Comparative Evaluation of Chest Ultrasonography and Computed Tomography as Predictors of Malignant Pleural Effusion: A Prospective Study.

Malignant pleural effusion (MPE) is a manifestation of advanced cancer that requires a prompt and accurate diagnosis. Ultrasonography (US) and computed tomography (CT) are valuable imaging techniques for evaluating pleural effusions; however, their relative predictive ability for a malignant origin remains debatable. This prospective study aimed to compare chest US with CT findings as predictors of malignancy in patients with undiagnosed exudative pleural effusion. Fifty-four adults with undiagnosed exudative pleural effusions underwent comprehensive clinical evaluation including chest US, CT, and histopathologic biopsy. Blinded radiologists evaluated the US and CT images for features suggestive of malignancy, based on predefined criteria. Diagnostic performance measures were calculated using histopathology as a reference standard. Of the 54 patients, 33 (61.1%) had MPEs confirmed on biopsy. No significant differences between US and CT were found in detecting parietal pleural abnormalities, lung lesions, chest wall invasion, or liver metastasis. US outperformed CT in identifying diaphragmatic pleural thickening ≥10 mm (33.3% vs. 6.1%, p < 0.001) and nodularity (45.5% vs. 3%, p < 0.001), whereas CT was superior for mediastinal thickening (48.5% vs. 15.2%, p = 0.002). For diagnosing MPE, diaphragmatic nodularity detected by US had 45.5% sensitivity and 100% specificity, whereas CT mediastinal thickening had 48.5% sensitivity and 90.5% specificity. Both US and CT demonstrate reasonable diagnostic performance for detecting MPE, with particular imaging findings favoring a malignant origin. US may be advantageous for evaluating diaphragmatic pleural involvement, whereas CT is more sensitive to mediastinal abnormalities.

The COVEG score to predict severity and mortality among hospitalized patients with COVID-19.

INTRODUCTION: COVID-19 severity and mortality predictors could determine admission criteria and reduce mortality. We aimed to evaluate the clinical-laboratory features of hospitalized patients with COVID-19 to develop a novel score of severity and mortality. METHODOLOGY: This retrospective cohort study was conducted using data from patients with COVID-19 who were admitted to five Egyptian university hospitals. Demographics, comorbidities, clinical manifestations, laboratory parameters, the duration of hospitalization, and disease outcome were analyzed, and a score to predict severity and mortality was developed. RESULTS: A total of 1308 patients with COVID-19, with 996 (76.1%) being moderate and 312 (23.9%) being severe cases, were included. The mean age was 46.5 ± 17.1 years, and 61.6% were males. The overall mortality was 12.6%. Regression analysis determined significant predictors, and a ROC curve defined cut-off values. The COVEG severity score was defined by age ≥ 54, D-dimer ≥ 0.795, serum ferritin ≥ 406, C-reactive protein ≥ 30.1, and neutrophil: lymphocyte ratio ≥ 2.88. The COVEG mortality score was based on COVEG severity and the presence of cardiac diseases. Both COVEG scores had high predictive values (area under the curve 0.882 and 0.883, respectively). CONCLUSIONS: COVEG score predicts the severity and mortality of patients with COVID-19 accurately.

Sleep disturbances following recovery from COVID-19: a comparative cross-sectional study in Egypt.

BACKGROUND: Sequelae from COVID-19 are increasingly being reported, but sleep disturbances after recovery from the disease have had little attention. AIMS: This study aimed to identify and compare sleep disturbances and associated correlates among adults who have recovered from COVID-19 with those who have never been infected with the disease. METHODS: The sample included 85 adults who have recovered from COVID-19 and 85 adults who have never been infected (matched on age, sex, education and socioeconomic level). Individuals were recruited from Zagazig University Hospitals, Egypt from 1 September to 29 November 2020. Participants were interviewed using a sociodemographic and clinical checklist, the Pittsburgh Sleep Quality Index and the Hospital Anxiety Depression Scale. RESULTS: Most (77%) of the recovered cases had experienced sleep disturbances, compared with 46% of controls. Individuals who had recovered from COVID-19 were more likely to have poor subjective sleep quality (odds ratio (OR) 1.5, 95% confidence interval (CI): 1.1-2.1), prolonged sleep latency (OR 1.8, 95% CI: 1.3-2.6), shorter sleep duration (OR 1.6, 95% CI: 1.1-2.2), reduced sleep efficiency (OR 3.8, 95% CI: 2.0-7.1), frequent daytime dysfunction (OR 1.9, 95% CI: 1.2-3.1) and poor global Pittsburgh Sleep Quality Index score (OR 3.0, 95% CI: 1.5-6.0). Depressive (P = 0.002) and anxiety (P = 0.003) symptoms were associated with a poor global Pittsburgh Sleep Quality Index score among recovered female participants (P = 0,034) who had low-to-medium education level (P = 0.004). CONCLUSIONS: Further studies (e.g. population-based longitudinal studies) are needed on sleep disturbances as a potential sequelae of COVID-19, because it can impair mental and physical well-being.