Predictors of in-hospital mortality in elderly unvaccinated patients during SARS-CoV-2 Alpha variants epidemic
Zenya Saito, Shota Uchiyama, Saiko Nishioka, Kentaro Tamura, Nobumasa Tamura, Kazuyoshi Kuwano
Infection Prevention in Practice, doi:10.1016/j.infpip.2024.100341
Background: COVID-19, caused by SARS-CoV-2, has caused a global pandemic. This study aimed to identify predictors of in-hospital mortality in unvaccinated elderly patients with COVID-19 by comparing various predictive factors between the survivors and nonsurvivors. Methods: We retrospectively selected 132 unvaccinated patients aged over 65 years with COVID-19 at a hospital in Kanagawa, Japan, during SARS-CoV-2 Alpha variants epidemic. We compared the clinical characteristics, laboratory and radiological findings, treatment, and complications of the survivors and non-survivors. In logistic regression analysis, variables that were significant in the univariate analysis were subjected to multivariate analysis using the variable increase method. Results: There were 119 and 13 patients in the survivor and non-survivor groups, respectively. Multivariate regression revealed increasing odds with the presence of ARDS and DIC (odd ratio (OR) ¼ 16.35, 34.36; P¼0.002, 0.001, respectively) and prolonged hospital stay (OR ¼ 1.17; P¼0.004). Conclusions: We found the complications of ARDS and DIC and hospital length of stay to be independent predictors of in-hospital mortality in elderly unvaccinated patients with COVID-19. Establishing treatments and prevention methods for ARDS and DIC could result in lower mortality rates.
Credit author statement Zenya Saito: Conceptualization, Original draft, Writing, Reviewing and Editing. Shota Uchiyama: Data collection, investigation, reviewing final manuscript. Saiko Nishioka: Data collection, investigation, reviewing final manuscript. Kentaro Tamura: Data collection, investigation, reviewing final manuscript. Nobumasa Tamura: Data collection, investigation, reviewing final manuscript. Kazuyoshi Kuwano: Validation, Methodology, Data analysis, Supervision.
Conflict of interest statement The authors declare that they have no competing interests.
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