Aspergillosis in Critically Ill Patients with and Without COVID-19 in a Tertiary Hospital in Southern Brazil.
Mariana Rodrigues TrápagaVanice Rodrigues PoesterRossana Patrícia BassoBianca Dos Santos BlanLívia Silveira MunhozAlessandro C PasqualottoTalita da Fontoura WernerMaria Letícia FigurelliDavid A StevensAndrea von GrollMelissa Orzechowski XavierPublished in: Mycopathologia (2024)
The impact of invasive pulmonary aspergillosis (IPA) on non-neutropenic critically ill patients in intensive care units (ICU) has been demonstrated in recent decades. Furthermore, after the start of the COVID-19 pandemic, COVID-19 associated with pulmonary aspergillosis (CAPA) has become a major concern in ICUs. However, epidemiological data from different regions are scarce. We evaluated the prevalence and clinical-epidemiological data of IPA in patients with COVID-19 requiring mechanical ventilation (MV) in the ICU ("severe COVID-19") and non-COVID ICU patients in MV of a tertiary hospital in the southern region of Brazil. Eighty-seven patients admitted between June 2020 and August 2022 were included; 31 with severe COVID-19. For the diagnosis of IPA or CAPA, algorithms including host factors and mycological criteria (positive culture for Aspergillus spp., immunoassay for galactomannan detection, and/or qPCR) were utilized. The overall incidence of IPA and CAPA in our ICU was 73 cases/1000 ICU hospitalizations. Aspergillosis occurred in 13% (4/31) of the COVID-19 patients, and in 16% (9/56) of the critically ill patients without COVID-19, with mortality rates of 75% (3/4) and 67% (6/9), respectively. Our results highlight the need for physicians enrolled in ICU care to be aware of aspergillosis and for more access of the patients to sensitive and robust diagnostic tests by biomarkers detection.
Keyphrases
- mechanical ventilation
- intensive care unit
- coronavirus disease
- sars cov
- end stage renal disease
- acute respiratory distress syndrome
- ejection fraction
- chronic kidney disease
- newly diagnosed
- risk factors
- machine learning
- respiratory failure
- healthcare
- peritoneal dialysis
- prognostic factors
- primary care
- electronic health record
- early onset
- coronary artery disease
- deep learning
- cardiovascular events
- drug induced
- sensitive detection
- quality improvement
- pain management
- artificial intelligence
- real time pcr