Real-world evaluation of imipenem/cilastatin/relebactam across US medical centres.

We assessed 160 patients who received imipenem/cilastatin/relebactam for ≥2 days. At treatment initiation, the median Charlson Comorbidity Index was 5, 45% were in the intensive care unit, and 19% required vasopressor support. The in-hospital mortality rate was 24%. These data advance our understanding of real-world indications and outcomes of imipenem/cilastatin/relebactam use.

Health-related impact of illness associated with excessive daytime sleepiness in patients with obstructive sleep apnea.

OBJECTIVES: This real-world study aimed to characterize the impact of illness of excessive daytime sleepiness (EDS) in patients with obstructive sleep apnea (OSA) who are adherent to continuous positive airway pressure (CPAP). METHODS: This cross-sectional study surveyed participants in Evidation Health's Achievement app (November 2020-January 2021), a mobile consumer platform that encourages users to develop healthy habits and provides incentives to participate in research. Participants were US-resident adults who self-reported a physician diagnosis of OSA and adherence to CPAP (≥4 hours/night, ≥5 nights/week) for≥6 months. The survey included the Functional Outcomes of Sleep Questionnaire-Short Version (FOSQ-10), Epworth Sleepiness Scale (ESS), and questions regarding comorbidities, CPAP use, caffeine consumption, and physical activity. EDS was defined as ESS score >10. There were more female than male participants; therefore, data were reported separately for females/males. RESULTS: In total, 476 participants (female, n = 283 [59%]; mean [SD] age, 49.7 [10.8] years; obese, 74.4%) completed the survey; 209 had EDS (mean [SD] ESS, 13.8 [2.5]) and 267 did not (mean [SD] ESS, 6.3 [2.5]). Self-reported duration of CPAP use was consistent between the EDS/no EDS cohorts, with most participants using CPAP for 7 to 9 hours/night, 7 nights/week. Participants with EDS commonly reported anxiety ([EDS/no EDS] males: 31.5%/20.0%; females: 53.7%/39.5%), depression (males: 35.6%/24.2%; females: 55.9%/44.9%), and insomnia (males: 19.2%/6.7%; females: 25.7%/12.9%) and showed impairment on the FOSQ-10 ([EDS/no EDS] males: 80.8%/35.0%; females: 91.9%/53.1%). Participants with EDS reported that sleepiness 'very often' prevented physical activity and influenced dietary choices. CONCLUSION: EDS influences choices related to physical activity, caffeine consumption, and diet in patients who are adherent with CPAP. More research is needed to understand the association between EDS and choices of CPAP-adherent patients. Future research should explore the health-related consequences of residual EDS associated with OSA and whether they can be mitigated by improving EDS.

Approximately 1 billion people worldwide have a sleep disorder called obstructive sleep apnea (OSA). People with OSA experience a blockage in their upper airway during sleep, which can lead to snoring, gasping for air, difficulty breathing, and disturbed sleep. As a result, 50%­80% of people with OSA experience excessive daytime sleepiness (EDS) ­ the irresistible need to sleep during the daytime. Many people (9%­22%) who are treated for OSA using continuous positive airway pressure (CPAP) still experience EDS. Previous studies have described the negative impact EDS has on people, such as decreased work productivity and increased risk of motor vehicle accidents. However, it is unclear if EDS impacts health, behavioral choices, and lifestyle in the real world. This study surveyed people with OSA and collected their Fitbit data using Evidation's Achievement app. People with OSA and EDS reported having anxiety, depression, insomnia, and other health problems more often than people with OSA without EDS. Also, people with OSA and EDS reported having more impairments in daytime functioning than people with OSA without EDS. Furthermore, people with OSA and EDS reported that sleepiness 'very often' prevented them from engaging in physical activity. Women with OSA and EDS said that sleepiness 'very often' influenced their food choices. People with OSA and EDS also used caffeine and 'sometimes' used physical activity to stay awake during the daytime. Findings from this study show people with OSA alter their lifestyle and behavioral choices because of EDS.

A Prospective Real-World Study Exploring Associations Between Passively Collected Tracker Data and Headache Burden Among Individuals with Tension-Type Headache and Migraine.

INTRODUCTION: Prevalence and burden of headache disorders in real-world settings is relatively unstudied. We explored the associations between passively collected activity data, headache burden, and quality of life in headache sufferers. METHODS: Data from wearable activity tracking devices and daily short questionnaires were collected over 12 weeks to assess occurrence of headache, activity, quality of life and self-rated health. Variables were analyzed using a series of mixed-effects models and stratified based on headache type. Multiple linear and logistic regressions were used to analyze treatment preferences. RESULTS: Behaviors inferred from activity tracker data suggested that individuals slept more, had reduced physical activity, and had lower maximum heart rate on days with headache. As headache-specific impact on quality of life increased, activity and maximum heart rate decreased and sleep increased. Headache days with higher self-rated health were associated with less napping, higher step count and maximum heart rate, correlating with increased activity. Migraineurs experienced greater burden in everyday life compared with tension-type headache sufferers. CONCLUSION: This study adds to existing evidence that activity trackers can be used to quantify headache burden in real-world settings and aid in understanding symptom management.

Big data hurdles in precision medicine and precision public health.

BACKGROUND: Nowadays, trendy research in biomedical sciences juxtaposes the term 'precision' to medicine and public health with companion words like big data, data science, and deep learning. Technological advancements permit the collection and merging of large heterogeneous datasets from different sources, from genome sequences to social media posts or from electronic health records to wearables. Additionally, complex algorithms supported by high-performance computing allow one to transform these large datasets into knowledge. Despite such progress, many barriers still exist against achieving precision medicine and precision public health interventions for the benefit of the individual and the population. MAIN BODY: The present work focuses on analyzing both the technical and societal hurdles related to the development of prediction models of health risks, diagnoses and outcomes from integrated biomedical databases. Methodological challenges that need to be addressed include improving semantics of study designs: medical record data are inherently biased, and even the most advanced deep learning's denoising autoencoders cannot overcome the bias if not handled a priori by design. Societal challenges to face include evaluation of ethically actionable risk factors at the individual and population level; for instance, usage of gender, race, or ethnicity as risk modifiers, not as biological variables, could be replaced by modifiable environmental proxies such as lifestyle and dietary habits, household income, or access to educational resources. CONCLUSIONS: Data science for precision medicine and public health warrants an informatics-oriented formalization of the study design and interoperability throughout all levels of the knowledge inference process, from the research semantics, to model development, and ultimately to implementation.