Drug utilisation patterns & clinical outcomes in hospitalised COVID-19 patients: A geospatial & machine learning approach.

Abstract

Background & objectives Coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed challenges in clinical management due to a lack of established treatment guidelines. This study aimed to analyse drug utilisation patterns and identify factors influencing clinical outcomes in COVID-19 patients. Methods A retrospective study was conducted on 380 confirmed COVID-19 patients admitted between April and June 2021 at a tertiary hospital in Sikkim, India. Study participants demographics, medications, comorbidities, outcomes, and geospatial data were collected with due approval from the Institutional Ethics Committee. Machine learning classification and regression models were used for analysis. Results The Random Forest classification model achieved the highest accuracy of 90.7 per cent and an AUROC score of 0.86. Methylprednisolone use was associated with an 11.4 per cent mortality rate. Geospatial analysis identified significant mortality clustering in the East district for female study participants and in the East and North districts for male study participants, with a Moran's I index of 0.125080 and a z-score of 8.642819, indicating statistically significant spatial clustering. Interpretation & conclusions The study provides insights into COVID-19 management practices and outcomes. Machine learning identified relationships between factors associated with mortality, which could be due to advanced disease state, associated co-morbidities or post-treatment issues. Further prospective studies are needed to validate findings and address limitations.