The Internal COVID-19 Database Registry: A Rural, Community Hospital’s Experience with the Pandemic

M. Assaad,Apurwa Karki

The Guthrie journal of the Donald Guthrie Foundation for Medical Research(2023)

引用 0|浏览0
暂无评分
摘要
Background: The COVID-19 pandemic has brought to light several long-term complications that may be challenging for clinicians to manage. Therefore, it is imperative for physicians to be able to identify patients at risk for infection and to understand the deleterious physiologic and clinical effects the virus can have on long-term lung function. Methods: The authors utilized the internal COVID-19 database registry at a rural, community hospital to identify the presence of pulmonary-specific conditions that may have predisposed the patient population to infection. Data were collected from 1,329 patients admitted to the hospital between March 2020 and November 2021. Data regarding patient demographics, vaccination status, baseline pulmonary conditions, and pulmonary function tests were collected. All statistical analysis was carried out in R statistical programming language (R Core Team, 2021). Results: The pulmonary condition that had the highest mortality rate (42.86%) was obesity hypoventilation syndrome; however, the total number of patients with this diagnosis was only 7. With regards to change in pulmonary function parameters following admission, the only variable that was found to be statistically significant was a decrease in DLCO (diffusing capacity) for patients who survived. Interestingly, there was no statistically significant difference in mortality among vaccinated patients. Conclusions: Although some confounding variables may exist, the registry database may prove clinically useful to help guide physicians in establishing disease patterns and better managing long-term complications from COVID-19. Additional variables of interest to measure may include new imaging findings of pulmonary fibrosis, development of pulmonary hypertension, and new oxygen requirements.
更多
查看译文
关键词
pandemic,community hospitals,rural
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要