Discrepancies in the clinical and radiological profiles of COVID-19: A case-based discussion and review of literature

Kumar, Hemant; Fernandez, Cornelius James; Kolpattil, Sangeetha; Munavvar, Mohamed; Pappachan, Joseph M

doi:10.4329/wjr.v13.i4.75

Cited by 4 publications

(8 citation statements)

References 99 publications

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“…Most of the patients (94.3%, 533) were diagnosed by a positive reverse transcription polymerase chain reaction (RT-PCR) amplification, which, according to Kumar et al 11 , is still the gold standard for diagnosing COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Adult patients admitted to a tertiary hospital for COVID-19 and risk factors associated with severity: a retrospective cohort study

Santos

Stein

Barilli

et al. 2022

Rev. Inst. Med. trop. S. Paulo

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COVID-19 is a disease whose knowledge is still under construction, high transmissibility, with no consensual treatment available to everyone. Therefore, the identification of patients at higher risk of evolving to the critical form of the disease is fundamental. The study aimed to determine risk factors associated with the severity of COVID-19 in adults patients. This is an observational, retrospective study from a cohort of adult patients with COVID-19 admitted to a public hospital from March to August 2020, whose medical records were evaluated. For the association of possible severity predictors, a Poisson regression was used. The primary outcome was the critical form of the disease (need for admission to the Intensive Care Unit and/or invasive mechanical ventilation). We included 565 patients: mostly men; 55.5% of those who progressed to the critical form of the disease were over sixty years old. Hypertension, diabetes mellitus and obesity were the most frequent comorbidities. There were 39.8% of patients who progressed to the critical form of the disease. The hospital mortality rate was 22.1%, and that of critical patients was 46.7%. The independent factors associated with the severity of the disease were obesity [RR = 1.33 (95% CI 1.07 to 1.66; p = 0.011)], SpO 2 /FiO 2 ratio ≤ 315 [RR = 2.20 (95% CI 1.79 to 2.71; p = 0.000)], C-reactive protein > 100 mg/L [RR = 1.65 (95% CI 1.33 to 2.06; p = 0.000)], and lymphocytes < 1,000/µL [RR = 1.44 (95% CI 1.18 to 1.75; p = 0.000)]. Advanced age and comorbidities were dependent factors strongly associated with the critical form of the disease.

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Section: Discussionmentioning

confidence: 99%

Adult patients admitted to a tertiary hospital for COVID-19 and risk factors associated with severity: a retrospective cohort study

Santos

Stein

Barilli

et al. 2022

Rev. Inst. Med. trop. S. Paulo

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“…We appreciate Kumar et al [ 1 ] consideration of our study in their paper on the discrepancies in the clinical and radiological profiles of coronavirus disease 2019 (COVID-19)[ 1 ]. In this paper, the use of chest computed tomography (CT), chest X-ray (CXR), and lung ultrasound (LUS) as possible diagnostic tools for the COVID-19 is discussed.…”

Section: To the Editormentioning

confidence: 99%

“…The authors cite the findings from two versions of the Cochrane Review titled “Thoracic imaging tests for the diagnosis of COVID-19.” As more information becomes available, this systematic review has aimed to keep pace with the new data. The most recent version of the review, published in March 2021, has shown differences in the sensitivities and specificities of these three image modalities compared to the findings in prior versions reported in the article by Kumar et al [ 1 ].…”

Section: To the Editormentioning

confidence: 99%

“…Firstly, the authors cited the initial review by Salameh et al [ 2 ], which determined that CXR had a pooled sensitivity of CXR is 82.1% (95%CI: 62.5-92.7)[ 1 , 2 ] in patients who had COVID-19. The second version of the review, by Islam et al [ 3 ], determined that CXR had a sensitivity ranging from 56.9% to 89.0% and specificity ranging from 11.1% to 88.9%[ 1 , 3 ] in patients with COVID-19. As opposed to the first two versions, in the third and most recent version, there was a sufficient number of studies, evaluating the diagnostic accuracy of CXR, to perform a meta-analysis.…”

Section: To the Editormentioning

confidence: 99%

“…Secondly, the article by Kumar et al [ 1 ] states that chest CT may not be capable of discriminating COIVD-19 from other respiratory diseases[ 1 ]. The review by Salameh et al [ 2 ] obtained a pooled specificity of 18.1% (95%CI: 3.71-55.8)[ 2 ] for chest CT, in cases where CT scans were used as the primary diagnostic test, which was subsequently updated to 61.1% (95%CI: 42.3-77.1) in the subsequent edition[ 3 ].…”

Section: To the Editormentioning

confidence: 99%

See 2 more Smart Citations

Diagnostic accuracy of thoracic imaging modalities for the detection of COVID-19

Dawit¹,

Absi²,

Islam³

et al. 2022

WJR

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The ongoing coronavirus disease 2019 (COVID-19) pandemic continues to present diagnostic challenges. The use of thoracic radiography has been studied as a method to improve the diagnostic accuracy of COVID-19. The ‘Living’ Cochrane Systematic Review on the diagnostic accuracy of imaging tests for COVID-19 is continuously updated as new information becomes available for study. In the most recent version, published in March 2021, a meta-analysis was done to determine the pooled sensitivity and specificity of chest X-ray (CXR) and lung ultrasound (LUS) for the diagnosis of COVID-19. CXR gave a sensitivity of 80.6% (95%CI: 69.1-88.6) and a specificity of 71.5% (95%CI: 59.8-80.8). LUS gave a sensitivity rate of 86.4% (95%CI: 72.7-93.9) and specificity of 54.6% (95%CI: 35.3-72.6). These results differed from the findings reported in the recent article in this journal where they cited the previous versions of the study in which a meta-analysis for CXR and LUS could not be performed. Additionally, the article states that COVID-19 could not be distinguished, using chest computed tomography (CT), from other respiratory diseases. However, the latest review version identifies chest CT as having a specificity of 80.0% (95%CI: 74.9-84.3), which is much higher than the previous version which indicated a specificity of 61.1% (95%CI: 42.3-77.1). Therefore, CXR, chest CT and LUS have the potential to be used in conjunction with other methods in the diagnosis of COVID-19.

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Optimal feature selection for COVID-19 detection with CT images enabled by metaheuristic optimization and artificial intelligence

Torse

Khanai

Pai

et al. 2023

Multimed Tools Appl

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There is a broad range of novel Coronaviruses (CoV) such as the common cold, cough, and severe lung infections. The mutation of this virus, which originally started as COVID-19 in Wuhan, China, has continued the rapid spread globally. As the mutated form of this virus spreads across the world, testing and screening procedures of patients have become tedious for healthcare departments in largely populated countries such as India. To diagnose COVID-19 pneumonia by radiological methods, high-resolution computed tomography (CT) of the chest has been considered the most precise method of examination. The use of modern artificial intelligence (AI) techniques on chest high-resolution computed tomography (HRCT) images can help to detect the disease, especially in remote areas with a lack of specialized physicians. This article presents a novel metaheuristic algorithm for automatic COVID-19 detection using a least square support vector machine (LSSVM) classifier for three classes namely normal, COVID, and pneumonia. The proposed model results in a classification accuracy of 87.2% and an F1-score of 86.3% for multiclass classifications from simulations. The analysis of information transfer rate (ITR) revealed that the modified quantum-based marine predators algorithm (Mq-MPA) feature selection algorithm reduces the classification time of LSSVM by 23% when compared to the deep learning models.

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Discrepancies in the clinical and radiological profiles of COVID-19: A case-based discussion and review of literature

Cited by 4 publications

References 99 publications

Adult patients admitted to a tertiary hospital for COVID-19 and risk factors associated with severity: a retrospective cohort study

Adult patients admitted to a tertiary hospital for COVID-19 and risk factors associated with severity: a retrospective cohort study

Diagnostic accuracy of thoracic imaging modalities for the detection of COVID-19

Optimal feature selection for COVID-19 detection with CT images enabled by metaheuristic optimization and artificial intelligence

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