ABSTRACT
Objective: To determine the role of the serial portable chest X-ray in the diagnosis and quantification of patients with confirmed COVID-19 admitted to a tertiary care hospital. Methods: A retrospective study was conducted at Dow Institute of Radiology, Dow University of Health Sciences. Confirmed positive cases of COVID-19 from November 2020 to January 2021 were retrospectively studied. Patients' demographics and clinical characteristics, chest X-ray findings, and outcomes were retrieved through electronic medical records. Baseline and final follow-up chest X-rays findings were compared by using chest X-ray severity score. Multivariable logistic regression was used to evaluate the relationship between patients' characteristics and patient outcomes. Results: The study included 329 patients with a mean age of 56.43 ± 13.10 years (range 16-85 years). Peripheral consolidation and ground glass opacities (89.4%) were the most common X-ray findings followed by bilateral lung involvement (79.0%) and perihilar consolidation/ground glass opacities (69.9%). Among the patients who were admitted, 61.4% were discharged, 49.5% had prolonged length of stay ≥10 days, and 37.7% died. After adjustment of all patients' characteristics, the multivariate model showed no significant difference in chest X-ray severity score in relation to the patient's outcome. Patients who were admitted to the intensive care unit, and received oxygen support, bilevel positive airway pressure, and a ventilator were significantly associated with the outcome of being discharged, prolonged hospital stay, and death. Conclusion: Peripheral consolidation and ground glass opacities were the most common chest X-ray findings in admitted COVID-19 patients. No significant difference in chest X-ray severity score was noted in the primary outcome of being discharged, prolonged hospital stay, and death. There is no requirement for daily chest X-rays in hospitalized patients until required in the condition of worsening symptoms or significant intervention such as endotracheal intubation.
ABSTRACT
BACKGROUND & OBJECTIVE: Radiology has played a significant role in the diagnosis and quantifying the severity of COVID 19 pulmonary disease. This study was conducted to assess patterns and severity of COVID-19 pulmonary disease based on radiological imaging. METHODS: A prospective observational study was conducted in a large tertiary care public sector teaching hospital of Karachi, Pakistan from June 2020 till August 2020. All confirmed and suspected COVID-19 patients referred for chest X-rays and computed tomography (CT) scans were evaluated along with RT-PCR results. Suspected patients were followed for RT-PCR. Radiological features and severity of imaging studies were determined. RESULTS: Of 533 patients in whom X-rays were performed, majority had severe/critical findings, i.e., 304 (57.03%). Of 97 patients in whom CT scan was performed, mild/moderate findings were observed in 63 (64.94%) patients. Of 472 patients with abnormal X-rays, majority presented with alveolar pattern 459 (97.2%), bilateral lung involvement 453 (89.6%), and consolidation 356 (75.4%). Moreover, lobar predominance showed lower zone preponderance in 446 (94.5%) patients. Of 88 patients with abnormal CT findings, ground-glass opacity (GGO) 87 (98.9%) and crazy paving 69 (78.4%) were the most common findings. An insignificantly higher association of PCR positive cases was observed with severe/critical X-rays (p-value 0.076) and CT scan findings (p-value 0.431). CONCLUSION: Most common patterns on CT scans were GGO and crazy paving. While on chest radiographs, bilateral lung involvement with alveolar pattern and consolidation were most common findings. On X-rays, majority had severe/critical whereas CT scan had mild/moderate findings.
ABSTRACT
Since the coronavirus disease (COVID-19) outbreak in December 2019, studies have been addressing diverse aspects in relation to COVID-19 and Variant of Concern 202012/01 (VOC 202012/01) such as potential symptoms and predictive tools. However, limited work has been performed towards the modelling of complex associations between the combined demographic attributes and varying nature of the COVID-19 infections across the globe. This study presents an intelligent approach to investigate the multi-dimensional associations between demographic attributes and COVID-19 global variations. We gather multiple demographic attributes and COVID-19 infection data (by 8 January 2021) from reliable sources, which are then processed by intelligent algorithms to identify the significant associations and patterns within the data. Statistical results and experts' reports indicate strong associations between COVID-19 severity levels across the globe and certain demographic attributes, e.g. female smokers, when combined together with other attributes. The outcomes will aid the understanding of the dynamics of disease spread and its progression, which in turn may support policy makers, medical specialists and society, in better understanding and effective management of the disease.