Analysis of Population Differences in Digital Conversations About Cancer Clinical Trials: Advanced Data Mining and Extraction Study.

BACKGROUND: Racial and ethnic diversity in clinical trials for cancer treatment is essential for the development of treatments that are effective for all patients and for identifying potential differences in toxicity between different demographics. Mining of social media discussions about clinical trials has been used previously to identify patient barriers to enrollment in clinical trials; however, a comprehensive breakdown of sentiments and barriers by various racial and ethnic groups is lacking. OBJECTIVE: The aim of this study is to use an innovative methodology to analyze web-based conversations about cancer clinical trials and to identify and compare conversation topics, barriers, and sentiments between different racial and ethnic populations. METHODS: We analyzed 372,283 web-based conversations about cancer clinical trials, of which 179,339 (48.17%) of the discussions had identifiable race information about the individual posting the conversations. Using sophisticated machine learning software and analyses, we were able to identify key sentiments and feelings, topics of interest, and barriers to clinical trials across racial groups. The stage of treatment could also be identified in many of the discussions, allowing for a unique insight into how the sentiments and challenges of patients change throughout the treatment process for each racial group. RESULTS: We observed that only 4.01% (372,283/9,284,284) of cancer-related discussions referenced clinical trials. Within these discussions, topics of interest and identified clinical trial barriers discussed by all racial and ethnic groups throughout the treatment process included health care professional interactions, cost of care, fear, anxiety and lack of awareness, risks, treatment experiences, and the clinical trial enrollment process. Health care professional interactions, cost of care, and enrollment processes were notably discussed more frequently in minority populations. Other minor variations in the frequency of discussion topics between ethnic and racial groups throughout the treatment process were identified. CONCLUSIONS: This study demonstrates the power of digital search technology in health care research. The results are also valuable for identifying the ideal content and timing for the delivery of clinical trial information and resources for different racial and ethnic groups.

Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review.

ABSTRACT: Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer-Current Efforts; Collaborative Opportunities; and Moving Forward-Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders.

Standing Up Together.

Nurses represent the heart, soul, and future of the movement to end cancer. We are the ultimate collaborators. We interact across the spectrum of healthcare professionals, and we know the patients better than anyone. As the first line in the inpatient setting and the primary contact in the outpatient world, we often are the first to identify and document patient responses and the first to realize that something is off for a patient or family member. We do all of this while our patients face some of the biggest crises of their lives.

Development and validation of a clinical prediction rule for hospitalization due to pneumonia or influenza or death during influenza epidemics among community-dwelling elderly persons.

BACKGROUND: Uncertainties among health care providers and patients about the risk of serious influenza-associated complications and the potential benefits of vaccination may contribute to unsatisfactorily low influenza vaccination rates. To quantify the risk of serious outcomes (hospitalization due to pneumonia or influenza or death due to any cause) during influenza seasons, we developed a clinical prediction rule for the probability of hospitalization due to pneumonia or influenza or death among elderly persons. METHODS: We developed the clinical prediction rule using data from linked administrative databases in a cohort of 16,280 noninstitutionalized and unvaccinated elderly persons. Validation of the rule was conducted in 5 unvaccinated and 6 vaccinated cohorts, each consisting of >11,000 elderly members of 3 managed care organizations. Logistic regression was used to produce a prognostic score on the basis of the following predictors: age; sex; presence of pulmonary, cardiac, and renal disease; dementia or stroke and cancer; number of outpatient visits; and hospitalization due to pneumonia or influenza during the previous year. RESULTS: Reliability of the regression model was good (P=.65, by goodness-of-fit test), and it discriminated well between those who did and those who did not experience an outcome (area under the receiver-operating curve, 0.83; 95% confidence interval, 0.81-0.85). Validation revealed moderately lower but acceptable discriminating values (0.72-0.81). In the derivation cohort, the prognostic accuracy of the rule was high when a cutoff score for the upper 50th percentile was used: > or =10 of 1000 subjects with a score in the upper 50th percentile were predicted to have an outcome, and 89% of all outcomes were observed in this high-risk group, whereas <10 of 1000 subjects with a score in the lower 50th percentile were predicted to have an outcome, and only 11% of outcomes occurred in this group. Among unvaccinated subjects in the single-derivation cohort and the 11 validation cohorts combined, the outcome event rates were 35 events/1000 subjects in the higher-risk group and 6 events/1000 subjects in the lower-risk group. With vaccination, these event rates dropped by 15 events/1000 subjects and 2 events/1000 subjects, respectively. CONCLUSIONS: This prediction rule may be a useful tool to complement other age-based strategies, to further encourage vaccination, especially among those at the highest risk of serious complications due to influenza.

Influence of high-risk medical conditions on the effectiveness of influenza vaccination among elderly members of 3 large managed-care organizations.

This serial cohort study assessed the risk of hospitalization or death associated with influenza and the effectiveness of influenza vaccination among subgroups of elderly members of 3 managed-care organizations in the United States. Data on baseline characteristics and outcomes were obtained from computerized databases. A total of 122,974 (1996-1997 season) and 158,454 (1997-1998 season) persons were included in the cohorts. Among unvaccinated persons, hospitalizations for pneumonia/influenza or death occurred in 8.2 of 1000 healthy and 38.4 of 1000 high-risk persons in year 1, and in 8.2 of 1000 healthy and 29.3 of 1000 high-risk persons in year 2. After adjustments, vaccination was associated with a 48% reduction in the incidence of hospitalization or death (95% confidence interval [CI], 42-52) in year 1 and 31% (95% CI, 26-37) in year 2. Effectiveness estimates were statistically significant and generally consistent across the healthy and high-risk subgroups. The absolute risk reduction, however, was 2.4- to 4.7-fold higher among high-risk than among healthy elderly persons. All elderly individuals may substantially benefit from vaccination. However, the impact of influenza is greater in persons with high-risk medical conditions.