Telemedicine Applications for Cancer Rehabilitation: Scoping Review.

BACKGROUND: Cancer is a significant public health issue worldwide. Treatments such as surgery, chemotherapy, and radiation therapy often cause psychological and physiological side effects, affecting patients' ability to function and their quality of life (QoL). Physical activity is crucial to cancer rehabilitation, improving physical function and QoL and reducing cancer-related fatigue. However, many patients face barriers to accessing cancer rehabilitation due to socioeconomic factors, transportation issues, and time constraints. Telerehabilitation can potentially overcome these barriers by delivering rehabilitation remotely. OBJECTIVE: The aim of the study is to identify how telemedicine is used for the rehabilitation of patients with cancer. METHODS: This scoping review followed recognized frameworks. We conducted an electronic literature search on PubMed for studies published between January 2015 and May 2023. Inclusion criteria were studies reporting physical therapy telerehabilitation interventions for patients with cancer, including randomized and nonrandomized controlled trials, feasibility studies, and usability studies. In total, 21 studies met the criteria and were included in the final review. RESULTS: Our search yielded 37 papers, with 21 included in the final review. Randomized controlled trials comprised 47% (n=10) of the studies, with feasibility studies at 33% (n=7) and usability studies at 19% (n=4). Sample sizes were typically 50 or fewer participants in 57% (n=12) of the reports. Participants were generally aged 65 years or younger (n=17, 81%), with a balanced gender distribution. Organ-specific cancers were the focus of 66% (n=14) of the papers, while 28% (n=6) included patients who were in the posttreatment period. Web-based systems were the most used technology (n=13, 61%), followed by phone call or SMS text messaging-based systems (n=9, 42%) and mobile apps (n=5, 23%). Exercise programs were mainly home based (n=19, 90%) and included aerobic (n=19, 90%), resistance (n=13, 61%), and flexibility training (n=7, 33%). Outcomes included improvements in functional capacity, cognitive functioning, and QoL (n=10, 47%); reductions in pain and hospital length of stay; and enhancements in fatigue, physical and emotional well-being, and anxiety. Positive effects on feasibility (n=3, 14%), acceptability (n=8, 38%), and cost-effectiveness (n=2, 9%) were also noted. Functional outcomes were frequently assessed (n=19, 71%) with tools like the 6-minute walk test and grip strength tests. CONCLUSIONS: Telerehabilitation for patients with cancer is beneficial and feasible, with diverse approaches in study design, technologies, exercises, and outcomes. Future research should focus on developing standardized methodologies, incorporating objective measures, and exploring emerging technologies like virtual reality, wearable or noncontact sensors, and artificial intelligence to optimize telerehabilitation interventions. Addressing these areas can enhance clinical practice and improve outcomes for remote rehabilitation with patients.

Extraction of Substance Use Information From Clinical Notes: Generative Pretrained Transformer-Based Investigation.

BACKGROUND: Understanding the multifaceted nature of health outcomes requires a comprehensive examination of the social, economic, and environmental determinants that shape individual well-being. Among these determinants, behavioral factors play a crucial role, particularly the consumption patterns of psychoactive substances, which have important implications on public health. The Global Burden of Disease Study shows a growing impact in disability-adjusted life years due to substance use. The successful identification of patients' substance use information equips clinical care teams to address substance-related issues more effectively, enabling targeted support and ultimately improving patient outcomes. OBJECTIVE: Traditional natural language processing methods face limitations in accurately parsing diverse clinical language associated with substance use. Large language models offer promise in overcoming these challenges by adapting to diverse language patterns. This study investigates the application of the generative pretrained transformer (GPT) model in specific GPT-3.5 for extracting tobacco, alcohol, and substance use information from patient discharge summaries in zero-shot and few-shot learning settings. This study contributes to the evolving landscape of health care informatics by showcasing the potential of advanced language models in extracting nuanced information critical for enhancing patient care. METHODS: The main data source for analysis in this paper is Medical Information Mart for Intensive Care III data set. Among all notes in this data set, we focused on discharge summaries. Prompt engineering was undertaken, involving an iterative exploration of diverse prompts. Leveraging carefully curated examples and refined prompts, we investigate the model's proficiency through zero-shot as well as few-shot prompting strategies. RESULTS: Results show GPT's varying effectiveness in identifying mentions of tobacco, alcohol, and substance use across learning scenarios. Zero-shot learning showed high accuracy in identifying substance use, whereas few-shot learning reduced accuracy but improved in identifying substance use status, enhancing recall and F1-score at the expense of lower precision. CONCLUSIONS: Excellence of zero-shot learning in precisely extracting text span mentioning substance use demonstrates its effectiveness in situations in which comprehensive recall is important. Conversely, few-shot learning offers advantages when accurately determining the status of substance use is the primary focus, even if it involves a trade-off in precision. The results contribute to enhancement of early detection and intervention strategies, tailor treatment plans with greater precision, and ultimately, contribute to a holistic understanding of patient health profiles. By integrating these artificial intelligence-driven methods into electronic health record systems, clinicians can gain immediate, comprehensive insights into substance use that results in shaping interventions that are not only timely but also more personalized and effective.

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.

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.

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.

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.

Gene Expression Markers of Prognostic Importance for Prostate Cancer Risk in Patients with Benign Prostate Hyperplasia.

Comparative analyses utilizing publicly available big data have the potential to generate novel hypotheses and knowledge. However, this approach is underutilized in the realm of cancer research, particularly for prostate cancer. While the general progression of prostate cancer is now well understood, how individual cell types transition from healthy, to pre-cancerous, to cancerous cell types, remains to be further elucidated. To address this, we re-analyzed two publicly available single-cell RNA-seq datasets of prostate cancer and benign prostate hyperplasia cell types. The differential expression analysis of 15,505 epithelial cell profiles across 18,638 genes revealed 791 genes that were up regulated in prostate cancer epithelial cells. Here we report six markers that show significant upregulation in prostate cancer cells relative to BPH epithelial cells: HPN (5.62X), RAC3 (3.51X), CD24 (2.18X), HOXC6 (1.77X), AGR2 (1.71X), and IGFBP2 (1.28X). In particular, the significant differential expression of AGR2 further supports its clinical relevance in supplementing prostate-specific antigen screening for detecting prostate cancer. These findings have the potential to further advance our knowledge of genes governing the development of cancer in prostate epithelial cells. Clinical Relevance- Our results establish the importance of 6 prostate cancer markers (HPN, RAC3, CD24, HOXC6, AGR2, and IGFBP3) in distinguishing between prostate cancer epithelial cells and benign prostate hyperplasia epithelial cells.

Telerehabilitation for Patients with Cancer: A Scoping Review.

This study was aimed at identifying how telemedicine is used for rehabilitation of patients with cancer. An electronic literature search was conducted using the PubMed database covering January 2015 to October 2020. To be included in the review, studies had to report telerehabilitation interventions for patients with cancer. Randomized controlled trials, quasi-experimental studies, as well as feasibility and usability studies were included, and reviews were excluded. Overall, 33 eligible studies were found but only 22 were considered for inclusion. After a detailed analysis, 16 studies were included. Most of the studies concluded that telehealth systems supporting physical exercise were effective to improve function, quality of life, pain, satisfaction and muscle strength. Limitations in most of the studies included non-randomized design and limited number of subjects. We conluded that more studies are needed for stronger evidence of this type of treatment and to facilitate clinical practice in this field.

Using EHR Data to Identify Social Determinants of Health Affecting Disparities in Cancer Survival.

The aim of this pilot study was to identify social determinants of health (SDH) that affect disparities in cancer survival. A limited dataset was generated by querying electronic medical records (EHR) from an academic medical center in New York City between January 2003 and November 2020. Socio-demographic characteristics that affected survival in 22,096 cancer patients were analyzed using descriptive statistics and logistic regression analyses. Two subsets of adult patients were identified: patients who were deceased less than 1 year after diagnosis and patients who survived over 5 years after diagnosis. Percentage of individuals with short survival in Blacks and Whites was respectively 41.4% and 22.2% for lung cancer, 9.8% and 7.1% for colorectal cancer, 2.9% and 0.7% for breast cancer, 6.8% and 4.0% for multiple myeloma, and 1.4% and 0.8% for prostate cancer. Logistic regression identified SDH factors increasing likelihood of shorter survival that included older age, and being male, Black or Hispanic. We concluded that further analysis of a broader spectrum of SDH factors is warranted.

Identifying Determinants of Disparities in Lung Cancer Survival Rates from Electronic Health Record Data.

The goal of this pilot study was to identify significant factors that affect disparities in lung cancer survival. A de-identified dataset was generated by querying electronic health records (EHR) from an academic medical center in New York City between January 2003 and November 2020. Socio-demographic characteristics, cancer stage, and genetic profile were analyzed using logistic regression. Two subsets of adult patients were identified: patients who were deceased less than 1 year after diagnosis and patients who survived over 5 years after diagnosis. Male, Black and Hispanic patients and those who were diagnosed in later stages were the people most susceptible to a shorter length of survival after cancer diagnoses. In addition, we identified three genetic oncodrivers (KRAS, EGFR and TP53) which were highly correlated with the length of survival after lung cancer diagnoses and their distribution was associated with race. We concluded that EHR data provide important insights on cancer survival disparities.

Machine Learning Approaches for Early Prostate Cancer Prediction Based on Healthcare Utilization Patterns.

The goal of this study was to build a machine learning model for early prostate cancer prediction based on healthcare utilization patterns. We examined the frequency and pattern changes of healthcare utilization in 2916 prostate cancer patients 3 years prior to their prostate cancer diagnoses and explored several supervised machine learning techniques to predict possible prostate cancer diagnosis. Analysis of patients' medical activities between 1 year and 2 years prior to their prostate cancer diagnoses using XGBoost model provided the best prediction accuracy with high F1 score (0.9) and AUC score (0.73). These pilot results indicated that application of machine learning to healthcare utilization patterns may result in early identification of prostate cancer diagnosis.

Utilizing Shared Big Data to Identify Liver Cancer Dedifferentiation Markers.

Big data reanalysis has the potential to generate novel comparative analyses which aim to generate novel hypotheses and knowledge. However, this approach is underutilized in the realm of cancer research, particularly for cancer stem cells (CSCs). CSCs are a rare subset of tumor cells, which dedifferentiate from healthy adult cells, and have the potential for self-renewal and treatment resistance. This analysis utilizes two publically available single-cell RNA-seq datasets of liver cancer and adult liver cell types to demonstrate how reanalysis of big data can lead to valuable new discoveries. We identify 519 differentially expressed genes between liver CSCs and healthy liver cell types. Here we report the potential novel liver CSC dedifferentiation factor, Msh Homeobox 2, which was significantly upregulated in liver CSCs by 1.36 fold (p-value < 1E-10). These findings have the potential to further advance our knowledge of genes governing the formation of CSCs.

Usability Inspection of a Mobile Cancer Telerehabilitation System.

Cancer rehabilitation (CR) has been shown to address physical impairments and progressive disablement experienced by patients with cancer. Access to CR is limited by multiple barriers. Previous studies demonstrated that telemedicine approaches may facilitate access to rehabilitation services however usability and acceptance of cancer telerehabilitation has not been systematically evaluated. This goal of this study was usability inspection of a mobile cancer telerehabilitation system based on cognitive walkthroughs and heuristics evaluations, supplemented by surveys to capture health literacy, patient engagement, and acceptance. The System Usability Scale provided a standardized reference for usability and satisfaction, and the mean result of 83.2±16.9 placed this mobile interface in the top 10th percentile. A semi-structured qualitative interview provided actionable feedback, which informed the next iteration of this project. Overall, this mobile telerehabilitation system was considered by cancer patients easy-to-use, satisfying, and engaging with 91% of participants planning to use it in the future.

A Platform for Integrating and Sharing Cancer Stem Cell Data.

Advancements in cancer research and treatment have highlighted the need for standardization and sharing of cancer stem cell (CSC) data to facilitate research transparency and to promote collaboration within the scientific community. Although previous applications have attempted to gather and disseminate these data, currently no platform organizes the heterogeneous CSC information into a harmonized project-based framework. The aim of our platform, ReMeDy, is to provide an intelligent informatics solution integrating diverse CSC characteristics, outcomes information, and omics data across clinical, preclinical and in vitro studies. These heterogeneous data streams are organized within a multi-modular framework, subjected to a stringent validation by using standardized ontologies, and stored in a searchable format. To test usefulness of our approach for capturing diverse data related to CSCs, we integrated data from 52 publicly-available CSC projects. We validated the robustness of the platform, by efficiently organizing diverse data elements, and demonstrated its potential for promoting future knowledge discovery driven by aggregation of published data. Next steps include expanding number of uploaded CSC projects and developing additional data visualization tools. The platform is accessible through https://remedy.mssm.edu/.

Intelligent Integrative Platform for Sharing Heterogenuous Stem Cell Research Data.

Recent studies demonstrated that comparative analysis of stem cell research data sets originating from multiple studies can produce new information and help with hypotheses generation. Effective approaches for incorporating multiple diverse heterogeneous data sets collected from stem cell projects into a harmonized project-based framework have been lacking. Here, we provide an intelligent informatics solution for integrating comprehensive characterizations of stem cells with research subject and project outcome information. Our platform is the first to seamlessly integrate information from iPSCs and cancer stem cell research into a single platform, using a multi-modular common data element framework. Heterogeneous data is validated using predefined ontologies and stored in a relational database, to ensure data quality and ease of access. Testing was performed using 103 published, publicly-available iPSC and cancer stem cell projects conducted in clinical, preclinical and in vitro evaluations. We validated the robustness of the platform, by seamlessly harmonizing diverse data elements, and demonstrated its potential for knowledge generation through the aggregation and harmonization of data. Future aims of this project include increasing the database size using crowdsourcing and natural language processing functionalities. The platform is publicly available at https://remedy.mssm.edu/.

Identification of Liver Cancer Stem Cell Stemness Markers Using a Comparative Analysis of Public Data Sets.

PURPOSE: Comparative reanalysis of single-cell transcriptomics data to gain useful novel insights into cancer stem cells (CSCs), which are a rare subset of cells within tumors, characterized by their capability to self-renew and differentiate, and their role in tumorigenicity. PATIENTS AND METHODS: This project utilized publically available liver single-cell RNA-seq datasets of liver cancer and liver progenitor cell types to demonstrate how shared large amounts of data can generate new and valuable information. The data were analyzed using EdgeR differential expression analysis, with focus on a set of 34 known stemness markers. RESULTS: We showed that the expression of stemness markers SOX9, KRT19, KRT7, and CD24, and Yamanaka factors Oct4 and SOX2 in CSCs was significantly elevated relative to progenitor cell types, potentially explaining their increased differentiation and replication potential. CONCLUSION: These results help to further document the complementary expression changes that give CSCs their distinct phenotypic profile. Our findings have potential significance to advance our knowledge of the important genes relevant to CSCs.

Comparative Analysis of Public Data Sets to Identify Stemness Markers That Differentiate Liver Cancer Stem Cells.

Cancer stem cells (CSCs) represent an important field in translational medicine for generating novel cancer treatments. To identify important stemness markers in liver CSCs that potentially explain their resistance to treatment, we analyzed 10865 single-cell RNA-seq samples across 42684 coding and non-coding genes. Our results show that CSCs have significantly increased expression of two Yamanaka factors (Oct4, 2.14X and SOX2, 1.13X) and three stemness factors (CD44, 3.25X; KRT7, 2.2X; SOX9, 1.71X), relative to liver progenitor cells. Our study demonstrates the potential power of harnessing shared big data for driving translational medicine for novel hypothesis generation.

Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City.

OBJECTIVE: The COVID-19 pandemic is a global public health crisis, with over 33 million cases and 999 000 deaths worldwide. Data are needed regarding the clinical course of hospitalised patients, particularly in the USA. We aimed to compare clinical characteristic of patients with COVID-19 who had in-hospital mortality with those who were discharged alive. DESIGN: Demographic, clinical and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed COVID-19 between 27 February and 2 April 2020 were identified through institutional electronic health records. We performed a retrospective comparative analysis of patients who had in-hospital mortality or were discharged alive. SETTING: All patients were admitted to the Mount Sinai Health System, a large quaternary care urban hospital system. PARTICIPANTS: Participants over the age of 18 years were included. PRIMARY OUTCOMES: We investigated in-hospital mortality during the study period. RESULTS: A total of 2199 patients with COVID-19 were hospitalised during the study period. As of 2 April, 1121 (51%) patients remained hospitalised, and 1078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 µg/mL, C reactive protein was 162 mg/L and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 µg/mL, C reactive protein was 79 mg/L and procalcitonin was 0.09 ng/mL. CONCLUSIONS: In our cohort of hospitalised patients, requirement of intensive care and mortality were high. Patients who died typically had more pre-existing conditions and greater perturbations in inflammatory markers as compared with those who were discharged.

Mining Electronic Dental Records to Identify Dry Socket Risk Factors.

The goal of this study was to investigate risk factors for developing dry sockets in patients after dental extractions. Data were collected directly from electronic dental records (EDR) and were utilized for selecting dry socket cases and controls to conduct a nested case-control study. Case-control matching was based on sex, age range, maxilla-mandible location, and anterior-posterior location. From 83 self-reported health survey questions, 7 questions were found to have predictive potential based on a significant chi-squared test. Stepwise conditional logistic regression showed a statistically significant association between the development of dry socket and a history of serious illness (OR=1.4; 95% CI:1.02-1.95), cancer (OR=2.6; 95% CI:1.13-5.83), and frequent mouth sores (OR=1.9; 95% CI:1.09-3.33). These results corroborated previous reports on potential involvement of impaired immune response in dry socket development. EDR may be an important source for uncovering predictive factors that play a role in prevention and management of oral health.

Peri-implantitis prevalence, incidence rate, and risk factors: A study of electronic health records at a U.S. dental school.

OBJECTIVES: We assessed peri-implantitis prevalence, incidence rate, and associated risk factors by analyzing electronic oral health records (EHRs) in an educational institution. METHODS: We used a validated reference cohort comprising all patients receiving dental implants over a 3.5-year period (2,127 patients and 6,129 implants). Electronic oral health records of a random 10% subset were examined for an additional follow-up of ≥2.5 years to assess the presence of radiographic bone loss, defined as >2 mm longitudinal increase in the distance between the implant shoulder and the supporting peri-implant bone level (PBL) between time of placement and follow-up. "Intact" implants had no or ≤2 mm PBL increase from baseline. Electronic oral health record notes were reviewed to corroborate a definitive peri-implantitis diagnosis at implants with progressive bone loss. A nested case-control analysis of peri-implantitis-affected implants randomly matched by age with "intact" implants from peri-implantitis-free individuals identified putative risk factors. RESULTS: The prevalence of peri-implantitis over an average follow-up of 2 years was 34% on the patient level and 21% on the implant level. Corresponding incidence rates were 0.16 and 0.10 per patient-year and implant-year, respectively. Multiple conditional logistic regression identified ill-fitting fixed prosthesis (OR = 5.9; 95% CI: 1.6-21.1), cement-retained prosthesis (OR = 4.5; 2.1-9.5), and radiographic evidence of periodontitis (OR = 3.6; 1.7-7.6) as statistically associated with peri-implantitis. Implant location in the mandible (OR = 0.02; 0.003-0.2) and use of antibiotics in conjunction with implant surgery (OR = 0.19; 0.05-0.7) emerged as protective exposures. CONCLUSIONS: Approximately 1/3 of the patients and 1/5 of all implants experienced peri-implantitis. Ill-fitting/ill-designed fixed and cement-retained restorations, and history of periodontitis emerged as the principal risk factors for peri-implantitis.