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Aim: Life‐threatening infections significantly impact the care of children undergoing therapy for acute lymphoblastic leukemia (ALL) who are at risk of severe sepsis due to both host and treatment factors. Our aim was to develop a life‐threatening infection risk prediction model that would allow remote rapid triage of patients to reduce time to first dose of antibiotics and sepsis‐related mortality. Methods: A retrospective analysis of 2068 fever episodes during ALL therapy was used for model building and subsequent internal validation. Results: Three hundred and seventy‐seven patients were treated for ALL in two institutions with comparable critical and supportive care resources. A total of 55 patients accounted for 71 admissions to the critical care unit for sepsis that led to eight septic deaths during a 16‐year study period. A retrospective analysis of risk factors for sepsis enabled us to build a model focused on 13 variables that discriminated admissions requiring critical care well: area under the receiver operating characteristic curve of.82; 95% CI.76–.87, p<.001, and Brier score of.033. Significant univariate predictors included neutropenia, presence of symptoms of abdominal pain, diarrhea, fever during induction or steroid‐based phases, and the lack of any localizing source of infection at time of presentation. Conclusion: We have developed a risk prediction model that can reliably identify ALL patients undergoing treatment who are at a higher risk of life‐threatening sepsis. Clinical applicability can potentially be extended to low‐middle income settings, and its utility should be further studied in real‐world settings. [ABSTRACT FROM AUTHOR]

Aim: Life-threatening infections significantly impact the care of children undergoing therapy for acute lymphoblastic leukemia (ALL) who are at risk of severe sepsis due to both host and treatment factors. Our aim was to develop a life-threatening infection risk prediction model that would allow remote rapid triage of patients to reduce time to first dose of antibiotics and sepsis-related mortality., Methods: A retrospective analysis of 2068 fever episodes during ALL therapy was used for model building and subsequent internal validation., Results: Three hundred and seventy-seven patients were treated for ALL in two institutions with comparable critical and supportive care resources. A total of 55 patients accounted for 71 admissions to the critical care unit for sepsis that led to eight septic deaths during a 16-year study period. A retrospective analysis of risk factors for sepsis enabled us to build a model focused on 13 variables that discriminated admissions requiring critical care well: area under the receiver operating characteristic curve of .82; 95% CI .76-.87, p<.001, and Brier score of .033. Significant univariate predictors included neutropenia, presence of symptoms of abdominal pain, diarrhea, fever during induction or steroid-based phases, and the lack of any localizing source of infection at time of presentation., Conclusion: We have developed a risk prediction model that can reliably identify ALL patients undergoing treatment who are at a higher risk of life-threatening sepsis. Clinical applicability can potentially be extended to low-middle income settings, and its utility should be further studied in real-world settings., (© 2022 John Wiley & Sons Australia, Ltd.)