Confirmed or unconfirmed cases of 2019 novel coronavirus pneumonia in Italian patients: a retrospective analysis of clinical features

Association of Serum Ferritin With Severity of Disease in Real-Time Reverse Transcription-Polymerase Chain Reaction Negative COVID-19 Patients

BACKGROUND

Coronavirus disease 2019 (COVID-19) is still causing disastrous effects in various parts of the world through recurring waves. Real-time reverse transcription polymerase chain reaction (RT-PCR)-negative COVID-19 is particularly challenging as these patients are less likely to receive treatment and more likely to progress to severe disease. Thus, it is imperative to find markers that can predict the severity of disease at an early stage. The objective of the present study was to analyze the association of ferritin levels with severe disease in RT-PCR-negative COVID-19 patients.

METHODS

A prospective cross-sectional analytical study was conducted in adults with COVID-19 pneumonia with a negative RT-PCR test from October 2020 to September 2021. Hematologic, biochemical, and inflammatory parameters were investigated within 24 h of hospitalization. Demographic, clinical, and laboratory findings were compared between patients with and without severe disease.

RESULTS

A total of 220 patients were included. The mean age of the study participants was 47.3 ± 14.2 years, and 55.5% (n=122) were male. C-reactive protein, D-dimer, and ferritin levels were significantly higher in patients with severe disease (p<0.01). Receiver operating characteristic curve analyses were performed, and ferritin was found as significant predictor of severe disease (area under the curve=0.642, p<0.001).

CONCLUSION

Early analysis of ferritin can predict the severity of disease in COVID-19 patients, irrespective of the RT-PCR status.

Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19

BACKGROUND

COVID-19 illness is highly variable, ranging from infection with no symptoms through to pneumonia and life-threatening consequences. Symptoms such as fever, cough, or loss of sense of smell (anosmia) or taste (ageusia), can help flag early on if the disease is present. Such information could be used either to rule out COVID-19 disease, or to identify people who need to go for COVID-19 diagnostic tests. This is the second update of this review, which was first published in 2020.

OBJECTIVES

To assess the diagnostic accuracy of signs and symptoms to determine if a person presenting in primary care or to hospital outpatient settings, such as the emergency department or dedicated COVID-19 clinics, has COVID-19.

SEARCH METHODS

We undertook electronic searches up to 10 June 2021 in the University of Bern living search database. In addition, we checked repositories of COVID-19 publications. We used artificial intelligence text analysis to conduct an initial classification of documents. We did not apply any language restrictions.

SELECTION CRITERIA

Studies were eligible if they included people with clinically suspected COVID-19, or recruited known cases with COVID-19 and also controls without COVID-19 from a single-gate cohort. Studies were eligible when they recruited people presenting to primary care or hospital outpatient settings. Studies that included people who contracted SARS-CoV-2 infection while admitted to hospital were not eligible. The minimum eligible sample size of studies was 10 participants. All signs and symptoms were eligible for this review, including individual signs and symptoms or combinations. We accepted a range of reference standards.

DATA COLLECTION AND ANALYSIS

Pairs of review authors independently selected all studies, at both title and abstract, and full-text stage. They resolved any disagreements by discussion with a third review author. Two review authors independently extracted data and assessed risk of bias using the QUADAS-2 checklist, and resolved disagreements by discussion with a third review author. Analyses were restricted to prospective studies only. We presented sensitivity and specificity in paired forest plots, in receiver operating characteristic (ROC) space and in dumbbell plots. We estimated summary parameters using a bivariate random-effects meta-analysis whenever five or more primary prospective studies were available, and whenever heterogeneity across studies was deemed acceptable.

MAIN RESULTS

We identified 90 studies; for this update we focused on the results of 42 prospective studies with 52,608 participants. Prevalence of COVID-19 disease varied from 3.7% to 60.6% with a median of 27.4%. Thirty-five studies were set in emergency departments or outpatient test centres (46,878 participants), three in primary care settings (1230 participants), two in a mixed population of in- and outpatients in a paediatric hospital setting (493 participants), and two overlapping studies in nursing homes (4007 participants). The studies did not clearly distinguish mild COVID-19 disease from COVID-19 pneumonia, so we present the results for both conditions together. Twelve studies had a high risk of bias for selection of participants because they used a high level of preselection to decide whether reverse transcription polymerase chain reaction (RT-PCR) testing was needed, or because they enrolled a non-consecutive sample, or because they excluded individuals while they were part of the study base. We rated 36 of the 42 studies as high risk of bias for the index tests because there was little or no detail on how, by whom and when, the symptoms were measured. For most studies, eligibility for testing was dependent on the local case definition and testing criteria that were in effect at the time of the study, meaning most people who were included in studies had already been referred to health services based on the symptoms that we are evaluating in this review. The applicability of the results of this review iteration improved in comparison with the previous reviews. This version has more studies of people presenting to ambulatory settings, which is where the majority of assessments for COVID-19 take place. Only three studies presented any data on children separately, and only one focused specifically on older adults. We found data on 96 symptoms or combinations of signs and symptoms. Evidence on individual signs as diagnostic tests was rarely reported, so this review reports mainly on the diagnostic value of symptoms. Results were highly variable across studies. Most had very low sensitivity and high specificity. RT-PCR was the most often used reference standard (40/42 studies). Only cough (11 studies) had a summary sensitivity above 50% (62.4%, 95% CI 50.6% to 72.9%)); its specificity was low (45.4%, 95% CI 33.5% to 57.9%)). Presence of fever had a sensitivity of 37.6% (95% CI 23.4% to 54.3%) and a specificity of 75.2% (95% CI 56.3% to 87.8%). The summary positive likelihood ratio of cough was 1.14 (95% CI 1.04 to 1.25) and that of fever 1.52 (95% CI 1.10 to 2.10). Sore throat had a summary positive likelihood ratio of 0.814 (95% CI 0.714 to 0.929), which means that its presence increases the probability of having an infectious disease other than COVID-19. Dyspnoea (12 studies) and fatigue (8 studies) had a sensitivity of 23.3% (95% CI 16.4% to 31.9%) and 40.2% (95% CI 19.4% to 65.1%) respectively. Their specificity was 75.7% (95% CI 65.2% to 83.9%) and 73.6% (95% CI 48.4% to 89.3%). The summary positive likelihood ratio of dyspnoea was 0.96 (95% CI 0.83 to 1.11) and that of fatigue 1.52 (95% CI 1.21 to 1.91), which means that the presence of fatigue slightly increases the probability of having COVID-19. Anosmia alone (7 studies), ageusia alone (5 studies), and anosmia or ageusia (6 studies) had summary sensitivities below 50% but summary specificities over 90%. Anosmia had a summary sensitivity of 26.4% (95% CI 13.8% to 44.6%) and a specificity of 94.2% (95% CI 90.6% to 96.5%). Ageusia had a summary sensitivity of 23.2% (95% CI 10.6% to 43.3%) and a specificity of 92.6% (95% CI 83.1% to 97.0%). Anosmia or ageusia had a summary sensitivity of 39.2% (95% CI 26.5% to 53.6%) and a specificity of 92.1% (95% CI 84.5% to 96.2%). The summary positive likelihood ratios of anosmia alone and anosmia or ageusia were 4.55 (95% CI 3.46 to 5.97) and 4.99 (95% CI 3.22 to 7.75) respectively, which is just below our arbitrary definition of a 'red flag', that is, a positive likelihood ratio of at least 5. The summary positive likelihood ratio of ageusia alone was 3.14 (95% CI 1.79 to 5.51). Twenty-four studies assessed combinations of different signs and symptoms, mostly combining olfactory symptoms. By combining symptoms with other information such as contact or travel history, age, gender, and a local recent case detection rate, some multivariable prediction scores reached a sensitivity as high as 90%.

AUTHORS' CONCLUSIONS

Most individual symptoms included in this review have poor diagnostic accuracy. Neither absence nor presence of symptoms are accurate enough to rule in or rule out the disease. The presence of anosmia or ageusia may be useful as a red flag for the presence of COVID-19. The presence of cough also supports further testing. There is currently no evidence to support further testing with PCR in any individuals presenting only with upper respiratory symptoms such as sore throat, coryza or rhinorrhoea. Combinations of symptoms with other readily available information such as contact or travel history, or the local recent case detection rate may prove more useful and should be further investigated in an unselected population presenting to primary care or hospital outpatient settings. The diagnostic accuracy of symptoms for COVID-19 is moderate to low and any testing strategy using symptoms as selection mechanism will result in both large numbers of missed cases and large numbers of people requiring testing. Which one of these is minimised, is determined by the goal of COVID-19 testing strategies, that is, controlling the epidemic by isolating every possible case versus identifying those with clinically important disease so that they can be monitored or treated to optimise their prognosis. The former will require a testing strategy that uses very few symptoms as entry criterion for testing, the latter could focus on more specific symptoms such as fever and anosmia.

The broad spectrum of COVID-like patients initially negative at RT-PCR testing: a cohort study

BACKGROUND

Patients that arrive in the emergency department (ED) with COVID-19-like syndromes testing negative at the first RT-PCR represent a clinical challenge because of the lack of evidence about their management available in the literature. Our first aim was to quantify the proportion of patients testing negative at the first RT-PCR performed in our Emergency Department (ED) that were confirmed as having COVID-19 at the end of hospitalization by clinical judgment or by any subsequent microbiological testing. Secondly, we wanted to identify which variables that were available in the first assessment (ED variables) would have been useful in predicting patients, who at the end of the hospital stay were confirmed as having COVID-19 (false-negative at the first RT-PCR).

METHODS

We retrospectively collected data of 115 negative patients from2020, March 1st to 2020, May 15th. Three experts revised patients' charts collecting information on the whole hospital stay and defining patients as COVID-19 or NOT-COVID-19. We compared ED variables in the two groups by univariate analysis and logistic regression.

RESULTS

We classified 66 patients as COVID-19 and identified the other 49 as having a differential diagnosis (NOT-COVID), with a concordance between the three experts of 0.77 (95% confidence interval (95%CI) 0.66- 0.73). Only 15% of patients tested positive to a subsequent RT-PCR test, accounting for 25% of the clinically suspected. Having fever (odds ratio (OR) 3.32, (95%CI 0.97-12.31), p = 0.06), showing a typical pattern at the first lung ultrasound (OR 6.09, (95%CI 0.87-54.65), p = 0.08) or computed tomography scan (OR 4.18, (95%CI 1.11-17.86), p = 0.04) were associated with a higher probability of having COVID-19.

CONCLUSIONS

In patients admitted to ED with COVID-19 symptoms and negative RT-PCR a comprehensive clinical evaluation integrated with lung ultrasound and computed tomography could help to detect COVID-19 patients with a false negative RT-PCR result.

Comparison of clinical, laboratory and radiological features in confirmed and unconfirmed COVID-19 patients

Background: We aimed to compare the clinical, laboratory and radiological findings of confirmed COVID-19 and unconfirmed patients. Methods: This was a single-center, retrospective study. Results: Overall, 620 patients (338 confirmed COVID-19 and 282 unconfirmed) were included. Confirmed COVID-19 patients had higher percentages of close contact with a confirmed or probable case. In univariate analysis, the presence of myalgia and dyspnea, decreased leukocyte, neutrophil and platelet counts were best predictors for SARS-CoV-2 RT-PCR positivity. Multivariate analyses revealed that only platelet count was an independent predictor for SARS-CoV-2 RT-PCR positivity. Conclusion: Routine complete blood count may be helpful for distinguishing COVID-19 from other respiratory illnesses at an early stage, while PCR testing is unique for the diagnosis of COVID-19.

Spatio-temporal characteristics of the novel coronavirus attention network and its influencing factors in China

The outbreak of a novel coronavirus pneumonia (COVID-19), wherein more than 200 million people have been infected and millions have died, poses a great threat to achieving the United Nations 2030 sustainable development goal (SDGs). Based on the Baidu index of 'novel coronavirus', this paper analyses the spatial and temporal characteristics of and factors that influenced the attention network for COVID-19 from January 9, 2020, to April 15, 2020. The study found that (1) Temporally, the attention in the new coronavirus network showed an upward trend from January 9 to January 29, with the largest increase from January 23 to January 29 and a peak on January 29, and then a slow downward trend. The level of attention in the new coronavirus network was basically flat when comparing January 22 and March 4. (2) Spatially, first, from the perspective of regional differences, the network attention in the eastern and central regions decreased in turn. The network users in the eastern region exhibited the highest attention to the new coronavirus, especially in Guangdong, Shandong, Jiangsu and other provinces and cities. The network attention in Tibet, Xinjiang, Qinghai and Ningxia in the western region was the lowest in terms of the national network attention. Second, from the perspective of interprovincial differences, the attention in the new coronavirus network was highly consistent with the Hu Huanyong line of China's population boundary. The east of the Hu Huanyong line is densely populated, and the network showed high concern, mostly ranking at the third to fifth levels. (3) The number of Internet users in the information technology field, the population, and the culture and age characteristics of individuals are important factors that influence the novel coronavirus attention network.

An IoT Framework for Screening of COVID-19 Using Real-Time Data from Wearable Sensors

Experts have predicted that COVID-19 may prevail for many months or even years before it can be completely eliminated. A major problem in its cure is its early screening and detection, which will decide on its treatment. Due to the fast contactless spreading of the virus, its screening is unusually difficult. Moreover, the results of COVID-19 tests may take up to 48 h. That is enough time for the virus to worsen the health of the affected person. The health community needs effective means for identification of the virus in the shortest possible time. In this study, we invent a medical device utilized consisting of composable sensors to monitor remotely and in real-time the health status of those who have symptoms of the coronavirus or those infected with it. The device comprises wearable medical sensors integrated using the Arduino hardware interfacing and a smartphone application. An IoT framework is deployed at the backend through which various devices can communicate in real-time. The medical device is applied to determine the patient's critical status of the effects of the coronavirus or its symptoms using heartbeat, cough, temperature and Oxygen concentration (SpO2) that are evaluated using our custom algorithm. Until now, it has been found that many coronavirus patients remain asymptomatic, but in case of known symptoms, a person can be quickly identified with our device. It also allows doctors to examine their patients without the need for physical direct contact with them to reduce the possibility of infection. Our solution uses rule-based decision-making based on the physiological data of a person obtained through sensors. These rules allow to classify a person as healthy or having a possibility of infection by the coronavirus. The advantage of using rules for patient's classification is that the rules can be updated as new findings emerge from time to time. In this article, we explain the details of the sensors, the smartphone application, and the associated IoT framework for real-time, remote screening of COVID-19.

Diagnosis of COVID-19 in Patients with Negative Nasopharyngeal Swabs: Reliability of Radiological and Clinical Diagnosis and Accuracy Versus Serology

Background: The diagnosis of Coronavirus disease 2019 (COVID-19) relies on the positivity of nasopharyngeal swab. However, a significant percentage of symptomatic patients may test negative. We evaluated the reliability of COVID-19 diagnosis made by radiologists and clinicians and its accuracy versus serology in a sample of patients hospitalized for suspected COVID-19 with multiple negative swabs. Methods: Admission chest CT-scans and clinical records of swab-negative patients, treated according to the COVID-19 protocol or deceased during hospitalization, were retrospectively evaluated by two radiologists and two clinicians, respectively. Results: Of 254 patients, 169 swab-confirmed cases and one patient without chest CT-scan were excluded. A total of 84 patients were eligible for the reliability study. Of these, 21 patients died during hospitalization; the remaining 63 underwent serological testing and were eligible for the accuracy evaluation. Of the 63, 26 patients showed anti-Sars-Cov-2 antibodies, while 37 did not. The inter-rater agreement was “substantial” (kappa 0.683) between radiologists, “moderate” (kappa 0.454) between clinicians, and only “fair” (kappa 0.341) between radiologists and clinicians. Both radiologic and clinical evaluations showed good accuracy compared to serology. Conclusions: The radiologic and clinical diagnosis of COVID-19 for swab-negative patients proved to be sufficiently reliable and accurate to allow a diagnosis of COVID-19, which needs to be confirmed by serology and follow-up.

False-negative RT-PCR for COVID-19 and a diagnostic risk score: a retrospective cohort study among patients admitted to hospital

OBJECTIVE

To describe the characteristics and outcomes of patients with a clinical diagnosis of COVID-19 and false-negative SARS-CoV-2 reverse transcription-PCR (RT-PCR), and develop and internally validate a diagnostic risk score to predict risk of COVID-19 (including RT-PCR-negative COVID-19) among medical admissions.

DESIGN

Retrospective cohort study.

SETTING

Two hospitals within an acute NHS Trust in London, UK.

PARTICIPANTS

All patients admitted to medical wards between 2 March and 3 May 2020.

OUTCOMES

Main outcomes were diagnosis of COVID-19, SARS-CoV-2 RT-PCR results, sensitivity of SARS-CoV-2 RT-PCR and mortality during hospital admission. For the diagnostic risk score, we report discrimination, calibration and diagnostic accuracy of the model and simplified risk score and internal validation.

RESULTS

4008 patients were admitted between 2 March and 3 May 2020. 1792 patients (44.8%) were diagnosed with COVID-19, of whom 1391 were SARS-CoV-2 RT-PCR positive and 283 had only negative RT-PCRs. Compared with a clinical reference standard, sensitivity of RT-PCR in hospital patients was 83.1% (95% CI 81.2%-84.8%). Broadly, patients with false-negative RT-PCR COVID-19 and those confirmed by positive PCR had similar demographic and clinical characteristics but lower risk of intensive care unit admission and lower in-hospital mortality (adjusted OR 0.41, 95% CI 0.27-0.61). A simple diagnostic risk score comprising of age, sex, ethnicity, cough, fever or shortness of breath, National Early Warning Score 2, C reactive protein and chest radiograph appearance had moderate discrimination (area under the receiver-operator curve 0.83, 95% CI 0.82 to 0.85), good calibration and was internally validated.

CONCLUSION

RT-PCR-negative COVID-19 is common and is associated with lower mortality despite similar presentation. Diagnostic risk scores could potentially help triage patients requiring admission but need external validation.