ABSTRACT
OBJECTIVES: We aimed to report the emergence of azole-resistant invasive aspergillosis in hematologic patients admitted to a tertiary hospital in Spain during the last 4 months. METHODS: Prospective, descriptive study was performed to describe and follow all consecutive proven and probable invasive aspergillosis resistant to azoles from hematological cohort during the last 4 months. All patients had fungal cultures and antifungal susceptibility or real-time PCR detection for Aspergillus species and real-time PCR detection for azole-resistant mutation. RESULTS: Four cases of invasive aspergillosis were diagnosed in 4 months. Three of them had azole-resistant aspergillosis. Microbiological diagnosis was achieved in three cases by means of fungal culture isolation and subsequent antifungal susceptibility whereas one case was diagnosed by PCR-based aspergillus and azole resistance detection. All the azole-resistant aspergillosis presented TR34/L98H mutation. Patients with azole-resistant aspergillosis had different hematologic diseases: multiple myeloma, lymphoblastic acute leukemia, and angioimmunoblastic T lymphoma. Regarding risk factors, one had prolonged neutropenia, two had corticosteroids, and two had viral co-infection. Two of the patients developed aspergillosis under treatment with azoles. CONCLUSION: We have observed a heightened risk of azole-resistant aspergillosis caused by A. fumigatus harboring the TR34/L98H mutation in patients with hematologic malignancies. The emergence of azole-resistant aspergillosis raises concerns for the community, highlighting the urgent need for increased surveillance and the importance of susceptibility testing and new drugs development.
ABSTRACT
INTRODUCTION: The impact of remdesivir on mortality in patients with COVID-19 is still controversial. We aimed to identify clinical phenotype clusters of COVID-19 hospitalized patients with highest benefit from remdesivir use and validate these findings in an external cohort. METHODS: We included consecutive patients hospitalized between February 2020 and February 2021 for COVID-19. The derivation cohort comprised subjects admitted to Hospital Clinic of Barcelona. The validation cohort included patients from Hospital Universitari Mutua de Terrassa (Terrassa) and Hospital Universitari La Fe (Valencia), all tertiary centers in Spain. We employed K-means clustering to group patients according to reverse transcription polymerase chain reaction (rRT-PCR) cycle threshold (Ct) values and lymphocyte counts at diagnosis, and pre-test symptom duration. The impact of remdesivir on 60-day mortality in each cluster was assessed. RESULTS: A total of 1160 patients (median age 66, interquartile range (IQR) 55-78) were included. We identified five clusters, with mortality rates ranging from 0 to 36.7%. Highest mortality rate was observed in the cluster including patients with shorter pre-test symptom duration, lower lymphocyte counts, and lower Ct values at diagnosis. The absence of remdesivir administration was associated with worse outcome in the high-mortality cluster (10.5% vs. 36.7%; p < 0.001), comprising subjects with higher viral loads. These results were validated in an external multicenter cohort of 981 patients. CONCLUSIONS: Patients with COVID-19 exhibit varying mortality rates across different clinical phenotypes. K-means clustering aids in identifying patients who derive the greatest mortality benefit from remdesivir use.
ABSTRACT
INTRODUCTION: Artificial intelligence (AI) and machine learning (ML) have the potential to revolutionize the management of febrile neutropenia (FN) and drive progress toward personalized medicine. AREAS COVERED: In this review, we detail how the collection of a large number of high-quality data can be used to conduct precise mathematical studies with ML and AI. We explain the foundations of these techniques, covering the fundamentals of supervised and unsupervised learning, as well as the most important challenges, e.g. data quality, 'black box' model interpretation and overfitting. To conclude, we provide detailed examples of how AI and ML have been used to enhance predictions of chemotherapy-induced FN, detection of bloodstream infections (BSIs) and multidrug-resistant (MDR) bacteria, and anticipation of severe complications and mortality. EXPERT OPINION: There is promising potential of implementing accurate AI and ML models whilst managing FN. However, their integration as viable clinical tools poses challenges, including technical and implementation barriers. Improving global accessibility, fostering interdisciplinary collaboration, and addressing ethical and security considerations are essential. By overcoming these challenges, we could transform personalized care for patients with FN.
