Pharmacotherapy from Pre-COVID to Post-COVID: Longitudinal Trends and Predictive Indicators for Long COVID Symptoms

从新冠疫情前到新冠疫情后的药物治疗:长期新冠症状的纵向趋势和预测指标

阅读:3

Abstract

BACKGROUND/OBJECTIVES: A significant number of COVID-19 cases experience persistent symptoms after the acute infection phase, a condition known as long COVID or post-acute sequelae of COVID-19. Approved prevention and treatment options for long COVID are currently lacking. Given the heterogeneous nature of long COVID, a personalized medicine approach is essential for effective disease management. This study aimed to describe trends in pharmacotherapy from pre-COVID to post-COVID phases to gain insights into COVID-19 treatment strategies and assess whether pre-COVID pharmacotherapy can predict long COVID symptoms as a health status indicator. METHODS: In the Precision Medicine for more Oxygen (P4O2) COVID-19 study, 95 long COVID patients were comprehensively evaluated through post-COVID outpatient clinics and study visits. This study focused on descriptive analysis of the pharmacotherapy patterns across different phases: pre-COVID-19, acute COVID, and post-COVID. Furthermore, associations between pre-COVID medication and long COVID outcomes were analyzed with regression analyses. RESULTS: We observed peaks in the use of certain medications during the acute infection phase, including corticosteroids and antithrombotic agents, with a decrease in the use of renin-angiotensin system inhibitors. Consistently high use of alimentary tract medications was found across all phases. Pre-COVID respiratory medications were associated with fatigue symptoms, while antiinfectives and cardiovascular drugs were linked to fewer persisting long COVID symptom categories. CONCLUSION: Our findings provide longitudinal, descriptive pharmacotherapy insights and suggest that medication history can be a valuable health status indicator in characterizing patients for personalized disease management strategies, considering the heterogeneous nature of long COVID.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。