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2857. A Statistical Model to Predict Invasive Fungal Disease in Pediatric Cancer and Hematopoietic Stem Cell Transplantation Patients

BACKGROUND: Invasive fungal diseases (IFDs) are devastating opportunistic infections that result in significant morbidity and death in pediatric cancer and hematopoietic stem cell transplantation (SCT) patients. Identification of risk factors for IFD will help clinical decisions relevant to the diag...

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Autores principales: Alali, Muayad, Kumar, Madan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6809245/
http://dx.doi.org/10.1093/ofid/ofz359.162
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author Alali, Muayad
Kumar, Madan
author_facet Alali, Muayad
Kumar, Madan
author_sort Alali, Muayad
collection PubMed
description BACKGROUND: Invasive fungal diseases (IFDs) are devastating opportunistic infections that result in significant morbidity and death in pediatric cancer and hematopoietic stem cell transplantation (SCT) patients. Identification of risk factors for IFD will help clinical decisions relevant to the diagnosis and management of IFD in a timely manner. Despite this, data evaluating prediction risk tools for IFD in pediatric cancer are limited. METHODS: We conducted a retrospective review of pediatric oncology patients with a diagnosis of febrile neutropenia (FN) at UChicago Comer Children’s Hospital from July 2009 to December 2016. We analyzed 13 clinical, laboratory, and treatment-related risk factors for IFD including (age, gender, underlying diagnosis, SCT status, graft vs. host disease, chemotherapy in the last 2 weeks, temperature, height, fever duration, presence of hypotension, absolute neutrophil count, duration of neutropenia, absolute monocyte count, and the absolute lymphocyte count (ALC)). IFD was stratified as possible, probable, and proven according to the latest EORTC/MSG criteria (2008). Multivariable logistic regression risk prediction models were developed with separate analyses for all suspected IFD cases and only proven and probable cases. RESULTS: A total of 667 FN episodes (FNEs) were identified in 265 patients. IFD was diagnosed in 62 episodes (9.2%) of which 13 (1.9%) were proven, 27 (4%) probable, and 22 (3.3%) possible. Five variables obtained were significantly more common in IFD. Patients presenting with hypotension and fever >5 days were highly associated with IFD (P < 0.001). SCT receipts (P < 0.01), neutropenia longer than 10 days (P = 0.02), and ALC <300 mm(3) at time of presentation (P = 0.03) were additional risk factors. The final model performs very well compared with other published models with a receiver operating characteristic–area under the curve (ROC-AUC) of 86.5 for all IFD cases and ROC-AUC of 84.5 for proven, probable IFD cases. CONCLUSION: Our findings showed important clinical markers for the development of IFD in pediatric oncology patients. A predictive regression model including identified significant factors has been created. Risk stratification with prospective external validation using this model can be used to refine the clinical approach. [Image: see text] [Image: see text] DISCLOSURES: All Authors: No reported Disclosures.
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spelling pubmed-68092452019-10-28 2857. A Statistical Model to Predict Invasive Fungal Disease in Pediatric Cancer and Hematopoietic Stem Cell Transplantation Patients Alali, Muayad Kumar, Madan Open Forum Infect Dis Abstracts BACKGROUND: Invasive fungal diseases (IFDs) are devastating opportunistic infections that result in significant morbidity and death in pediatric cancer and hematopoietic stem cell transplantation (SCT) patients. Identification of risk factors for IFD will help clinical decisions relevant to the diagnosis and management of IFD in a timely manner. Despite this, data evaluating prediction risk tools for IFD in pediatric cancer are limited. METHODS: We conducted a retrospective review of pediatric oncology patients with a diagnosis of febrile neutropenia (FN) at UChicago Comer Children’s Hospital from July 2009 to December 2016. We analyzed 13 clinical, laboratory, and treatment-related risk factors for IFD including (age, gender, underlying diagnosis, SCT status, graft vs. host disease, chemotherapy in the last 2 weeks, temperature, height, fever duration, presence of hypotension, absolute neutrophil count, duration of neutropenia, absolute monocyte count, and the absolute lymphocyte count (ALC)). IFD was stratified as possible, probable, and proven according to the latest EORTC/MSG criteria (2008). Multivariable logistic regression risk prediction models were developed with separate analyses for all suspected IFD cases and only proven and probable cases. RESULTS: A total of 667 FN episodes (FNEs) were identified in 265 patients. IFD was diagnosed in 62 episodes (9.2%) of which 13 (1.9%) were proven, 27 (4%) probable, and 22 (3.3%) possible. Five variables obtained were significantly more common in IFD. Patients presenting with hypotension and fever >5 days were highly associated with IFD (P < 0.001). SCT receipts (P < 0.01), neutropenia longer than 10 days (P = 0.02), and ALC <300 mm(3) at time of presentation (P = 0.03) were additional risk factors. The final model performs very well compared with other published models with a receiver operating characteristic–area under the curve (ROC-AUC) of 86.5 for all IFD cases and ROC-AUC of 84.5 for proven, probable IFD cases. CONCLUSION: Our findings showed important clinical markers for the development of IFD in pediatric oncology patients. A predictive regression model including identified significant factors has been created. Risk stratification with prospective external validation using this model can be used to refine the clinical approach. [Image: see text] [Image: see text] DISCLOSURES: All Authors: No reported Disclosures. Oxford University Press 2019-10-23 /pmc/articles/PMC6809245/ http://dx.doi.org/10.1093/ofid/ofz359.162 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Abstracts
Alali, Muayad
Kumar, Madan
2857. A Statistical Model to Predict Invasive Fungal Disease in Pediatric Cancer and Hematopoietic Stem Cell Transplantation Patients
title 2857. A Statistical Model to Predict Invasive Fungal Disease in Pediatric Cancer and Hematopoietic Stem Cell Transplantation Patients
title_full 2857. A Statistical Model to Predict Invasive Fungal Disease in Pediatric Cancer and Hematopoietic Stem Cell Transplantation Patients
title_fullStr 2857. A Statistical Model to Predict Invasive Fungal Disease in Pediatric Cancer and Hematopoietic Stem Cell Transplantation Patients
title_full_unstemmed 2857. A Statistical Model to Predict Invasive Fungal Disease in Pediatric Cancer and Hematopoietic Stem Cell Transplantation Patients
title_short 2857. A Statistical Model to Predict Invasive Fungal Disease in Pediatric Cancer and Hematopoietic Stem Cell Transplantation Patients
title_sort 2857. a statistical model to predict invasive fungal disease in pediatric cancer and hematopoietic stem cell transplantation patients
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6809245/
http://dx.doi.org/10.1093/ofid/ofz359.162
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