Post-COVID-19 pulmonary fibrosis with prognostic risk factors: A study in the hilly state of North India

Abstract

Context: Coronavirus disease 19 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). As it is a novel coronavirus infection, its acute as well as long-term pulmonary alterations in terms of radiological imaging and pathology need to be explored. Aim: To analyze the risk factors associated with the development of pulmonary fibrosis in post-COVID-19 patients. Settings and Design: A retrospective study in a tertiary center in a hilly state of North India. Methods and Material: A retrospective study was carried out in a hilly state of North India during the second wave of the COVID-19 pandemic. A baseline computed tomography (CT) within 1 month and a follow-up CT within 3–6 months of the onset of patients’ symptoms was done. The patients were assigned two groups based on the presence of fibrosis [reticular pattern (inter/intralobular septal thickening), parenchymal bands, bronchiectasis, and/ or honeycombing] on follow-up CT. The patients’ demographic profiles, clinical records, blood laboratory findings, treatment given, baseline CT, and follow-up CT findings were compared between the fibrosis and non-fibrosis groups. Statistical analysis used: The data entry was done in the Microsoft EXCEL spreadsheet, and the final analysis was done with the use of Statistical Package for Social Sciences (SPSS) software, IBM manufacturer, Chicago, USA, ver 25.0. Results: Of the total 72 patients, two had no abnormality on baseline CT. Forty (57.14%) out of 70 patients already had fibrosis on baseline CT, which increased to involve 47 (67.14%) patients on follow-up CT. It was analyzed that old age, smoking, comorbid status, low SpO2, longer hospitalization, lymphopenia, and increased severity of COVID-19 disease were independent risk factors for the development of pulmonary fibrosis in post-COVID-19 pneumonia. Conclusions: A better understanding of the underlying mechanism and pathogenesis can help in filling up of gaps in risk stratification of COVID-19.