Evaluating clinical characteristics studies produced early in the Covid-19 pandemic: A systematic review

Characteristics and outcomes of COVID-19 patients admitted to hospital with and without respiratory symptoms

Background

COVID-19 is primarily known as a respiratory illness; however, many patients present to hospital without respiratory symptoms. The association between non-respiratory presentations of COVID-19 and outcomes remains unclear. We investigated risk factors and clinical outcomes in patients with no respiratory symptoms (NRS) and respiratory symptoms (RS) at hospital admission.

Methods

This study describes clinical features, physiological parameters, and outcomes of hospitalised COVID-19 patients, stratified by the presence or absence of respiratory symptoms at hospital admission. RS patients had one or more of: cough, shortness of breath, sore throat, runny nose or wheezing; while NRS patients did not.

Results

Of 178,640 patients in the study, 86.4 % presented with RS, while 13.6 % had NRS. NRS patients were older (median age: NRS: 74 vs RS: 65) and less likely to be admitted to the ICU (NRS: 36.7 % vs RS: 37.5 %). NRS patients had a higher crude in-hospital case-fatality ratio (NRS 41.1 % vs. RS 32.0 %), but a lower risk of death after adjusting for confounders (HR 0.88 [0.83-0.93]).

Conclusion

Approximately one in seven COVID-19 patients presented at hospital admission without respiratory symptoms. These patients were older, had lower ICU admission rates, and had a lower risk of in-hospital mortality after adjusting for confounders.

The need to strengthen the evaluation of the impact of Artificial Intelligence-based decision support systems on healthcare provision

Despite the renewed interest in Artificial Intelligence-based clinical decision support systems (AI-CDS), there is still a lack of empirical evidence supporting their effectiveness. This underscores the need for rigorous and continuous evaluation and monitoring of processes and outcomes associated with the introduction of health information technology. We illustrate how the emergence of AI-CDS has helped to bring to the fore the critical importance of evaluation principles and action regarding all health information technology applications, as these hitherto have received limited attention. Key aspects include assessment of design, implementation and adoption contexts; ensuring systems support and optimise human performance (which in turn requires understanding clinical and system logics); and ensuring that design of systems prioritises ethics, equity, effectiveness, and outcomes. Going forward, information technology strategy, implementation and assessment need to actively incorporate these dimensions. International policy makers, regulators and strategic decision makers in implementing organisations therefore need to be cognisant of these aspects and incorporate them in decision-making and in prioritising investment. In particular, the emphasis needs to be on stronger and more evidence-based evaluation surrounding system limitations and risks as well as optimisation of outcomes, whilst ensuring learning and contextual review. Otherwise, there is a risk that applications will be sub-optimally embodied in health systems with unintended consequences and without yielding intended benefits.

Clinical and Laboratory Characteristics of Pediatric COVID-19 Population—A Bibliometric Analysis

The literature on the COVID-19 landscape has rapidly expanded in the pandemic period. The current study undertakes a bibliometric analysis of research in the topic of the clinical and laboratory characteristics of pediatric COVID-19 cases. Our aim is to perform a comprehensive bibliometric review of current research trends and patterns of this research domain. Publications retrieved from the Web of Science Core Collection and VOSviewer were used for analysis and network visualization. We analyzed geographical distribution and temporal trends, collaboration and citation patterns of authors, institutions, and countries, and core research themes from co-occurrence of keywords and terms. The analysis showed that contributions in the research field were from 302 publications, 1104 institutions, 62 countries, and 172 journals. Many publications were authored by American and Chinese authors, and many were published in the Pediatric Infectious Disease Journal, Pediatric Pulmonology, and Frontiers in Pediatrics. The top cited and co-cited journals were the New England Journal of Medicine, Nature, JAMA, Lancet Infectious Diseases, and BMJ. The network visualization maps of keywords and terms offered a global overview of the clinical and laboratory characteristics of pediatric COVID-19 patients. The bibliometric profile of the researched domain, based on analyzing a large collection of publications/data, could (i) enrich the researchers and non-researchers understanding of the field existing patterns and trends, and (ii) be useful in clinical practice (diagnostic and management) and public health policy.

SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results

OBJECTIVES

SARS-CoV-2 rapid antigen tests (RAT) provide fast identification of infectious patients when RT-PCR results are not immediately available. We aimed to develop a prediction model for identification of false negative (FN) RAT results.

METHODS

In this multicenter trial, patients with documented paired results of RAT and RT-PCR between October 1st 2020 and January 31st 2021 were retrospectively analyzed regarding clinical findings. Variables included demographics, laboratory values and specific symptoms. Three different models were evaluated using Bayesian logistic regression.

RESULTS

The initial dataset contained 4,076 patients. Overall sensitivity and specificity of RAT was 62.3% and 97.6%. 2,997 cases with negative RAT results (FN: 120; true negative: 2,877; reference: RT-PCR) underwent further evaluation after removal of cases with missing data. The best-performing model for predicting FN RAT results containing 10 variables yielded an area under the curve of 0.971. Sensitivity, specificity, PPV and NPV for 0.09 as cut-off value (probability for FN RAT) were 0.85, 0.99, 0.7 and 0.99.

CONCLUSION

FN RAT results can be accurately identified through ten routinely available variables. Implementation of a prediction model in addition to RAT testing in clinical care can provide decision guidance for initiating appropriate hygiene measures and therefore helps avoiding nosocomial infections.