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High-resolution computed tomography (HRCT) thorax in Covid-19 Pneumonia
Journal International Medical Sciences Academy ; 34(2):72-76, 2021.
Article in English | EMBASE | ID: covidwho-1733044
ABSTRACT

Background:

Covid-19 disease has become a dreadful and deadly disease over last one year with numerous methods described in recent medical literature for early diagnosis ad monitory the course of disease. High-resolution computed tomography (HRCT) thorax findings in Covid-19 pneumonia have been described with high specificity & moderate sensitivity. Aims &

Objectives:

We conducted this study with the aim and objectives of determining the common pulmonary radiological findings in HRCT thorax of Covid-19 patients and their relationship with category of clinical symptoms as well as correlation with CT severity score. Materials and

Methods:

This retrospective, observational & cross-sectional study included HRCT thorax of 100 cases proven to be RT-PCR positive for Covid-19 disease. The spectrum of pulmonary findings was noted along with their frequency in different symptom category of patients. CT severity score was also correlated with category of symptoms.

Results:

Approximately two-third patients in our study revealed radiological findings on HRCT thorax with GGO being the commonest finding followed by consolidation & characteristic signs in the decreasing order of frequency. More than half of patients in mild and more than 90% patients in moderate category revealed findings on HRCT thorax with average CT severity score of <10 in mild and >11 in moderate category of symptoms.

Conclusions:

Though HRCT thorax cannot be used as a screening tool for diagnosis of Covid-19 disease owing to its moderate sensitivity yet its high degree of specificity especially in moderate category of patients makes it useful in objective assessment during treatment as well as in predicting the prognosis of patients high CT severity score.
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Collection: Databases of international organizations Database: EMBASE Language: English Journal: Journal International Medical Sciences Academy Year: 2021 Document Type: Article

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Collection: Databases of international organizations Database: EMBASE Language: English Journal: Journal International Medical Sciences Academy Year: 2021 Document Type: Article