exacerbation frequency and airway colonization. Earlier treatment of exacerbations is likely to reduce severity.

OBJECTIVE

This study tested the hypothesis that, in a population with bronchiectasis, COPD, or both who have frequent exacerbations and airway colonization, changes in symptom scores or physiological variables within 10 days prior to an exacerbation would allow the prediction of the event.

METHODS

We performed a 6-month, longitudinal, observational, cohort study among 30 participants with bronchiectasis, COPD, or both; at least 2 exacerbations per year; and colonization with Pseudomonas aeruginosa or Haemophilus influenzae. Daily symptom and physiological data were collected, comprising pulse rate, blood pressure, oxygen saturation, peak flow rate, step count, weight, and temperature. Exacerbations (defined as the onset of new antibiotic use for respiratory symptoms) were collected, and predictive values for abnormal values in the 10 days prior to an exacerbation were calculated.

RESULTS

A total of 30 participants were recruited, collecting a total of 39,534 physiological and 25,334 symptom data points across 5358 participant-days; these included 78 exacerbations across 27 participants, with the remaining 3 participants not having exacerbations within the 6-month observation period. Peak flow rate, oxygen saturation, and weight were significantly different at the point of exacerbation (all P<.001), but no significant trends around exacerbation were noted and no clinically beneficial predictive value was found in the overall or individually adjusted model. Symptom scores tended to worsen for 10 days on either side of an exacerbation but were of insufficient magnitude for prediction, with area under the receiver operating characteristic curve values of ranging from 0.4 to 0.6.

CONCLUSIONS

Within this small cohort with bronchiectasis, COPD, or both and airway colonization, physiological and symptom variables did not show sufficient predictive value for exacerbations to be of clinical utility. The self-management education provided as standard of care may be superior to either of these approaches, but benefit in another or larger cohort cannot be excluded.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/resprot.6636.

The effect of telemedicine employing telemonitoring instruments on readmissions of patients with heart failure and/or COPD: a systematic review

Background

Hospital readmissions pose a challenge for modern healthcare systems. Our aim was to assess the efficacy of telemedicine incorporating telemonitoring of patients' vital signs in decreasing readmissions with a focus on a specific patient population particularly prone to rehospitalization: patients with heart failure (HF) and/or chronic obstructive pulmonary disease (COPD) through a comparative effectiveness systematic review.

Methods

Three major electronic databases, including PubMed, Scopus, and ProQuest's ABI/INFORM, were searched for English-language articles published between 2012 and 2023. The studies included in the review employed telemedicine incorporating telemonitoring technologies and quantified the effect on hospital readmissions in the HF and/or COPD populations.

Results

Thirty scientific articles referencing twenty-nine clinical studies were identified (total of 4,326 patients) and were assessed for risk of bias using the RoB2 (nine moderate risk, six serious risk) and ROBINS-I tools (two moderate risk, two serious risk), and the Newcastle-Ottawa Scale (three good-quality, four fair-quality, two poor-quality). Regarding the primary outcome of our study which was readmissions: the readmission-related outcome most studied was all-cause readmissions followed by HF and acute exacerbation of COPD readmissions. Fourteen studies suggested that telemedicine using telemonitoring decreases the readmission-related burden, while most of the remaining studies suggested that it had a neutral effect on hospital readmissions. Examination of prospective studies focusing on all-cause readmission resulted in the observation of a clearer association in the reduction of all-cause readmissions in patients with COPD compared to patients with HF (100% vs. 8%).

Conclusions

This systematic review suggests that current telemedicine interventions employing telemonitoring instruments can decrease the readmission rates of patients with COPD, but most likely do not impact the readmission-related burden of the HF population. Implementation of novel telemonitoring technologies and conduct of more high-quality studies as well as studies of populations with ≥2 chronic disease are necessary to draw definitive conclusions.

Systematic Review Registration

This study is registered at the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY), identifier (INPLASY202460097).

Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis

BACKGROUND

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with high mortality, morbidity, and poor quality of life and constitute a substantial burden to patients and health care systems. New approaches to prevent or reduce the severity of AECOPD are urgently needed. Internationally, this has prompted increased interest in the potential of remote patient monitoring (RPM) and digital medicine. RPM refers to the direct transmission of patient-reported outcomes, physiological, and functional data, including heart rate, weight, blood pressure, oxygen saturation, physical activity, and lung function (spirometry), directly to health care professionals through automation, web-based data entry, or phone-based data entry. Machine learning has the potential to enhance RPM in chronic obstructive pulmonary disease by increasing the accuracy and precision of AECOPD prediction systems.

OBJECTIVE

This study aimed to conduct a dual systematic review. The first review focuses on randomized controlled trials where RPM was used as an intervention to treat or improve AECOPD. The second review examines studies that combined machine learning with RPM to predict AECOPD. We review the evidence and concepts behind RPM and machine learning and discuss the strengths, limitations, and clinical use of available systems. We have generated a list of recommendations needed to deliver patient and health care system benefits.

METHODS

A comprehensive search strategy, encompassing the Scopus and Web of Science databases, was used to identify relevant studies. A total of 2 independent reviewers (HMGG and CM) conducted study selection, data extraction, and quality assessment, with discrepancies resolved through consensus. Data synthesis involved evidence assessment using a Critical Appraisal Skills Programme checklist and a narrative synthesis. Reporting followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.

RESULTS

These narrative syntheses suggest that 57% (16/28) of the randomized controlled trials for RPM interventions fail to achieve the required level of evidence for better outcomes in AECOPD. However, the integration of machine learning into RPM demonstrates promise for increasing the predictive accuracy of AECOPD and, therefore, early intervention.

CONCLUSIONS

This review suggests a transition toward the integration of machine learning into RPM for predicting AECOPD. We discuss particular RPM indices that have the potential to improve AECOPD prediction and highlight research gaps concerning patient factors and the maintained adoption of RPM. Furthermore, we emphasize the importance of a more comprehensive examination of patient and health care burdens associated with RPM, along with the development of practical solutions.

Exacerbation predictive modelling using real-world data from the myCOPD app

Background

Acute exacerbations of COPD (AECOPD) are episodes of breathlessness, cough and sputum which are associated with the risk of hospitalisation, progressive lung function decline and death. They are often missed or diagnosed late. Accurate timely intervention can improve these poor outcomes. Digital tools can be used to capture symptoms and other clinical data in COPD. This study aims to apply machine learning to the largest available real-world digital dataset to develop AECOPD Prediction tools which could be used to support early intervention and improve clinical outcomes.

Objective

To create and validate a machine learning predictive model that forecasts exacerbations of COPD 1-8 days in advance. The model is based on routine patient-entered data from myCOPD self-management app.

Method

Adaptations of the AdaBoost algorithm were employed as machine learning approaches. The dataset included 506 patients users between 2017 and 2021. 55,066 app records were available for stable COPD event labels and 1263 records of AECOPD event labels. The data used for training the model included COPD assessment test (CAT) scores, symptom scores, smoking history, and previous exacerbation frequency. All exacerbation records used in the model were confined to the 1-8 days preceding a self-reported exacerbation event.

Results

TheEasyEnsemble Classifier resulted in a Sensitivity of 67.0 % and a Specificity of 65 % with a positive predictive value (PPV) of 5.0 % and a negative predictive value (NPV) of 98.9 %. An AdaBoost model with a cost-sensitive decision tree resulted in a a Sensitivity of 35.0 % and a Specificity of 89.0 % with a PPV of 7.08 % and NPV of 98.3 %.

Conclusion

This preliminary analysis demonstrates that machine learning approaches to real-world data from a widely deployed digital therapeutic has the potential to predict AECOPD and can be used to confidently exclude the risk of exacerbations of COPD within the next 8 days.

Mortality prevention as the centre of COPD management

COPD is a major healthcare problem and cause of mortality worldwide. COPD patients at increased mortality risk are those who are more symptomatic, have lower lung function and lower diffusing capacity of the lung for carbon monoxide, decreased exercise capacity, belong to the emphysematous phenotype and those who have concomitant bronchiectasis. Mortality risk seems to be greater in patients who experience COPD exacerbations and in those who suffer from concomitant cardiovascular and/or metabolic diseases. To predict the risk of death in COPD patients, several composite scores have been created using different parameters. In previous years, large studies (also called mega-trials) have evaluated the efficacy of different therapies on COPD mortality, but until recently only nonpharmaceutical interventions have proven to be effective. However, recent studies on fixed combinations of triple therapy (long-acting β-agonists, long-acting muscarinic antagonists and inhaled corticosteroids) have provided encouraging results, showing for the first time a reduction in mortality compared to dual therapies. The aim of the present review is to summarise available data regarding mortality risk in COPD patients and to describe pharmacological therapies that have shown effectiveness in reducing mortality.

Rationale and Design of Healthy at Home for COPD: an Integrated Remote Patient Monitoring and Virtual Pulmonary Rehabilitation Pilot Study

Chronic Obstructive Pulmonary Disease (COPD) is a common, costly, and morbid condition. Pulmonary rehabilitation, close monitoring, and early intervention during acute exacerbations of symptoms represent a comprehensive approach to improve outcomes, but the optimal means of delivering these services is uncertain. Logistical, financial, and social barriers to providing healthcare through face-to-face encounters, paired with recent developments in technology, have stimulated interest in exploring alternative models of care. The Healthy at Home study seeks to determine the feasibility of a multimodal, digitally enhanced intervention provided to participants with COPD longitudinally over six months. This paper details the recruitment, methods, and analysis plan for the study, which is recruiting 100 participants in its pilot phase. Participants were provided with several integrated services including a smartwatch to track physiological data, a study app to track symptoms and study instruments, access to a mobile integrated health program for acute clinical needs, and a virtual comprehensive pulmonary support service. Participants shared physiologic, demographic, and symptom reports, electronic health records, and claims data with the study team, facilitating a better understanding of their symptoms and potential care needs longitudinally. The Healthy at Home study seeks to develop a comprehensive digital phenotype of COPD by tracking and responding to multiple indices of disease behavior and facilitating early and nuanced responses to changes in participants' health status. This study is registered at Clinicaltrials.gov (NCT06000696).

Cognitive and Emotional Responses to Chronic Obstructive Pulmonary Disease Exacerbations and Patterns of Care Seeking

Rationale: Cognitive and emotional responses associated with care seeking for chronic obstructive pulmonary disease (COPD) exacerbations are not well understood.Objectives: We sought to define care-seeking profiles based on whether and when U.S. veterans seek care for COPD exacerbations and compare cognitive and emotional responses with exacerbation symptoms across the profiles.Methods: This study analyzes data from a 1-year prospective observational cohort study of individuals with COPD. Cognitive and emotional responses to worsening symptoms were measured with the Response to Symptoms Questionnaire, adapted for COPD. Seeking care was defined as contacting or visiting a healthcare provider or going to the emergency department. Participants were categorized into four care-seeking profiles based on the greatest delay in care seeking for exacerbations when care was sought: 0-3 days (early), 4-7 days (short delay), >7 days (long delay), or never sought care for any exacerbation. The proportion of exacerbations for which participants reported cognitive and emotional responses was estimated for each care-seeking profile, stratified by the timing of when care was sought.Results: There were 1,052 exacerbations among 350 participants with Response to Symptoms Questionnaire responses. Participants were predominantly male (96%), and the mean age was 69.3 ± 7.2 years. For the 409 (39%) exacerbations for which care was sought, the median delay was 3 days. Those who sought care had significantly more severe COPD (forced expiratory volume in 1 s, modified Medical Research Council dyspnea scale) than those who never sought care. Regardless of the degree of delay until seeking care at one exacerbation, participants consistently reported experiencing serious symptoms if they sought care compared with events for which participants did not seek care (e.g., among early care seekers when care was sought, 36%; when care was not sought, 25%). Similar findings were seen in participants' assessment of the importance of getting care (e.g., among early care seekers when care was sought, 90%; when care was not sought, 52%) and their assessment of anxiety about the symptoms (e.g., among early care seekers when care was sought, 33%; when care was not sought, 17%).Conclusions: Delaying or not seeking care for COPD exacerbations was common. Regardless of care-seeking profile, cognitive and emotional responses to symptoms when care was sought differed from responses when care was not sought. Emotional and cognitive response to COPD exacerbations should be considered when developing individualized strategies to encourage seeking care for exacerbations.Clinical trial registered with www.clinicaltrials.gov (NCT02725294).

A machine learning model for predicting acute exacerbation of in-home chronic obstructive pulmonary disease patients

PURPOSE

This study utilized intelligent devices to remotely monitor patients with chronic obstructive pulmonary disease (COPD), aiming to construct and evaluate machine learning (ML) models that predict the probability of acute exacerbations of COPD (AECOPD).

METHODS

Patients diagnosed with COPD Group C/D at our hospital between March 2019 and June 2021 were enrolled in this study. The diagnosis of COPD Group C/D and AECOPD was based on the GOLD 2018 guidelines. We developed a series of machine learning (ML)-based models, including XGBoost, LightGBM, and CatBoost, to predict AECOPD events. These models utilized data collected from portable spirometers and electronic stethoscopes within a five-day time window. The area under the ROC curve (AUC) was used to assess the effectiveness of the models.

RESULTS

A total of 66 patients were enrolled in COPD groups C/D, with 32 in group C and 34 in group D. Using observational data within a five-day time window, the ML models effectively predict AECOPD events, achieving high AUC scores. Among these models, the CatBoost model exhibited superior performance, boasting the highest AUC score (0.9721, 95 % CI: 0.9623-0.9810). Notably, the boosting tree methods significantly outperformed the time-series based methods, thanks to our feature engineering efforts. A post-hoc analysis of the CatBoost model reveals that features extracted from the electronic stethoscope (e.g., max/min vibration energy) hold more importance than those from the portable spirometer.

CONCLUSIONS

The tree-based boosting models prove to be effective in predicting AECOPD events in our study. Consequently, these models have the potential to enhance remote monitoring, enable early risk assessment, and inform treatment decisions for homebound patients with chronic COPD.

Characterising user engagement with mHealth for chronic disease self-management and impact on machine learning performance

Mobile Health (mHealth) has the potential to be transformative in the management of chronic conditions. Machine learning can leverage self-reported data collected with apps to predict periods of increased health risk, alert users, and signpost interventions. Despite this, mHealth must balance the treatment burden of frequent self-reporting and predictive performance and safety. Here we report how user engagement with a widely used and clinically validated mHealth app, myCOPD (designed for the self-management of Chronic Obstructive Pulmonary Disease), directly impacts the performance of a machine learning model predicting an acute worsening of condition (i.e., exacerbations). We classify how users typically engage with myCOPD, finding that 60.3% of users engage frequently, however, less frequent users can show transitional engagement (18.4%), becoming more engaged immediately ( < 21 days) before exacerbating. Machine learning performed better for users who engaged the most, however, this performance decrease can be mostly offset for less frequent users who engage more near exacerbation. We conduct interviews and focus groups with myCOPD users, highlighting digital diaries and disease acuity as key factors for engagement. Users of mHealth can feel overburdened when self-reporting data necessary for predictive modelling and confidence of recognising exacerbations is a significant barrier to accurate self-reported data. We demonstrate that users of mHealth should be encouraged to engage when they notice changes to their condition (rather than clinically defined symptoms) to achieve data that is still predictive for machine learning, while reducing the likelihood of disengagement through desensitisation.

Implementation of the Care Bundle for the Management of Chronic Obstructive Pulmonary Disease with/without Heart Failure

Chronic obstructive pulmonary disease (COPD) is often part of a more complex cardiopulmonary disease, especially in older patients. The differential diagnosis of the acute exacerbation of COPD and/or heart failure (HF) in emergency settings is challenging due to their frequent coexistence and symptom overlap. Both conditions have a detrimental impact on each other's prognosis, leading to increased mortality rates. The timely diagnosis and treatment of COPD and coexisting factors like left ventricular overload or HF in inpatient and outpatient care can improve prognosis, quality of life, and long-term outcomes, helping to avoid exacerbations and hospitalization, which increase future exacerbation risk. This work aims to address existing gaps, providing management recommendations for COPD with/without HF, particularly when both conditions coexist. During virtual meetings, a panel of experts (the authors) discussed and reached a consensus on the differential and paired diagnosis of COPD and HF, providing suggestions for risk stratification, accurate diagnosis, and appropriate therapy for inpatients and outpatients. They emphasize that when COPD and HF are concomitant, both conditions should receive adequate treatment and that recommended HF treatments are not contraindicated in COPD and have favorable effects. Accurate diagnosis and therapy is crucial for effective treatment, reducing hospital readmissions and associated costs. The management considerations discussed in this study can potentially be extended to address other cardiopulmonary challenges frequently encountered by COPD patients.

ERS International Congress 2023: highlights from the Respiratory Intensive Care Assembly

Early career members of Assembly 2 (Respiratory Intensive Care) attended the 2023 European Respiratory Society International Congress in Milan, Italy. The conference covered acute and chronic respiratory failure. Sessions of interest to our assembly members and to those interested in respiratory critical care are summarised in this article and include the latest updates in respiratory intensive care, in particular acute respiratory distress syndrome and mechanical ventilation.

Impact of COPD on mortality: An 8-year observational retrospective healthcare claims database cohort study

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of morbidity and mortality. Here we present a large observational study on the association of COPD and exacerbations with mortality (AvoidEx Mortality).

METHODS

A real-world, observational cohort study with longitudinal analyses of German healthcare claims data in patients ≥40 years of age with a COPD diagnosis from 2011 to 2018 (n = 250,723) was conducted. Patients entered the cohort (index date) upon the first COPD diagnosis. To assess the impact of COPD on all-cause death, a propensity score-matched control group of non-COPD patients was constructed. The number and severity of exacerbations during a 12-month pre-index period were used to form subgroups. For each exacerbation subgroup the exacerbations during 12 months prior to death were analysed.

RESULTS

COPD increases the all-cause mortality risk by almost 60% (HR 1.57 (95% CI 1.55-1.59)) in comparison to matched non-COPD controls, when controlling for other baseline covariates. The cumulative risk of death after 8 years was highest in patients with a history of more than one moderate or severe exacerbation. Among all deceased COPD patients, 17.2% had experienced a severe, and 34.8% a moderate exacerbation, within 3 months preceding death. Despite increasing exacerbation rates towards death, more than the half of patients were not receiving any recommended pharmacological COPD therapy in the year before death.

CONCLUSION

Our study illustrates the impact of COPD on mortality risk and highlights the need for consequent COPD management comprising exacerbation assessment and treatment.

Inhaled versus systemic corticosteroids for acute exacerbations of COPD: a systematic review and meta-analysis

This meta-analysis compares the efficacy and safety of inhaled versus systemic corticosteroids for COPD exacerbations.Following a pre-registered protocol, we appraised eligible randomised controlled trials (RCTs) according to Cochrane methodology, performed random-effects meta-analyses for all outcomes prioritised in the European Respiratory Society COPD core outcome set and rated the certainty of evidence as per Grading of Recommendations Assessment, Development and Evaluation methodology.We included 20 RCTs totalling 2140 participants with moderate or severe exacerbations. All trials were at high risk of methodological bias. Low-certainty evidence did not reveal significant differences between inhaled and systemic corticosteroids for treatment failure rate (relative risk 1.75, 95% CI 0.76-4.02, n=569 participants); breathlessness (mean change: standardised mean difference (SMD) -0.11, 95% CI -0.36-0.15, n=239; post-treatment scores: SMD -0.18, 95% CI -0.41-0.05, n=293); serious adverse events (relative risk 1.47, 95% CI 0.56-3.88, n=246); or any other efficacy outcomes. Moderate-certainty evidence implied a tendency for fewer adverse events with inhaled compared to systemic corticosteroids (relative risk 0.80, 95% CI 0.64-1.0, n=480). Hyperglycaemia and oral fungal infections were observed more frequently with systemic and inhaled corticosteroids, respectively.Limited available evidence suggests potential noninferiority of inhaled to systemic corticosteroids in COPD exacerbations. Appropriately designed and powered RCTs are warranted to confirm these findings.

Severe Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Are There Significant Differences Between Hospitalized and Emergency Department Patients?

Rationale

Current guidelines define a severe acute exacerbation of chronic obstructive pulmonary disease (AECOPD) as an increase in symptoms requiring hospital admission or emergency department (ED) visit. Little is known about whether or not subjects requiring hospitalization and those needing only ED care have similar patient profiles and if their clinical outcomes appear comparable.

Objective

The main goals of this study were to compare the demographic and clinical characteristics of patients treated for an AECOPD with an inpatient admission versus an ED visit and to review if hospital resource utilization was different between the 2 groups after discharge.

Methods

Subjects treated in 2022 at Montfort Hospital for an AECOPD were reviewed. Patient demographic information was collected in addition to spirometry results and blood eosinophil counts on file. Supplemental oxygen use and medication lists were also recorded. Patients with an initial hospital admission were compared to those requiring only ED care with univariate and multivariate analyses. We also assessed if subjects were again treated for an AECOPD up to 6 months post initial discharge, and if so, the type of hospital visits (hospitalization or ED).

Measurements and Main Results

A total of 135 individuals necessitated hospitalization and 79 received ED care for the treatment of an AECOPD. On univariate analysis, patients requiring an inpatient stay appeared older and were more likely to have spirometry results on file. A greater proportion of hospitalized individuals were on supplemental oxygen and prescribed at least one long-acting inhaled medication. These studied variables remained significant after multivariate logistic regression analysis. Subjects with an initial inpatient admission were also more likely to require hospitalization upon repeat presentation for a severe AECOPD.

Conclusion

Given the important differences observed in both patient characteristics and hospital resource utilization, this study supports considering an AECOPD requiring inpatient admission versus an ED visit as distinct categories of events.

MISSION ABC: transforming respiratory care through one-stop multidisciplinary clinics - an observational study

OBJECTIVES

The Modern Innovative Solutions to Improve Outcomes in Asthma, Breathlessness and Chronic Obstructive Pulmonary Disease (COPD) (MABC) service aimed to enhance disease management for chronic respiratory conditions through specialist multidisciplinary clinics, predominantly in the community. This study assesses the outcomes of these clinics.

DESIGN

This study used a prospective, longitudinal, participatory action research approach.

SETTING

The study was conducted in primary care practices across Hampshire, UK.

PARTICIPANTS

Adults aged 16 years and above with poorly controlled asthma or COPD, as well as those with undifferentiated breathlessness not under specialist care, were included.

INTERVENTIONS

Participants received care through the multidisciplinary, specialist-led MABC clinics.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary outcomes included disease activity, quality of life and healthcare utilisation. Secondary outcomes encompassed clinic attendance, diagnostic changes, patient activation, participant and healthcare professional experiences and cost-effectiveness.

RESULTS

A total of 441 participants from 11 general practitioner practices were recruited. Ninety-six per cent of participants would recommend MABC clinics. MABC assessments led to diagnosis changes for 64 (17%) participants with asthma and COPD and treatment adjustments for 252 participants (57%). Exacerbations decreased significantly from 236 to 30 after attending the clinics (p<0.005), with a mean reduction of 0.53 exacerbation events per participant. Reductions were also seen in unscheduled and out-of-hours primary care attendance, emergency department visits and hospital admissions (all p<0.005). Cost savings from reduced exacerbations and healthcare utilisation offset increased medication costs and clinic expenses.

CONCLUSIONS

Specialist-supported multidisciplinary teams in MABC clinics improved diagnosis accuracy and adherence to guidelines. High patient satisfaction, disease control improvements and reduced exacerbations resulted in decreased unscheduled healthcare use and cost savings.

TRIAL REGISTRATION NUMBER

NCT03096509.

Breathlessness and "exacerbation" questions predictive for incident COPD (MARKO study): data after two years of follow-up

Aims

To determine the predictability of the MARKO questionnaire and/or its domains, individually or in combination with other markers and characteristics (age, gender, smoking history, lung function, 6-min walk test (6 MWT), exhaled breath temperature (EBT), and hsCRP for the incident chronic obstructive pulmonary disease (COPD) in subjects at risk over 2 years follow-up period).

Participants and Methods

Patients, smokers/ex-smokers with >20 pack-years, aged 40-65 years of both sexes were recruited and followed for 2 years. After recruitment and signing the informed consent at the GP, a detailed diagnostic workout was done by the pulmonologist; they completed three self-assessment questionnaires-MARKO, SGRQ and CAT, detailed history and physical, laboratory (CBC, hsCRP), lung function tests with bronchodilator and EBT. At the 2 year follow-up visit they performed: the same three self-assessment questionnaires, history and physical, lung function tests and EBT.

Results

A sample of 320 subjects (41.9% male), mean (SD) age 51.9 (7.4) years with 36.4 (17.4) pack-years of smoking was reassessed after 2.1 years. Exploratory factor analysis of MARKO questionnaire isolated three distinct domains (breathlessness and fatigue, "exacerbations", cough and expectorations). We have determined a rate for incident COPD that was 4.911/100 person-years (95% CI [3.436-6.816]). We found out that questions about breathlessness and "exacerbations", and male sex were predictive of incident COPD after two years follow-up (AUC 0.79, 95% CI [0.74-0.84], p < 0.001). When only active smokers were analyzed a change in EBT after a cigarette (ΔEBT) was added to a previous model (AUC 0.83, 95% CI [0.78-0.88], p < 0.001).

Conclusion

Our preliminary data shows that the MARKO questionnaire combined with EBT (change after a cigarette smoke) could potentially serve as early markers of future COPD in smokers.

Development of Multivariable Prediction Models for the Identification of Patients Admitted to Hospital with an Exacerbation of COPD and the Prediction of Risk of Readmission: A Retrospective Cohort Study using Electronic Medical Record Data

BACKGROUND

Approximately 20% of patients who are discharged from hospital for an acute exacerbation of COPD (AECOPD) are readmitted within 30 days. To reduce this, it is important both to identify all individuals admitted with AECOPD and to predict those who are at higher risk for readmission.

OBJECTIVES

To develop two clinical prediction models using data available in electronic medical records: 1) identifying patients admitted with AECOPD and 2) predicting 30-day readmission in patients discharged after AECOPD.

METHODS

Two datasets were created using all admissions to General Internal Medicine from 2012 to 2018 at two hospitals: one cohort to identify AECOPD and a second cohort to predict 30-day readmissions. We fit and internally validated models with four algorithms.

RESULTS

Of the 64,609 admissions, 3,620 (5.6%) were diagnosed with an AECOPD. Of those discharged, 518 (15.4%) had a readmission to hospital within 30 days. For identification of patients with a diagnosis of an AECOPD, the top-performing models were LASSO and a four-variable regression model that consisted of specific medications ordered within the first 72 hours of admission. For 30-day readmission prediction, a two-variable regression model was the top performing model consisting of number of COPD admissions in the previous year and the number of non-COPD admissions in the previous year.

CONCLUSION

We generated clinical prediction models to identify AECOPDs during hospitalization and to predict 30-day readmissions after an acute exacerbation from a dataset derived from available EMR data. Further work is needed to improve and externally validate these models.

Using an electronic diary and wristband accelerometer to detect exacerbations and activity levels in COPD: a feasibility study

Background

Early and accurate identification of acute exacerbations of COPD may lead to earlier treatment and prevent hospital admission. Electronic diaries have been developed for symptom monitoring and accelerometers to monitor activity. However, it is unclear whether this technology is usable in the COPD population. This study aimed to assess the feasibility of an electronic diary (eDiary) for symptom reporting using the MoreCare app and activity monitoring with the Garmin Vivofit 2 in COPD.

Methods

Participants were recruited from the London COPD Cohort. Participants were provided a Garmin Vivofit 2 activity monitor and an android tablet with the MoreCare app for a period of 3 months.

Results

25 COPD patients were recruited (mean±sd age 70.8±7.1 years, forced expiratory volume in 1 s (FEV1) 49.8±14.8% predicted). Age, gender, disease severity and exacerbation frequency had no impact on eDiary compliance. There was a moderate positive correlation between median daily very active minutes and FEV1 % pred (ρ=0.62, p=0.005). Daily step counts decreased during the initial 7 days of exacerbation and recovery compared to a pre-exacerbation baseline. A decision-tree model identified change in sputum colour, change in step count, severity of cold, exacerbation history and use of rescue medication as the most important predictors of acute exacerbations of COPD in this cohort.

Conclusions

Symptom and activity monitoring using digital technology is feasible in COPD. Further large-scale digital health studies are needed to assess whether eDiaries can be used to identify patients at risk of exacerbation and guide early intervention.

Risk Factors for Recurrent Exacerbations in the General-Practitioner-Based Swiss Chronic Obstructive Pulmonary Disease (COPD) Cohort

BACKGROUND

Patients with chronic obstructive pulmonary disease (COPD) often suffer from acute exacerbations. Our objective was to describe recurrent exacerbations in a GP-based Swiss COPD cohort and develop a statistical model for predicting exacerbation.

METHODS

COPD cohort demographic and medical data were recorded for 24 months, by means of a questionnaire-based COPD cohort. The data were split into training (75%) and validation (25%) datasets. A negative binomial regression model was developed using the training dataset to predict the exacerbation rate within 1 year. An exacerbation prediction model was developed, and its overall performance was validated. A nomogram was created to facilitate the clinical use of the model.

RESULTS

Of the 229 COPD patients analyzed, 77% of the patients did not experience exacerbation during the follow-up. The best subset in the training dataset revealed that lower forced expiratory volume, high scores on the MRC dyspnea scale, exacerbation history, and being on a combination therapy of LABA + ICS (long-acting beta-agonists + Inhaled Corticosteroids) or LAMA + LABA (Long-acting muscarinic receptor antagonists + long-acting beta-agonists) at baseline were associated with a higher rate of exacerbation. When validated, the area-under-curve (AUC) value was 0.75 for one or more exacerbations. The calibration was accurate (0.34 predicted exacerbations vs 0.28 observed exacerbations).

CONCLUSION

Nomograms built from these models can assist clinicians in the decision-making process of COPD care.

Machine learning for the development of diagnostic models of decompensated heart failure or exacerbation of chronic obstructive pulmonary disease

Heart failure (HF) and chronic obstructive pulmonary disease (COPD) are two chronic diseases with the greatest adverse impact on the general population, and early detection of their decompensation is an important objective. However, very few diagnostic models have achieved adequate diagnostic performance. The aim of this trial was to develop diagnostic models of decompensated heart failure or COPD exacerbation with machine learning techniques based on physiological parameters. A total of 135 patients hospitalized for decompensated heart failure and/or COPD exacerbation were recruited. Each patient underwent three evaluations: one in the decompensated phase (during hospital admission) and two more consecutively in the compensated phase (at home, 30 days after discharge). In each evaluation, heart rate (HR) and oxygen saturation (Ox) were recorded continuously (with a pulse oximeter) during a period of walking for 6 min, followed by a recovery period of 4 min. To develop the diagnostic models, predictive characteristics related to HR and Ox were initially selected through classification algorithms. Potential predictors included age, sex and baseline disease (heart failure or COPD). Next, diagnostic classification models (compensated vs. decompensated phase) were developed through different machine learning techniques. The diagnostic performance of the developed models was evaluated according to sensitivity (S), specificity (E) and accuracy (A). Data from 22 patients with decompensated heart failure, 25 with COPD exacerbation and 13 with both decompensated pathologies were included in the analyses. Of the 96 characteristics of HR and Ox initially evaluated, 19 were selected. Age, sex and baseline disease did not provide greater discriminative power to the models. The techniques with S and E values above 80% were the logistic regression (S: 80.83%; E: 86.25%; A: 83.61%) and support vector machine (S: 81.67%; E: 85%; A: 82.78%) techniques. The diagnostic models developed achieved good diagnostic performance for decompensated HF or COPD exacerbation. To our knowledge, this study is the first to report diagnostic models of decompensation potentially applicable to both COPD and HF patients. However, these results are preliminary and warrant further investigation to be confirmed.

Differences in the lipid metabolism profile and clinical characteristics between eosinophilic and non-eosinophilic acute exacerbation of chronic obstructive pulmonary disease

Objective: In this study, we aimed to investigate the differences in serum lipid metabolite profiles and their relationship with clinical characteristics between patients with eosinophilic and non-eosinophilic AECOPD. Methods: A total of 71 AECOPD patients were enrolled. Eosinophilic AECOPD was defined as blood EOS% ≥ 2% (n = 23), while non-eosinophilic AECOPD, as blood EOS< 2% (n = 48). Clinical data were collected, and serum lipid metabolism profiles were detected by liquid chromatography-mass spectrometry (LC-MS). The XCMS software package was used to pre-process the raw data, and then, lipid metabolite identification was achieved through a spectral match using LipidBlast library. Differences in lipid profiles and clinical features between eosinophilic and non-eosinophilic groups were analyzed by generalized linear regression. The least absolute shrinkage and selection operator (LASSO) was applied to screen the most characteristic lipid markers for the eosinophilic phenotype. Results: Eosinophilic AECOPD patients had less hypercapnic respiratory failures, less ICU admissions, a shorter length of stay in the hospital, and a lower fibrinogen level. In the lipid metabolism profiles, 32 significantly different lipid metabolites were screened through a t-test adjusted by using FDR (FDR-adjusted p < 0.05 and VIP> 1). Nine differential lipid metabolites were found to be associated with the three clinical features, namely, hypercapnia respiratory failure, ICU admission, and fibrinogen in further integration analysis. The species of triacylglycerol (TAG), phosphatidylcholine (PC), lysophosphatidylcholine (LPC), and diacylglyceryl trimethylhomoserine (DGTS) were high in these eosinophilic AECOPD. The LASSO was applied, and three lipid metabolites were retained, namely, LPC (16:0), TAG (17:0/17:2/17:2), and LPC (20:2). The logistic regression model was fitted using these three markers, and the area under the ROC curve of the model was 0.834 (95% CI: 0.740-0.929). Conclusion: Patients with eosinophilic AECOPD had a unique lipid metabolism status. Species of TAGs and LPCs were significantly increased in this phenotype and were associated with better clinical outcomes.

Personal and community-level exposure to air pollution and daily changes in respiratory symptoms and oxygen saturation among adults with COPD

Background

Air pollution exposure is associated with hospital admissions for Chronic Obstructive Pulmonary Disease (COPD). Few studies have investigated whether daily personal exposure to air pollutants affects respiratory symptoms and oxygenation among COPD patients.

Methodology

We followed 30 former smokers with COPD for up to 4 non-consecutive 30-day periods in different seasons. Participants recorded worsening of respiratory symptoms (sub-categorized as breathing or bronchitis symptoms) by daily questionnaire, and oxygen saturation by pulse oximeter. Personal and community-level exposure to fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), and ozone (O₃) were measured by portable air quality monitors and stationary monitors in the Boston area. We used generalized and multi-level linear mixed-effects models to estimate associations of the 24-hour average of each pollutant in the previous day with changes in respiratory symptoms and oxygen saturation.

Results

Higher community-level exposure to air pollutants was associated with worsening respiratory symptoms. An interquartile range (IQR) higher community-level O₃ was associated with a 1.35 (95%CI: 1.07-1.70) higher odds of worsening respiratory symptoms. The corresponding ORs for community-level PM_2.5 and NO₂ were 1.18 (95%CI: 1.02-1.37) and 1.06 (95%CI: 0.90-1.25), respectively. Community-level NO₂ was associated with worsening bronchitis symptoms (OR=1.25, 95%CI: 1.00-1.56), but not breathing symptoms. Personal PM_2.5 exposure was associated with lower odds of worsening respiratory symptoms (OR=0.91; 95%CI: 0.81-1.01). Personal exposure to NO₂ was associated with 0.11% lower oxygen saturation (95%CI: -0.22, 0.00) per IQR.

Conclusions

In this COPD population, there was a pattern of worsening respiratory symptoms associated with community-level exposure to O₃ and PM_2.5, and worsening oxygenation associated with personal exposure to NO₂.

Use of Remote Cardiorespiratory Monitoring is Associated with a Reduction in Hospitalizations for Subjects with COPD

Background

Chronic obstructive pulmonary disease (COPD) is prevalent and results in high healthcare resource utilization. The largest impact on health status and proportion of healthcare costs in COPD are related to hospitalizations for acute exacerbations. Accordingly, the Centers for Medicare & Medicaid Services have advocated for remote patient monitoring (RPM) to aid in chronic disease management. However, there has been a lack of evidence for the effectiveness of RPM in reducing the need for unplanned hospitalizations for patients with COPD.

Methods

This pre/post study was a retrospective analysis of unplanned hospitalizations in a cohort of COPD subjects started on RPM at a large, outpatient pulmonary practice. The study included all subjects with at least one unplanned, all-cause hospitalization or emergency room visit in the prior year, who had elected to enroll in an RPM service for assistance with clinical management. Additional inclusion criteria included being on RPM for at least 12 months and a patient of the practice for at least two years (12 months pre- and post-initiation of RPM).

Results

The study included 126 subjects. RPM was associated with a significantly lower rate of unplanned hospitalizations per patient per year (1.09 ± 0.07 versus 0.38 ± 0.06, P<0.001).

Conclusion

Unplanned, all-cause hospitalization rates were lower in subjects started on RPM for COPD when compared to their prior year. These results support the potential of RPM to improve the long-term management of COPD.

Exacerbation in patients with stable COPD in China: analysis of a prospective, 52-week, nationwide, observational cohort study (REAL)

BACKGROUND

Chronic obstructive pulmonary disease (COPD) management in China is inadequate and there is a need to improve care and outcomes for patients nationwide.

OBJECTIVES

The REAL study was designed to generate reliable information on COPD management from a representative sample of Chinese patients with COPD. Here, we present study outcomes related to acute exacerbations.

DESIGN

A 52-week, multicentre, prospective, observational study.

METHODS

Outpatients (aged ⩾ 40 years) enrolled from 25 tertiary and 25 secondary hospitals across six geographic regions in China were followed for 12 months. Risk factors for COPD exacerbation and disease severity by exacerbation were assessed using multivariate Poisson and ordinal logistic regression models, respectively.

RESULTS

Between June 2017 and January 2019, 5013 patients were enrolled, with 4978 included in the analysis. Mean (standard deviation) age was 66.2 (8.9) years. More patients presented with exacerbations in secondary versus tertiary hospitals (59.4% versus 40.2%) and in rural versus urban areas (53.2% versus 46.3%). Overall exacerbation rates differed across regions (range: 0.27-0.84). Patients from secondary versus tertiary hospitals had higher rates of overall exacerbation (0.66 versus 0.47), severe exacerbation (0.44 versus 0.18) and exacerbation that resulted in hospitalisation (0.41 versus 0.16). Across regions and hospital tiers, the rates of overall exacerbation and exacerbations that resulted in hospitalisation were highest in patients with very severe COPD (based on the severity of airflow limitation or GOLD 2017 combined assessment). Strong predictors of exacerbation included demographic and clinical characteristics, modified Medical Research Council scores, mucus purulence, exacerbation history and the use of maintenance mucolytic treatment.

CONCLUSION

COPD exacerbation rates varied across regions and were higher in secondary compared with tertiary hospitals in China. Understanding the factors associated with COPD exacerbation may facilitate improved management of COPD exacerbations in China.

REGISTRATION

The trial was registered on 20 March 2017 (ClinicalTrials.gov: NCT03131362; https://clinicaltrials.gov/ct2/show/NCT03131362).

PLAIN LANGUAGE SUMMARY

Exacerbations in patients with chronic obstructive pulmonary disease in ChinaBackground: Chronic obstructive pulmonary disease (COPD) causes progressive and irreversible airflow limitation. As the disease progresses, patients often experience a flare up of symptoms referred to as an exacerbation. There is inadequate management of COPD in China and, therefore, there is a need to improve care and outcomes for patients across the country.Objective: This study aimed to generate reliable information on exacerbations among Chinese patients with COPD to help inform future management strategies.Study design and methods: Patients (aged ⩾ 40 years) were enrolled from 25 secondary and 25 tertiary hospitals across six regions of China. Physicians collected data over 1 year during routine outpatient visits.Results: There were more patients who experienced an exacerbation in secondary versus tertiary hospitals (59% versus 40%) and in rural versus urban areas (53% versus 46%). Patients in different geographic regions experienced varying frequencies of exacerbations over 1 year. Compared with patients from tertiary hospitals, patients from secondary hospitals experienced exacerbations (including exacerbations that were severe and those that resulted in hospitalisation) at a higher frequency over 1 year. Patients with very severe disease experienced exacerbations (including exacerbations that resulted in hospitalisation) at the highest frequency over 1 year, regardless of the patient's geographic region or hospital tier. Patients who had certain characteristics and symptoms, had exacerbation(s) over the previous year, or received medication that aids in the clearance of mucus were more likely to experience exacerbations.Conclusion: The frequency of exacerbations among Chinese patients with COPD varied between patients living in different geographic regions and between patients presenting to different hospital tiers. Understanding the factors related to the occurrence of an exacerbation may help physicians better manage the disease.

[What Is an Acute COPD Exacerbation? Results of a Survey among Primary Care Physicians in the German-Speaking Part of Switzerland]

What Is an Acute COPD Exacerbation? Results of a Survey among Primary Care Physicians in the German-Speaking Part of Switzerland Abstract. Acute exacerbations have a relevant impact on morbidity and mortality in patients with chronic obstructive pulmonary disease (COPD), which is why prophylactic and early treatment have become indispensable. However, COPD exacerbations are significantly under-diagnosed, possibly due to linguistic discrepancies between physician and patient. The aim of this study was to disclose how exacerbations are perceived by the GPs (general practitioners) and their patients and what linguistic conventions they use. This survey showed that GPs and their patients quite often have a divergent notion a common of COPD exacerbations.

Development and Validation of a Multivariable Prediction Model to Identify Acute Exacerbation of COPD and Its Severity for COPD Management in China (DETECT Study): A Multicenter, Observational, Cross-Sectional Study

Purpose

There is an unmet clinical need for an accurate and objective diagnostic tool for early detection of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). DETECT (NCT03556475) was a multicenter, observational, cross-sectional study aiming to develop and validate multivariable prediction models for AECOPD occurrence and severity in patients with chronic obstructive pulmonary disease (COPD) in China.

Patients and Methods

Patients aged ≥40 years with moderate/severe COPD, AECOPD, or no COPD were consecutively enrolled between April 22, 2020, and January 18, 2021, across seven study sites in China. Multivariable prediction models were constructed to identify AECOPD occurrence (primary outcome) and AECOPD severity (secondary outcome). Candidate variables were selected using a stepwise procedure, and the bootstrap method was used for internal model validation.

Results

Among 299 patients enrolled, 246 were included in the final analysis, of whom 30.1%, 40.7%, and 29.3% had COPD, AECOPD, or no COPD, respectively. Mean age was 64.1 years. Variables significantly associated with AECOPD occurrence (P<0.05) and severity (P<0.05) in the final models included COPD disease-related characteristics, as well as signs and symptoms. Based on cut-off values of 0.374 and 0.405 for primary and secondary models, respectively, the performance of the primary model constructed to identify AECOPD occurrence (AUC: 0.86; sensitivity: 0.84; specificity: 0.77), and of the secondary model for AECOPD severity (AUC: 0.81; sensitivity: 0.90; specificity: 0.73) indicated high diagnostic accuracy and clinical applicability.

Conclusion

By leveraging easy-to-collect patient and disease data, we developed identification tools that can be used for timely detection of AECOPD and its severity. These tools may help physicians diagnose AECOPD in a timely manner, before further disease progression and possible hospitalizations.

Remote patient monitoring in the management of chronic obstructive pulmonary disease

Remote patient monitoring allows monitoring high-risk patients through implementation of an expanding number of technologies in coordination with a healthcare team to augment care, with the potential to provide early detection of exacerbation, prompt access to therapy and clinical services, and ultimately improved patient outcomes and decreased healthcare utilization.In this review, we describe the application of remote patient monitoring in chronic obstructive pulmonary disease including the potential benefits and possible barriers to implementation both for the individual and the healthcare system.

A machine-learning-based prediction method for easy COPD classification based on pulse oximetry clinical use

Chronic Obstructive Pulmonary Disease (COPD) is a progressive, obstructive lung disease that restricts airflow from the lungs. COPD patients are at risk of sudden and acute worsening of symptoms called exacerbations. Early identification and classification of COPD exacerbation can reduce COPD risks and improve patient’s healthcare and management. Pulse oximetry is a non-invasive technique used to assess patients with acutely worsening symptoms. As part of manual diagnosis based on pulse oximetry, clinicians examine three warning signs to classify COPD patients. This may lack high sensitivity and specificity which requires a blood test. However, laboratory tests require time, further delayed treatment and additional costs. This research proposes a prediction method for COPD patients’ classification based on pulse oximetry three manual warning signs and the resulting derived few key features that can be obtained in a short time. The model was developed on a robust physician labeled dataset with clinically diverse patient cases. Five classification algorithms were applied on the mentioned dataset and the results showed that the best algorithm is XGBoost with the accuracy of 91.04%, precision of 99.86%, recall of 82.19%, F1 measure value of 90.05% with an AUC value of 95.8%. Age, current and baseline heart rate, current and baseline pulse ox. (SPO2) were found the top most important predictors. These findings suggest the strength of XGBoost model together with the availability and the simplicity of input variables in classifying COPD daily living using a (wearable) pulse oximeter.

REducing delay through edUcation on eXacerbations for people with chronic lung disease: Study protocol of a single-arm pre-post study

AIM

This study protocol aims to examine the effectiveness and preconditions of a self-management program-named REducing Delay through edUcation on eXacerbations (REDUX)-in China.

BACKGROUND

The high disease burden in people with chronic lung disease is mainly due to exacerbations. There is a need for effective exacerbation-management interventions. A nurse-led program, REDUX, helped patients self-manage exacerbations.

DESIGN

A single-arm pre-post study.

METHODS

Fifty-four patients and 24 healthcare professionals (HCPs) in Chinese primary care will be included. The core element of the program is a personalized action plan. HCPs will receive training in using the action plan to help patients manage exacerbations. The intervention will start when a patient is referred to the nurse for a post-exacerbation consultation and ends when the patient presents for the second post-exacerbation consultation. During the first post-exacerbation consultation, the patient and nurse will create the action plan. The primary outcomes in patients will include the delays between the onset of exacerbation and recognition, between exacerbation recognition and action, between exacerbation recognition and consultation with a doctor, and when the patients feel better after receiving medical help from HCPs. The secondary outcomes will include preconditions of the program. The ethics approval was obtained in September 2021.

DISCUSSION

This study will discuss a culturally adapted nurse-led self-management intervention for people with chronic lung disease in China. The intervention could help Chinese HCPs provide efficient care and reduce their workload. Furthermore, it will inform future research on tailoring nurse-led self-management interventions in different contexts.

IMPACT

The study will contribute to the evidence on the effectiveness and preconditions of REDUX in China. If effective, the result will assist the nursing of people with chronic lung disease.

TRIAL REGISTRATION

Registered in the Chinese clinical trial registry (ID: 2100051782).