Detecting COPD exacerbations early using daily telemonitoring of symptoms and k-means clustering: a pilot study

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

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

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.

A Review of Machine Learning Algorithms for Biomedical Applications

As the amount and complexity of biomedical data continue to increase, machine learning methods are becoming a popular tool in creating prediction models for the underlying biomedical processes. Although all machine learning methods aim to fit models to data, the methodologies used can vary greatly and may seem daunting at first. A comprehensive review of various machine learning algorithms per biomedical applications is presented. The key concepts of machine learning are supervised and unsupervised learning, feature selection, and evaluation metrics. Technical insights on the major machine learning methods such as decision trees, random forests, support vector machines, and k-nearest neighbors are analyzed. Next, the dimensionality reduction methods like principal component analysis and t-distributed stochastic neighbor embedding methods, and their applications in biomedical data analysis were reviewed. Moreover, in biomedical applications predominantly feedforward neural networks, convolutional neural networks, and recurrent neural networks are utilized. In addition, the identification of emerging directions in machine learning methodology will serve as a useful reference for individuals involved in biomedical research, clinical practice, and related professions who are interested in understanding and applying machine learning algorithms in their research or practice.

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

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

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Are older adults considered in asthma and chronic obstructive pulmonary disease mobile health research? A scoping review

BACKGROUND

The use of mobile health (mHealth) for asthma and chronic obstructive pulmonary disease (COPD) is rapidly growing and may help address the complex respiratory care needs of our ageing population. However, little is currently known about how airways mHealth is developed and used among older adults (≥65 years).

OBJECTIVE

To identify if and how older adults with asthma and COPD have been incorporated across the mHealth research cycle.

METHODS

We searched Ovid MEDLINE, EMBASE, CINAHL and the Cochrane Central Registry of Controlled Trials for studies pertaining to the development or evaluation of asthma and COPD mHealth for adults published after 2010. Study, participant and mHealth details, including any considerations of older age, were extracted, synthesised and charted.

RESULTS

A total of 334 studies of 191 mHealth tools were identified. Adults ≥65 years old were included in 33.3% of asthma mHealth studies and 85.3% of COPD studies. Discussions of older age focused on barriers to technology use. Methodologic and/or analytic considerations of older age were mostly absent throughout the research cycle. Among the 28 instances quantitative age-related analyses were detailed, 12 described positive mHealth use and satisfaction outcomes in older adults versus negative or equivocal outcomes.

CONCLUSION

We identified an overall lack of consideration for older age throughout the airways mHealth research cycle, even among COPD mHealth studies that predominantly included older adults. We also found a contrast between the perceptions of how older age might negatively influence mHealth use and available quantitative evaluations. Future airways mHealth research must better integrate the needs and concerns of older adults.

Early detection and prediction of acute exacerbation of chronic obstructive pulmonary disease

Chronic Obstructive Pulmonary Disease (COPD) is characterized by persistent respiratory symptoms and airflow limitation. Acute exacerbation of COPD (AECOPD) is an acute worsening of respiratory symptoms, which needs additional treatment and can result in worsening health status, increasing risks of hospitalization and mortality. Therefore, it is necessary to early recognize and diagnose exacerbations of COPD. This review introduces the updated definition of COPD exacerbations, the current clinical assessment tools, and the current potential biomarkers. The application of mobile health care in COPD management for early identification and diagnosis is also included in this review.

Early detection of COPD patients' symptoms with personal environmental sensors: a remote sensing framework using probabilistic latent component analysis with linear dynamic systems

In this study, we present a cohort study involving 106 COPD patients using portable environmental sensor nodes with attached air pollution sensors and activity-related sensors, as well as daily symptom records and peak flow measurements to monitor patients' activity and personal exposure to air pollution. This is the first study which attempts to predict COPD symptoms based on personal air pollution exposure. We developed a system that can detect COPD patients' symptoms one day in advance of symptoms appearing. We proposed using the Probabilistic Latent Component Analysis (PLCA) model based on 3-dimensional and 4-dimensional spectral dictionary tensors for personalised and population monitoring, respectively. The model is combined with Linear Dynamic Systems (LDS) to track the patients' symptoms. We compared the performance of PLCA and PLCA-LDS models against Random Forest models in the identification of COPD patients' symptoms, since tree-based classifiers were used for remote monitoring of COPD patients in the literature. We found that there was a significant difference between the classifiers, symptoms and the personalised versus population factors. Our results show that the proposed PLCA-LDS-3D model outperformed the PLCA and the RF models between 4 and 20% on average. When we used only air pollutants as input, the PLCA-LDS-3D forecasting results in personalised and population models were 48.67 and 36.33% accuracy for worsening of lung capacity and 38.67 and 19% accuracy for exacerbation of COPD patients' symptoms, respectively. We have shown that indicators of the quality of an individual's environment, specifically air pollutants, are as good predictors of the worsening of respiratory symptoms in COPD patients as a direct measurement.

Remote patient monitoring in the management of chronic obstructive pulmonary disease

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

Estimating influences of unemployment and underemployment on mental health during the COVID-19 pandemic: who suffers the most?

OBJECTIVES

The aim of the study was to evaluate whether unemployment and underemployment are associated with mental distress and whether employment insecurity and its mental health consequences are disproportionately concentrated among specific social groups in the United States during the COVID-19 pandemic.

STUDY DESIGN

This is a population-based longitudinal study.

METHODS

Data came from the Understanding America Study, a population-based panel in the United States. Between April and May 2020, 3548 adults who were not out of the labor force were surveyed. Analyses using targeted maximum likelihood estimation examined the association of employment insecurity with depression, assessed using the 2-item Patient Health Questionnaire, and anxiety, measured with the 2-item Generalized Anxiety Disorder scale. Stratified models were evaluated to examine whether employment insecurity and its mental health consequences are disproportionately concentrated among specific social groups.

RESULTS

Being unemployed or underemployed was associated with increased odds of having depression (adjusted odds ratio [AOR] = 1.66, 95% confidence interval [CI] = 1.36-2.02) and anxiety (AOR = 1.50, 95% CI = 1.26, 1.79), relative to having a full-time job. Employment insecurity was disproportionately concentrated among Hispanics (54.3%), Blacks (60.6%), women (55.9%), young adults (aged 18-29 years; 57.0%), and those without a college degree (62.7%). Furthermore, Hispanic workers, subsequent to employment insecurity, experienced worse effects on depression (AOR = 2.08, 95% CI = 1.28, 3.40) and anxiety (AOR = 1.95, 95% CI = 1.24, 3.09). Those who completed high school or less reported worse depression subsequent to employment insecurity (AOR = 2.44, 95% CI = 1.55, 3.85).

CONCLUSIONS

Both unemployment and underemployment threaten mental health during the pandemic, and the mental health repercussions are not felt equally across the population. Employment insecurity during the pandemic should be considered an important public health concern that may exacerbate pre-existing mental health disparities during and after the pandemic.

Treating COPD Patients with Inhaled Medications in the Era of COVID-19 and Beyond: Options and Rationales for Patients at Home

COVID-19 has affected millions of patients, caregivers, and clinicians around the world. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spreads via droplets and close contact from person to person, and there has been an increased concern regarding aerosol drug delivery due to the potential aerosolizing of viral particles. To date, little focus has been given to aerosol drug delivery to patients with COVID-19 treated at home to minimize their hospital utilization. Since most hospitals were stressed with multiple admissions and experienced restricted healthcare resources in the era of COVID-19 pandemic, treating patients with COPD at home became essential to minimize their hospital utilization. However, guidance on how to deliver aerosolized medications safely and effectively to this patient population treated at home is still lacking. In this paper, we provide some strategies and rationales for device and interface selection, delivery technique, and infection control for patients with COPD who are being treated at home in the era of COVID-19 and beyond.

An 11-Item Measure of User- and Human-Centered Design for Personal Health Tools (UCD-11): Development and Validation

BACKGROUND

Researchers developing personal health tools employ a range of approaches to involve prospective users in design and development.

OBJECTIVE

The aim of this paper was to develop a validated measure of the human- or user-centeredness of design and development processes for personal health tools.

METHODS

We conducted a psychometric analysis of data from a previous systematic review of the design and development processes of 348 personal health tools. Using a conceptual framework of user-centered design, our team of patients, caregivers, health professionals, tool developers, and researchers analyzed how specific practices in tool design and development might be combined and used as a measure. We prioritized variables according to their importance within the conceptual framework and validated the resultant measure using principal component analysis with Varimax rotation, classical item analysis, and confirmatory factor analysis.

RESULTS

We retained 11 items in a 3-factor structure explaining 68% of the variance in the data. The Cronbach alpha was .72. Confirmatory factor analysis supported our hypothesis of a latent construct of user-centeredness. Items were whether or not: (1) patient, family, caregiver, or surrogate users were involved in the steps that help tool developers understand users or (2) develop a prototype, (3) asked their opinions, (4) observed using the tool or (5) involved in steps intended to evaluate the tool, (6) the process had 3 or more iterative cycles, (7) changes between cycles were explicitly reported, (8) health professionals were asked their opinion and (9) consulted before the first prototype was developed or (10) between initial and final prototypes, and (11) a panel of other experts was involved.

CONCLUSIONS

The User-Centered Design 11-item measure (UCD-11) may be used to quantitatively document the user/human-centeredness of design and development processes of patient-centered tools. By building an evidence base about such processes, we can help ensure that tools are adapted to people who will use them, rather than requiring people to adapt to tools.

Length of hospital stay prediction with an integrated approach of statistical-based fuzzy cognitive maps and artificial neural networks

Chronic obstructive pulmonary disease (COPD) is a global burden, which is estimated to be the third leading cause of death worldwide by 2030. The economic burden of COPD grows continuously because it is not a curable disease. These conditions make COPD an important research field of artificial intelligence (AI) techniques in medicine. In this study, an integrated approach of the statistical-based fuzzy cognitive maps (SBFCM) and artificial neural networks (ANN) is proposed for predicting length of hospital stay of patients with COPD, who admitted to the hospital with an acute exacerbation. The SBFCM method is developed to determine the input variables of the ANN model. The SBFCM conducts statistical analysis to prepare preliminary information for the experts and then collects expert opinions accordingly, to define a conceptual map of the system. The integration of SBFCM and ANN methods provides both statistical data and expert opinion in the prediction model. In the numerical application, the proposed approach outperformed the conventional approach and other machine learning algorithms with 79.95% accuracy, revealing the power of expert opinion involvement in medical decisions. A medical decision support framework is constructed for better prediction of length of hospital stay and more effective hospital management.

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.

How stable is lung function in patients with stable chronic obstructive pulmonary disease when monitored using a telehealth system? A longitudinal and home-based study

Background

Many telehealth systems have been designed to identify signs of exacerbations in patients with chronic obstructive pulmonary disease (COPD), but few previous studies have reported the nature of recorded lung function data and what variations to expect in this group of individuals. The aim of the study was to evaluate the nature of individual diurnal, day-to-day and long-term variation in important prognostic markers of COPD exacerbations by employing a telehealth system developed in-house.

Methods

Eight women and five men with COPD performed measurements (spirometry, pulse oximetry and the COPD assessment test (CAT)) three times per week for 4–6 months using the telehealth system. Short-term and long-term individual variations were assessed using the relative density and weekly means respectively. Quality of the spirometry measurements (forced expiratory volume in one second (FEV₁) and inspiratory capacity (IC)) was assessed employing the criteria of American Thoracic Society (ATS)/European Respiratory Society (ERS) guidelines.

Results

Close to 1100 measurements of both FEV₁ and IC were performed during a total of 240 patient weeks. The two standard deviation ranges for intra-individual short-term variation were approximately ±210 mL and ± 350 mL for FEV₁ and IC respectively. In long-term, spirometry values increased and decreased without notable changes in symptoms as reported by CAT, although it was unusual with a decrease of more than 50 mL per measurement of FEV₁ between three consecutive measurement days. No exacerbation occurred. There was a moderate to strong positive correlation between FEV₁ and IC, but weak or absent correlation with the other prognostic markers in the majority of the participants.

Conclusions

Although FEV₁ and IC varied within a noticeable range, no corresponding change in symptoms occurred. Therefore, this study reveals important and, to our knowledge, previously not reported information about short and long-term variability in prognostic markers in stable patients with COPD. The present data are of significance when defining criteria for detecting exacerbations using telehealth strategies.

Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease

OBJECTIVES

Identifying acute exacerbations in chronic obstructive pulmonary disease (AECOPDs) is of utmost importance for reducing the associated mortality and financial burden. In this research, the authors aimed to develop identification models for AECOPDs and to compare the relative performance of different modeling paradigms to find the best model for this task.

METHODS

Data were extracted from electronic medical records (EMRs) of patients with chronic obstructive pulmonary disease who admitted to the China-Japan Friendship Hospital between February 2011 and March 2017. Five machine learning algorithms (random forest, support vector machine, logistic regression, K-nearest neighbor and naïve Bayes) were used to develop the AECOPDs identification models. Feature selection was performed to find an optimal feature subset. 10-folds cross-validation was used to find the best hyperparameters for each model. The following metrics:  area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value were used to evaluate the performance of these models.

RESULTS

A total of 303 EMRs (AECOPDs patients:135; None AECOPDs patients: 168) were included in the study. The SVM model obtained the best performance (sensitivity: 0.80, specificity: 0.83, positive predictive value:0.81, negative predictive value:0.85 and area under the receiver operating characteristic curve: 0.90) after performing feature selection.

CONCLUSIONS

Our research confirms that the proposed model based on the support vector machine is a powerful tool to identify AECOPDs patients, and it is promising to provide decision support for clinicians when they are struggling to give a confirmed clinical diagnosis.

Artificial intelligence and machine learning in respiratory medicine

Introduction: The application of artificial intelligence (AI) and machine learning (ML) in medicine and in particular in respiratory medicine is an increasingly relevant topic.Areas covered: We aimed to identify and describe the studies published on the use of AI and ML in the field of respiratory diseases. The string '(((pulmonary) OR respiratory)) AND ((artificial intelligence) OR machine learning)' was used in PubMed as a search strategy. The majority of studies identified corresponded to the area of chronic obstructive pulmonary disease (COPD), in particular to COPD and chest computed tomography scans, interpretation of pulmonary function tests, exacerbations and treatment. Another field of interest is the application of AI and ML to the diagnosis of interstitial lung disease, and a few other studies were identified on the fields of mechanical ventilation, interpretation of images on chest X-ray and diagnosis of bronchial asthma.Expert opinion: ML may help to make clinical decisions but will not replace the physician completely. Human errors in medicine are associated with large financial losses, and many of them could be prevented with the help of AI and ML. AI is particularly useful in the absence of conclusive evidence of decision-making.

Telemonitoring to Manage Chronic Obstructive Pulmonary Disease: Systematic Literature Review

Background

Chronic obstructive pulmonary disease (COPD) is a leading cause of death throughout the world. Telemedicine has been utilized for many diseases and its prevalence is increasing in the United States. Telemonitoring of patients with COPD has the potential to help patients manage disease and predict exacerbations.

Objective

The objective of this review is to evaluate the effectiveness of telemonitoring to manage COPD. Researchers want to determine how telemonitoring has been used to observe COPD and we are hoping this will lead to more research in telemonitoring of this disease.

Methods

This review was conducted in accordance with the Assessment for Multiple Systematic Reviews (AMSTAR) and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Authors performed a systematic review of the PubMed and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases to obtain relevant articles. Articles were then accepted or rejected by group consensus. Each article was read and authors identified barriers and facilitators to effectiveness of telemonitoring of COPD.

Results

Results indicate that conflicting information exists for the effectiveness of telemonitoring of patients with COPD. Primarily, 13 out of 29 (45%) articles stated that patient outcomes were improved overall with telemonitoring, while 11 of 29 (38%) indicated no improvement. Authors identified the following facilitators: reduced need for in-person visits, better disease management, and bolstered patient-provider relationship. Important barriers included low-quality data, increased workload for providers, and cost.

Conclusions

The high variability between the articles and the ways they provided telemonitoring services created conflicting results from the literature review. Future research should emphasize standardization of telemonitoring services and predictability of exacerbations.

A comparison between discrete and continuous time Bayesian networks in learning from clinical time series data with irregularity

BACKGROUND

Recently, mobile devices, such as smartphones, have been introduced into healthcare research to substitute paper diaries as data-collection tools in the home environment. Such devices support collecting patient data at different time points over a long period, resulting in clinical time-series data with high temporal complexity, such as time irregularities. Analysis of such time series poses new challenges for machine-learning techniques. The clinical context for the research discussed in this paper is home monitoring in chronic obstructive pulmonary disease (COPD).

OBJECTIVE

The goal of the present research is to find out which properties of temporal Bayesian network models allow to cope best with irregularly spaced multivariate clinical time-series data.

METHODS

Two mainstream temporal Bayesian network models of multivariate clinical time series are studied: dynamic Bayesian networks, where the system is described as a snapshot at discrete time points, and continuous time Bayesian networks, where transitions between states are modeled in continuous time. Their capability of learning from clinical time series that vary in nature are extensively studied. In order to compare the two temporal Bayesian network types for regularly and irregularly spaced time-series data, three typical ways of observing time-series data were investigated: (1) regularly spaced in time with a fixed rate; (2) irregularly spaced and missing completely at random at discrete time points; (3) irregularly spaced and missing at random at discrete time points. In addition, similar experiments were carried out using real-world COPD patient data where observations are unevenly spaced.

RESULTS

For regularly spaced time series, the dynamic Bayesian network models outperform the continuous time Bayesian networks. Similarly, if the data is missing completely at random, discrete-time models outperform continuous time models in most situations. For more realistic settings where data is not missing completely at random, the situation is more complicated. In simulation experiments, both models perform similarly if there is strong prior knowledge available about the missing data distribution. Otherwise, continuous time Bayesian networks perform better. In experiments with unevenly spaced real-world data, we surprisingly found that a dynamic Bayesian network where time is ignored performs similar to a continuous time Bayesian network.

CONCLUSION

The results confirm conventional wisdom that discrete-time Bayesian networks are appropriate when learning from regularly spaced clinical time series. Similarly, we found that time series where the missingness occurs completely at random, dynamic Bayesian networks are an appropriate choice. However, for complex clinical time-series data that motivated this research, the continuous-time models are at least competitive and sometimes better than their discrete-time counterparts. Furthermore, continuous-time models provide additional benefits of being able to provide more fine-grained predictions than discrete-time models, which will be of practical relevance in clinical applications.

Symptom clusters in chronic obstructive pulmonary disease: A systematic review

AIM

To conduct a comprehensive literature review to identify symptom clusters commonly present in Chronic obstructive pulmonary disease (COPD) patients.

BACKGROUND

COPD is the fourth leading cause of death worldwide. Substantial research has been studied regarding single symptoms that burden patients with this disease and the profound impacts that these symptoms can have on physical and psychological health. However, these symptoms rarely occur in isolation and limited research has been conducted identifying clinically significant relationships or clusters of symptoms associated with COPD afflicted patients.

METHODS

PubMed, Web of Science, and Embase databases were used to identify potential articles limited to records published between 2005 and 2018 with human-conducted trials on adults with COPD, examining symptom clusters in this population. Only 5 studies met inclusion criteria.

RESULTS

Across the five studies, 596 participants were included with a mean age of 70.49. Two themes emerged including psychological symptom clusters and respiratory-related symptom clusters. Anxiety-related symptoms appeared to be a common theme among psychological symptom clusters and varied greatly based on instrument selection. Inconsistent results were found in respiratory-related symptom clusters, but included difficulty breathing as a common symptom component. Only one study examined for stability of symptoms over time.

CONCLUSION

There were inconsistent results across all studies which may be contributed to the heterogeneity amongst patients, instruments administered, and statistical approach. Future research should be conducted to further elucidate COPD related symptom clusters, their effects on somatic and cognitive health, and the stability of these symptom clusters over time.

Continuous remote monitoring of COPD patients-justification and explanation of the requirements and a survey of the available technologies

Remote patient monitoring should reduce mortality rates, improve care, and reduce costs. We present an overview of the available technologies for the remote monitoring of chronic obstructive pulmonary disease (COPD) patients, together with the most important medical information regarding COPD in a language that is adapted for engineers. Our aim is to bridge the gap between the technical and medical worlds and to facilitate and motivate future research in the field. We also present a justification, motivation, and explanation of how to monitor the most important parameters for COPD patients, together with pointers for the challenges that remain. Additionally, we propose and justify the importance of electrocardiograms (ECGs) and the arterial carbon dioxide partial pressure (PaCO2) as two crucial physiological parameters that have not been used so far to any great extent in the monitoring of COPD patients. We cover four possibilities for the remote monitoring of COPD patients: continuous monitoring during normal daily activities for the prediction and early detection of exacerbations and life-threatening events, monitoring during the home treatment of mild exacerbations, monitoring oxygen therapy applications, and monitoring exercise. We also present and discuss the current approaches to decision support at remote locations and list the normal and pathological values/ranges for all the relevant physiological parameters. The paper concludes with our insights into the future developments and remaining challenges for improvements to continuous remote monitoring systems. Graphical abstract ᅟ.

Implementation of Activity Sensor Equipment in the Homes of Chronic Obstructive Pulmonary Disease Patients

INTRODUCTION

Telemedicine care models for managing advanced chronic obstructive pulmonary disease (COPD) may benefit from the addition of motion sensing, spirometry, and tablet-based symptom diary tracking.

METHODS

We conducted a feasibility study of telemedicine in the home setting using multiple activity sensor monitoring equipment. Deployment and monitoring were supported by home health nurses with technical advice from the equipment makers as needed. Data analytics for motion sensing was provided by the research sponsor, but was not used for care decisions. On study intake, a health risk assessment, Quality of Life (SF-36) survey, and the St. George Respiratory Questionnaire were administered to assess patients' self-perception of quality of life, activities of daily life function, and difficulty living with COPD.

RESULTS

Twenty-eight patients were enrolled and data were gathered for a minimum of 6 months and maximum of 9 months. The researchers demonstrated that augmentation of traditional telemedicine methods with motion sensing, spirometry, and symptom diaries appears feasible. The technical, process, logistics barriers, and solutions required for system deployment are described. The researchers demonstrated that augmentation of traditional telemedicine methods with motion sensing, spirometry, and symptom diaries appears feasible.

CONCLUSIONS

Further exploration will be needed to determine the value of this information in preventing outcomes relevant to patients.

Classification of Exacerbation Frequency in the COPDGene Cohort Using Deep Learning With Deep Belief Networks

This study aims to develop an automatic classifier based on deep learning for exacerbation frequency in patients with chronic obstructive pulmonary disease (COPD). A three-layer deep belief network (DBN) with two hidden layers and one visible layer was employed to develop classification models and the models' robustness to exacerbation was analyzed. Subjects from the COPDGene cohort were labeled with exacerbation frequency, defined as the number of exacerbation events per year. A total of 10 300 subjects with 361 features each were included in the analysis. After feature selection and parameter optimization, the proposed classification method achieved an accuracy of 91.99%, using a ten-fold cross validation experiment. The analysis of DBN weights showed that there was a good visual spatial relationship between the underlying critical features of different layers. Our findings show that the most sensitive features obtained from the DBN weights are consistent with the consensus showed by clinical rules and standards for COPD diagnostics. We, thus, demonstrate that DBN is a competitive tool for exacerbation risk assessment for patients suffering from COPD.

Machine learning approaches to personalize early prediction of asthma exacerbations

Patient telemonitoring results in an aggregation of significant amounts of information about patient disease trajectory. However, the potential use of this information for early prediction of exacerbations in adult asthma patients has not been systematically evaluated. The aim of this study was to explore the utility of telemonitoring data for building machine learning algorithms that predict asthma exacerbations before they occur. The study dataset comprised daily self-monitoring reports consisting of 7001 records submitted by adult asthma patients during home telemonitoring. Predictive modeling included preparation of stratified training datasets, predictive feature selection, and evaluation of resulting classifiers. Using a 7-day window, a naive Bayesian classifier, adaptive Bayesian network, and support vector machines were able to predict asthma exacerbation occurring on day 8, with sensitivity of 0.80, 1.00, and 0.84; specificity of 0.77, 1.00, and 0.80; and accuracy of 0.77, 1.00, and 0.80, respectively. Our study demonstrated that machine learning techniques have significant potential in developing personalized decision support for chronic disease telemonitoring systems. Future studies may benefit from a comprehensive predictive framework that combines telemonitoring data with other factors affecting the likelihood of developing acute exacerbation. Approaches implemented for advanced asthma exacerbation prediction may be extended to prediction of exacerbations in patients with other chronic health conditions.

Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review

Major reported factors associated with the limited effectiveness of home telemonitoring interventions in chronic respiratory conditions include the lack of useful early predictors, poor patient compliance and the poor performance of conventional algorithms for detecting deteriorations. This article provides a systematic review of existing algorithms and the factors associated with their performance in detecting exacerbations and supporting clinical decisions in patients with chronic obstructive pulmonary disease (COPD) or asthma. An electronic literature search in Medline, Scopus, Web of Science and Cochrane library was conducted to identify relevant articles published between 2005 and July 2015. A total of 20 studies (16 COPD, 4 asthma) that included research about the use of algorithms in telemonitoring interventions in asthma and COPD were selected. Differences on the applied definition of exacerbation, telemonitoring duration, acquired physiological signals and symptoms, type of technology deployed and algorithms used were found. Predictive models with good clinically reliability have yet to be defined, and are an important goal for the future development of telehealth in chronic respiratory conditions. New predictive models incorporating both symptoms and physiological signals are being tested in telemonitoring interventions with positive outcomes. However, the underpinning algorithms behind these models need be validated in larger samples of patients, for longer periods of time and with well-established protocols. In addition, further research is needed to identify novel predictors that enable the early detection of deteriorations, especially in COPD. Only then will telemonitoring achieve the aim of preventing hospital admissions, contributing to the reduction of health resource utilization and improving the quality of life of patients.

ChronicOnline: Implementing a mHealth solution for monitoring and early alerting in chronic obstructive pulmonary disease

Lack of time or economic difficulties prevent chronic obstructive pulmonary disease patients from communicating regularly with their physicians, thus inducing exacerbation of their chronic condition and possible hospitalization. Enhancing Chronic patients’ Health Online proposes a new, sustainable and innovative business model that provides at low cost and at significant savings to the national health system, a preventive health service for chronic obstructive pulmonary disease patients, by combining human medical expertise with state-of-the-art online service delivery based on cloud computing, service-oriented architecture, data analytics, and mobile applications. In this article, we implement the frontend applications of the Enhancing Chronic patients’ Health Online system and describe their functionality and the interfaces available to the users.

Prediction of exacerbation onset in chronic obstructive pulmonary disease patients

The objective of this study was to develop an algorithm for prediction of exacerbation onset in Chronic Obstructive Pulmonary Disease (COPD) patients based on continuous self-monitoring of physiological parameters from telehome-care monitoring. 151 physiological parameters of COPD patients were monitored on a daily/weekly basis for up to 2 years. Data were segmented in 30-day periods leading up to an exacerbation (exacerbation episode) and starting from a 14-day recovery period post-exacerbation (control episode) and tested in 6 intervals to predict exacerbation onset using k-nearest neighbour (k = 1, 3, 5). A classifier with sensitivity of 73%, specificity of 74%, positive predictive value of 69%, negative predictive value of 78% and an accuracy of 74% was achieved using data intervals consisting of 5 days. Intelligent processing of physiological recordings have potential for predicting exacerbation onset.

Computerised Analysis of Telemonitored Respiratory Sounds for Predicting Acute Exacerbations of COPD

Chronic obstructive pulmonary disease (COPD) is one of the commonest causes of death in the world and poses a substantial burden on healthcare systems and patients' quality of life. The largest component of the related healthcare costs is attributable to admissions due to acute exacerbation (AECOPD). The evidence that might support the effectiveness of the telemonitoring interventions in COPD is limited partially due to the lack of useful predictors for the early detection of AECOPD. Electronic stethoscopes and computerised analyses of respiratory sounds (CARS) techniques provide an opportunity for substantial improvement in the management of respiratory diseases. This exploratory study aimed to evaluate the feasibility of using: (a) a respiratory sensor embedded in a self-tailored housing for ageing users; (b) a telehealth framework; (c) CARS and (d) machine learning techniques for the remote early detection of the AECOPD. In a 6-month pilot study, 16 patients with COPD were equipped with a home base-station and a sensor to daily record their respiratory sounds. Principal component analysis (PCA) and a support vector machine (SVM) classifier was designed to predict AECOPD. 75.8% exacerbations were early detected with an average of 5 ± 1.9 days in advance at medical attention. The proposed method could provide support to patients, physicians and healthcare systems.