Report of three azole-resistant Aspergillus fumigatus cases with TR34/L98H mutation in hematological patients in Barcelona, Spain.

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.

Improving management of febrile neutropenia in oncology patients: the role of artificial intelligence and machine learning.

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.

Infection epidemiology in relation to different therapy phases in patients with haematological malignancies receiving CAR T-cell therapy.

BACKGROUND: We described the real-life epidemiology and causes of infections on the different therapy phases in patients undergoing chimeric antigen receptor (CAR) T-cells directed towards CD19+ or BCMA+ cells. METHODS: All consecutive patients receiving CAR T-cell therapy at our institution were prospectively followed-up. We performed various comparative analyses of all patients and subgroups with and without infections. RESULTS: Ninety-one adults mainly received CAR T-cell therapy for acute leukaemia (53%) and lymphoma (33%). We documented a total of 77 infections in 47 (52%) patients, 37 (48%) during the initial neutropenic phase and 40 (52%) during the non-neutropenic phase. Infections during the neutropenic phase were mainly due to bacterial (29, 78%): catheter infections (11 [38%] cases), endogenous source (5 [17%]), and Clostridioides difficile (5 [17%]). Patients receiving corticosteroids after CAR T-cell therapy had a higher risk of endogenous infection (100% vs. 16%; p = .006). During the non-neutropenic phase, bacterial infections remained very frequent (24, 60%), mainly with catheter source (8, 33%). Respiratory tract infections were common (17, 43%). CONCLUSIONS: Infections after CAR T-cell therapy were frequent. During the neutropenic phase, it is essential to prevent nosocomial infections and balance the use of antibiotics to lower endogenous bacteraemia and Clostridial infection rates.

Breakthrough invasive fungal infection among patients with haematologic malignancies: A national, prospective, and multicentre study.

OBJECTIVES: We describe the current epidemiology, causes, and outcomes of breakthrough invasive fungal infections (BtIFI) in patients with haematologic malignancies. METHODS: BtIFI in patients with ≥ 7 days of prior antifungals were prospectively diagnosed (36 months across 13 Spanish hospitals) according to revised EORTC/MSG definitions. RESULTS: 121 episodes of BtIFI were documented, of which 41 (33.9%) were proven; 53 (43.8%), probable; and 27 (22.3%), possible. The most frequent prior antifungals included posaconazole (32.2%), echinocandins (28.9%) and fluconazole (24.8%)-mainly for primary prophylaxis (81%). The most common haematologic malignancy was acute leukaemia (64.5%), and 59 (48.8%) patients had undergone a hematopoietic stem-cell transplantation. Invasive aspergillosis, principally caused by non-fumigatus Aspergillus, was the most frequent BtIFI with 55 (45.5%) episodes recorded, followed by candidemia (23, 19%), mucormycosis (7, 5.8%), other moulds (6, 5%) and other yeasts (5, 4.1%). Azole resistance/non-susceptibility was commonly found. Prior antifungal therapy widely determined BtIFI epidemiology. The most common cause of BtIFI in proven and probable cases was the lack of activity of the prior antifungal (63, 67.0%). At diagnosis, antifungal therapy was mostly changed (90.9%), mainly to liposomal amphotericin-B (48.8%). Overall, 100-day mortality was 47.1%; BtIFI was either the cause or an essential contributing factor to death in 61.4% of cases. CONCLUSIONS: BtIFI are mainly caused by non-fumigatus Aspergillus, non-albicans Candida, Mucorales and other rare species of mould and yeast. Prior antifungals determine the epidemiology of BtIFI. The exceedingly high mortality due to BtIFI warrants an aggressive diagnostic approach and early initiation of broad-spectrum antifungals different than those previously used.

Real-life epidemiology and current outcomes of hospitalized adults with invasive fungal infections.

We aimed to describe the current epidemiology of both hosts with invasive fungal infections (IFIs) and causative fungi. And, detail outcomes of these infections at 12 weeks in a real-life cohort of hospitalized patients. The study was retrospective and observational to describe IFI diagnosed in a tertiary hospital (February 2017-December 2021). We included all consecutive patients meeting criteria for proven or probable IFI according to EORTC-MSG and other criteria. A total of 367 IFIs were diagnosed. 11.7% were breakthrough infections, and 56.4% were diagnosed in the intensive care unit. Corticosteroid use (41.4%) and prior viral infection (31.3%) were the most common risk factors for IFI. Lymphoma and pneumocystis pneumonia were the most common baseline and fungal diseases. Only 12% of IFI occurred in patients with neutropenia. Fungal cultures were the most important diagnostic tests (85.8%). The most frequent IFIs were candidemia (42.2%) and invasive aspergillosis (26.7%). Azole-resistant Candida strains and non-fumigatus Aspergillus infections represented 36.1% and 44.5% of the cases, respectively. Pneumocystosis (16.9%), cryptococcosis (4.6%), and mucormycosis (2.7%) were also frequent, as well as mixed infections (3.4%). Rare fungi accounted for 9.5% of infections. Overall, IFI mortality at 12 weeks was 32.2%; higher rates were observed for Mucorales (55.6%), Fusarium (50%), and mixed infections (60%). We documented emerging changes in both hosts and real-life IFI epidemiology. Physicians should be aware of these changes to suspect infections and be aggressive in diagnoses and treatments. Currently, outcomes for such clinical scenarios remain extremely poor.

Current epidemiology of the host and fungi and IFI treatments are changing. Real-life data on this subject are scarce. We present our most recent evidence to highlight the importance of the ongoing challenges that require further investigation and clinical adjustments.