We conclude that predictive modeling can be used to benchmark the importance of different features on the models derived with data from different hospitals. This might help to optimize crucial processes in a specific hospital, even in other scenarios beyond Patient Blood Management.
While self-reported Coronavirus Disease 2019 (COVID-19) symptom checklists have been extensively used during the pandemic, they have not been sufficiently validated from a psychometric perspective. We, therefore, used advanced psychometric modelling to explore the construct validity and internal consistency of an online self-reported COVID-19 symptom checklist and suggested adaptations where necessary. Fit to the Rasch model was examined in a sample of 1638 Austrian citizens who completed the checklist on up to 20 days during a lockdown. The items’ fatigue’, ‘headache’ and ‘sneezing’ had the highest likelihood to be affirmed. The longitudinal application of the symptom checklist increased the fit to the Rasch model. The item ‘cough’ showed a significant misfit to the fundamental measurement model and an additional dependency to ‘dry cough/no sputum production’. Several personal factors, such as gender, age group, educational status, COVID-19 test status, comorbidities, immunosuppressive medication, pregnancy and pollen allergy led to systematic differences in the patterns of how symptoms were affirmed. Raw scores’ adjustments ranged from ±0.01 to ±0.25 on the metric scales (0 to 10). Except for some basic adaptations that increases the scale’s construct validity and internal consistency, the present analysis supports the combination of items. More accurate item wordings co-created with laypersons would lead to a common understanding of what is meant by a specific symptom. Adjustments for personal factors and comorbidities would allow for better clinical interpretations of self-reported symptom data.
Heart failure (HF) is a chronic disease with high hospitalizations rates and increased mortality. The six-minute walk test (6MWT) is a standardized method to assess the functional health of HF patients by measuring the distance walked in six minutes [1]. The improved 6MWT with new filter algorithms, based on the development of smartphone application [9], can be used everywhere with a good global navigation satellite system (GNSS) signal. This paper tests multiple filtering approaches to reduce the measured distance error in the application.We were able to decrease the average relative error of the application from 4.5% to 2.0%. Additionally, the number of experiments with relative error over our chosen clinically significant difference of 5% is reduced by 90%. Keeping these results in mind, the system can be considered clinically acceptable.
The demand for extended care for people suffering from heart failure is omnipresent. Wearables providing continuous heart rate measurement through optical sensors are of great interest due to their ease of use without the need for medical staff and their low cost. In this study, seven wearables were tested in fifteen measurement runs, with a duration of fourteen-hour each, and compared to a reference sensor. By calculating the Pearson correlation and the root mean square error, as well as the graphical representation by a Bland Altman plot, it was found that these wearables lack sufficient accuracy and may not be suitable for medical purposes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.