Review finds core outcome set uptake in new studies and systematic reviews needs improvement.

OBJECTIVES: To review evidence about the uptake of core outcome sets (COS). A COS is an agreed standardized set of outcomes that should be measured and reported, as a minimum, in all clinical trials in a specific area of health or healthcare. STUDY DESIGN AND SETTING: This article provides an analysis of what is known about the uptake of COS in research. Similarities between COS and outcomes recommended by stakeholders in the evidence ecosystem is reviewed and actions taken by them to facilitate COS uptake described. RESULTS: COS uptake is low in most research areas. Common facilitators relate to trialist awareness and understanding. Common barriers were not including in the development process all specialties that might use the COS and the lack of recommendations for how to measure the outcomes. Increasingly, COS developers are considering strategies for promoting uptake earlier in the process, including actions beyond traditional dissemination approaches. An overlap between COS and outcomes in regulatory documents and health technology assessments is good. An increasing number and variety of organizations are recommending COS be considered. CONCLUSION: We suggest actions for various stakeholders for improving COS uptake. Research is needed to assess the impact of these actions to identify effective evidence-based strategies.

Impact of the COVID-19 pandemic on eye strain and dry eye symptoms.

PURPOSE: Among adult individuals with dry eye, assess the self-reported impact of the COVID-19 pandemic on (1) dry eye-related visual function, (2) reading efficiency, and (3) dry eye treatments used. METHODS: In June-July 2020, we conducted an online survey of adults with dry eye who spent at least somewhat more time at home during the pandemic than before. Consistent with TFOS DEWS II guidelines, we categorized respondents into mild, moderate, or severe dry eye based on treatment usage. RESULTS: We included 388 respondents: 97 respondents (25%) with mild, 80 (21%) with moderate, and 211 (54%) with severe dry eye. In all three groups, screen/reading time generally doubled during the pandemic. Reduced work-related efficiency was noted by a considerable proportion of respondents (moderate dry eye: 51%, mild: 39%, and severe: 38%). Compared with respondents with mild dry eye, respondents with moderate dry eye were considerably more likely to note worsening symptoms: eye pain (OR = 2.57, 95% CI 1.22-5.41), headache from eye symptoms (OR = 2.34, 95% CI 1.11-4.90), and difficulty concentrating because of eye symptoms (OR = 2.79, 95% CI 1.37-5.66). Respondents with moderate dry eye with Sjögren's syndrome were most likely to note these. Respondents with severe dry eye were more likely than respondents with mild dry eye to report losing access to dry eye-related treatments (OR = 2.62, 95% CI 1.36-5.03). CONCLUSIONS: The COVID-19 pandemic-related eye strain may be impacting symptoms, performance, and ultimately employment, especially for those with moderate dry eye. This may be compounding the already-high dry eye-related societal burden.

Development and validation of a predictive model for critical illness in adult patients requiring hospitalization for COVID-19.

BACKGROUND: Identifying factors that can predict severe disease in patients needing hospitalization for COVID-19 is crucial for early recognition of patients at greatest risk. OBJECTIVE: (1) Identify factors predicting intensive care unit (ICU) transfer and (2) develop a simple calculator for clinicians managing patients hospitalized with COVID-19. METHODS: A total of 2,685 patients with laboratory-confirmed COVID-19 admitted to a large metropolitan health system in Georgia, USA between March and July 2020 were included in the study. Seventy-five percent of patients were included in the training dataset (admitted March 1 to July 10). Through multivariable logistic regression, we developed a prediction model (probability score) for ICU transfer. Then, we validated the model by estimating its performance accuracy (area under the curve [AUC]) using data from the remaining 25% of patients (admitted July 11 to July 31). RESULTS: We included 2,014 and 671 patients in the training and validation datasets, respectively. Diabetes mellitus, coronary artery disease, chronic kidney disease, serum C-reactive protein, and serum lactate dehydrogenase were identified as significant risk factors for ICU transfer, and a prediction model was developed. The AUC was 0.752 for the training dataset and 0.769 for the validation dataset. We developed a free, web-based calculator to facilitate use of the prediction model (https://icucovid19.shinyapps.io/ICUCOVID19/). CONCLUSION: Our validated, simple, and accessible prediction model and web-based calculator for ICU transfer may be useful in assisting healthcare providers in identifying hospitalized patients with COVID-19 who are at high risk for clinical deterioration. Triage of such patients for early aggressive treatment can impact clinical outcomes for this potentially deadly disease.