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Background: The current coronavirus disease-19 (COVID-19) pandemic call attention to the key role informatics play in healthcare. The present study discovers an independent role of computerised tomography chest (CT) scans in prognosis of COVID-19 using classification learning algorithms. Methods: In this retrospective study, 57 RT PCR positive COVID-19 patients were enrolled from SMS Medical College, Jaipur (Rajasthan, India) after approval from the Institutional Ethics Committee. A set of 21 features including clinical findings and laboratory parameters and chest CT severity score were recorded. The CT score with mild, moderate and severe categories was chosen as response variable. The dimensionality reduction of feature space was performed and classifiers including, decision trees, K-nearest neighbours, support vector machine and ensemble learning were trained with principal components. The model with highest accuracy and area under the ROC curve (AUC) was selected. Results: The median age of patients was 55 years (range: 20-99 years) with 37 males. The feature space was reduced from 21 to 7 predictors, that included fever, cough, fibrin degradation products, haemoglobin, neutrophil-lymphocyte ratio, ferritin and procalcitonin. The linear support vector machine was chosen as the best classifier with 73.7 % and 0.69 accuracy and AUC for severe CT chest score, respectively. The variance contributed by first three principal components were 97.5 %, 2.4 % and 0.0 %, respectively. Conclusion: In view of low degree of relationships between predictors and chest CT scan severity score category as interpreted from accuracy and AUC it can be concluded that chest CT scan has an independent role in the prognosis of COVID-19 patients.

Background/Aim: The recent pandemic of Severe Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV-2) is yet another scourge from the coronaviridae family that causes illnesses ranging from common cold to more severe diseases such as Middle East respiratory syndrome (MERS-CoV) and severe acute respiratory syndrome (SARS-CoV). The numbers are still on the rise, despite a country wide lockdown and yet no definitive drugs and or/vaccines are available to manage the active COVID-19 cases. Methods: The present research design was a hospital based observational descriptive study conducted at S. M. S. Medical College and Attached hospitals, Jaipur, that analysed data of all the patients with COVID-19 related mortality, admitted between 1 April to 4 May 2020. Patients included in this study were RT-PCR confirmed cases of SARS-CoV-2 using nasopharyngeal and oropharyngeal swab samples. Results: The mean age of patients with COVID-19 related mortality was 53.41 ± 18.42 year with majority of patients belonging to age group of more than 60 years (41.18 %) followed closely by COVID-19 positive patients in age range of 45 to 60 years (33.33 %). The male to female ratio was 1.68: 1. Mean time lag between hospitalization and death reported was 6.18 days. Majority of the patients admitted (72.5 %) succumbed within 3 days of hospitalization. Eleven patients (21 %) were brought dead to the hospital who were tested COVID-19 positive after death. Most common comorbidity reported in patients with COVID-19 related mortality was hypertension (30 %) followed by diabetes mellitus (27.5 %). Conclusion: Hypertension and diabetes mellitus might be independent risk factors making an individual susceptible to COVID-19 related death. Elderly patients also have a greater risk of mortality. The non-availability of definitive management protocol and/or vaccine against COVID-19 makes public health preventive measures of social distancing, use of masks and frequent handwashing an important modality in the fight against COVID-19.