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Objectives: This study aims to identify infection etiology in febrile neutropenia (FN) is vital. This study explores different microbiological approaches and their impact on diagnosing infections in patients with hematologic malignancies and FN. Methods: This is a retrospective analysis conducted at the Hospital Clinic of Barcelona details microbiological testing strategies used to diagnose infections at FN onset between January 2020 and July 2022. Results: A total of 4520 microbiological tests were ordered in 462 FN episodes, achieving a 10% test positivity rate, with 200 (43.3%) episodes showing microbiological documentation of infection. Blood cultures (40.4%), non-culture blood tests (21.2%), and respiratory tract samples (16.2%) were the most requested. Blood cultures exhibited the highest (16.9%) test positivity rates, whereas non-culture blood tests showed the lowest (3.3%). Bacterial infections were present in 149 of 462 (32.3%) FN episodes. Viral infections (66 of 462, 14.3%)—notably, respiratory viruses—were also frequent. Mortality rate at 60 days was 9.1%; documented infections were associated with a higher risk (15%). Conclusions: In the current landscape of antimicrobial diagnostics, our findings revealed the highest reported rate of microbiologically documented infections at FN onset. Bacterial infections are common; however, our data reiterate the significance of viral infections in causing fever. Optimizing FN management during respiratory viral infections remains a challenge for antimicrobial de-escalation. The low positivity rates observed in certain diagnostic tests emphasize the need for cost-effective diagnostic stewardship.

Clinical Decision Support Systems (CDSS) are computer-based tools that leverage the analysis of large volumes of health data to assist healthcare professionals in making clinical decisions, whether preventive, diagnostic, or therapeutic. This review examines the impact of CDSS on clinical practice, highlighting both their potential benefits and their limitations and challenges. We detail the experience of clinical medical professionals in the development of a virtual control center for COVID-19 patients (C3 COVID-19) in Spain during the SARS-CoV-2 pandemic. This tool enabled real-time monitoring of clinical data for hospitalized COVID-19 patients, optimizing personalized and informed medical decision-making. CDSS can offer significant advantages, such as improving the quality of inpatient care, promoting evidence-based clinical and therapeutic decision-making, facilitating treatment personalization, and enhancing healthcare system efficiency and productivity. However, the implementation of CDSS presents challenges, including the need for physicians to become familiar with the systems and software, and the necessity for ongoing updates and technical support of the systems., (©The Author 2024. Published by Sociedad Española de Quimioterapia. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)(https://creativecommons.org/licenses/by-nc/4.0/).)

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 TR 34 /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., (© 2024. The Author(s).)

Background: This study aimed to describe documented infections associated with postinfusion fever after CAR T-cell therapy and to evaluate daily changes in vital signs, laboratory results, and the National Early Warning Score (NEWS) in patients with and without confirmed bacterial infections following fever onset, with the objective of assisting in antibiotic stewardship., Methods: This was a retrospective, observational study including all consecutive adult patients who received CAR T-cell therapy. Documented infection in the first fever episode after infusion, and clinical and analytic trend comparison of patients with bacterial documented infections and those without documented infections, are described., Results: Among 152 patients treated with CAR T-cell therapy, 87 (57.2%) had fever within 30 days of infusion, with a median time from infusion to fever of 3 (interquartile range, 2-5) days. Of these 87 patients, 82 (94.3%) received broad-spectrum antibiotics. Infection was documented in 9 (10.3%) patients and only 4 (4.6%) had bacterial infections. Clinical signs and biomarkers were similar in patients with bacterial documented infection and in those without documented infection at fever onset. Fever, tachycardia, and high C-reactive protein levels remained high during the first 3 days after CAR T-cell infusion, even when no infection was documented., Conclusions: Fever is a common symptom following CAR T-cell infusion and is largely treated with broad-spectrum antibiotics. However, confirmed bacterial documented infections after the first fever post-CAR T-cell infusion are very unusual. Because clinical parameters and biomarkers are not useful for identifying infectious fever, other methods should be assessed to ensure the proper use of antibiotics., Competing Interests: Potential conflicts of interest. C. G.-V. has received honoraria for talks on behalf of Gilead Sciences, MSD, Pfizer, Janssen, Novartis, Basilea, GSK, Shionogi, AbbVie, and Advanz Pharma, and grant support from Gilead Sciences, Pfizer, GSK, MSD, and Pharmamar. A. S. has received honoraria for talks on behalf of Merck Sharp and Dohme, Pfizer, Novartis, Angelini, Menarini, and Gilead Sciences as well as grant support from Pfizer and Gilead Sciences. J. M. has received honoraria for talks on behalf of Merck Sharp and Dohme, Pfizer, Novartis, and Angelini. O. P. has received honoraria for talks on behalf of BMS and Qiagen and consulting for Sanofi. C. F. d. L. declares receiving grants through his institution from BMS, Janssen, and Amgen; honoraria from Amgen, Janssen, BMS, GSK, and Sanofi; support for attending meetings or travel from Janssen, BMS, GSK, and Amgen; and participation in data and safety monitoring boards or advisory boards with Janssen, BMS, Amgen, Pfizer, and Sanofi. All other authors report no potential conflicts of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

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.