Background: Panic attacks are an impairing mental health problem that affects about one in 10 US adults every year. Current DSM criteria describe panic attacks as unexpected, occurring without warning or triggering events. The unexpected nature of panic attacks not only leads to increased anxiety for the individual but has also made panic attacks particularly challenging to study. However, recent evidence suggests that individuals who experience such attacks could identify attack triggers. Objective: We aimed to explore both retrospectively and prospectively, qualitative, and quantitative factors associated with the onset of panic attacks. Method: We remotely recruited a diverse sample of 87 individuals who regularly experienced panic attacks from 30 states in the US. Participants responded to daily questions relating to their panic attacks and wellness behaviors each day for 28 days. We also considered daily community level factors captured by the Hedonometer, a metric which estimates population-level happiness daily using a random 10% of all public tweets. Results: Consistent with our prior work, most participants (95%) were able to retrospectively identify a trigger for their attack. Worse individual mood was associated with greater likelihood of experiencing a same-day panic attack over and above other individual wellness factors. Worse individually reported mood and state-based population level mood as indicated by the Hedonometer were associated with greater likelihood of next-day panic attack. Conclusions: These promising results suggest that individuals who experience panic attacks may be able to expect the unexpected. The importance of individual and state-based population level mood in panic attack risk could be used to ultimately inform future prevention and intervention efforts.

IntroductionThe number of new biomedical manuscripts published on important topics exceeds the capacity of single persons to read. Integration of literature is an even more elusive task. This article describes a pilot study of a scalable online system to integrate data from 1000 articles on COVID-19.MethodsArticles were imported from PubMed using the query ‘COVID-19’. The full text of articles reporting new data was obtained and the results extracted manually. An online software system was used to enter the results. Similar results were bundled using note fields in parent–child order. Each extracted result was linked to the source article. Each new data entry comprised at least four note fields: (1) result, (2) population or sample, (3) description of the result and (4) topic. Articles underwent iterative rounds of group review over remote sessions.ResultsScreening 4126 COVID-19 articles resulted in a selection of 1000 publications presenting new data. The results were extracted and manually entered in note fields. Integration from multiple publications was achieved by sharing parent note fields by child entries. The total number of extracted primary results was 12 209. The mean number of results per article was 15.1 (SD 12.0). The average number of parent note fields for each result note field was 6.8 (SD 1.4). The total number of all note fields was 28 809. Without sharing of parent note fields, there would have been a total of 94 986 note fields.ConclusionThis pilot study demonstrates the feasibility of a scalable online system to extract results from 1000 manuscripts. Using four types of notes to describe each result provided standardisation of data entry and information integration. There was substantial reduction in complexity and reduction in total note fields by sharing of parent note fields. We conclude that this system provides a method to scale up extraction of information on very large topics.

Panic attacks are an impairing mental health problem that impacts more than one out of every 10 adults in the United States (US). Clinical guidelines suggest panic attacks occur without warning and their unexpected nature worsens their impact on quality of life. Individuals who experience panic attacks would benefit from advance warning of when an attack is likely to occur so that appropriate steps could be taken to manage or prevent it. Our recent work suggests that an individual’s likelihood of experiencing a panic attack can be predicted by self-reported mood and community-level Twitter-derived mood the previous day. Prior work also suggests that physiological markers may indicate a pending panic attack. However, the ability of objective physiological, behavioral, and environmental measures to predict next-day panic attacks has not yet been explored. To address this question, we consider data from 38 individuals who regularly experienced panic attacks recruited from across the US. Participants responded to daily questions about their panic attacks for 28 days and provided access to data from their Apple Watches. Results indicate that objective measures of ambient noise (louder) and resting heart rate (higher) are related to the likelihood of experiencing a panic attack the next day. These preliminary results suggest, for the first time, that panic attacks may be predictable from data passively collected by consumer wearable devices, opening the door to improvements in how panic attacks are managed and to the development of new preventative interventions.Clinical RelevanceObjective data from consumer wearables may predict when an individual is at high risk for experiencing a next-day panic attack. This information could guide treatment decisions, help individuals manage their panic, and inform the development of new preventative interventions.

Statement of the problem: Unconscious bias and systemic racism is evident in published reports that describe persistent asymmetric outcomes in our entire health care system including oncology. Framework of the solution: There already is a very large set of publications that describe the extent and outcomes of health disparities. An extensive data set also describes mitigation strategies. Changing the outcomes includes policy changes within the health care system but also with regulatory agencies and the legislative branch of government. It is critical that these different systems are armed with the totality of available information in a manner that can be leveraged to improve the health care of all. We have developed a system of describing large sets of data manually extracted from published articles. These results are aggregated together independent of the framework of the manuscript so that similar outcomes can be placed side by side. This system can provide the necessary comprehensive data that is available today to begin to implement changes. Results to date: We have used COVID-19 publications as a prototype topic that has so many articles no single person can comprehend or manage. We extracted data from 1000 COVID-19 manuscripts that presented new data. This rendered 26,000 note fields arranged in a parent child relationship. The data base described 12,000 individual observations. A read only version is available at COVIDpublications.org. We are now applying this system to bias and stigma of the health care profession to persons who use drugs, and a demo of this project is available at (https://app.refbin.com/app/embed?m=1188). We have now established the rules to manually extract data from any clinical article that presents new data. This involves 4 types of note fields per observation arranged in parent child relationships. 1) The observation, 2) description of the observation, 3) the population, and 4) the topic. This system allows the observations from an unlimited number of studies to share parents. This results in about a 5-fold reduction in the total number of note fields. It also allows grouping of information so that a user can scan the data base and access the entirety of information without specifically knowing what they are looking for. Conclusions: We are expanding this data base bias and systemic racism of the health care system on persons with substance use disorder to include the broader range of patients. By capturing all of the data that is known we hope to influence implementation of improved health care to patients including those with cancer. These results will be presented in October. Citation Format: Shania Lunna, Samuel Gauthier, Stacia Richard, Rachel M Bombardier, David N Krag. Extraction and organization of all published results on impact of systemic racism on treatment of cancer patients [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-047.