Prediction of Chronic Obstructive Pulmonary Disease Exacerbation Events by Using Patient Self-reported Data in a Digital Health App: Statistical Evaluation and Machine Learning Approach

Acute exacerbation prediction of COPD based on Auto-metric graph neural network with inspiratory and expiratory chest CT images

Chronic obstructive pulmonary disease (COPD) is a widely prevalent disease with significant mortality and disability rates and has become the third leading cause of death globally. Patients with acute exacerbation of COPD (AECOPD) often substantially suffer deterioration and death. Therefore, COPD patients deserve special consideration regarding treatment in this fragile population for pre-clinical health management. Based on the above, this paper proposes an AECOPD prediction model based on the Auto-Metric Graph Neural Network (AMGNN) using inspiratory and expiratory chest low-dose CT images. This study was approved by the ethics committee in the First Affiliated Hospital of Guangzhou Medical University. Subsequently, 202 COPD patients with inspiratory and expiratory chest CT Images and their annual number of AECOPD were collected after the exclusion. First, the inspiratory and expiratory lung parenchyma images of the 202 COPD patients are extracted using a trained ResU-Net. Then, inspiratory and expiratory lung Radiomics and CNN features are extracted from the 202 inspiratory and expiratory lung parenchyma images by Pyradiomics and pre-trained Med3D (a heterogeneous 3D network), respectively. Last, Radiomics and CNN features are combined and then further selected by the Lasso algorithm and generalized linear model for determining node features and risk factors of AMGNN, and then the AECOPD prediction model is established. Compared to related models, the proposed model performs best, achieving an accuracy of 0.944, precision of 0.950, F1-score of 0.944, ad area under the curve of 0.965. Therefore, it is concluded that our model may become an effective tool for AECOPD prediction.

Use of Ecological Momentary Assessment to Measure Dyspnea in COPD

Dyspnea is an unpredictable and distressing symptom of chronic obstructive pulmonary disease (COPD). Dyspnea is challenging to measure due to the heterogeneity of COPD and recall bias associated with retrospective reports. Ecological Momentary Assessment (EMA) is a technique used to collect symptoms in real-time within a natural environment, useful for monitoring symptom trends and risks of exacerbation in COPD. EMA can be integrated into mobile health (mHealth) platforms for repeated data collection and used alongside physiological measures and behavioral activity monitors. The purpose of this paper is to discuss the use of mHealth and EMA for dyspnea measurement, consider clinical implications of EMA in COPD management, and identify needs for future research in this area.

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

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

A history of home mechanical ventilation: The past, present and future

This state-of-the-art review provides an overview of the history of home mechanical ventilation (HMV), including early descriptions of mechanical ventilation from ancient and Renaissance perspectives and the mass development of ventilators designed for long-term use during the poliomyelitis epidemic. Seminal data from key clinical trials supports the application of HMV in certain patients with chronic obstructive pulmonary disease, neuromuscular disease and obesity-related respiratory failure. Innovative engineering coupled with refined physiological understanding now permits widespread delivery of home mechanical ventilation to a global population, using portable devices with advanced ventilatory modes and telemonitoring capabilities. Exponential growth in digital technology continues, and ongoing research is needed to understand how to harness clinical and physiological data to benefit patients and healthcare services in a clinically- and cost-effective manner.

Content, Mechanism, and Outcome of Effective Telehealth Solutions for Management of Chronic Obstructive Pulmonary Diseases: A Narrative Review

Telehealth (TH) solutions for Chronic Obstructive Pulmonary Disease (COPD) are promising behavioral therapeutic interventions and can help individuals living with COPD to improve their health status. The linking content, mechanism, and outcome of TH interventions reported in the literature related to COPD care are unknown. This paper aims to summarize the existing literature about structured TH solutions in COPD care. We conducted an electronic search of the literature related to TH solutions for COPD management up to October 2023. Thirty papers presented TH solutions as an innovative treatment to manage COPD. TH and digital health solutions are used interchangeably in the literature, but both have the potential to improve care, accessibility, and quality of life. To date, current TH solutions in COPD care have a variety of content, mechanisms, and outcomes. TH solutions can enhance education as well as provide remote monitoring. The content of TH solutions can be summarized as symptom management, prompt physical activity, and psychological support. The mechanism of TH solutions is manipulated by factors such as content, mode of delivery, strategy, and intensity. The most common outcome measures with TH solutions were adherence to treatment, health status, and quality of life. Implementing effective TH with a COPD care bundle must consider important determinants such as patient's needs, familiarity with the technology, healthcare professional support, and data privacy. The development of effective TH solutions for COPD management also must consider patient engagement as a positive approach to optimizing implementation and effectiveness.

Exploring the Validity of GOLD 2023 Guidelines: Should GOLD C and D Be Combined?

Introduction

The GOLD (Global Initiative for Chronic Obstructive Lung Disease) 2023 guidelines proposed important changes to the stratification of disease severity using the "ABCD" assessment tool. The highest risk groups "C" and "D" were combined into a single category "E" based on exacerbation history, no longer considering symptomology.

Purpose

We quantify the differential disease progression of individuals initially stratified by the GOLD 2022 "ABCD" scheme to evaluate these proposed changes.

Patients and Methods

We utilise data collected from 1529 users of the myCOPD mobile app, a widely used and clinically validated app supporting people living with COPD in the UK. For patients in each GOLD group, we quantify symptoms using COPD Assessment Tests (CAT) and rate of exacerbation over a 12-month period post classification.

Results

CAT scores for users initially classified into GOLD C and GOLD D remain significantly different after 12 months (Kolmogorov-Smirnov statistic = 0.59, P = 8.2 × 10-23). Users initially classified into GOLD C demonstrate a significantly lower exacerbation rate over the 12 months post classification than those initially in GOLD D (Kolmogorov-Smirnov statistic = 0.26; P = 3.1 × 10-2; all exacerbations). Further, those initially classified as GOLD B have higher CAT scores and exacerbation rates than GOLD C in the following 12 months.

Conclusion

CAT scores remain important for stratifying disease progression both in-terms of symptomology and future exacerbation risk. Based on this evidence, the merger of GOLD C and GOLD D should be reconsidered.

Unleashing the Power of Very Small Data to Predict Acute Exacerbations of Chronic Obstructive Pulmonary Disease

Introduction

In this article, we explore to what extent it is possible to leverage on very small data to build machine learning (ML) models that predict acute exacerbations of chronic obstructive pulmonary disease (AECOPD).

Methods

We build ML models using the small data collected during the eHealth Diary telemonitoring study between 2013 and 2017 in Sweden. This data refers to a group of multimorbid patients, namely 18 patients with chronic obstructive pulmonary disease (COPD) as the major reason behind previous hospitalisations. The telemonitoring was supervised by a specialised hospital-based home care (HBHC) unit, which also was responsible for the medical actions needed.

Results

We implement two different ML approaches, one based on time-dependent covariates and the other one based on time-independent covariates. We compare the first approach with standard COX Proportional Hazards (CPH). For the second one, we use different proportions of synthetic data to build models and then evaluate the best model against authentic data.

Discussion

To the best of our knowledge, the present ML study shows for the first time that the most important variable for an increased risk of future AECOPDs is "maintenance medication changes by HBHC". This finding is clinically relevant since a sub-optimal maintenance treatment, requiring medication changes, puts the patient in risk for future AECOPDs.

Conclusion

The experiments return useful insights about the use of small data for ML.

The Efficacy and Safety of Budesonide/Glycopyrronium/Formoterol in the Treatment of COPD in the Elderly

Objective

Chronic obstructive pulmonary disease (COPD) is a major and difficult disease of the chronic respiratory system that is common and frequent, with a huge disease burden. The aim of this study was to investigate the efficacy and safety of budesonide/glyburide/formoterol fumarate (BGF) in the treatment of COPD.

Methods

A comprehensive literature search was conducted in PubMed, Embase, Cochrane Library, and Web of Science. The basic features of the seven pieces of literature were identified using the search strategy. The sample size range was 130∼1264.

Results

The effects of BGF increased FEV1 in patients with COPD (mean difference = 2.86, 95%CI: 2.71–3.01, p < 0.00001). The effects of BGF improved in patients with ≥1 TEAE in patients with COPD, and was not statistically significant after treatment (Odds rate = 1.00, 95%CI: 0.85–1.17, p=0.97). The effects of BGF increased in patients with TEAEs related a to study treatment in patients with COPD (odds rate = 1.27, 95% CI: 1.03–1.57, p=0.02). The effects of BGF in decreased patients with serious TEAEs in patients with COPD (odds rate = −0.02, 95% CI: −0.03–−0.00, p=0.04). The effects of BGF decreased the death rate in patients with COPD, and were not statistically significant after treatment (odds rate = 0.77, 95% CI: 0.31–1.97, p=0.59). The effects of BGF decreased the hypertension rate in patients with COPD (odds rate = 0.92, 95% CI: 0.44–1.89, p=0.81), and was not statistically significant after treatment. The effects of BGF increased pneumonia in patients with COPD (odds rate = 1.55, 95% CI: 0.81–2.97, p=0.19), and were not statistically significant after treatment. The effects of BGF increased FEV1, increased patients with TEAEs related a to study treatment, and decreased patients with serious TEAEs in patients with COPD.

Conclusion

This study elucidates the efficacy and safety of BGF in the treatment of COPD with a view to providing a clinical reference.

Health-Screening-Based Chronic Obstructive Pulmonary Disease and Its Effect on Cardiovascular Disease Risk

Chronic obstructive pulmonary disease (COPD) is considered a major cause of death worldwide, and various studies have been conducted for its early diagnosis. Our work developed a scoring system by predicting and validating COPD and performed predictive model implementations. Participants who underwent a health screening between 2017 and 2020 were extracted from the Korea National Health and Nutrition Examination Survey (KNHANES) database. COPD individuals were defined as aged 40 years or older with prebronchodilator forced expiratory volume in 1 s/forced vital capacity (FEV1/FVC < 0.7). The logistic regression model was performed, and the C-index was used for variable selection. Receiver operating characteristic (ROC) curves with area under the curve (AUC) values were generated for evaluation. Age, sex, waist circumference and diastolic blood pressure were used to predict COPD and to develop a COPD score based on a multivariable model. A simplified model for COPD was validated with an AUC value of 0.780 from the ROC curves. In addition, we evaluated the association of the derived score with cardiovascular disease (CVD). COPD scores showed significant performance in COPD prediction. The developed score also showed a good effect on the diagnostic ability for CVD risk. In the future, studies comparing the diagnostic accuracy of the derived scores with standard diagnostic tests are needed.