Standard blood laboratory values as a clinical support tool to distinguish between SARS-CoV-2 positive and negative patients

Standard blood laboratory parameters may have diagnostic potential, if polymerase-chain-reaction (PCR) tests are not available on time. We evaluated standard blood laboratory parameters of 655 COVID-19 patients suspected to be infected with SARS-CoV-2, who underwent PCR testing in one of five hospitals in Vienna, Austria. We compared laboratory parameters, clinical characteristics, and outcomes between positive and negative PCR-tested patients and evaluated the ability of those parameters to distinguish between groups. Of the 590 patients (20–100 years, 276 females and 314 males), 208 were PCR-positive. Positive compared to negative PCR-tested patients had significantly lower levels of leukocytes, neutrophils, basophils, eosinophils, lymphocytes, neutrophil-to-lymphocyte ratio, monocytes, and thrombocytes; while significantly higher levels were detected with erythrocytes, hemoglobin, hematocrit, C-reactive-protein, ferritin, activated-partial-thromboplastin-time, alanine-aminotransferase, aspartate-aminotransferase, lipase, creatine-kinase, and lactate-dehydrogenase. From all blood parameters, eosinophils, ferritin, leukocytes, and erythrocytes showed the highest ability to distinguish between COVID-19 positive and negative patients (area-under-curve, AUC: 72.3–79.4%). The AUC of our model was 0.915 (95% confidence intervals, 0.876–0.955). Leukopenia, eosinopenia, elevated erythrocytes, and hemoglobin were among the strongest markers regarding accuracy, sensitivity, specificity, positive and negative predictive value, positive and negative likelihood ratio, and post-test probabilities. Our findings suggest that especially leukopenia, eosinopenia, and elevated hemoglobin are helpful to distinguish between COVID-19 positive and negative tested patients.

Standard Blood Laboratory Values As A Clinical Support Tool to Distinguish Between SARS-CoV-2 Positive and Negative Patients

Standard blood laboratory parameters may have diagnostic potential, if polymerase-chain-reaction (PCR) tests are not available on time. We evaluated standard blood laboratory parameters of 655 COVID-19 patients suspected to be infected with SARS-CoV-2, who underwent PCR testing in one of five hospitals in Vienna, Austria. We compared laboratory parameters, clinical characteristics, and outcomes between positive and negative PCR-tested patients and evaluated the ability of those parameters to distinguish between groups. Of the 590 patients (20-100years, 276 females and 314 males), 208 were PCR-positive. Positive compared to negative PCR-tested patients had significantly lower levels of leukocytes, neutrophils, basophils, eosinophils, lymphocytes, neutrophil-to-lymphocyte ratio, monocytes, and thrombocytes; while significantly higher levels were detected with erythrocytes, hemoglobin, hematocrit, C-reactive-protein, ferritin, activated-partial-thromboplastin-time, alanine-aminotransferase, aspartate-aminotransferase, lipase, creatine-kinase, and lactate-dehydrogenase. From all blood parameters, eosinophils, ferritin, leukocytes, and erythrocytes showed the highest ability to distinguish between COVID-19 positive and negative patients (area-under-curve: 72.3-79.4%). Leukopenia, eosinopenia, elevated erythrocytes, and hemoglobin were among the strongest markers regarding accuracy, sensitivity, specificity, positive and negative predictive value, positive and negative likelihood ratio, and post-test probabilities. Our findings suggest that especially leukopenia, eosinopenia, as well as elevated erythrocytes, hemoglobin, and ferritin are helpful to distinguish between COVID-19 positive and negative tested patients.

Dynamic support database clinical support tool: inter-rater reliability

Purpose The dynamic support database (DSD) clinical support tool structures the risk of admission rating for individuals with intellectual disabilities. This study aims to investigate inter-rater reliability between multi-disciplinary health care professionals within the North West of England. Design/methodology/approach A small-scale quantitative study investigated reliability between raters on the DSD clinical support tool. A data set of 60 rating tools for 30 individuals was used. Descriptive statistics and Kappa coefficient explored agreement. Findings The DSD clinical support tool was found to have strong inter-rater reliability between individual items and the differences between individual scores were spread suggesting variance found could not be attributed to specific questions. Strong inter-rater reliability was found in the overall ratings. Research limitations/implications Results suggest the DSD clinical support tool provides stratification for risk of admission ratings independent of who completes it. Future studies could investigate inter-rater reliability between organisations, i.e. health and social care professionals, and use a larger data sample to ensure generalisability. Replication of the study within child and adolescent services using the children’s DSD clinical support tool is also recommended. Originality/value The DSD clinical support tool has been implemented within the child and adult intellectual disability services across the North West. As more teams across England consider its implementation, the study provides reassurance that coding agreement is high, allowing for stratification for risk of admission independent of the rater.

Stakeholder Engagement in Developing an Electronic Clinical Support Tool for Tobacco Prevention in Adolescent Primary Care

Following guideline recommendations to promote tobacco prevention in adolescent primary care, we developed a patient-facing clinical support tool. The electronic tool screens patients for use and susceptibility to conventional and alternative tobacco products, and promotes patient–provider communication. The purpose of this paper is to describe the iterative stakeholder engagement process used in the development of the tool. During the pre-testing phase, we consulted with scientists, methodologists, clinicians, and Citizen Scientists. Throughout the development phase, we engaged providers from three clinics in focus groups. Usability testing was conducted via in-depth, cognitive interviewing of adolescent patients. Citizen Scientists (n = 7) played a critical role in the final selection of educational content and interviewer training by participating in mock-up patient interviews. Cognitive interviews with patients (n = 16) ensured that systems were in place for the feasibility trial and assessed ease of navigation. Focus group participants (n = 24) offered recommendations for integrating the tool into clinical workflow and input on acceptability and appropriateness, and anticipated barriers and facilitators for adoption and feasibility. Engaging key stakeholders to discuss implementation outcomes throughout the implementation process can improve the quality, applicability, and relevance of the research, and enhance implementation success.