Current challenges in COVID-19 diagnosis: a narrative review and implications for clinical practice

Ground-glass opacity on emergency department chest X-ray: a risk factor for in-hospital mortality and organ failure in elderly admitted for COVID-19

INTRODUCTION

Ground-glass opacity is commonly seen on radiographic imaging tests of patients admitted for COVID-19. The main objective of this study is to determine if the presence of ground-glass opacity on chest X-rays carried out at the Emergency Department correlates with significantly higher mortality. A secondary objective is to clarify which characteristics are associated with those patients who presented ground-glass opacity.

METHODS

Data were obtained from our 2020 hospital admission records. Consequently, this is a retrospective cohort study. Our cohort consists of 300 admissions from a group of elderly with a mean age of 81.6. There were 49.3% women (148/300) and 50.7% men (152/300).

RESULTS

The presence of ground-glass opacity on chest X-rays is a risk factor for in-hospital mortality (RR = 1.6), heart failure (RR = 4.3), respiratory failure (RR = 1.5), acute kidney injury (RR = 1.3) and ICU admission (RR = 2.7).

CONCLUSION

Based on these results, the variable 'finding ground-glass opacity on chest X-rays carried out at the Emergency Department' should be assessed for inclusion in the different calculators that estimate the prognosis/mortality rate of patients admitted for COVID-19.

The three facets of the SARS-CoV-2 pandemic during the first two waves in the northern, central, and southern Italy

BACKGROUND

There is a scarcity of information in literature regarding the clinical differences and comorbidities of patients affected by Coronavirus disease 2019 (COVID-19), which could clarify the different prevalence of the outcomes (composite and only death) between several Italian regions.

OBJECTIVE

This study aimed to assess the heterogeneity of clinical features of patients with COVID-19 upon hospital admission and disease outcomes in the northern, central, and southern Italian regions.

METHODS

An observational cohort multicenter retrospective study including 1210 patients who were admitted for COVID-19 in Infectious diseases, Pulmonology, Endocrinology, Geriatrics and Internal Medicine Units in Italian cities stratified between north (263 patients); center (320 patients); and south (627 patients), during the first and second pandemic waves of SARS-CoV-2 (from February 1, 2020 to January 31, 2021). The data, obtained from clinical charts and collected in a single database, comprehended demographic characteristics, comorbidities, hospital and home pharmacological therapies, oxygen therapy, laboratory values, discharge, death and Intensive care Unit (ICU) transfer. Death or ICU transfer were defined as composite outcomes.

RESULTS

Male patients were more frequent in the northern Italian region than in the central and southern regions. Diabetes mellitus, arterial hypertension, chronic pulmonary and chronic kidney diseases were the comorbidities more frequent in the southern region; cancer, heart failure, stroke and atrial fibrillation were more frequent in the central region. The prevalence of the composite outcome was recorded more frequently in the southern region. Multivariable analysis showed a direct association between the combined event and age, ischemic cardiac disease, and chronic kidney disease, in addition to the geographical area.

CONCLUSIONS

Statistically significant heterogeneity was observed in patients with COVID-19 characteristics at admission and outcomes from northern to southern Italy. The higher frequency of ICU transfer and death in the southern region may depend on the wider hospital admission of frail patients for the availability of more beds since the burden of COVID-19 on the healthcare system was less intense in southern region. In any case, predictive analysis of clinical outcomes should consider that the geographical differences that may reflect clinical differences in patient characteristics, are also related to access to health-care facilities and care modalities. Overall, the present results caution against generalizability of prognostic scores in COVID-19 patients derived from hospital cohorts in different settings.

Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database

Background and Objective

The spread of coronavirus has been challenging for the healthcare system's proper management and diagnosis during the rapid spread and control of the infection. Real-time reverse transcription-polymerase chain reaction (RT-PCR), though considered the standard testing measure, has low sensitivity and is time-consuming, which restricts the fast screening of individuals. Therefore, computer tomography (CT) is used to complement the traditional approaches and provide fast and effective screening over other diagnostic methods. This work aims to appraise the importance of chest CT findings of COVID-19 and post-COVID in the diagnosis and prognosis of infected patients and to explore the ways and means to integrate CT findings for the development of advanced Artificial Intelligence (AI) tool-based predictive diagnostic techniques.

Methods

The retrospective study includes a 188 patient database with COVID-19 infection confirmed by RT-PCR testing, including post-COVID patients. Patients underwent chest high-resolution computer tomography (HRCT), where the images were evaluated for common COVID-19 findings and involvement of the lung and its lobes based on the coverage region. The radiological modalities analyzed in this study may help the researchers in generating a predictive model based on AI tools for further classification with a high degree of reliability.

Results

Mild to moderate ground glass opacities (GGO) with or without consolidation, crazy paving patterns, and halo signs were common COVID-19 related findings. A CT score is assigned to every patient based on the severity of lung lobe involvement.

Conclusion

Typical multifocal, bilateral, and peripheral distributions of GGO are the main characteristics related to COVID-19 pneumonia. Chest HRCT can be considered a standard method for timely and efficient assessment of disease progression and management severity. With its fusion with AI tools, chest HRCT can be used as a one-stop platform for radiological investigation and automated diagnosis system.

COVID-19: A state of art on immunological responses, mutations, and treatment modalities in riposte

Over the last few years, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) unleashed a global public health catastrophe that had a substantial influence on human physical and mental health, the global economy, and socio-political dynamics. SARS-CoV-2 is a respiratory pathogen and the cause of ongoing COVID-19 pandemic, which testified how unprepared humans are for pandemics. Scientists and policymakers continue to face challenges in developing ideal therapeutic agents and vaccines, while at the same time deciphering the pathology and immunology of SARS-CoV-2. Challenges in the early part of the pandemic included the rapid development of diagnostic assays, vaccines, and therapeutic agents. The ongoing transmission of COVID-19 is coupled with the emergence of viral variants that differ in their transmission efficiency, virulence, and vaccine susceptibility, thus complicating the spread of the pandemic. Our understanding of how the human immune system responds to these viruses as well as the patient groups (such as the elderly and immunocompromised individuals) who are often more susceptible to serious illness have both been aided by this epidemic. COVID-19 causes different symptoms to occur at different stages of infection, making it difficult to determine distinct treatment regimens employed for the various clinical phases of the disease. Unsurprisingly, determining the efficacy of currently available medications and developing novel therapeutic strategies have been a process of trial and error. The global scientific community collaborated to research and develop vaccines at a neck-breaking speed. This review summarises the overall picture of the COVID-19 pandemic, different mutations in SARS-CoV-2, immune response, and the treatment modalities against SARS-CoV-2.

Evaluation of risk factors for developing COVID-19 in healthcare professionals working at two university hospitals in Turkey

BACKGROUND: Healthcare workers (HCWs) were seriously affected by the coronavirus disease 2019 (COVID-19). It is a priority to protect HCWs against COVID-19 and ensure the continuity of the health care system. OBJECTIVE: To evaluate the risk factors for COVID-19 in HCWs and the effectiveness of the measures taken on protection. METHODS: A nested case-control study was conducted in two hospitals serving on the same campus which are affiliated with a university from Turkey, between 03.12.2020 and 05.22.2020. We aimed to recruit three controls working in the same unit with the cases diagnosed with COVID-19 by polymerase chain reaction (PCR) and whose SARS-CoV-2 PCR test is negative. Self-reported data were collected from the HCWs by the face-to-face method. Descriptive and analytical methods were used and a logistic regression model was built. Results: The study was completed with 271 HCWs, 72 cases, and 199 controls. Household contact with a COVID-19 patient or a patient with symptoms compatible with COVID-19 was found to be significantly higher in the cases than in the controls (p = 0.02, p <  0.001). When the measures for control the COVID-19 were analyzed, using a medical mask (OR = 0.28, 95% confidence interval = 0.11–0.76, p = 0.01) by COVID-19 patient and using the respiratory mask by HCWs (OR = 0.13, 95% CI = 0.03–0.52, p = 0.004) during close contact was found to be protective against COVID-19 transmission. Conclusion: This study showed an association with using medical masks by the patients as an important protective precaution for the transmission of COVID-19 to HCWs. Respiratory masks should be used by HCWs while in close contact with COVID-19 patients regardless of aerosol-producing procedures.

Clinical epidemiological applicability of real-time polymerase chain reaction for COVID-19

OBJECTIVES

Real-time polymerase chain reaction is currently used as a confirmatory test for coronavirus disease 2019 (COVID-19). The test results are interpreted as positive, negative, or inconclusive, and are used only for a qualitative classification of patients. However, the test results can be quantitated using threshold count (Ct) values to determine the amount of virus present in the sample. Therefore, this study investigated the diagnostic usefulness of Ct results through various quantitative analyzes, along with an analysis of clinical and epidemiological characteristics.

METHODS

Clinical and epidemiological data from 4,642 COVID-19 patients in April 2021 were analyzed, including the Ct values of the RNA-dependent RNA polymerase (RdRp), envelope (E), and nucleocapsid (N) genes. Clinical and epidemiological data (sex, age, underlying diseases, and early symptoms) were collected through a structured questionnaire. A correlation analysis was used to examine the relationships between variables.

RESULTS

All 3 genes showed statistically significant relationships with symptoms and severity levels. The Ct values of the RdRp gene decreased as the severity of the patients increased. Moreover, statistical significance was observed for the presence of underlying diseases and dyspnea.

CONCLUSION

Ct values were found to be related to patients' clinical and epidemiological characteristics. In particular, since these factors are closely related to symptoms and severity, Ct values can be used as primary data for predicting patients' disease prognosis despite the limitations of this method. Conducting follow-up studies to validate this approach might enable using the data from this study to establish policies for preventing COVID-19 infection and spread.