Prevention and treatment of bacterial infections in patients with haematological cancers and haematopoietic stem cell transplantation: headways and shortcomings

Abstract

Background: There has been an unprecedented increase in the number of immunocompromised (IC) patients in clinical practice due to various reasons. Bacterial infections are a major cause of morbidity and mortality in this population. Emerging antibacterial resistance poses a significant challenge for prophylaxis and treatment.

Objectives: We aim to provide an update on antibacterial prophylaxis and management, particularly in high-risk IC patients, including those with acute leukaemia and haematopoietic stem cell transplantation.

Sources: We reviewed original articles, systematic reviews, metanalyses, and guidelines using PubMed, Scopus, and Web of Science.

Content: We discussed the pros and cons of fluoroquinolone prophylaxis in neutropenic patients in the context of personalized medicine. We also attempted to give an outline of empirical treatment of presumed bacterial infections and targeted therapy options for documented bacterial infections, considering the recent surge of multiresistant bacteria in haematological cancer patients and local epidemiology. The shortcomings of the current strategies and future needs are discussed in detail.

Implications: Antibacterial prophylaxis with fluoroquinolones may still have a role in preventing bacterial infections in carefully selected patients with high-risk haematology. Empirical treatment algorithms still need to be adjusted according to host and local factors. The use of rapid diagnostic methods may lessen the need for broad-spectrum empirical antibiotic usage. However, these tests may not be easily available due to budget constraints in countries with limited resources but high rates of bacterial resistance. Although new antimicrobials provide opportunities for effective and less toxic treatment of highly resistant bacterial infections, large-scale data from IC patients are very limited. Using data-driven approaches with artificial intelligence tools may guide the selection of appropriate patients who would benefit most from such prophylactic and treatment regimens.

Keywords: Artificial intelligence; Bacterial infection; Haematological cancer; Immunosuppression; Machine learning; Multidrug resistance; Neutropenia; Prophylaxis; Stem cell transplantation; Targeted therapy.