Cargando…

A prediction model for bacteremia and transfer to intensive care in pediatric and adolescent cancer patients with febrile neutropenia

Febrile neutropenia (FN) is a common condition in children receiving chemotherapy. Our goal in this study was to develop a model for predicting blood stream infection (BSI) and transfer to intensive care (TIC) at time of presentation in pediatric cancer patients with FN. We conducted an observationa...

Descripción completa

Detalles Bibliográficos
Autores principales: Alali, Muayad, Mayampurath, Anoop, Dai, Yangyang, Bartlett, Allison H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9076887/
https://www.ncbi.nlm.nih.gov/pubmed/35523855
http://dx.doi.org/10.1038/s41598-022-11576-z
_version_ 1784702024818884608
author Alali, Muayad
Mayampurath, Anoop
Dai, Yangyang
Bartlett, Allison H.
author_facet Alali, Muayad
Mayampurath, Anoop
Dai, Yangyang
Bartlett, Allison H.
author_sort Alali, Muayad
collection PubMed
description Febrile neutropenia (FN) is a common condition in children receiving chemotherapy. Our goal in this study was to develop a model for predicting blood stream infection (BSI) and transfer to intensive care (TIC) at time of presentation in pediatric cancer patients with FN. We conducted an observational cohort analysis of pediatric and adolescent cancer patients younger than 24 years admitted for fever and chemotherapy-induced neutropenia over a 7-year period. We excluded stem cell transplant recipients who developed FN after transplant and febrile non-neutropenic episodes. The primary outcome was onset of BSI, as determined by positive blood culture within 7 days of onset of FN. The secondary outcome was transfer to intensive care (TIC) within 14 days of FN onset. Predictor variables include demographics, clinical, and laboratory measures on initial presentation for FN. Data were divided into independent derivation (2009–2014) and prospective validation (2015–2016) cohorts. Prediction models were built for both outcomes using logistic regression and random forest and compared with Hakim model. Performance was assessed using area under the receiver operating characteristic curve (AUC) metrics. A total of 505 FN episodes (FNEs) were identified in 230 patients. BSI was diagnosed in 106 (21%) and TIC occurred in 56 (10.6%) episodes. The most common oncologic diagnosis with FN was acute lymphoblastic leukemia (ALL), and the highest rate of BSI was in patients with AML. Patients who had BSI had higher maximum temperature, higher rates of prior BSI and higher incidence of hypotension at time of presentation compared with patients who did not have BSI. FN patients who were transferred to the intensive care (TIC) had higher temperature and higher incidence of hypotension at presentation compared to FN patients who didn’t have TIC. We compared 3 models: (1) random forest (2) logistic regression and (3) Hakim model. The areas under the curve for BSI prediction were (0.79, 0.65, and 0.64, P < 0.05) for models 1, 2, and 3, respectively. And for TIC prediction were (0.88, 0.76, and 0.65, P < 0.05) respectively. The random forest model demonstrated higher accuracy in predicting BSI and TIC and showed a negative predictive value (NPV) of 0.91 and 0.97 for BSI and TIC respectively at the best cutoff point as determined by Youden’s Index. Likelihood ratios (LRs) (post-test probability) for RF model have potential utility of identifying low risk for BSI and TIC (0.24 and 0.12) and high-risk patients (3.5 and 6.8) respectively. Our prediction model has a very good diagnostic performance in clinical practices for both BSI and TIC in FN patients at the time of presentation. The model can be used to identify a group of individuals at low risk for BSI who may benefit from early discharge and reduced length of stay, also it can identify FN patients at high risk of complications who might benefit from more intensive therapies at presentation.
format Online
Article
Text
id pubmed-9076887
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-90768872022-05-08 A prediction model for bacteremia and transfer to intensive care in pediatric and adolescent cancer patients with febrile neutropenia Alali, Muayad Mayampurath, Anoop Dai, Yangyang Bartlett, Allison H. Sci Rep Article Febrile neutropenia (FN) is a common condition in children receiving chemotherapy. Our goal in this study was to develop a model for predicting blood stream infection (BSI) and transfer to intensive care (TIC) at time of presentation in pediatric cancer patients with FN. We conducted an observational cohort analysis of pediatric and adolescent cancer patients younger than 24 years admitted for fever and chemotherapy-induced neutropenia over a 7-year period. We excluded stem cell transplant recipients who developed FN after transplant and febrile non-neutropenic episodes. The primary outcome was onset of BSI, as determined by positive blood culture within 7 days of onset of FN. The secondary outcome was transfer to intensive care (TIC) within 14 days of FN onset. Predictor variables include demographics, clinical, and laboratory measures on initial presentation for FN. Data were divided into independent derivation (2009–2014) and prospective validation (2015–2016) cohorts. Prediction models were built for both outcomes using logistic regression and random forest and compared with Hakim model. Performance was assessed using area under the receiver operating characteristic curve (AUC) metrics. A total of 505 FN episodes (FNEs) were identified in 230 patients. BSI was diagnosed in 106 (21%) and TIC occurred in 56 (10.6%) episodes. The most common oncologic diagnosis with FN was acute lymphoblastic leukemia (ALL), and the highest rate of BSI was in patients with AML. Patients who had BSI had higher maximum temperature, higher rates of prior BSI and higher incidence of hypotension at time of presentation compared with patients who did not have BSI. FN patients who were transferred to the intensive care (TIC) had higher temperature and higher incidence of hypotension at presentation compared to FN patients who didn’t have TIC. We compared 3 models: (1) random forest (2) logistic regression and (3) Hakim model. The areas under the curve for BSI prediction were (0.79, 0.65, and 0.64, P < 0.05) for models 1, 2, and 3, respectively. And for TIC prediction were (0.88, 0.76, and 0.65, P < 0.05) respectively. The random forest model demonstrated higher accuracy in predicting BSI and TIC and showed a negative predictive value (NPV) of 0.91 and 0.97 for BSI and TIC respectively at the best cutoff point as determined by Youden’s Index. Likelihood ratios (LRs) (post-test probability) for RF model have potential utility of identifying low risk for BSI and TIC (0.24 and 0.12) and high-risk patients (3.5 and 6.8) respectively. Our prediction model has a very good diagnostic performance in clinical practices for both BSI and TIC in FN patients at the time of presentation. The model can be used to identify a group of individuals at low risk for BSI who may benefit from early discharge and reduced length of stay, also it can identify FN patients at high risk of complications who might benefit from more intensive therapies at presentation. Nature Publishing Group UK 2022-05-06 /pmc/articles/PMC9076887/ /pubmed/35523855 http://dx.doi.org/10.1038/s41598-022-11576-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Alali, Muayad
Mayampurath, Anoop
Dai, Yangyang
Bartlett, Allison H.
A prediction model for bacteremia and transfer to intensive care in pediatric and adolescent cancer patients with febrile neutropenia
title A prediction model for bacteremia and transfer to intensive care in pediatric and adolescent cancer patients with febrile neutropenia
title_full A prediction model for bacteremia and transfer to intensive care in pediatric and adolescent cancer patients with febrile neutropenia
title_fullStr A prediction model for bacteremia and transfer to intensive care in pediatric and adolescent cancer patients with febrile neutropenia
title_full_unstemmed A prediction model for bacteremia and transfer to intensive care in pediatric and adolescent cancer patients with febrile neutropenia
title_short A prediction model for bacteremia and transfer to intensive care in pediatric and adolescent cancer patients with febrile neutropenia
title_sort prediction model for bacteremia and transfer to intensive care in pediatric and adolescent cancer patients with febrile neutropenia
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9076887/
https://www.ncbi.nlm.nih.gov/pubmed/35523855
http://dx.doi.org/10.1038/s41598-022-11576-z
work_keys_str_mv AT alalimuayad apredictionmodelforbacteremiaandtransfertointensivecareinpediatricandadolescentcancerpatientswithfebrileneutropenia
AT mayampurathanoop apredictionmodelforbacteremiaandtransfertointensivecareinpediatricandadolescentcancerpatientswithfebrileneutropenia
AT daiyangyang apredictionmodelforbacteremiaandtransfertointensivecareinpediatricandadolescentcancerpatientswithfebrileneutropenia
AT bartlettallisonh apredictionmodelforbacteremiaandtransfertointensivecareinpediatricandadolescentcancerpatientswithfebrileneutropenia
AT alalimuayad predictionmodelforbacteremiaandtransfertointensivecareinpediatricandadolescentcancerpatientswithfebrileneutropenia
AT mayampurathanoop predictionmodelforbacteremiaandtransfertointensivecareinpediatricandadolescentcancerpatientswithfebrileneutropenia
AT daiyangyang predictionmodelforbacteremiaandtransfertointensivecareinpediatricandadolescentcancerpatientswithfebrileneutropenia
AT bartlettallisonh predictionmodelforbacteremiaandtransfertointensivecareinpediatricandadolescentcancerpatientswithfebrileneutropenia