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1.
Br J Radiol ; 95(1134): 20211028, 2022 Jun 01.
Article in English | MEDLINE | ID: covidwho-1862216

ABSTRACT

OBJECTIVE: The purpose was to evaluate reader variability between experienced and in-training radiologists of COVID-19 pneumonia severity on chest radiograph (CXR), and to create a multireader database suitable for AI development. METHODS: In this study, CXRs from polymerase chain reaction positive COVID-19 patients were reviewed. Six experienced cardiothoracic radiologists and two residents classified each CXR according to severity. One radiologist performed the classification twice to assess intraobserver variability. Severity classification was assessed using a 4-class system: normal (0), mild (1), moderate (2), and severe (3). A median severity score (Rad Med) for each CXR was determined for the six radiologists for development of a multireader database (XCOMS). Kendal Tau correlation and percentage of disagreement were calculated to assess variability. RESULTS: A total of 397 patients (1208 CXRs) were included (mean age, 60 years SD ± 1), 189 men). Interobserver variability between the radiologists ranges between 0.67 and 0.78. Compared to the Rad Med score, the radiologists show good correlation between 0.79-0.88. Residents show slightly lower interobserver agreement of 0.66 with each other and between 0.69 and 0.71 with experienced radiologists. Intraobserver agreement was high with a correlation coefficient of 0.77. In 220 (18%), 707 (59%), 259 (21%) and 22 (2%) CXRs there was a 0, 1, 2 or 3 class-difference. In 594 (50%) CXRs the median scores of the residents and the radiologists were similar, in 578 (48%) and 36 (3%) CXRs there was a 1 and 2 class-difference. CONCLUSION: Experienced and in-training radiologists demonstrate good inter- and intraobserver agreement in COVID-19 pneumonia severity classification. A higher percentage of disagreement was observed in moderate cases, which may affect training of AI algorithms. ADVANCES IN KNOWLEDGE: Most AI algorithms are trained on data labeled by a single expert. This study shows that for COVID-19 X-ray severity classification there is significant variability and disagreement between radiologist and between residents.


Subject(s)
COVID-19 , Algorithms , Artificial Intelligence , COVID-19/diagnostic imaging , Humans , Male , Middle Aged , Radiography, Thoracic , Radiologists , Retrospective Studies
2.
The Egyptian Journal of Radiology and Nuclear Medicine ; 53(1), 2022.
Article in English | EuropePMC | ID: covidwho-1601740

ABSTRACT

Background Chest CT scan has an important role in the diagnosis and management of COVID-19 infection. A major concern in radiologic assessment of the patients is the radiation dose. Research has been done to evaluate low-dose chest CT in the diagnosis of pulmonary lesions with promising findings. We decided to determine diagnostic performance of ultra-low-dose chest CT in comparison to low-dose CT for viral pneumonia during the COVID-19 pandemic. Results 167 patients underwent both low-dose and ultra-low-dose chest CT scans. Two radiologists blinded to the diagnosis independently examined ultra-low-dose chest CT scans for findings consistent with COVID-19 pneumonia. In case of any disagreement, a third senior radiologist made the final diagnosis. Agreement between two CT protocols regarding ground-glass opacity, consolidation, reticulation, and nodular infiltration were recorded. On low-dose chest CT, 44 patients had findings consistent with COVID-19 infection. Ultra-low-dose chest CT had sensitivity and specificity values of 100% and 98.4%, respectively for diagnosis of viral pneumonia. Two patients were falsely categorized to have pneumonia on ultra-low-dose CT scan. Positive predictive value and negative predictive value of ultra-low-dose CT scan were respectively 95.7% and 100%. There was good agreement between low-dose and ultra-low-dose methods (kappa = 0.97;P < 0.001). Perfect agreement between low-dose and ultra-low-dose scans was found regarding diagnosis of ground-glass opacity (kappa = 0.83, P < 0.001), consolidation (kappa = 0.88, P < 0.001), reticulation (kappa = 0.82, P < 0.001), and nodular infiltration (kappa = 0.87, P < 0.001). Conclusion Ultra-low-dose chest CT scan is comparable to low-dose chest CT for detection of lung infiltration during the COVID-19 outbreak while maintaining less radiation dose. It can also be used instead of low-dose chest CT scan for patient triage in circumstances where rapid-abundant PCR tests are not available.

3.
Heart Lung ; 50(1): 13-20, 2021.
Article in English | MEDLINE | ID: covidwho-856730

ABSTRACT

BACKGROUND: Chest computed tomography (CT) scan is frequently used in the diagnosis of COVID-19 pneumonia. OBJECTIVES: This study investigates the predictive value of CT severity score (CSS) for length-of-stay (LOS) in hospital, initial disease severity, ICU admission, intubation, and mortality. METHODS: In this retrospective study, initial CT scans of consecutively admitted patients with COVID-19 pneumonia were reviewed in a tertiary hospital. The association of CSS with the severity of disease upon admission and the final adverse outcomes was assessed using Pearson's correlation test and logistic regression, respectively. RESULTS: Total of 121 patients (60±16 years), including 54 women and 67 men, with positive RT-PCR tests were enrolled. We found a significant but weak correlation between CSS and qSOFA, as a measure of disease severity (r: 0.261, p = 0.003). No significant association was demonstrated between CSS and LOS. Patients with CSS>8 had at least three-fold higher risk of ICU admission, intubation, and mortality. CONCLUSIONS: CSS in baseline CT scan of patients with COVID-19 pneumonia can predict adverse outcomes and is weakly correlated with initial disease severity.


Subject(s)
COVID-19 , Female , Humans , Length of Stay , Male , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed
4.
Pol Arch Intern Med ; 130(7-8): 629-634, 2020 08 27.
Article in English | MEDLINE | ID: covidwho-761202

ABSTRACT

INTRODUCTION: Currently, there are known contributing factors but no comprehensive methods for predicting the mortality risk or intensive care unit (ICU) admission in patients with novel coronavirus disease 2019 (COVID­19). OBJECTIVES: The aim of this study was to explore risk factors for mortality and ICU admission in patients with COVID­19, using computed tomography (CT) combined with clinical laboratory data. PATIENTS AND METHODS: Patients with polymerase chain reaction-confirmed COVID­19 (n = 63) from university hospitals in Tehran, Iran, were included. All patients underwent CT examination. Subsequently, a total CT score and the number of involved lung lobes were calculated and compared against collected laboratory and clinical characteristics. Univariable and multivariable proportional hazard analyses were used to determine the association among CT, laboratory and clinical data, ICU admission, and in­hospital death. RESULTS: By univariable analysis, in­hospital mortality was higher in patients with lower oxygen saturation on admission (below 88%), higher CT scores, and a higher number of lung lobes (more than 4) involved with a diffuse parenchymal pattern. By multivariable analysis, in­hospital mortality was higher in those with oxygen saturation below 88% on admission and a higher number of lung lobes involved with a diffuse parenchymal pattern. The risk of ICU admission was higher in patients with comorbidities (hypertension and ischemic heart disease), arterial oxygen saturation below 88%, and pericardial effusion. CONCLUSIONS: We can identify factors affecting in­hospital death and ICU admission in COVID-19. This can help clinicians to determine which patients are likely to require ICU admission and to inform strategic healthcare planning in critical conditions such as the COVID­19 pandemic.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Real-Time Polymerase Chain Reaction , Adult , Age Distribution , Aged , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Female , Humans , Iran , Male , Middle Aged , Pandemics , Pneumonia, Viral/epidemiology , Poland/epidemiology , SARS-CoV-2 , Sex Distribution , Tomography, X-Ray Computed , Young Adult
5.
J Antimicrob Chemother ; 75(11): 3379-3385, 2020 11 01.
Article in English | MEDLINE | ID: covidwho-723194

ABSTRACT

BACKGROUND: Currently no effective antiviral therapy has been found to treat COVID-19. The aim of this trial was to assess if the addition of sofosbuvir and daclatasvir improved clinical outcomes in patients with moderate or severe COVID-19. METHODS: This was an open-label, multicentre, randomized controlled clinical trial in adults with moderate or severe COVID-19 admitted to four university hospitals in Iran. Patients were randomized into a treatment arm receiving sofosbuvir and daclatasvir plus standard care, or a control arm receiving standard care alone. The primary endpoint was clinical recovery within 14 days of treatment. The study is registered with IRCT.ir under registration number IRCT20200128046294N2. RESULTS: Between 26 March and 26 April 2020, 66 patients were recruited and allocated to either the treatment arm (n = 33) or the control arm (n = 33). Clinical recovery within 14 days was achieved by 29/33 (88%) in the treatment arm and 22/33 (67%) in the control arm (P = 0.076). The treatment arm had a significantly shorter median duration of hospitalization [6 days (IQR 4-8)] than the control group [8 days (IQR 5-13)]; P = 0.029. Cumulative incidence of hospital discharge was significantly higher in the treatment arm versus the control (Gray's P = 0.041). Three patients died in the treatment arm and five in the control arm. No serious adverse events were reported. CONCLUSIONS: The addition of sofosbuvir and daclatasvir to standard care significantly reduced the duration of hospital stay compared with standard care alone. Although fewer deaths were observed in the treatment arm, this was not statistically significant. Conducting larger scale trials seems prudent.


Subject(s)
Antiviral Agents/administration & dosage , Betacoronavirus , Coronavirus Infections/drug therapy , Imidazoles/administration & dosage , Patient Admission/trends , Pneumonia, Viral/drug therapy , Sofosbuvir/administration & dosage , Adult , Aged , COVID-19 , Carbamates , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/epidemiology , Drug Therapy, Combination , Female , Humans , Iran/epidemiology , Male , Middle Aged , Pandemics , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/epidemiology , Pyrrolidines , SARS-CoV-2 , Severity of Illness Index , Treatment Outcome , Valine/analogs & derivatives
6.
SN Compr Clin Med ; 2(9): 1366-1376, 2020.
Article in English | MEDLINE | ID: covidwho-718568

ABSTRACT

We investigated significant predictors of poor in-hospital outcomes for patients admitted with viral pneumonia during the COVID-19 outbreak in Tehran, Iran. Between February 22 and March 22, 2020, patients who were admitted to three university hospitals during the COVID-19 outbreak in Tehran, Iran were included. Demographic, clinical, laboratory, and chest CT scan findings were gathered. Two radiologists evaluated the distribution and CT features of the lesions and also scored the extent of lung involvement as the sum of three zones in each lung. Of 228 included patients, 45 patients (19.7%) required ICU admission and 34 patients (14.9%) died. According to regression analysis, older age (OR = 1.06; P < 0.001), blood oxygen saturation (SpO2) < 88% (OR = 2.88; P = 0.03), and higher chest CT total score (OR = 1.10; P = 0.03) were significant predictors for in-hospital death. The same three variables were also recognized as significant predictors for invasive respiratory support: SpO2 < 88% (OR = 3.97, P = 0.002), older age (OR = 1.05, P < 0.001), and higher CT total score (OR = 1.13, P = 0.008). Potential predictors of invasive respiratory support and in-hospital death in patients with viral pneumonia were older age, SpO2 < 88%, and higher chest CT score.

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