Assessing the Barriers and Facilitators to Pulmonary Rehabilitation Referrals Using the Consolidated Framework for Implementation Research (CFIR).

Chronic obstructive pulmonary disease (COPD) is a global health issue causing significant illness and death. Pulmonary Rehabilitation (PR) offers non-pharmacological treatment, including education, exercise, and psychological support which was shown to improve clinical outcomes. In both stable COPD and after an acute exacerbation, PR has been demonstrated to increase exercise capacity, decrease dyspnea, and enhance quality of life. Despite these benefits, referrals for PR for COPD treatment remain low. This study aims to evaluate the perceptions of healthcare providers for referring a COPD patient to PR. Semi-structured qualitative interviews were conducted with pulmonary specialists, hospitalists, and emergency department physicians. Domains and constructs from the Consolidated Framework for Implementation Research (CFIR) were applied to the qualitative data to organize, analyze, and identify the barriers and facilitators to referring COPD patients. The findings from this study will help guide strategies to improve the referral process for PR.

Deep Learning Approaches to Predict Exercise Exertion Levels Using Wearable Physiological Data.

Using physiological data from wearable devices, the study aimed to predict exercise exertion levels by building deep learning classification and regression models. Physiological data were obtained using an unobtrusive chest-worn ECG sensor and portable pulse oximeter from healthy individuals who performed 16-minute cycling exercise sessions. During each session, real-time ECG, pulse rate, oxygen saturation, and revolutions per minute (RPM) data were collected at three intensity levels. Subjects' ratings of perceived exertion (RPE) were collected once per minute. Each 16-minute exercise session was divided into eight 2-minute windows. The self-reported RPEs, heart rate, RPMs, and oxygen saturation levels were averaged for each window to form the predictive features. In addition, heart rate variability (HRV) features were extracted from the ECG for each window. Different feature selection algorithms were used to choose top-ranked predictors. The best predictors were then used to train and test deep learning models for regression and classification analysis. Our results showed the highest accuracy and F1 score of 98.2% and 98%, respectively in training the models. For testing the models, the highest accuracy and F1 score were 80%.

Association of Diagnostic Discrepancy with Length of Stay and Mortality in Congestive Heart Failure Patients Admitted to the Emergency Department.

The goal of this study was to analyze diagnostic discrepancies between emergency department (ED) and hospital discharge diagnoses in patients with congestive heart failure admitted to the ED. Using a synthetic dataset from the Department of Veterans Affairs, the patients' primary diagnoses were compared at two levels: diagnostic category and body system. With 12,621 patients and 24,235 admission cases, the study found a 58% mismatch rate at the category level, which was reduced to 30% at the body system level. Diagnostic categories associated with higher levels of mismatch included aplastic anemia, pneumonia, and bacterial infections. In contrast, diagnostic categories associated with lower levels of mismatch included alcohol-related disorders, COVID-19, cardiac dysrhythmias, and gastrointestinal hemorrhage. Further investigation revealed that diagnostic mismatches are associated with longer hospital stays and higher mortality rates. These findings highlight the importance of reducing diagnostic uncertainty, particularly in specific diagnostic categories and body systems, to improve patient care following ED admission.

Immersive Technologies for Depression Care: Scoping Review.

BACKGROUND: Depression significantly impacts quality of life, affecting approximately 280 million people worldwide. However, only 16.5% of those affected receive treatment, indicating a substantial treatment gap. Immersive technologies (IMTs) such as virtual reality (VR) and augmented reality offer new avenues for treating depression by creating immersive environments for therapeutic interventions. Despite their potential, significant gaps exist in the current evidence regarding the design, implementation, and use of IMTs for depression care. OBJECTIVE: We aim to map the available evidence on IMT interventions targeting depression treatment. METHODS: This scoping review followed a methodological framework, and we systematically searched databases for studies on IMTs and depression. The focus was on randomized clinical trials involving adults and using IMTs. The selection and charting process involved multiple reviewers to minimize bias. RESULTS: The search identified 16 peer-reviewed articles, predominantly from Europe (n=10, 63%), with a notable emphasis on Poland (n=9, 56%), which contributed to more than half of the articles. Most of the studies (9/16, 56%) were conducted between 2020 and 2021. Regarding participant demographics, of the 16 articles, 5 (31%) exclusively involved female participants, and 7 (44%) featured participants whose mean or median age was >60 years. Regarding technical aspects, all studies focused on VR, with most using stand-alone VR headsets (14/16, 88%), and interventions typically ranging from 2 to 8 weeks, predominantly in hospital settings (11/16, 69%). Only 2 (13%) of the 16 studies mentioned using a specific VR design framework in planning their interventions. The most frequently used therapeutic approach was Ericksonian psychotherapy, used in 56% (9/16) of the studies. Notably, none of the articles reported using an implementation framework or identified barriers and enablers to implementation. CONCLUSIONS: This scoping review highlights the growing interest in using IMTs, particularly VR, for depression treatment but emphasizes the need for more inclusive and comprehensive research. Future studies should explore varied therapeutic approaches and cost-effectiveness as well as the inclusion of augmented reality to fully realize the potential of IMTs in mental health care.

Towards a Patient-Centered Design of a Cancer Telerehabilitation System.

Successful implementation of telehealth platforms requires a detailed understanding of patient's needs, preferences, and attitudes toward a home-based platform. The goal of this study was to identify patient-centered characteristics of a cancer rehabilitation system based on cognitive evaluation of user interface and semi-structured qualitative interviews. Quantitative and qualitative feedback from 29 patients with metastatic urogenital cancer was collected after using a cancer telerehabilitation system. Heuristic evaluation, cognitive walkthrough, and analysis of qualitative interviews demonstrated a high level of support for the concept of home-based cancer telerehabilitation by cancer patients. Post-task surveys demonstrated sufficient usability and satisfaction scores from the participants. The patients provided valuable and insightful comments on how to further improve the functionality and interface of the platform. Further improvement of the system usability, consistency, and accessibility based on the patient-centered design principles will significantly facilitate the implementation of cancer telerehabilitation in clinical practice.

Machine Learning Approaches for Exercise Exertion Level Classification Using Data from Wearable Physiologic Monitors.

This research aimed to develop a model for real-time prediction of aerobic exercise exertion levels. ECG signals were registered during 16-minute cycling exercises. Perceived ratings of exertion (RPE) were collected each minute from the study participants. Based on the reported RPE, each consecutive minute of the exercise was assigned to the "high exertion" or "low exertion" class. The characteristics of heart rate variability (HRV) in time and frequency domains were used as predictive features. The top ten ranked predictive features were selected using the minimum redundancy maximum relevance (mRMR) algorithm. The support vector machine demonstrated the highest accuracy with an F1 score of 82%.

Assessing Patient Perspectives on Pulmonary Telerehabilitation Using Thematic Analysis of Semi-Structured Qualitative Interviews.

The study was aimed at exploring patients' experiences after the completion of a 12-month pulmonary telerehabilitation (PR) program. Semi-structured qualitative interviews were conducted with 16 COPD patients. The interviews were analyzed using a thematic approach to identify patterns and themes. The patients exhibited high acceptability and satisfaction with the remote PR program and provided valuable input for its improvement. These insights will be used for the implementation of a patient-centered COPD telerehabilitation system.

NLP-Assisted Differential Diagnosis of Chronic Obstructive Pulmonary Disease Exacerbation.

Chronic Obstructive Pulmonary Disease (COPD) frequently coincides with other comorbidities such as congestive heart failure, hypertension, coronary artery disease, or atrial fibrillation. The exhibition of overlapping sets of symptoms associated with these conditions prevents early identification of an acute exacerbation upon admission to a hospital. Early identification of the underlying cause of exacerbation allows timely prescription of an optimal treatment plan as well as allows avoiding unnecessary clinical tests and specialist consultations. The aim of this study was to develop a predictive model for early identification of COPD exacerbation by using the clinical notes generated within 24 hours of admission to the hospital. The study cohort included patients with a prior diagnosis of COPD. Four predictive models have been developed, among which the support vector machine showed the best performance based on the resulting 80% F1 score.

Pneumococcal Vaccination Lowers the Risk of Alzheimer's Disease: A Study Utilizing Data from the IBM® MarketScan® Database.

Previous studies demonstrated an association between influenza vaccination and the likelihood of developing Alzheimer's disease. This study was aimed at assessing whether pneumococcal vaccinations are associated with a lower risk of Alzheimer's disease based on analysis of data from the IBM® MarketScan® Database. Vaccinated and unvaccinated matched cohorts were generated using propensity-score matching with the greedy nearest-neighbor matching algorithm. The conditional logistic regression method was used to estimate the relationship between pneumococcal vaccination and the onset of Alzheimer's disease. There were 142,874 subjects who received the pneumococcal vaccine and 14,392 subjects who did not. The conditional logistic regression indicated that the people who received the pneumococcal vaccine had a significantly lower risk of developing Alzheimer's disease as compared to the people who did not receive any pneumococcal vaccine (OR=0.37; 95%CI: 0.33-0.42; P-value < .0001). Our findings demonstrated that the pneumococcal vaccine was associated with a 63% reduction in the risk of Alzheimer's disease among US adults aged 65 and older.

Identifying Determinants of Survival Disparities in Multiple Myeloma Patients Using Electronic Health Record Data.

Multiple myeloma (MM) is one of the most common hematological malignancies. The goal of this study was to analyze the sociodemographic, economic, and genetic characteristics of long-term and short-term survival of multiple myeloma patients using EHR data from an academic medical center in New York City. The de-identified analytical dataset comprised 2,111 patients with MM who were stratified based on the length of survival into two groups. Demographic variables, cancer stage, income level, and genetic mutations were analyzed using descriptive statistics and logistic regression. Age, race, and cancer stage were all significant factors that affected the length of survival of multiple myeloma patients. In contrast, gender and income level were not significant factors based on the multivariate adjusted analysis. Older adults, African American patients, and patients who were diagnosed with stage III of multiple myeloma were the people most likely to exhibit short-term survival after the MM diagnosis.

Mixed-Methods Assessment of a Virtual Reality-Based System for Pulmonary Rehabilitation.

Barriers to pulmonary rehabilitation (PR) (e.g., finances, mobility, and lack of awareness about the benefits of PR). Reducing these barriers by providing COPD patients with convenient access to PR educational and exercise training may help improve the adoption of PR. Virtual reality (VR) is an emerging technology that may provide an interactive and engaging method of supporting a home-based PR program. The goal of this study was to systematically evaluate the feasibility of a VR app for a home-based PR education and exercise program using a mixed-methods design. 18 COPD patients were asked to complete three brief tasks using a VR-based PR application. Afterward, patients completed a series of quantitative and qualitative assessments to evaluate the usability, acceptance, and overall perspectives and experience of using a VR system to engage with PR education and exercise training. The findings from this study demonstrate the high acceptability and usability of the VR system to promote participation in a PR program. Patients were able to successfully operate the VR system with minimal assistance. This study examines patient perspectives thoroughly while leveraging VR-based technology to facilitate access to PR. The future development and deployment of a patient-centered VR-based system in the future will consider patient insights and ideas to promote PR in COPD patients.

Exercise Exertion Levels Prediction Based on Real-Time Wearable Physiological Signal Monitoring.

The real-time revolutions per minute (RPM) data, ECG signal, pulse rate, and oxygen saturation levels were collected during 16-minute cycling exercises. In parallel, ratings of perceived exertion (RPE) were collected each minute from the study participants. A 2-minute moving window, with one minute shift, was applied to each 16-minute exercise session to divide it into a total of fifteen 2-minute windows. Based on the self-reported RPE, each exercise window was labeled as "high exertion" or "low exertion" classes. The heart rate variability (HRV) characteristics in time and frequency domains were extracted from the collected ECG signals for each window. In addition, collected oxygen saturation levels, pulse rate, and RPMs were averaged for each window. The best predictive features were then selected using the minimum redundancy maximum relevance (mRMR) algorithm. Top selected features were then used to assess the accuracy of five ML classifiers to predict the level of exertion. The Naïve Bayes model demonstrated the best performance with an accuracy of 80% and an F1 score of 79%.

The Efficacy of Long-Term Hydroxychloroquine Use in the Prevention of COVID-19: A Retrospective Cohort Study.

The use of hydroxychloroquine (HCQ) in the prevention or treatment of COVID-19 remains controversial due to the insufficient supporting evidence and clinical studies indicating that it does not reduce COVID-19 mortality. Its potential protective effects against SARS-CoV-2 are still unclear. Big data resources, such as MarketScan database containing over 30 million insured participants annually, have not been used systematically to assess the association between long-term HCQ use and the risk of COVID-19. This retrospective study aimed to determine the protective effect of HCQ using the MarketScan database. We examined COVID-19 incidence from January to September 2020 among adult patients with systemic lupus erythematosus or rheumatoid arthritis who had received HCQ for at least 10 months in 2019 compared to those who did not. Propensity score matching was used to control for confounding variables and make the HCQ and non-HCQ groups comparable in this study. After matching at the ratio of 1:2, the analytical dataset comprised 13,932 patients who received HCQ for over 10 months and 27,754 HCQ-naïve patients. Multivariate logistic regression showed that long-term HCQ use was associated with a lower likelihood of COVID-19 in patients who had been receiving HCQ for over 10 months (OR=0.78, 95% CI: 0.69-0.88). These findings suggest that long-term HCQ use may provide protection against COVID-19.

Patient Perceptions of a Virtual Reality-Based System for Pulmonary Rehabilitation: A Qualitative Analysis.

The objective of this study was to evaluate the attitudes, beliefs, and perspectives of patients diagnosed with Chronic Obstructive Pulmonary Disease (COPD) while using a virtual reality (VR) system supporting a home-based pulmonary rehabilitation (PR) program. Patients with a history of COPD exacerbations were asked to use a VR app for home-based PR and then undergo semi-structured qualitative interviews to provide their feedback on using the VR app. The mean age of the patients was 72±9 years ranging between 55 and 84 years old. The qualitative data were analyzed using a deductive thematic analysis. Findings from this study indicated the high acceptability and usability of the VR-based system for engaging in a PR program. This study offers a thorough examination of patient perceptions while utilizing a VR-based technology to facilitate access to PR. Future development and deployment of a patient-centered VR-based system will consider patient insights and suggestions to support COPD self-management according to patient requirements, preferences, and expectations.

Combining NLP and Machine Learning for Differential Diagnosis of COPD Exacerbation Using Emergency Room Data.

Chronic Obstructive Pulmonary Disease (COPD) exacerbation exhibits a set of overlapping symptoms with various forms of cardiovascular disease, which makes its early identification challenging. Timely identification of the underlying condition that caused acute admission of COPD patients in the emergency room (ER) may improve patient care and reduce care costs. This study aims to use machine learning combined with natural language processing (NLP) of ER notes to facilitate differential diagnosis in COPD patients admitted to ER. Using unstructured patient information extracted from the notes documented at the very first hours of admission to the hospital, four machine learning models were developed and tested. The random forest model demonstrated the best performance with F1 score of 93%.

Using Electronic Medical Records and Clinical Notes to Predict the Outcome of Opioid Treatment Program.

Opioid addiction is a serious public health problem in the US, and this study aimed to explore how natural language processing (NLP) can be used to identify factors that contribute to distress in individuals with opioid addiction, and then use this information along with structured data to predict the outcome of opioid treatment programs (OTP). The study analyzed medical records data and clinical notes of 1,364 patients, out of which 136 succeeded in the program and 1,228 failed. The results showed that several factors influenced the success of patients in the program, including sex, race, education, employment, secondary substance, tobacco use, and type of residences. XGBoost with down sampling was the best model. The accuracy of the model was 0.71 and the AUC score was 0.64. The study highlights the importance of using both structured and unstructured data to evaluate the effectiveness of OTP.

Towards an Open-Source Platform to Harmonize and Share Research Results from COVID-19 Interventional and Observational Studies.

As the SARS-CoV-2 virus continues to remain a universal threat on a global scale, a large number of COVID-19 clinical trials and observational studies are being conducted and published. Currently, 9,202 COVID-19 clinical trials have been registered on ClinicalTrials.gov and 293,187 COVID-19 articles were indexed in PubMed. To fully capitalize on the voluminous number of publications reporting COVID-19 interventional and observational studies, their results should be freely accessible via an open-source harmonized shared resource. We introduced ReMeDy (https://remedy.mssm.edu/), an intelligent integrative informatics platform aimed to harmonize and cross-link diverse COVID-19 trial outcomes and observational data. We tested the potential of the platform by uploading 52 COVID-19 clinical trials and 48 COVID-19 observational retrospective studies. ReMeDy was validated based on its capability to store and organize diverse data. The next steps include developing a crowdsourcing functionality coupled with automated outcome extraction using natural language processing.

Assessing Patient Perspectives of a Cancer Telerehabilitation Platform Using Thematic Analysis of Semi-Structured Qualitative Interviews.

Cancer-related physical impairments and functional decline affect most patients receiving chemotherapy. Despite evidence that exercise can improve these symptoms, access to exercise-based rehabilitation for cancer patients is limited. Providing telerehabilitation services has shown promising results in alleviating these barriers to access. An in-depth understanding of patient perspectives on cancer telerehabilitation is imperative for the successful development of patient-centered interfaces and functionality. The goal of this study was to explore patients' views and experiences based on a walkthrough of a mobile cancer telerehabilitation system. After the walkthrough, semi-structured qualitative interviews were conducted in 29 cancer patients undergoing chemotherapy. The interviews were analyzed using a thematic analysis approach to deductively identify patterns and themes. Patients responded with approval for the telerehabilitation system, particularly its convenience and ease of use. Patients with reported low technology literacy adapted to the system with minimal problems. The thematic analysis results provided an in-depth understanding of the patients' needs and preferences of the interface and functionality of the telerehabilitation system. These valuable insights will be considered for future development and implementation of a patient-centered cancer telerehab system.

Using Big Data to Uncover Association Between Sildenafil Use and Reduced Risk of Alzheimer's Disease.

Alzheimer's disease is a chronic neurodegenerative disease with multiple pathogenesis pathways. Sildenafil, one of the phosphodiesterase-5 inhibitors, was proven to have effective benefits in transgenic Alzheimer's disease mice. The purpose of the study was to investigate the relationship between sildenafil use and the risk of Alzheimer's disease based on the IBM® MarketScan® Database covering over 30 million employees and family members per year. Sildenafil and non-sildenafil-matched cohorts were generated using propensity-score matching with the greedy nearest-neighbor algorithm. The propensity score stratified univariate analysis and the Cox regression model showed that sildenafil use was significantly associated with a 60% risk reduction of developing Alzheimer's disease (HR=0.40; 95%CI:0.38-0.44; P<.0001) compared to the cohort of individuals who did not take sildenafil. Sex-stratified analyses revealed that sildenafil was related to a lower risk of Alzheimer's disease in subgroups of both males and females. Our findings demonstrated a significant association between sildenafil use and a lower risk of Alzheimer's disease.