Neutropenia and Infection Prophylaxis in Childhood Cancer

Villeneuve, Stephanie; Aftandilian, Catherine

doi:10.1007/s11912-022-01192-5

Cited by 13 publications

(9 citation statements)

References 87 publications

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“…When methotrexate and vincristine were both included in a chemotherapy regimen, there was the highest support for infection co-occurrence at >0.80. Patients who receive these chemotherapy drugs have been shown to benefit from antimicrobial prophylaxis, although the types of antimicrobial prophylaxis administered may vary [ 11 , 12 ].…”

Section: Resultsmentioning

confidence: 99%

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An Interpretable Machine Learning Framework for Rare Disease: A Case Study to Stratify Infection Risk in Pediatric Leukemia

Al-Hussaini,

White,

Varmeziar

et al. 2024

JCM

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Background: Datasets on rare diseases, like pediatric acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), have small sample sizes that hinder machine learning (ML). The objective was to develop an interpretable ML framework to elucidate actionable insights from small tabular rare disease datasets. Methods: The comprehensive framework employed optimized data imputation and sampling, supervised and unsupervised learning, and literature-based discovery (LBD). The framework was deployed to assess treatment-related infection in pediatric AML and ALL. Results: An interpretable decision tree classified the risk of infection as either “high risk” or “low risk” in pediatric ALL (n = 580) and AML (n = 132) with accuracy of ∼79%. Interpretable regression models predicted the discrete number of developed infections with a mean absolute error (MAE) of 2.26 for bacterial infections and an MAE of 1.29 for viral infections. Features that best explained the development of infection were the chemotherapy regimen, cancer cells in the central nervous system at initial diagnosis, chemotherapy course, leukemia type, Down syndrome, race, and National Cancer Institute risk classification. Finally, SemNet 2.0, an open-source LBD software that links relationships from 33+ million PubMed articles, identified additional features for the prediction of infection, like glucose, iron, neutropenia-reducing growth factors, and systemic lupus erythematosus (SLE). Conclusions: The developed ML framework enabled state-of-the-art, interpretable predictions using rare disease tabular datasets. ML model performance baselines were successfully produced to predict infection in pediatric AML and ALL.

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Section: Resultsmentioning

confidence: 99%

“…Prophylactic antibacterial or antifungal treatments are an option [ 10 , 11 , 12 ]. However, physicians must balance the prophylactic prevention of infection with the risk of anti-infectious drug resistance and polypharmacy.…”

Section: Introductionmentioning

confidence: 99%

An Interpretable Machine Learning Framework for Rare Disease: A Case Study to Stratify Infection Risk in Pediatric Leukemia

Al-Hussaini,

White,

Varmeziar

et al. 2024

JCM

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“…The current generation of chemotherapies is frequently aggressive and can cause extended periods of neutropenia, which increases the risk of infection and significantly raises morbidity and mortality for patients (11) . Bacterial infections accounted for the majority of infections (249, 39.2%), while secondary fungal infections (47, 9.4%) and probable viral infections (24, 4.8%) also played a significant role.…”

Section: Discussionmentioning

confidence: 99%

“…This is partly owing to a lack of knowledge regarding the incidence, seriousness, and effects of viral infections in this population. (11) .…”

Section: Discussionmentioning

confidence: 99%

Prevalence of Antibiotic Resistance in Pediatric Malignancy Patients: A Two-Year Retrospective Study in Basra City

Jabbar¹,

Hadi²

2023

The Egyptian Journal of Hospital Medicine

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Background: The most frequent side effects of chemotherapy are infections, which have substantial morbidity and mortality rates in immunocompromised patients. Aim: To screen the prevalence of resistance between antibiotics administered to infected patients in hematological malignancy wards. Subjects and Methods: A retrospective cross-sectional study of pediatric oncology patients was conducted from January 2021 to June 2022 at Basra children hospital. The study included 500 patients with hematological malignancies. Results: This study involved 57.8% male and 42.2% female patients. Leukemia was the most common diagnosis 59% while other solid tumors was 41%. Febrile neutropenia was only 5.8%, bacterial infection was predominant at 49.8% while the fungal infection was 9.4%, and 4.8% suspected viral infection. We found that the high percentage of antibiotics used were Cephalosporin (41.4%) and the lower percentage of antibiotics were Quinolone 0.4%. Conclusion:The most frequent form of infection was a bacterial infection, and the most frequently prescribed antibiotic for oncology patients was cephalosporin. The chest represented the most severely infected place.

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“…Literature reports show that infection-related mortality (IRM) in patients with ALL accounts for 30% of deaths and 64% of treatment-related mortality. Most deaths are due to bacterial infections (68%), followed by fungal and viral infections (20% and 12%, respectively) 3 . Risk factors for the development of infections in patients undergoing anticancer therapy include Caucasian race, female sex, age (higher risk in infants and adolescents), Down syndrome, steroid use, the presence of a central venous catheter, and a prolonged neutropenic phase 2 , 4 , 5 .…”

Section: Introductionmentioning

confidence: 99%

Incidence of bacterial and fungal infections in Polish pediatric patients with acute lymphoblastic leukemia during the pandemic

Zawitkowska,

Drabko,

Lejman

et al. 2023

Sci Rep

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The most common complications related to the treatment of childhood acute lymphoblastic leukemia (ALL) are infections. The aim of the study was to analyze the incidence and mortality rates among pediatric patients with ALL who were treated in 17 Polish pediatric hematology centers in 2020–2021 during the pandemic. Additionally, we compared these results with those of our previous study, which we conducted in the years 2012–2017. The retrospective analysis included 460 patients aged 1–18 years with newly diagnosed ALL. In our study, 361/460 (78.5%) children were reported to have microbiologically documented bacterial infections during chemotherapy. Ten patients (2.8%) died due to sepsis. Fungal infections were reported in 99 children (21.5%), of whom five (5.1%) died due to the infection. We especially observed an increase in bacterial infections during the pandemic period compared to the previous study. The directions of our actions should be to consider antibiotic prophylaxis, shorten the duration of hospitalization, and educate parents and medical staff about complications (mainly infections) during anticancer therapy. It is necessary to continue clinical studies evaluating infection prophylaxis to improve outcomes in childhood ALL patients.

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Neutropenia and Infection Prophylaxis in Childhood Cancer

Cited by 13 publications

References 87 publications

An Interpretable Machine Learning Framework for Rare Disease: A Case Study to Stratify Infection Risk in Pediatric Leukemia

An Interpretable Machine Learning Framework for Rare Disease: A Case Study to Stratify Infection Risk in Pediatric Leukemia

Prevalence of Antibiotic Resistance in Pediatric Malignancy Patients: A Two-Year Retrospective Study in Basra City

Incidence of bacterial and fungal infections in Polish pediatric patients with acute lymphoblastic leukemia during the pandemic

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