Artificial intelligence-powered remote monitoring of patients with chronic obstructive pulmonary disease.
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ABSTRACT:
SUBMITTER: Li X
PROVIDER: S-EPMC8280054 | biostudies-literature | 2021 Jun
REPOSITORIES: biostudies-literature
Publications
Artificial intelligence-powered remote monitoring of patients with chronic obstructive pulmonary disease.
Li Xuying X Zhou Hao-Peng HP Zhou Zhi-Jun ZJ Du Nan N Zhong Er-Heng EH Zhai Ke K Liu Nathan N Zhou Linfu L
Chinese medical journal 20210616 13
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Project description:BackgroundAlthough remote home monitoring (RHM) has the capacity to prevent exacerbations in patients with chronic obstructive pulmonary disease (COPD), evidence regarding its effectiveness remains unclear. The objective of this study was to determine the effectiveness of RHM in patients with COPD.MethodsA systematic review of the scholarly literature published within the last 10 years was conducted using internationally recognized guidelines. Search strategies were applied to several electronic databases and clinical trial registries through March 2020 to identify studies comparing RHM to 'no remote home monitoring' (no RHM) or comparing RHM with provider's feedback to RHM without feedback. To critically appraise the included randomized studies, the Cochrane Collaboration risk of bias tool (ROB) was used. The quality of included non-randomized interventional and comparative observational studies was evaluated using the ACROBAT-NRSI tool from the Cochrane Collaboration. The quality of evidence relating to key outcomes was assessed using Grading of Recommendations, Assessment, Development and Evaluations (GRADE) on the following: health-related quality of life (HRQoL), patient experience and number of exacerbations, number of emergency room (ER) visits, COPD-related hospital admissions, and adherence as the proportion of patients who completed the study. Three independent reviewers assessed methodologic quality and reviewed the studies.ResultsSeventeen randomized controlled trials (RCTs) and two comparative observational studies were included in the review. The primary finding of this systematic review is that a considerable amount of evidence relating to the efficacy/effectiveness of RHM exists, but its quality is low. Although RHM is safe, it does not appear to improve HRQoL (regardless of the type of RHM), lung function or self-efficacy, or to reduce depression, anxiety, or healthcare resource utilization. The inclusion of regular feedback from providers may reduce COPD-related hospital admissions. Though adherence RHM remains unclear, both patient and provider satisfaction were high with the intervention.ConclusionsAlthough a considerable amount of evidence to the effectiveness of RHM exists, due to heterogeneity of care settings and the low-quality evidence, they should be interpreted with caution.
| S-EPMC9107164 | biostudies-literature
Project description: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.
| S-EPMC10470466 | biostudies-literature
Project description: Not available
| S-EPMC8543678 | biostudies-literature
Project description:Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) plays a pivotal role in preventing autoimmunity and fostering anticancer immunity by interacting with B7 proteins CD80 and CD86. CTLA-4 is the first immune checkpoint targeted with a monoclonal antibody inhibitor. Checkpoint inhibitors have generated durable responses in many cancer patients, representing a revolutionary milestone in cancer immunotherapy. However, therapeutic efficacy is limited to a small portion of patients, and immune-related adverse events are noteworthy, especially for monoclonal antibodies directed against CTLA-4. Previously, small molecules have been developed to impair the CTLA-4: CD80 interaction; however, they directly targeted CD80 and not CTLA-4. In this study, we performed artificial intelligence (AI)-powered virtual screening of approximately ten million compounds to target CTLA-4. We validated primary hits with biochemical, biophysical, immunological, and experimental animal assays. We then optimized lead compounds and obtained inhibitors with an inhibitory concentration of 1 micromole in disrupting the interaction between CTLA-4 and CD80. Unlike ipilimumab, these small molecules did not degrade CTLA-4. Several compounds inhibited tumor development prophylactically and therapeutically in syngeneic and CTLA-4-humanized mice. This project supports an AI-based framework in designing small molecules targeting immune checkpoints for cancer therapy.
2024-02-15 | GSE228560 | GEO
Project description: Not available
| S-EPMC10787416 | biostudies-literature
Project description:Diaphragm muscles in Chronic Obstructive Pulmonary Disease (COPD) patients undergo an adaptive fast to slow transformation that includes cellular adaptations. This project studies the signaling mechanisms responsible for this transformation. Keywords: other
2003-07-16 | GSE475 | GEO
Project description:Background and purposeChronic obstructive pulmonary disease (COPD), is a primary public health issue globally and in our country, which continues to increase due to poor awareness of the disease and lack of necessary preventive measures. COPD is the result of a blockage of the air sacs known as alveoli within the lungs; it is a persistent sickness that causes difficulty in breathing, cough, and shortness of breath. COPD is characterized by breathing signs and symptoms and airflow challenge because of anomalies in the airways and alveoli that occurs as the result of significant exposure to harmful particles and gases. The spirometry test (breath measurement test), used for diagnosing COPD, is creating difficulties in reaching hospitals, especially in patients with disabilities or advanced disease and in children. To facilitate the diagnostic treatment and prevent these problems, it is far evaluated that using photoplethysmography (PPG) signal in the diagnosis of COPD disease would be beneficial in order to simplify and speed up the diagnosis process and make it more convenient for monitoring. A PPG signal includes numerous components, including volumetric changes in arterial blood that are related to heart activity, fluctuations in venous blood volume that modify the PPG signal, a direct current (DC) component that shows the optical properties of the tissues, and modest energy changes in the body. PPG has typically received the usage of a pulse oximeter, which illuminates the pores and skin and measures adjustments in mild absorption. PPG occurring with every heart rate is an easy signal to measure. PPG signal is modeled by machine learning to predict COPD.MethodsDuring the studies, the PPG signal was cleaned of noise, and a brand-new PPG signal having three low-frequency bands of the PPG was obtained. Each of the four signals extracted 25 features. An aggregate of 100 features have been extracted. Additionally, weight, height, and age were also used as characteristics. In the feature selection process, we employed the Fisher method. The intention of using this method is to improve performance.ResultsThis improved PPG prediction models have an accuracy rate of 0.95 performance value for all individuals. Classification algorithms used in feature selection algorithm has contributed to a performance increase.ConclusionAccording to the findings, PPG-based COPD prediction models are suitable for usage in practice.
| S-EPMC10280226 | biostudies-literature
Project description:Investigation of whole genome gene expression level changes of the dynamic gene profiling of peripheral blood mononuclear cells (PBMCs) from patients with AECOPD) on day1, 3 and 10, compared to the normal people and stable COPD patients. A five chip study using total RNA recovered from Peripheral Blood Mononuclear Cell of Peripheral Blood.Evaluating the dynamic gene profiling of peripheral blood mononuclear cells (PBMCs) from patients with AECOPD) on day1, 3 and 10 after the hospital admission, to compared with healthy controls or patients with stable COPD. Slides were scanned at 5 μm/pixel resolution using an Axon GenePix 4000B scanner (Molecular Devices Corporation) piloted by GenePix Pro 6.0 software (Axon). Scanned images (TIFF format) were then imported into NimbleScan software (version 2.5) for grid alignment and expression data analysis. Expression data were normalized through quantile normalization and the Robust Multichip Average (RMA) algorithm included in the NimbleScan software. The Probe level (*_norm_RMA.pair) files and Gene level (*_RMA.calls) files were generated after normalization.
2014-08-14 | E-GEOD-60399 | biostudies-arrayexpress
Project description:Measuring genome-wide changes in transcript abundance in circulating peripheral whole blood cells is a useful way to study disease pathobiology and may help elucidate biomarkers and molecular mechanisms of disease. The sensitivity and interpretability of analyses carried out in this complex tissue, however, are significantly affected by its heterogeneity. It is therefore desirable to quantify this heterogeneity, either to account for it or to better model interactions that may be present between the abundance of certain transcripts, some cell types and some indication. Accurate enumeration of the many component cell types that make up peripheral whole blood can be costly, however, and may further complicate the sample collection process. Many approaches have been developed to infer the composition of a sample from high-dimensional transcriptomic and, more recently, epigenetic data. These approaches rely on the availability of isolated expression profiles for the cell types to be enumerated. These profiles are platform-specific, suitable datasets are rare, and generating them is expensive. No such dataset exists on the Affymetrix Gene ST platform. We present a freely-available, and open-source, multiresponse Gaussian model capable of accurately inferring the composition of peripheral whole blood samples from Affymetrix Gene ST expression profiles. The model was developed on a cohort of patients with chronic obstructive pulmonary disease (COPD) and tested in chronic heart failure patients.
2016-05-25 | GSE77344 | GEO
Project description:BackgroundChronic obstructive pulmonary disease (COPD) is the third leading cause of mortality worldwide. Reducing the number of COPD exacerbations is an important patient outcome and a major cost-saving approach. Both technology-enabled self-monitoring (SM) and remote monitoring (RM) programs have the potential to reduce exacerbations, but they have not been directly compared with each other. As RM is a more resource-intensive strategy, it is important to understand whether it is more effective than SM.ObjectiveThe objective of this study is to evaluate the impact of SM and RM on self-management behaviors, COPD disease knowledge, and respiratory status relative to standard care (SC).MethodsThis was a 3-arm open-label randomized controlled trial comparing SM, RM, and SC completed in an outpatient COPD clinic in a community hospital. Patients in the SM and RM groups recorded their vital signs (oxygen, blood pressure, temperature, and weight) and symptoms with the Cloud DX platform every day and were provided with a COPD action plan. Patients in the RM group also received access to a respiratory therapist (RT). The RT monitored their vital signs intermittently and contacted them when their vitals varied outside of predetermined thresholds. The RT also contacted patients once a week irrespective of their vital signs or symptoms. All patients were randomized to 1 of the 3 groups and assessed at baseline and 3 and 6 months after program initiation. The primary outcome was the Partners in Health scale, which measures self-management skills. Secondary outcomes included the St. George's Respiratory Questionnaire, Bristol COPD Knowledge Questionnaire, COPD Assessment Test, and modified-Medical Research Council Breathlessness Scale. Patients were also asked to self-report on health system usage.ResultsA total of 122 patients participated in the study, 40 in the SC, 41 in the SM, and 41 in the RM groups. Out of those patients, 7 in the SC, 5 in the SM, and 6 in the RM groups did not complete the study. There were no significant differences in the rates of study completion among the groups (P=.80).ConclusionsBoth SM and RM have shown promise in reducing acute care utilization and exacerbation frequencies. As far as we are aware, no studies to date have directly compared technology-enabled self-management with RM programs in COPD patients. We believe that this study will be an important contribution to the literature.Trial registrationClinicalTrials.gov NCT03741855; https://clinicaltrials.gov/ct2/show/NCT03741855.International registered report identifier (irrid)DERR1-10.2196/13920.
| S-EPMC6718086 | biostudies-literature