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Background: Chronic lymphocytic leukemia (CLL) is a hematological malignancy characterized by immune dysfunction, which significantly contributes to increased morbidity and mortality due to infections. Summary: Advancement in therapeutic strategies based on combination chemoimmunotherapy and targeted treatment have increased life expectancy for patients affected by CLL. However, mortality and morbidity due to infection showed no improvement over the last decades. Although therapy options are highly efficient in targeting leukemic cells, several studies highlighted the interactions of different treatments with the tumor microenvironment immune components, significantly impacting their clinical efficacy and fostering increased risk of infections. Key Messages: Given the profound immune dysfunction caused by CLL itself, treatment can thus represent a double-edged sword. Thus, it is essential to increase our understanding and awareness on how conventional therapies affect the disease-microenvironment-infection axis to ensure the best personalized strategy for each patient. This requires careful consideration of the advantages and disadvantages of efficient treatments, whether chemoimmunotherapy or targeted combinations, leading to risk of infectious complications. To this regard, our machine learning-based algorithm CLL Treatment-Infection Model, currently implemented into the local electronic health record system for Eastern Denmark, aims at early identification of patients at high risk of serious infections (PreVent-ACaLL; NCT03868722). We here review strategies for management of immune dysfunction and infections in CLL., BACKGROUND: Chronic lymphocytic leukemia (CLL) is a hematological malignancy characterized by immune dysfunction, which significantly contributes to increased morbidity and mortality due to infections. SUMMARY: Advancement in therapeutic strategies based on combination chemoimmunotherapy and targeted treatment have increased life expectancy for patients affected by CLL. However, mortality and morbidity due to infection showed no improvement over the last decades. Although therapy options are highly efficient in targeting leukemic cells, several studies highlighted the interactions of different treatments with the tumor microenvironment immune components, significantly impacting their clinical efficacy and fostering increased risk of infections. KEY MESSAGES: Given the profound immune dysfunction caused by CLL itself, treatment can thus represent a double-edged sword. Thus, it is essential to increase our understanding and awareness on how conventional therapies affect the disease-microenvironment-infection axis to ensure the best personalized strategy for each patient. This requires careful consideration of the advantages and disadvantages of efficient treatments, whether chemoimmunotherapy or targeted combinations, leading to risk of infectious complications. To this regard, our machine learning-based algorithm CLL Treatment-Infection Model, currently implemented into the local electronic health record system for Eastern Denmark, aims at early identification of patients at high risk of serious infections (PreVent-ACaLL; NCT03868722). We here review strategies for management of immune dysfunction and infections in CLL.