Exacerbations in Chronic Obstructive Pulmonary Disease: Identification and Prediction Using a Digital Health System

The Potential Use and Value of a Wearable Monitoring Bracelet for Patients With Chronic Obstructive Pulmonary Disease: Qualitative Study Investigating the Patient and Health Care Professional Perspectives

BACKGROUND

The occurrence of exacerbations has major effects on the health of people with chronic obstructive pulmonary disease (COPD). Monitoring devices that measure (vital) parameters hold promise for timely identification and treatment of exacerbations. Stakeholders' perspectives on the use of monitoring devices are of importance for the successful development and implementation of a device.

OBJECTIVE

This study aimed to explore the potential use and value of a wearable monitoring bracelet (MB) for patients with COPD at high risk for exacerbation. The perspectives of health care professionals as well as patients were examined, both immediately after hospitalization and over a longer period. Furthermore, potential facilitators and barriers to the use and implementation of an MB were explored.

METHODS

Data for this qualitative study were collected from January to April 2023. A total of 11 participants (eg, n=6 health care professionals [HCPs], 2 patients, and 3 additional patients) participated. In total, 2 semistructured focus groups were conducted via video calls; 1 with HCPs of various professional backgrounds and 1 with patients. In addition, 3 semistructured individual interviews were held with patients. The interviews and focus groups addressed attitudes, wishes, needs, as well as factors that could either support or impede the potential MB use. Data from interviews and focus groups were coded and analyzed according to the principles of the framework method.

RESULTS

HCPs and patients both predominantly emphasized the importance of an MB in terms of promptly identifying exacerbations by detecting deviations from normal (vital) parameters, and subsequently alerting users. According to HCPs, this is how an MB should support the self-management of patients. Most participants did not anticipate major differences in value and use of an MB between the short-term and the long-term periods after hospitalization. Facilitators of the potential use and implementation of an MB that participants highlighted were ease of use and some form of support for patients in using an MB and interpreting the data. HCPs as well as patients expressed concerns about potential costs as a barrier to use and implementation. Another barrier that HCPs mentioned, was the prerequisite of digital literacy for patients to be able to interpret and react to the data from an MB.

CONCLUSIONS

HCPs and patients both recognize that an MB could be beneficial and valuable to patients with COPD at high risk for exacerbation, in the short as well as the long term. In particular, they perceived value in supporting self-management of patients with COPD. Stakeholders would be able to use the obtained insights in support of the effective implementation of MBs in COPD patient care, which can potentially improve health care and the overall well-being of patients with COPD.

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.

Continuous Monitoring of Heart Rate Variability and Respiration for the Remote Diagnosis of Chronic Obstructive Pulmonary Disease: Prospective Observational Study

BACKGROUND

Conventional daytime monitoring in a single day may be influenced by factors such as motion artifacts and emotions, and continuous monitoring of nighttime heart rate variability (HRV) and respiration to assist in chronic obstructive pulmonary disease (COPD) diagnosis has not been reported yet.

OBJECTIVE

The aim of this study was to explore and compare the effects of continuously monitored HRV, heart rate (HR), and respiration during night sleep on the remote diagnosis of COPD.

METHODS

We recruited patients with different severities of COPD and healthy controls between January 2021 and November 2022. Vital signs such as HRV, HR, and respiration were recorded using noncontact bed sensors from 10 PM to 8 AM of the following day, and the recordings of each patient lasted for at least 30 days. We obtained statistical means of HRV, HR, and respiration over time periods of 7, 14, and 30 days by continuous monitoring. Additionally, the effects that the statistical means of HRV, HR, and respiration had on COPD diagnosis were evaluated at different times of recordings.

RESULTS

In this study, 146 individuals were enrolled: 37 patients with COPD in the case group and 109 participants in the control group. The median number of continuous night-sleep monitoring days per person was 56.5 (IQR 32.0-113.0) days. Using the features regarding the statistical means of HRV, HR, and respiration over 1, 7, 14, and 30 days, binary logistic regression classification of COPD yielded an accuracy, Youden index, and area under the receiver operating characteristic curve of 0.958, 0.904, and 0.989, respectively. The classification performance for COPD diagnosis was directionally proportional to the monitoring duration of vital signs at night. The importance of the features for diagnosis was determined by the statistical means of respiration, HRV, and HR, which followed the order of respiration > HRV > HR. Specifically, the statistical means of the duration of respiration rate faster than 21 times/min (RRF), high frequency band power of 0.15-0.40 Hz (HF), and respiration rate (RR) were identified as the top 3 most significant features for classification, corresponding to cutoff values of 0.1 minute, 1316.3 nU, and 16.3 times/min, respectively.

CONCLUSIONS

Continuous monitoring of nocturnal vital signs has significant potential for the remote diagnosis of COPD. As the duration of night-sleep monitoring increased from 1 to 30 days, the statistical means of HRV, HR, and respiration showed a better reflection of an individual's health condition compared to monitoring the vital signs in a single day or night, and better was the classification performance for COPD diagnosis. Further, the statistical means of RRF, HF, and RR are crucial features for diagnosing COPD, demonstrating the importance of monitoring HRV and respiration during night sleep.

The Bitemporal Lens Model-toward a holistic approach to chronic disease prevention with digital biomarkers

Objectives

We introduce the Bitemporal Lens Model, a comprehensive methodology for chronic disease prevention using digital biomarkers.

Materials and Methods

The Bitemporal Lens Model integrates the change-point model, focusing on critical disease-specific parameters, and the recurrent-pattern model, emphasizing lifestyle and behavioral patterns, for early risk identification.

Results

By incorporating both the change-point and recurrent-pattern models, the Bitemporal Lens Model offers a comprehensive approach to preventive healthcare, enabling a more nuanced understanding of individual health trajectories, demonstrated through its application in cardiovascular disease prevention.

Discussion

We explore the benefits of the Bitemporal Lens Model, highlighting its capacity for personalized risk assessment through the integration of two distinct lenses. We also acknowledge challenges associated with handling intricate data across dual temporal dimensions, maintaining data integrity, and addressing ethical concerns pertaining to privacy and data protection.

Conclusion

The Bitemporal Lens Model presents a novel approach to enhancing preventive healthcare effectiveness.

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.

Feedback stabilization of probabilistic finite state machines based on deep Q-network

Background

As an important mathematical model, the finite state machine (FSM) has been used in many fields, such as manufacturing system, health care, and so on. This paper analyzes the current development status of FSMs. It is pointed out that the traditional methods are often inconvenient for analysis and design, or encounter high computational complexity problems when studying FSMs.

Method

The deep Q-network (DQN) technique, which is a model-free optimization method, is introduced to solve the stabilization problem of probabilistic finite state machines (PFSMs). In order to better understand the technique, some preliminaries, including Markov decision process, ϵ-greedy strategy, DQN, and so on, are recalled.

Results

First, a necessary and sufficient stabilizability condition for PFSMs is derived. Next, the feedback stabilization problem of PFSMs is transformed into an optimization problem. Finally, by using the stabilizability condition and deep Q-network, an algorithm for solving the optimization problem (equivalently, computing a state feedback stabilizer) is provided.

Discussion

Compared with the traditional Q learning, DQN avoids the limited capacity problem. So our method can deal with high-dimensional complex systems efficiently. The effectiveness of our method is further demonstrated through an illustrative example.

Management of Chronic Obstructive Pulmonary Disease: Constraints in Patient Pathway and Mitigation Strategies

Introduction

Respiratory diseases, ranking the third in Portugal, contribute significantly to illness and mortality. Chronic obstructive pulmonary disease (COPD) is the third-leading cause of death globally. Identifying high-risk individuals and implementing early treatment is crucial due to the variability of COPD symptoms and exacerbations. This study aimed to identify effective strategies for preventing exacerbations and complications.

Methods

A Delphi involving 15 experts was performed. Experts included physicians, nurses, health managers, policymakers, public health experts, and patient organizations. Consensus was achieved at 73.3% for each strategy using a scale ranging from "agree" to "disagree." Three rounds were conducted to address six questions related to early diagnosis and patient follow-up. Challenges faced by the Portuguese Health System in managing COPD, obstacles in COPD exacerbation diagnosis and management, and effective strategies to overcome barriers were identified in the first round. The second and third rounds involved analyzing the gathered information and voting on each indicator to achieve consensus, respectively. Indicators were categorized into constraints and barriers, and strategies for reducing COPD exacerbations and disease burden.

Results

Out of a total of 134 valid indicators generated, 108 achieved consensus. Among the indicators agreed upon by experts, 18 pertained to barriers, challenges, and constraints, while 90 focused on action strategies for COPD. Among the strategies formulated, 25 consensus indicators target prevention strategies, 24 consensus indicators aim to enhance COPD referrals, and 41 consensus indicators focus on mitigating COPD exacerbations and reducing the overall disease burden.

Discussion/Conclusion

This study emphasizes the need for integrated investment in respiratory healthcare and recognition of the impact of COPD on patients, healthcare systems, and economies. Prevention and appropriate treatment of exacerbations are crucial for effective COPD management and reducing associated morbidity and mortality. Experts highlight the importance of improving coordination between different levels of care, integrating information systems, and decentralizing hospital responsibilities. The COVID-19 pandemic has further emphasized the importance of individual and collective respiratory health, necessitating investment in health promotion and COPD awareness.

Patent and Bibliometric Analysis of the Scientific Landscape of the Use of Pulse Oximeters and Their Prospects in the Field of Digital Medicine

This study conducted a comprehensive patent and bibliometric analysis to elucidate the evolving scientific landscape surrounding the development and application of pulse oximeters, including in the field of digital medicine. Utilizing data from the Lens database for the period of 2000-2023, we identified the United States, China, the Republic of Korea, Japan, Canada, Australia, Taiwan, and the United Kingdom as the predominant countries in patent issuance for pulse oximeter technology. Our bibliometric analysis revealed a consistent temporal trend in both the volume of publications and citations, underscoring the growing importance of pulse oximeters in digitally-enabled medical practice. Using the VOSviewer software(version 1.6.18), we discerned six primary research clusters: (1) measurement accuracy; (2) integration with the Internet of Things; (3) applicability across diverse pathologies; (4) telemedicine and mobile applications; (5) artificial intelligence and deep learning; and (6) utilization in anesthesiology, resuscitation, and intensive care departments. The findings of this study indicate the prospects for leveraging digital technologies in the use of pulse oximetry in various fields of medicine, with implications for advancing the understanding, diagnosis, prevention, and treatment of cardio-respiratory pathologies. The conducted patent and bibliometric analysis allowed the identification of technical solutions to reduce the risks associated with pulse oximetry: improving precision and validity, technically improved clinical diagnostic use, and the use of machine learning.

Experiences of Patients With Chronic Obstructive Pulmonary Disease Using the Apple Watch Series 6 Versus the Traditional Finger Pulse Oximeter for Home SpO2 Self-Monitoring: Qualitative Study Part 2

BACKGROUND

Amid the rise in mobile health, the Apple Watch now has the capability to measure peripheral blood oxygen saturation (SpO2). Although the company indicated that the Watch is not a medical device, evidence suggests that SpO2 measurements among patients with chronic obstructive pulmonary disease (COPD) are accurate in controlled settings. Yet, to our knowledge, the SpO2 function has not been validated for patients with COPD in naturalistic settings.

OBJECTIVE

This qualitative study explored the experiences of patients with COPD using the Apple Watch Series 6 versus a traditional finger pulse oximeter for home SpO2 self-monitoring.

METHODS

We conducted individual semistructured interviews with 8 female and 2 male participants with moderate to severe COPD, and transcripts were qualitatively analyzed. All received a watch to monitor their SpO2 for 5 months.

RESULTS

Due to respiratory distress, the watch was unable to collect reliable SpO2 measurements, as it requires the patient to remain in a stable position. However, despite the physical limitations and lack of reliable SpO2 values, participants expressed a preference toward the watch. Moreover, participants' health needs and their unique accessibility experiences influenced which device was more appropriate for self-monitoring purposes. Overall, all shared the perceived importance of prioritizing their physical COPD symptoms over device selection to manage their disease.

CONCLUSIONS

Differing results between participant preferences and smartwatch limitations warrant further investigation into the reliability and accuracy of the SpO2 function of the watch and the balance among self-management, medical judgment, and dependence on self-monitoring technology.

Remote patient monitoring strategies and wearable technology in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is highly prevalent and is associated with a heavy burden on patients and health systems alike. Exacerbations of COPD (ECOPDs) are a leading cause of acute hospitalization among all adult chronic diseases. There is currently a paradigm shift in the way that ECOPDs are conceptualized. For the first time, objective physiological parameters are being used to define/classify what an ECOPD is (including heart rate, respiratory rate, and oxygen saturation criteria) and therefore a mechanism to monitor and measure their changes, particularly in an outpatient ambulatory setting, are now of great value. In addition to pre-existing challenges on traditional 'in-person' health models such as geography and seasonal (ex. winter) impacts on the ability to deliver in-person visit-based care, the COVID-19 pandemic imposed additional stressors including lockdowns, social distancing, and the closure of pulmonary function labs. These health system stressors, combined with the new conceptualization of ECOPDs, rapid advances in sophistication of hardware and software, and a general openness by stakeholders to embrace this technology, have all influenced the propulsion of remote patient monitoring (RPM) and wearable technology in the modern care of COPD. The present article reviews the use of RPM and wearable technology in COPD. Context on the influences, factors and forces which have helped shape this health system innovation is provided. A focused summary of the literature of RPM in COPD is presented. Finally, the practical and ethical principles which must guide the transition of RPM in COPD into real-world clinical use are reviewed.

Clinicians' Perspectives of Wearable Technology to Detect and Monitor Exacerbations of Chronic Obstructive Pulmonary Disease: Mixed-Method Survey

Objective

To investigate clinicians' perspectives on the current use of wearable technology for detecting COPD exacerbations, and to identify potential facilitators and barriers to its adoption in clinical settings.

Methods

A mixed-method survey was conducted through an online survey platform involving clinicians working with COPD patients. The questionnaires were developed by an expert panel specialising in respiratory medicine at UCL. The questionnaire evaluated clinicians' perspectives on several aspects: the current extent of wearable technology utilisation, the perceived feasibility, and utility of these devices, as well as the potential facilitators and barriers that hinder its wider implementation.

Results

Data from 118 clinicians were included in the analysis. Approximately 80% of clinicians did not currently use information from wearable devices in routine clinical care. A majority of clinicians did not have confidence in the effectiveness of wearables and their consequent impact on health outcomes. However, clinicians highlighted the potential value of wearables in helping deliver personalised care and more rapid assistance. Ease of use, technical support and accessibility of data were considered facilitating factors for wearable utilisation. Costs and lack of technical knowledge were the most frequently reported barriers to wearable utilisation.

Conclusion

Clinicians' perspectives of the use of wearable technology to detect and monitor COPD exacerbations are variable. While accessibility and technical support facilitate wearable implementation, cost, technical issues, and knowledge act as barriers. Our findings highlight the facilitators and barriers to using wearables in patients with COPD and emphasise the need to assess patients' perspectives on wearable acceptability.

Novel breathing pattern analysis: Symmetric Projection Attractor Reconstruction improves identification of impending COPD re-exacerbations - a retrospective cohort analysis

Respiratory waveforms can be reduced to simple metrics, such as rate, but this may miss information about waveform shape and whole breathing pattern. A novel analysis method quantifying the whole waveform shape identifies AECOPD earlier. https://bit.ly/3M6uIEB.

Implementation of Tele-Intensive Care Unit Services During the COVID-19 Pandemic: A Systematic Literature Review and Updated Experience from Shandong Province

Background: While the use of telemedicine had been expanding before the initial outbreak of COVID-19, the pandemic has dramatically accelerated its implementation and expanded its usage in many hospitals. Tele-intensive care unit (ICU) is a specialized type of telemedicine that adapts available technologies to the unique needs of critically ill patients. We published an editorial in 2020 describing our initial experiences of Tele-ICU application in Shandong Province. Here, we update our insights gained over the past 2 years, and we provide a systematic review of the literature to compare our perspectives with those from other institutions. Methods: We performed a systematic literature review of publications describing the use of telemedicine in an ICU setting during COVID-19. The PubMed database was searched for studies published after January 1, 2020, which offered detailed descriptions of tele-ICU usage. Extracted data included details regarding tele-ICU technologies, descriptions of the institution, usage cases, assessments of tele-ICU effectiveness, and site-reported opinions (e.g., advantages, disadvantages). Results: We screened 162 studies resulting from the PubMed literature search, along with one expert recommendation. Of the 112 full-text articles retrieved, 11 were selected for inclusion in this qualitative summary. All were retrospective descriptions of tele-ICU experiences at a single site. Some pairs of included articles reported results from the same institution, with seven unique sites being described. Three sites employed centralized models of tele-ICU, while four allowed staff to participate from distant locations. Five sites collected user-reported feedback regarding tele-ICU. While the advantages and disadvantages described rarely overlapped directly between sites, many reported positive opinions of tele-ICU use overall. Conclusions: The potential applications of tele-ICU technologies vary widely, making them highly adaptable to the needs of individual institutions. Tele-ICU has proven invaluable to some hospitals during COVID-19 due to its effectiveness at aiding patient care while mitigating risk to health care workers.

Oximetría de pulso en enfermedades respiratorias

El pulsioxímetro es un dispositivo que utiliza principios de espectrofotometría y fotopletismografía para la medición de la saturación de oxígeno arterial, así como el ciclo cardiaco y respiratorio, lo que resulta útil para monitorear pacientes con compromisorespiratorio. En este trabajo se realiza una revisión bibliográfica de los principios físicos del pulsioxímetro y sus avances más recientes en pacientes con enfermedad pulmonar obstructiva crónica (EPOC), asma y COVID-19. Se encontró que la oximetría de pulso es una herramienta confiable y eficaz en el diagnóstico y la prevención de complicaciones en pacientes con estas enfermedades respiratorias.

MRI-assessed diaphragmatic function can predict frequent acute exacerbation of COPD: a prospective observational study based on telehealth-based monitoring system

BACKGROUND

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) have considerably high mortality and re-hospitalisation rate. Diaphragmatic dysfunction (DD) is common in COPD patients. However, whether diaphragmatic dysfunction is related to acute exacerbation is yet to be elucidated. This study aimed to evaluate the diaphragm function by magnetic resonance imaging (MRI) in COPD patients and assess whether the impact of DD may help predict AECOPD.

METHODS

20 healthy adult volunteers and 80 COPD patients were enrolled. The diaphragms function parameters were accessed by MRI. Patients were guided to start self-management by the Telehealth-based monitoring system following the enrolment. Events of acute exacerbation of COPD were recorded by the system and confirmed by healthcare providers. Binary univariate and multivariate logistic regression analyses were performed to investigate the factors associated with the frequency of AECOPD. Receiver operating characteristic (ROC) curves were further used to assess the value of prediction indexes.

RESULTS

Fifty-nine COPD patients completed a one-year follow-up based on the Telehealth-based monitoring system. The clinical outcomes showed that the diaphragm function parameters at the end of maximal breathing were lower in the COPD group than in the healthy control group (P < 0.05). ANOVA showed significant differences among Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages for diaphragm function parameters, including chest wall motion, lung area, upper-lower diameter, and the diaphragm thickening fraction at the end of maximal breathing (P < 0.05). Moreover, significant differences in diaphragm function parameters were observed between patients with infrequent AECOPD (n = 28) and frequent AECOPD (n = 31) based on the frequency of AECOPD (P < 0.05). The diaphragm thickening fraction and the chest wall motion were associated with AECOPD after adjusting for age, sex, BMI, and lung functions, and the combination of predictions showed better accuracy in predicting the frequency of AECOPD.

CONCLUSIONS

In COPD patients, diaphragm function parameters correlate with the severity of airflow limitation. The diaphragm thickening fraction and the chest wall motion were associated with the frequency of AECOPD and can predict it.

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.

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

Background

Asthma is a variable long-term condition. Currently, there is no cure for asthma and the focus is, therefore, on long-term management. Mobile health (mHealth) is promising for chronic disease management but to be able to realize its potential, it needs to go beyond simply monitoring. mHealth therefore needs to leverage machine learning to provide tailored feedback with personalized algorithms. There is a need to understand the extent of machine learning that has been leveraged in the context of mHealth for asthma management. This review aims to fill this gap.

Methods

We searched PubMed for peer-reviewed studies that applied machine learning to data derived from mHealth for asthma management in the last five years. We selected studies that included some human data other than routinely collected in primary care and used at least one machine learning algorithm.

Results

Out of 90 studies, we identified 22 relevant studies that were then further reviewed. Broadly, existing research efforts can be categorized into three types: 1) technology development, 2) attack prediction, 3) patient clustering. Using data from a variety of devices (smartphones, smartwatches, peak flow meters, electronic noses, smart inhalers, and pulse oximeters), most applications used supervised learning algorithms (logistic regression, decision trees, and related algorithms) while a few used unsupervised learning algorithms. The vast majority used traditional machine learning techniques, but a few studies investigated the use of deep learning algorithms.

Discussion

In the past five years, many studies have successfully applied machine learning to asthma mHealth data. However, most have been developed on small datasets with internal validation at best. Small sample sizes and lack of external validation limit the generalizability of these studies. Future research should collect data that are more representative of the wider asthma population and focus on validating the derived algorithms and technologies in a real-world setting.

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.

Development and validation of a multivariable mortality risk prediction model for COPD in primary care

Risk stratification of chronic obstructive pulmonary disease (COPD) patients is important to enable targeted management. Existing disease severity classification systems, such as GOLD staging, do not take co-morbidities into account despite their high prevalence in COPD patients. We sought to develop and validate a prognostic model to predict 10-year mortality in patients with diagnosed COPD. We constructed a longitudinal cohort of 37,485 COPD patients (149,196 person-years) from a UK-wide primary care database. The risk factors included in the model pertained to demographic and behavioural characteristics, co-morbidities, and COPD severity. The outcome of interest was all-cause mortality. We fitted an extended Cox-regression model to estimate hazard ratios (HR) with 95% confidence intervals (CI), used machine learning-based data modelling approaches including k-fold cross-validation to validate the prognostic model, and assessed model fitting and discrimination. The inter-quartile ranges of the three metrics on the validation set suggested good performance: 0.90–1.06 for model fit, 0.80–0.83 for Harrel’s c-index, and 0.40–0.46 for Royston and Saurebrei’s \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_D^2$$\end{document}RD2 with a strong overlap of these metrics on the training dataset. According to the validated prognostic model, the two most important risk factors of mortality were heart failure (HR 1.92; 95% CI 1.87–1.96) and current smoking (HR 1.68; 95% CI 1.66–1.71). We have developed and validated a national, population-based prognostic model to predict 10-year mortality of patients diagnosed with COPD. This model could be used to detect high-risk patients and modify risk factors such as optimising heart failure management and offering effective smoking cessation interventions.

A proof of concept for continuous, non-invasive, free-living vital signs monitoring to predict readmission following an acute exacerbation of COPD: a prospective cohort study

Background

The use of vital signs monitoring in the early recognition of an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) post-hospital discharge is limited. This study investigated whether continuous vital signs monitoring could predict an AECOPD and readmission.

Methods

35 people were recruited at discharge following hospitalisation for an AECOPD. Participants were asked to wear an Equivital LifeMonitor during waking hours for 6 weeks and to complete the Exacerbations of Chronic Pulmonary Disease Tool (EXACT), a 14-item symptom diary, daily. The Equivital LifeMonitor recorded respiratory rate (RR), heart rate (HR), skin temperature (ST) and physical activity (PA) every 15-s. An AECOPD was classified as mild (by EXACT score), moderate (prescribed oral steroids/antibiotics) or severe (hospitalisation).

Results

Over the 6-week period, 31 participants provided vital signs and symptom data and 14 participants experienced an exacerbation, of which, 11 had sufficient data to predict an AECOPD. HR and PA were associated with EXACT score (p < 0.001). Three days prior to an exacerbation, RR increased by mean ± SD 2.0 ± 0.2 breaths/min for seven out of 11 exacerbations and HR increased by 8.1 ± 0.7 bpm for nine of these 11 exacerbations.

Conclusions

Increased heart rate and reduced physical activity were associated with worsening symptoms. Even with high-resolution data, the variation in vital signs data remains a challenge for predicting AECOPDs. Respiratory rate and heart rate should be further explored as potential predictors of an impending AECOPD.

Trial registration: ISRCTN registry; ISRCTN12855961. Registered 07 November 2018—Retrospectively registered, https://www.isrctn.com/ISRCTN12855961

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02018-5.

Is Methadone Safe for Patients With Opioid Use Disorder and Coronavirus Disease 2019 Infection?

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic has led to not only increase in substance misuse, substance use disorder, and risk of overdose but also lack of access to treatment services. Due to lack of knowledge of the course and impact of COVID-19 and outcomes of it's interactions with existing treatments, the Substance Misuse Service Team initiated a safety improvement project to review the safety of opioid substitution treatment, particularly the safety of methadone. This preliminary retrospective cross-sectional audit of safety improvement intiative underscores the importance of providing treatment services to those with opioid use disorders and that methadone is safe among this population with a high burden of comorbidity, most of which leads to negative outcomes from COVID-19. The outcomes show that patients who have COVID-19 should continue with opioid substitution treatment with methadone. Although treatment with methadone is safe, symptomatic patients should be monitored. In addition, patients who take methadone at home should be educated on the risk of overdose due to, and adverse outcomes from, COVID-19 infection. Patients should monitor themselves using pulse oximeter for any signs of hypoxia.

An Integrated, Multimodal, Digital Health Solution for Chronic Obstructive Pulmonary Disease: Prospective Observational Pilot Study

BACKGROUND

Chronic obstructive pulmonary disease (COPD) affects millions of Americans and has a high economic impact partially due to frequent emergency room visits and hospitalizations. Advances in digital health have made it possible to collect data remotely from multiple devices to assist in managing chronic diseases such as COPD.

OBJECTIVE

In this pilot study, we evaluated the ability of patients with COPD to use the Wellinks mHealth platform to collect information from multiple modalities important to the management of COPD. We also assessed patient satisfaction and engagement with the platform.

METHODS

A single-site, observational, prospective pilot study (N=19) was conducted using the Wellinks platform in adults with COPD. All patients were aged over 30 years at screening, owned an iPhone, and were currently undergoing a treatment regimen that included nebulized therapy. Enrolled patients received a study kit consisting of the Flyp nebulizer, Smart One spirometer, the Nonin pulse oximeter, plus the Wellinks mHealth app, and training for all devices. For 8 weeks, participants were to enter daily symptoms and medication use manually; spirometry, nebulizer, and pulse oximeter data were automatically recorded. Data were sent to the attending physician in a monthly report. Patient satisfaction was measured via a 5-point scale and the Net Promoter Score (NPS) captured in interviews at the end of the observation period.

RESULTS

Average age of the patients was 79.6 (range 65-95) years. Participants (10 female; 9 male) had an average FEV1% (forced expiratory volume in 1 second as % of predicted for the patient) of 56.2% of predicted (range 23%-113%) and FEV1/forced vital capacity of 65%. COPD severity, as assessed by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification, was mild in 2 patients, moderate in 6, and severe/very severe in 11; 9 patients were on home oxygen. During this 8-week study, average use of the spirometer was 2.5 times/week, and the pulse oximeter 4.2 times/week. Medication use was manually documented 9.0 times/week, nebulizer use 1.9 times/week, and symptoms recorded 1.2 times/week on average. The correlation coefficients of home to office measurements for peak flow and FEV1 were high (r=0.94 and 0.96, respectively). Patients found the app valuable (13/16, 81%) and easy to use (15/16, 94%). The NPS was 59.

CONCLUSIONS

This study demonstrates that our cohort of patients with COPD engaged with the Wellinks mHealth platform avidly and consistently over the 8-week period, and that patient satisfaction was high, as indicated by the satisfaction survey and the NPS of 59. In this small, selected sample, patients were both willing to use the technology and capable of doing so successfully regardless of disease severity, age, or gender. The Wellinks mHealth platform was considered useful and valuable by patients, and can assist clinicians in improved, timely decision making for better COPD management.

Wearable Photoplethysmography for Cardiovascular Monitoring

Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD.

Wearable In-Ear PPG: Detailed Respiratory Variations Enable Classification of COPD

An ability to extract detailed spirometry-like breath-ing waveforms from wearable sensors promises to greatly improve respiratory health monitoring. Photoplethysmography (PPG) has been researched in depth for estimation of respiration rate, given that it varies with respiration through overall intensity, pulse amplitude and pulse interval. We compare and contrast the extraction of these three respiratory modes from both the ear canal and finger and show a marked improvement in the respiratory power for respiration induced intensity variations and pulse amplitude variations when recording from the ear canal. We next employ a data driven multi-scale method, noise assisted multivariate empirical mode decomposition (NA-MEMD), which allows for simultaneous analysis of all three respiratory modes to extract detailed respiratory waveforms from in-ear PPG. For rigour, we considered in-ear PPG recordings from healthy subjects, both older and young, patients with chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) and healthy subjects with artificially obstructed breathing. Specific in-ear PPG waveform changes are observed for COPD, such as a decreased inspiratory duty cycle and an increased inspiratory magnitude, when compared with expiratory magnitude. These differences are used to classify COPD from healthy and IPF waveforms with a sensitivity of 87% and an overall accuracy of 92%. Our findings indicate the promise of in-ear PPG for COPD screening and unobtrusive respiratory monitoring in ambulatory scenarios and in consumer wearables.

A capaciflector provides continuous and accurate respiratory rate monitoring for patients at rest and during exercise

Respiratory rate (RR) is a marker of critical illness, but during hospital care, RR is often inaccurately measured. The capaciflector is a novel sensor that is small, inexpensive, and flexible, thus it has the potential to provide a single-use, real-time RR monitoring device. We evaluated the accuracy of continuous RR measurements by capaciflector hardware both at rest and during exercise. Continuous RR measurements were made with capaciflectors at four chest locations. In healthy subjects (n = 20), RR was compared with strain gauge chest belt recordings during timed breathing and two different body positions at rest. In patients undertaking routine cardiopulmonary exercise testing (CPET, n = 50), RR was compared with pneumotachometer recordings. Comparative RR measurement bias and limits of agreement were calculated and presented in Bland-Altman plots. The capaciflector was shown to provide continuous RR measurements with a bias less than 1 breath per minute (BPM) across four chest locations. Accuracy and continuity of monitoring were upheld even during vigorous CPET exercise, often with narrower limits of agreement than those reported for comparable technologies. We provide a unique clinical demonstration of the capaciflector as an accurate breathing monitor, which may have the potential to become a simple and affordable medical device.Clinical trial number: NCT03832205 https://clinicaltrials.gov/ct2/show/NCT03832205 registered February 6th, 2019.

Telehealth for patients with interstitial lung diseases (ILD): results of an international survey of clinicians

INTRODUCTION

Clinicians and policymakers are promoting widespread use of home technology including spirometry to detect disease progression for patients with interstitial lung disease (ILD); the COVID-19 pandemic has accelerated this. Data collating clinicians' views on the potential utility of telehealth in ILD are limited.

AIM

This survey investigated clinicians' opinions about contemporary methods and practices used to monitor disease progression in patients with ILD using telehealth.

METHODS

Clinicians were invited to participate in a cross-sectional survey (SurveyMonkey) of 13 questions designed by an expert panel. Telehealth was defined as home monitoring of symptoms and physiological parameters with regular automatic transmission of data from the patient's home to the clinician. Data are presented as percentages of respondents.

RESULTS

A total of 207 clinicians from 23 countries participated in the survey. A minority (81, 39%) reported using telehealth. 50% (n=41) of these respondents completed a further question about the effectiveness of telehealth. A majority of respondents (32, 70%) rated it to be quite or more effective than face-to-face visit. There were a greater number of respondents using telehealth from Europe (94, 45%) than Asia (51, 25%) and America (24%). Clinicians reported the most useful telehealth monitoring technologies as smartphone apps (59%) and wearable sensors (30%). Telehealth was most frequently used for monitoring disease progression (70%), quality of life (63%), medication use (63%) and reducing the need for in-person visits (63%). Clinicians most often monitored symptoms (93%), oxygen saturation (74%) and physical activity (72%). The equipment perceived to be most effective were spirometers (43%) and pulse oximeters (33%). The primary barriers to clinicians' participation in telehealth were organisational structure (80%), technical challenges (63%) and lack of time and/or workload (63%). Clinicians considered patients' barriers to participation might include lack of awareness (76%), lack of knowledge using smartphones (60%) and lack of confidence in telehealth (56%).

CONCLUSION

The ILD clinicians completing this survey who used telehealth to monitor patients (n=81) supported its' clinical utility. Our findings emphasise the need for robust research in telehealth as a mode for the delivery of cost-effective healthcare services in ILD and highlight the need to assess patients' perspectives to improve telehealth utility in patients with ILD.

Digital healthcare in COPD management: a narrative review on the advantages, pitfalls, and need for further research

Chronic obstructive pulmonary disease (COPD) remains a leading cause of morbidity and mortality despite current treatment strategies which focus on smoking cessation, pulmonary rehabilitation, and symptomatic relief. A focus of COPD care is to encourage self-management, particularly during COVID-19, where much face-to-face care has been reduced or ceased. Digital health solutions may offer affordable and scalable solutions to support COPD patient education and self-management, such solutions could improve clinical outcomes and expand service reach for limited additional cost. However, optimal ways to deliver digital medicine are still in development, and there are a number of important considerations for clinicians, commissioners, and patients to ensure successful implementation of digitally augmented care. In this narrative review, we discuss advantages, pitfalls, and future prospects of digital healthcare, which offer a variety of tools including self-management plans, education videos, inhaler training videos, feedback to patients and healthcare professionals (HCPs), exacerbation monitoring, and pulmonary rehabilitation. We discuss the key issues with sustaining patient and HCP engagement and limiting attrition of use, interoperability with devices, integration into healthcare systems, and ensuring inclusivity and accessibility. We explore the essential areas of research beyond determining safety and efficacy to understand the acceptability of digital healthcare solutions to patients, clinicians, and healthcare systems, and hence ways to improve this and sustain engagement. Finally, we explore the regulatory challenges to ensure quality and engagement and effective integration into current healthcare systems and care pathways, while maintaining patients' autonomy and privacy. Understanding and addressing these issues and successful incorporation of an acceptable, simple, scalable, affordable, and future-proof digital solution into healthcare systems could help remodel global chronic disease management and fractured healthcare systems to provide best patient care and optimisation of healthcare resources to meet the global burden and unmet clinical need of COPD.

Home monitoring of physiology and symptoms to detect interstitial lung disease exacerbations and progression: a systematic review

Background

Acute exacerbations (AEs) and disease progression in interstitial lung disease (ILD) pose important challenges to clinicians and patients. AEs of ILD are variable in presentation but may result in rapid progression of ILD, respiratory failure and death. However, in many cases AEs of ILD may go unrecognised so that their true impact and response to therapy is unknown. The potential for home monitoring to facilitate early, and accurate, identification of AE and/or ILD progression has gained interest. With increasing evidence available, there is a need for a systematic review on home monitoring of patients with ILD to summarise the existing data. The aim of this review was to systematically evaluate the evidence for use of home monitoring for early detection of exacerbations and/or progression of ILD.

Method

We searched Ovid-EMBASE, MEDLINE and CINAHL using Medical Subject Headings (MeSH) terms in accordance with the PRISMA guidelines (PROSPERO registration number CRD42020215166).

Results

13 studies involving 968 patients have demonstrated that home monitoring is feasible and of potential benefit in patients with ILD. Nine studies reported that mean adherence to home monitoring was >75%, and where spirometry was performed there was a significant correlation (r=0.72–0.98, p<0.001) between home and hospital-based readings. Two studies suggested that home monitoring of forced vital capacity might facilitate detection of progression in idiopathic pulmonary fibrosis.

Conclusion

Despite the fact that individual studies in this systematic review provide supportive evidence suggesting the feasibility and utility of home monitoring in ILD, further studies are necessary to quantify the potential of home monitoring to detect disease progression and/or AEs.

First systematic review that provides supportive evidence for the feasibility and utility of home monitoring in ILD; further studies are necessary to evaluate approaches to detect exacerbation and/or progressionhttps://bit.ly/2Y8OCJL

Machine Learning Approaches for Predicting Acute Respiratory Failure, Ventilator Dependence, and Mortality in Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of mortality and contributes to high morbidity worldwide. Patients with COPD have a higher risk for acute respiratory failure, ventilator dependence, and mortality after hospitalization compared with the general population. Accurate and early risk detection will provide more information for early management and better decision making. This study aimed to build prediction models using patients’ characteristics, laboratory data, and comorbidities for early detection of acute respiratory failure, ventilator dependence, and mortality in patients with COPD after hospitalization. We retrospectively collected the electronic medical records of 5061 patients with COPD in three hospitals of the Chi Mei Medical Group, Taiwan. After data cleaning, we built three prediction models for acute respiratory failure, ventilator dependence, and mortality using seven machine learning algorithms. Based on the AUC value, the best model for mortality was built by the XGBoost algorithm (AUC = 0.817), the best model for acute respiratory failure was built by random forest algorithm (AUC = 0.804), while the best model for ventilator dependence was built by LightGBM algorithm (AUC = 0.809). A web service application was implemented with the best models and integrated into the existing hospital information system for physician’s trials and evaluations. Our machine learning models exhibit excellent predictive quality and can therefore provide physicians with a useful decision-making reference for the adverse prognosis of COPD patients.

Review of Artificial Intelligence techniques in Chronic Obstructive Lung Disease

BACKGROUND

Artificial Intelligence (AI) has proven to be an invaluable asset in the healthcare domain, where massive amounts of data are produced. Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous chronic condition with multiscale manifestations and complex interactions that represents an ideal target for AI.

OBJECTIVE

The aim of this review article is to appraise the adoption of AI in COPD research, and more specifically its applications to date along with reported results, potential challenges and future prospects.

METHODS

We performed a review of the literature from PubMed and DBLP and assembled studies published up to 2020, yielding 156 articles relevant to the scope of this review.

RESULTS

The resulting articles were assessed and organized into four basic contextual categories, namely: i) COPD diagnosis, ii) COPD prognosis, iii) Patient classification, iv) COPD management, and subsequently presented in an orderly manner based on a set of qualitative and quantitative criteria.

CONCLUSIONS

We observed considerable acceleration of research activity utilizing AI techniques in COPD research, especially in the last couple of years, nevertheless, the massive production of large and complex data in COPD calls for broader adoption of AI and more advanced techniques.

Addressing silent hypoxemia with COVID-19: Implementation of an outpatient pulse oximetry program in Vermont

Objectives

We initiated an outpatient pulse oximetry program to facilitate more rapid detection of clinical deterioration of persons with COVID-19.

Methods

Vermont residents in non-congregate settings with laboratory-confirmed SARS-CoV-2 infection were eligible for inclusion.

Results

Acceptance of pulse oximetry occurred more frequently among those who were older or symptomatic, spoke English, or who had underlying medical conditions.

Conclusions

We provide the first description of an outpatient pulse oximetry program for COVID-19 by a state health department in the U.S.

Remote Assessment of Lung Disease and Impact on Physical and Mental Health (RALPMH): Protocol for a Prospective Observational Study

BACKGROUND

Chronic lung disorders like chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) are characterized by exacerbations. They are unpleasant for patients and sometimes severe enough to cause hospital admission and death. Moreover, due to the COVID-19 pandemic, vulnerable populations with these disorders are at high risk, and their routine care cannot be done properly. Remote monitoring offers a low cost and safe solution for gaining visibility into the health of people in their daily lives, making it useful for vulnerable populations.

OBJECTIVE

The primary objective is to assess the feasibility and acceptability of remote monitoring using wearables and mobile phones in patients with pulmonary diseases. The secondary objective is to provide power calculations for future studies centered around understanding the number of exacerbations according to sample size and duration.

METHODS

Twenty participants will be recruited in each of three cohorts (COPD, IPF, and posthospitalization COVID). Data collection will be done remotely using the RADAR-Base (Remote Assessment of Disease And Relapse) mobile health (mHealth) platform for different devices, including Garmin wearable devices and smart spirometers, mobile app questionnaires, surveys, and finger pulse oximeters. Passive data include wearable-derived continuous heart rate, oxygen saturation, respiration rate, activity, and sleep. Active data include disease-specific patient-reported outcome measures, mental health questionnaires, and symptom tracking to track disease trajectory. Analyses will assess the feasibility of lung disorder remote monitoring (including data quality, data completeness, system usability, and system acceptability). We will attempt to explore disease trajectory, patient stratification, and identification of acute clinical events such as exacerbations. A key aspect is understanding the potential of real-time data collection. We will simulate an intervention to acquire responses at the time of the event to assess model performance for exacerbation identification.

RESULTS

The Remote Assessment of Lung Disease and Impact on Physical and Mental Health (RALPMH) study provides a unique opportunity to assess the use of remote monitoring in the evaluation of lung disorders. The study started in the middle of June 2021. The data collection apparatus, questionnaires, and wearable integrations were setup and tested by the clinical teams prior to the start of recruitment. While recruitment is ongoing, real-time exacerbation identification models are currently being constructed. The models will be pretrained daily on data of previous days, but the inference will be run in real time.

CONCLUSIONS

The RALPMH study will provide a reference infrastructure for remote monitoring of lung diseases. It specifically involves information regarding the feasibility and acceptability of remote monitoring and the potential of real-time data collection and analysis in the context of chronic lung disorders. It will help plan and inform decisions in future studies in the area of respiratory health.

TRIAL REGISTRATION

ISRCTN Registry ISRCTN16275601; https://www.isrctn.com/ISRCTN16275601.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/28873.

Early Detection of Symptom Exacerbation in Patients With SARS-CoV-2 Infection Using the Fitbit Charge 3 (DEXTERITY): Pilot Evaluation

Background

Some patients with COVID-19 experienced sudden death due to rapid symptom deterioration. Thus, it is important to predict COVID-19 symptom exacerbation at an early stage prior to increasing severity in patients. Patients with COVID-19 could experience a unique “silent hypoxia” at an early stage of the infection when they are apparently asymptomatic, but with rather low SpO₂ (oxygen saturation) levels. In order to continuously monitor SpO₂ in daily life, a high-performance wearable device, such as the Apple Watch or Fitbit, has become commercially available to monitor several biometric data including steps, resting heart rate (RHR), physical activity, sleep quality, and estimated oxygen variation (EOV).

Objective

This study aimed to test whether EOV measured by the wearable device Fitbit can predict COVID-19 symptom exacerbation.

Methods

We recruited patients with COVID-19 from August to November 2020. Patients were asked to wear the Fitbit for 30 days, and biometric data including EOV and RHR were extracted. EOV is a relative physiological measure that reflects users’ SpO₂ levels during sleep. We defined a high EOV signal as a patient’s oxygen level exhibiting a significant dip and recovery within the index period, and a high RHR signal as daily RHR exceeding 5 beats per day compared with the minimum RHR of each patient in the study period. We defined successful prediction as the appearance of those signals within 2 days before the onset of the primary outcome. The primary outcome was the composite of deaths of all causes, use of extracorporeal membrane oxygenation, use of mechanical ventilation, oxygenation, and exacerbation of COVID-19 symptoms, irrespective of readmission. We also assessed each outcome individually as secondary outcomes. We made weekly phone calls to discharged patients to check on their symptoms.

Results

We enrolled 23 patients with COVID-19 diagnosed by a positive SARS-CoV-2 polymerase chain reaction test. The patients had a mean age of 50.9 (SD 20) years, and 70% (n=16) were female. Each patient wore the Fitbit for 30 days. COVID-19 symptom exacerbation occurred in 6 (26%) patients. We were successful in predicting exacerbation using EOV signals in 4 out of 5 cases (sensitivity=80%, specificity=90%), whereas the sensitivity and specificity of high RHR signals were 50% and 80%, respectively, both lower than those of high EOV signals. Coincidental obstructive sleep apnea syndrome confirmed by polysomnography was detected in 1 patient via consistently high EOV signals.

Conclusions

This pilot study successfully detected early COVID-19 symptom exacerbation by measuring EOV, which may help to identify the early signs of COVID-19 exacerbation.

Trial Registration

University Hospital Medical Information Network Clinical Trials Registry UMIN000041421; https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000047290

Telemonitoring of daily activities compared to the six-minute walk test further completes the puzzle of oximetry-guided interventions

Pulmonary rehabilitation is based on a thorough patient assessment, including peripheral oxygen saturation (SpO₂) and heart rate (HR) at rest and on exertion. To understand whether exercise-field tests identify patients who desaturate (SpO₂ < 90%) during physical activities, this study compared the six-minute walk test (6MWT) and daily-life telemonitoring. Cross-sectional study including 100 patients referred for pulmonary rehabilitation. The 6MWT was performed in hospital with continuous assessment of SpO₂, HR, walked distance and calculated metabolic equivalent of tasks (METs). Patients were also evaluated in real-life by SMARTREAB telemonitoring, a combined oximetry-accelerometery with remote continuous assessment of SpO₂, HR and METs. SMARTREAB telemonitoring identified 24% more desaturators compared with the 6MWT. Moreover, there were significant mean differences between 6MWT and SMARTREAB in lowest SpO₂ of 7.2 ± 8.4% (P < 0.0005), in peak HR of - 9.3 ± 15.5% (P < 0.0005) and also in activity intensity of - 0.3 ± 0.8 METs (P < 0.0005). The 6MWT underestimates the proportion of patients with exercise-induced oxygen desaturation compared to real-life telemonitoring. These results help defining oximetry-guided interventions, such as telemedicine algorithms, oxygen therapy titration and regular physical activity assessment in pulmonary rehabilitation.

CONQUEST Quality Standards: For the Collaboration on Quality Improvement Initiative for Achieving Excellence in Standards of COPD Care

Background

Chronic obstructive pulmonary disease (COPD) are managed predominantly in primary care. However, key opportunities to optimize treatment are often not realized due to unrecognized disease and delayed implementation of appropriate interventions for both diagnosed and undiagnosed individuals. The COllaboratioN on QUality improvement initiative for achieving Excellence in STandards of COPD care (CONQUEST) is the first-of-its-kind, collaborative, interventional COPD registry. It comprises an integrated quality improvement program focusing on patients (diagnosed and undiagnosed) at a modifiable and higher risk of COPD exacerbations. The first step in CONQUEST was the development of quality standards (QS). The QS will be imbedded in routine primary and secondary care, and are designed to drive patient-centered, targeted, risk-based assessment and management optimization. Our aim is to provide an overview of the CONQUEST QS, including how they were developed, as well as the rationale for, and evidence to support, their inclusion in healthcare systems.

Methods

The QS were developed (between November 2019 and December 2020) by the CONQUEST Global Steering Committee, including 11 internationally recognized experts with a specialty and research focus in COPD. The process included an extensive literature review, generation of QS draft wording, three iterative rounds of review, and consensus.

Results

Four QS were developed: 1) identification of COPD target population, 2) assessment of disease and quantification of future risk, 3) non-pharmacological and pharmacological intervention, and 4) appropriate follow-up. Each QS is followed by a rationale statement and a summary of current guidelines and research evidence relating to the standard and its components.

Conclusion

The CONQUEST QS represent an important step in our aim to improve care for patients with COPD in primary and secondary care. They will help to transform the patient journey, by encouraging early intervention to identify, assess, optimally manage and followup COPD patients with modifiable high risk of future exacerbations.

Early identification and treatment of COPD exacerbation using remote respiratory monitoring

Remote patient monitoring (RPM) is increasingly more accessible and accurate. The optimal utilization of RPM requires medical conditions which have predictive physiologic changes and effective outpatient therapies. Respiratory rate elevation has been shown to be predictive of impending chronic obstructive pulmonary disease (COPD) exacerbation and treatment often focuses on home-based medical therapies. In this case, we report the successful treatment of a patient with an exacerbation of COPD based on pre-identification via respiratory RPM.

Telehealth interventions: remote monitoring and consultations for people with chronic obstructive pulmonary disease (COPD)

BACKGROUND

Chronic obstructive pulmonary disease (COPD, including bronchitis and emphysema) is a chronic condition causing shortness of breath, cough, and exacerbations leading to poor health outcomes. Face-to-face visits with health professionals can be hindered by severity of COPD or frailty, and by people living at a distance from their healthcare provider and having limited access to services. Telehealth technologies aimed at providing health care remotely through monitoring and consultations could help to improve health outcomes of people with COPD.

OBJECTIVES

To assess the effectiveness of telehealth interventions that allow remote monitoring and consultation and multi-component interventions for reducing exacerbations and improving quality of life, while reducing dyspnoea symptoms, hospital service utilisation, and death among people with COPD.

SEARCH METHODS

We identified studies from the Cochrane Airways Trials Register. Additional sources searched included the US National Institutes of Health Ongoing Trials Register, the World Health Organization International Clinical Trials Registry Platform, and the IEEEX Xplore Digital Library. The latest search was conducted in April 2020. We used the GRADE approach to judge the certainty of evidence for outcomes.

SELECTION CRITERIA

Eligible randomised controlled trials (RCTs) included adults with diagnosed COPD. Asthma, cystic fibrosis, bronchiectasis, and other respiratory conditions were excluded. Interventions included remote monitoring or consultation plus usual care, remote monitoring or consultation alone, and mult-component interventions from all care settings. Quality of life scales included St George's Respiratory Questionnaire (SGRQ) and the COPD Assessment Test (CAT). The dyspnoea symptom scale used was the Chronic Respiratory Disease Questionnaire Self-Administered Standardized Scale (CRQ-SAS).

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methodological procedures. We assessed confidence in the evidence for each primary outcome using the GRADE method. Primary outcomes were exacerbations, quality of life, dyspnoea symptoms, hospital service utilisation, and mortality; a secondary outcome consisted of adverse events.

MAIN RESULTS

We included 29 studies in the review (5654 participants; male proportion 36% to 96%; female proportion 4% to 61%). Most remote monitoring interventions required participants to transfer measurements using a remote device and later health professional review (asynchronous). Only five interventions transferred data and allowed review by health professionals in real time (synchronous). Studies were at high risk of bias due to lack of blinding, and certainty of evidence ranged from moderate to very low. We found no evidence on comparison of remote consultations with or without usual care. Remote monitoring plus usual care (8 studies, 1033 participants) Very uncertain evidence suggests that remote monitoring plus usual care may have little to no effect on the number of people experiencing exacerbations at 26 weeks or 52 weeks. There may be little to no difference in effect on quality of life (SGRQ) at 26 weeks (very low to low certainty) or on hospitalisation (all-cause or COPD-related; very low certainty). COPD-related hospital re-admissions are probably reduced at 26 weeks (hazard ratio 0.42, 95% confidence interval (CI) 0.19 to 0.93; 106 participants; moderate certainty). There may be little to no difference in deaths between intervention and usual care (very low certainty). We found no evidence for dyspnoea symptoms or adverse events. Remote monitoring alone (10 studies, 2456 participants) Very uncertain evidence suggests that remote monitoring may result in little to no effect on the number of people experiencing exacerbations at 41 weeks (odds ratio 1.02, 95% CI 0.67 to 1.55). There may be little to no effect on quality of life (SGRQ total at 17 weeks, or CAT at 38 and 52 weeks; very low certainty). There may be little to no effect on dyspnoea symptoms on the CRQ-SAS at 26 weeks (low certainty). There may be no difference in effects on the number of people admitted to hospital (very low certainty) or on deaths (very low certainty). We found no evidence for adverse events. Multi-component interventions with remote monitoring or consultation component (11 studies, 2165 participants) Very uncertain evidence suggests that multi-component interventions may have little to no effect on the number of people experiencing exacerbations at 52 weeks. Quality of life at 13 weeks may improve as seen in SGRQ total score (mean difference -9.70, 95% CI -18.32 to -1.08; 38 participants; low certainty) but not at 26 or 52 weeks (very low certainty). COPD assessment test (CAT) scores may improve at a mean of 38 weeks, but evidence is very uncertain and interventions are varied. There may be little to no effect on the number of people admitted to hospital at 33 weeks (low certainty). Multi-component interventions are likely to result in fewer people re-admitted to hospital at a mean of 39 weeks (OR 0.50, 95% CI 0.31 to 0.81; 344 participants, 3 studies; moderate certainty). There may be little to no difference in death at a mean of 40 weeks (very low certainty). There may be little to no effect on people experiencing adverse events (very low certainty). We found no evidence for dyspnoea symptoms.

AUTHORS' CONCLUSIONS

Remote monitoring plus usual care provided asynchronously may not be beneficial overall compared to usual care alone. Some benefit is seen in reduction of COPD-related hospital re-admissions, but moderate-certainty evidence is based on one study. We have not found any evidence for dyspnoea symptoms nor harms, and there is no difference in fatalities when remote monitoring is provided in addition to usual care. Remote monitoring interventions alone are no better than usual care overall for health outcomes. Multi-component interventions with asynchronous remote monitoring are no better than usual care but may provide short-term benefit for quality of life and may result in fewer re-admissions to hospital for any cause. We are uncertain whether remote monitoring is responsible for the positive impact on re-admissions, and we are unable to discern the long-term benefits of receiving remote monitoring as part of patient care. Owing to paucity of evidence, it is unclear which COPD severity subgroups would benefit from telehealth interventions. Given there is no evidence of harm, telehealth interventions may be beneficial as an additional health resource depending on individual needs based on professional assessment. Larger studies can determine long-term effects of these interventions.

The Performance of Digital Monitoring Devices for Oxygen Saturation and Respiratory Rate in COPD: A Systematic Review

Healthcare access and delivery for individuals with chronic obstructive pulmonary disease (COPD) who live in remote areas or who are susceptible to contracting communicable diseases, such as COVID-19, may be a challenge. Telehealth and remote monitoring devices can be used to overcome this issue. However, the accuracy of these devices must be ensured before forming healthcare decisions based on their outcomes. Therefore, a systematic review was performed to synthesize the evidence on the reliability, validity and responsiveness of digital devices used for tracking oxygen saturation (SpO₂) and/or respiratory rate (RR) in individuals with COPD, in remote settings. Three electronic databases were searched: MEDLINE (1996 to October 8, 2020), EMBASE (1996 to October 8, 2020) and CINAHL (1998 to October 8, 2020). Studies were included if they aimed to evaluate one or more measurement properties of a digital device measuring SpO₂ or RR in individuals with COPD. Six-hundred and twenty-five articles were identified and after screening, 7 studies matched the inclusion criteria; covering 11 devices measuring SpO₂ and/or RR. Studies reported on the reliability (n = 1), convergent validity (n = 1), concurrent validity (n = 2) and predictive validity (n = 2) of SpO₂ devices and on the convergent validity (n = 1), concurrent validity (n = 1) and predictive validity (n = 1) of RR devices. SpO₂ and RR devices were valid when compared against other respiration monitoring devices but were not precise in predicting exacerbation events. More well-designed measurement studies are needed to make firm conclusions about the accuracy of such devices.Supplemental data for this article is available online at https://doi.org/10.1080/15412555.2021.1945021 .

Validation of COPDPredict™: Unique Combination of Remote Monitoring and Exacerbation Prediction to Support Preventative Management of COPD Exacerbations

Background

COPDPredict™ is a novel digital application dedicated to providing early warning of imminent COPD (chronic obstructive pulmonary disease) exacerbations for prompt intervention. Exacerbation prediction algorithms are based on a decision tree model constructed from percentage thresholds for disease state changes in patient-reported wellbeing, forced expiratory volume in one second (FEV₁) and C-reactive protein (CRP) levels. Our study determined the validity of COPDPredict™ to identify exacerbations and provide timely notifications to patients and clinicians compared to clinician-defined episodes.

Methods

In a 6-month prospective observational study, 90 patients with COPD and frequent exacerbations registered wellbeing self-assessments daily using COPDPredict™ App and measured FEV₁ using connected spirometers. CRP was measured using finger-prick testing.

Results

Wellbeing self-assessment submissions showed 98% compliance. Ten patients did not experience exacerbations and treatment was unchanged. A total of 112 clinician-defined exacerbations were identified in the remaining 80 patients: 52 experienced 1 exacerbation; 28 had 2.2±0.4 episodes. Sixty-two patients self-managed using prescribed rescue medication. In 14 patients, exacerbations were more severe but responded to timely escalated treatment at home. Four patients attended the emergency room; with 2 hospitalised for <72 hours. Compared to the 6 months pre-COPDPredict™, hospitalisations were reduced by 98% (90 vs 2, p<0.001). COPDPredict™ identified COPD-related exacerbations at 7, 3 days (median, IQR) prior to clinician-defined episodes, sending appropriate alerts to patients and clinicians. Cross-tabulation demonstrated sensitivity of 97.9% (95% CI 95.7-99.2), specificity of 84.0% (95% CI 82.6-85.3), positive and negative predictive value of 38.4% (95% CI 36.4-40.4) and 99.8% (95% CI 99.5-99.9), respectively.

Conclusion

High sensitivity indicates that if there is an exacerbation, COPDPredict™ informs patients and clinicians accurately. The high negative predictive value implies that when an exacerbation is not indicated by COPDPredict™, risk of an exacerbation is low. Thus, COPDPredict™ provides safe, personalised, preventative care for patients with COPD.

Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study

Background

The World Health Organization has projected that by 2030, chronic obstructive pulmonary disease (COPD) will be the third-leading cause of mortality and the seventh-leading cause of morbidity worldwide. Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with an accelerated decline in lung function, diminished quality of life, and higher mortality. Accurate early detection of acute exacerbations will enable early management and reduce mortality.

Objective

The aim of this study was to develop a prediction system using lifestyle data, environmental factors, and patient symptoms for the early detection of AECOPD in the upcoming 7 days.

Methods

This prospective study was performed at National Taiwan University Hospital. Patients with COPD that did not have a pacemaker and were not pregnant were invited for enrollment. Data on lifestyle, temperature, humidity, and fine particulate matter were collected using wearable devices (Fitbit Versa), a home air quality–sensing device (EDIMAX Airbox), and a smartphone app. AECOPD episodes were evaluated via standardized questionnaires. With these input features, we evaluated the prediction performance of machine learning models, including random forest, decision trees, k-nearest neighbor, linear discriminant analysis, and adaptive boosting, and a deep neural network model.

Results

The continuous real-time monitoring of lifestyle and indoor environment factors was implemented by integrating home air quality–sensing devices, a smartphone app, and wearable devices. All data from 67 COPD patients were collected prospectively during a mean 4-month follow-up period, resulting in the detection of 25 AECOPD episodes. For 7-day AECOPD prediction, the proposed AECOPD predictive model achieved an accuracy of 92.1%, sensitivity of 94%, and specificity of 90.4%. Receiver operating characteristic curve analysis showed that the area under the curve of the model in predicting AECOPD was greater than 0.9. The most important variables in the model were daily steps walked, stairs climbed, and daily distance moved.

Conclusions

Using wearable devices, home air quality–sensing devices, a smartphone app, and supervised prediction algorithms, we achieved excellent power to predict whether a patient would experience AECOPD within the upcoming 7 days. The AECOPD prediction system provided an effective way to collect lifestyle and environmental data, and yielded reliable predictions of future AECOPD events. Compared with previous studies, we have comprehensively improved the performance of the AECOPD prediction model by adding objective lifestyle and environmental data. This model could yield more accurate prediction results for COPD patients than using only questionnaire data.

Validation of the Withings ScanWatch as a Wrist-Worn Reflective Pulse Oximeter: Prospective Interventional Clinical Study

BACKGROUND

A decrease in the level of pulse oxygen saturation as measured by pulse oximetry (SpO2) is an indicator of hypoxemia that may occur in various respiratory diseases, such as chronic obstructive pulmonary disease (COPD), sleep apnea syndrome, and COVID-19. Currently, no mass-market wrist-worn SpO2 monitor meets the medical standards for pulse oximeters.

OBJECTIVE

The main objective of this monocentric and prospective clinical study with single-blind analysis was to test and validate the accuracy of the reflective pulse oximeter function of the Withings ScanWatch to measure SpO2 levels at different stages of hypoxia. The secondary objective was to confirm the safety of this device when used as intended.

METHODS

To achieve these objectives, we included 14 healthy participants aged 23-39 years in the study, and we induced several stable plateaus of arterial oxygen saturation (SaO2) ranging from 100%-70% to mimic nonhypoxic conditions and then mild, moderate, and severe hypoxic conditions. We measured the SpO2 level with a Withings ScanWatch on each participant's wrist and the SaO2 from blood samples with a co-oximeter, the ABL90 hemoximeter (Radiometer Medical ApS).

RESULTS

After removal of the inconclusive measurements, we obtained 275 and 244 conclusive measurements with the two ScanWatches on the participants' right and left wrists, respectively, evenly distributed among the 3 predetermined SpO2 groups: SpO2≤80%, 80%

CONCLUSIONS

In conclusion, the Withings ScanWatch is able to measure SpO2 levels with adequate accuracy at a clinical grade. No undesirable effects or adverse events were reported during the study.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04380389; http://clinicaltrials.gov/ct2/show/NCT04380389.

Collapse

Key Words

• COPD
• COVID-19
• SpO2
• Withings ScanWatch
• accuracy
• connected watch
• neural network
• oximeter
• oxygen
• pulse oxygen saturation
• reflective pulse oximeter
• respiratory
• respiratory disease
• safety
• sleep apnea syndrome
• validation
• wearable

Collapse

MESH Headings

• Adult
• Female
• Humans
• Male
• Young Adult
• COVID-19/blood
• COVID-19/complications
• Healthy Volunteers
• Hypoxia/blood
• Hypoxia/complications
• Lung Diseases/blood
• Lung Diseases/complications
• Monitoring, Physiologic
• Oximetry/adverse effects
• Oximetry/standards
• Oxygen/blood
• Prospective Studies
• Single-Blind Method
• Wrist

Collapse

Grants Collapse

Affiliation(s)

Romain Kirszenblat Withings, Issy-Les-Moulineaux, France
Paul Edouard Withings, Issy-Les-Moulineaux, France

Collapse

A Home Telemedicine System for Continuous Respiratory Monitoring

This article presents a continuous home telemonitoring system for chronic respiratory patients using 5G connectivity developed in partnership with Vodafone as a part of the 5G Trial in Milan established by the Italian Ministry of Economic Development. The system features a wearable respiratory and activity monitor, an environmental sensor and a pulse oximeter sending the data through a 5G router to a Multi-Edge Computing server, incorporated in the Vodafone 5G infrastructure, where they are stored and accessible for visualization. In particular, activity, respiratory and environmental data are continuously streamed and collected. The solution has been tested on 18 healthy volunteers during non-supervised recordings lasting at least 48 hours. The combination of recognized activities and associated respiratory parameters provided statistically significant variations in breathing patterns between one activity and the other, thus giving more complete information to the clinicians than previously studied telemedicine systems based on spot-checks. In particular, statistically significant differences are found in tidal volume and minute ventilation between horizontal and vertical postures (p < 0.001) and between vertical postures and dynamic activities (p < 0.001); the respiratory rate shows statistically significant differences between horizontal and vertical postures (p < 0.001). Some environmental parameters have different mean values between day and night, such as carbon dioxide (p < 0.001). Trials on patients are needed to further study this telemedicine solution and make it commercially available in the future. The main further technical development suggested is the use of commercial 5G smartphones as routers, in order to make the system usable outside of home settings.

Potential value and impact of data mining and machine learning in clinical diagnostics

Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and machine learning to determine the relationships between variables from a large sample of data. It has previously been shown that data mining can improve the prediction and diagnostic precision of type 2 diabetes mellitus. A few studies have applied machine learning to assess hypertension and metabolic syndrome-related biomarkers, as well as refine the assessment of cardiovascular disease risk. Machine learning methods have also been applied to assess new biomarkers and survival outcomes in patients with renal diseases to predict the development of chronic kidney disease, disease progression, and renal graft survival. In the latter, random forest methods were found to be the best for the prediction of chronic kidney disease. Some studies have investigated the prognosis of nonalcoholic fatty liver disease and acute liver failure, as well as therapy response prediction in patients with viral disorders, using decision tree models. Machine learning techniques, such as Sparse High-Order Interaction Model with Rejection Option, have been used for diagnosing Alzheimer's disease. Data mining techniques have also been applied to identify the risk factors for serious mental illness, such as depression and dementia, and help to diagnose and predict the quality of life of such patients. In relation to child health, some studies have determined the best algorithms for predicting obesity and malnutrition. Machine learning has determined the important risk factors for preterm birth and low birth weight. Published studies of patients with cancer and bacterial diseases are limited and should perhaps be addressed more comprehensively in future studies. Herein, we provide an in-depth review of studies in which biochemical biomarker data were analyzed using machine learning methods to assess the risk of several common diseases, in order to summarize the potential applications of data mining methods in clinical diagnosis. Data mining techniques have now been increasingly applied to clinical diagnostics, and they have the potential to support this field.

Utilizing an Artificial Neural Network to Predict Self-Management in Patients With Chronic Obstructive Pulmonary Disease: An Exploratory Analysis

PURPOSE

The main objective of this study was to utilize an artificial neural network in an exploratory fashion to predict self-management behaviors based on reported symptoms in a sample of stable patients with chronic obstructive pulmonary disease (COPD).

DESIGN AND METHODS

Patient symptom data were collected over 21 consecutive days. Symptoms included distress due to cough, chest tightness, distress due to mucus, dyspnea with activity, dyspnea at rest, and fatigue. Self-management abilities were measured and recorded periodically throughout the study period and were the dependent variable for these analyses. Self-management ability scores were broken into three equal tertiles to signify low, medium, and high self-management abilities. Data were entered into a simple artificial neural network using a three-layer model. Accuracy of the neural network model was calculated in a series of three models that respectively used 7, 14, and 21 days of symptom data as input (independent variables). Symptom data were used to determine if the model could accurately classify participants into their respective self-management ability tertiles (low, medium, or high scores). Through analysis of synaptic weights, or the strength or amplitude of a connection between variables and parts of the neural network, the most important variables in classifying self-management abilities could be illuminated and served as another outcome in this study.

FINDINGS

The artificial neural network was able to predict self-management ability with 93.8% accuracy if 21 days of symptom data were included. The neural network performed best when predicting the low and high self-management abilities but struggled in predicting those with medium scores. By analyzing the synaptic weights, the most important variables determining self-management abilities were gender, followed by chest tightness, age, cough, breathlessness during activity, fatigue, breathlessness at rest, and phlegm.

CONCLUSIONS

The results of this study suggest that self-management abilities could potentially be predicted through understanding and reporting of patient's symptoms and use of an artificial neural network. Future research is clearly needed to expand on these findings.

CLINICAL RELEVANCE

Symptom presentation in chronically ill patients directly impacts self-management behaviors. Patients with COPD experience a number of symptoms that have the potential to impact their ability to manage their chronic disease, and artificial neural networks may help clinicians identify patients at risk for poor self-management abilities.

A two-layer probabilistic model to predict COPD exacerbations for patients in telehealth

Conventional one-layer models have yet to achieve clinically relevant classification rates in predicting exacerbations for patients with COPD. The present study investigates whether a two-layer probabilistic model can increase classification rates compared to a one-layer model. Continuous measurements of oxygen saturation, pulse rate, and blood pressure from nine patients with COPD were structured into 17 prodromal exacerbation periods and 398 control periods. A one-layer model was compared to a two-layer model based on prior probabilities using double cross-validation. The two models were compared by the area under the receiver operating characteristics curve and sensitivity at an arbitrarily set specificity of 0.95. This comparison was carried out across nine different classification algorithms. The area under the receiver operating characteristics curve was increased across all nine classification algorithms and by a mean value of 0.11. Sensitivity at an arbitrarily set specificity of 0.95 was also increased by a mean value of 0.13. In conclusion, a two-layer probabilistic model for predicting COPD exacerbations can increase classification rates compared to a one-layer model, and to a level of clinical relevance, for patients in telehealth.

The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

Respiratory rate is a fundamental vital sign that is sensitive to different pathological conditions (e.g., adverse cardiac events, pneumonia, and clinical deterioration) and stressors, including emotional stress, cognitive load, heat, cold, physical effort, and exercise-induced fatigue. The sensitivity of respiratory rate to these conditions is superior compared to that of most of the other vital signs, and the abundance of suitable technological solutions measuring respiratory rate has important implications for healthcare, occupational settings, and sport. However, respiratory rate is still too often not routinely monitored in these fields of use. This review presents a multidisciplinary approach to respiratory monitoring, with the aim to improve the development and efficacy of respiratory monitoring services. We have identified thirteen monitoring goals where the use of the respiratory rate is invaluable, and for each of them we have described suitable sensors and techniques to monitor respiratory rate in specific measurement scenarios. We have also provided a physiological rationale corroborating the importance of respiratory rate monitoring and an original multidisciplinary framework for the development of respiratory monitoring services. This review is expected to advance the field of respiratory monitoring and favor synergies between different disciplines to accomplish this goal.

Telemedicine in COPD: An Overview by Topics

COPD is a major cause of morbidity and mortality worldwide and carries a huge and growing economic and social burden. Telemedicine might allow the care of patients with limited access to health services and improve their self-management. During the COVID-19 pandemic, patient's safety represents one of the main reasons why we might use these tools to manage our patients. The authors conducted a literature search in MEDLINE database. The retrieval form of the Medical Subject Headings (Mesh) was ((Telemedicine OR Tele-rehabilitation OR Telemonitoring OR mHealth OR Ehealth OR Telehealth) AND COPD). We only included systematic reviews, reviews, meta-analysis, clinical trials and randomized-control trials, in the English language, with the selected search items in title or abstract, and published from January 1st 2015 to 31st May 2020 (n = 56). There was a positive tendency toward benefits in tele-rehabilitation, health-education and self-management, early detection of COPD exacerbations, psychosocial support and smoking cessation, but the heterogeneity of clinical trials and reviews limits the extent to which this value can be understood. Telemonitoring interventions and cost-effectiveness had contradictory results. The literature on teleconsultation was scarce during this period. The non-inferiority tendency of telemedicine programmes comparing to conventional COPD management seems an opportunity to deliver quality healthcare to COPD patients, with a guarantee of patient's safety, especially during the COVID-19 outbreak.