Evaluation of the Progress of Coronavirus Disease-19 Pneumonia using the British Society of Thoracic Imaging Reporting Model: A Validation Study

Abstract: Background: SARS-COV-2 (also known as severe acute respiratory syndrome corona virus-2), emerged as a pandemic and became an overwhelming global concern, causing substantial morbidity and mortality worldwide. Reverse transcription polymerase chain reaction (RT-PCR) is considered a gold standard in detecting clinically symptomatic patients but can have false negative and false positive results. As chest X-Ray (CXR) is considered as a baseline investigation in many hospitals, BSTI reporting model during COVID-19 pandemic has been a useful tool in diagnosis of COVID-19 pneumonia. Objective: To validate the British Society Thoracic Imaging (BSTI) coding system in the evaluation of the progress of the disease severity in patients with coronavirus disease-2019 (COVID-19) pneumonia. Materials and Methods: This is a cross sectional observational study. Total 450 CXRs (which included both the baseline and serial CXRs) of 225 COVID positive patients (RT-PCR positive for COVID-19 on nasal swabs) were included. These were retrospectively reviewed and reported by two Radiologists (having experience of at least 5 years in Radiology Reporting) in Corona Ward in Dr. Ruth K M Pfau Civil Hospital Karachi, Pakistan, for the duration of 10 months from 1st March 2020 till 31st December 2020. BSTI coding system was used to classify and interpret the CXR imaging findings as normal, definitive, indeterminate and non-COVID for baseline (CXR on 1st day of admission) and follow up CXRs (done in between 3rd and 7th day of admission). Data was analyzed using SPSS version 25. Numeric data was assessed for distribution using Shapiro-Wilks test. Median and interquartile range (IQR) were reported for numeric variables. Frequencies and percentages were reported for categorical data. Kappa statistics was applied to assess the agreement between BSTI scoring at baseline and follow-up CXRs. A p-value ≤0.05 was considered as statistically significant. Result: CXRs (including 225 baseline and 225 follow up CXRs) of 225 RT-PCR COVID-19 positive patients were analyzed. Interval change in BSTI coding system was noted, increase in frequency of probable/definitive COVID-19 findings were diagnosed on serial CXRs. The BSTI scoring at baseline and follow-up showed moderate agreement with kappa statistics as 60.3% (p=0.001). Conclusion: BSTI coding system can be helpful to classify the COVID-19 disease on CXR and filter for the prognosis of disease severity in the serial radiographs. Utilization of BSTI reporting model for reporting CXRs, even before RT-PCR, in future COVID pandemic can be considered as a useful tool.