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Machine Learning for Early Warning of Septic Shock in Children With Hematological Malignancies Accompanied by Fever or Neutropenia: A Single Center Retrospective Study
OBJECTIVES: The purpose of this article was to establish and validate clinically applicable septic shock early warning model (SSEW model) that can identify septic shock in hospitalized children with onco-hematological malignancies accompanied with fever or neutropenia. METHODS: Data from EMRs were c...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8240637/ https://www.ncbi.nlm.nih.gov/pubmed/34211848 http://dx.doi.org/10.3389/fonc.2021.678743 |
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author | Xiang, Long Wang, Hansong Fan, Shujun Zhang, Wenlan Lu, Hua Dong, Bin Liu, Shijian Chen, Yiwei Wang, Ying Zhao, Liebin Fu, Lijun |
author_facet | Xiang, Long Wang, Hansong Fan, Shujun Zhang, Wenlan Lu, Hua Dong, Bin Liu, Shijian Chen, Yiwei Wang, Ying Zhao, Liebin Fu, Lijun |
author_sort | Xiang, Long |
collection | PubMed |
description | OBJECTIVES: The purpose of this article was to establish and validate clinically applicable septic shock early warning model (SSEW model) that can identify septic shock in hospitalized children with onco-hematological malignancies accompanied with fever or neutropenia. METHODS: Data from EMRs were collected from hospitalized pediatric patients with hematological and oncological disease at Shanghai Children’s Medical Center. Medical records of patients (>30 days and <19 years old) with fever (≥38°C) or absolute neutrophil count (ANC) below 1.0 × 10(9)/L hospitalized with hematological or oncological disease between January 1, 2017 and August 1, 2019 were considered. Patients in whom septic shock was diagnosed during the observation period formed the septic shock group, whereas non-septic-shock group was the control group. In the septic shock group, the time points at 4, 8, 12, and 24 hours prior to septic shock were taken as observation points, and corresponding observation points were obtained in the control group after matching. We employed machine learning artificial intelligence (AI) to filter features and used XGBoost algorithm to build SSEW model. Area under the ROC curve (AU-ROC) was used to compare the effectiveness among the SSEW Model, logistic regression model, and pediatric sequential organ failure score (pSOFA) for early warning of septic shock. MAIN RESULTS: A total of 64 observation periods in the septic shock group and 2191 in the control group were included. AU-ROC of the SSEW model had higher predictive value for septic shock compared with the pSOFA score (0.93 vs. 0.76, Z = −2.73, P = 0.006). Further analysis showed that the AU-ROC of the SSEW model was superior to the pSOFA score at the observation points 4, 8, 12, and 24 h before septic shock. At the 24 h observation point, the SSEW model incorporated 14 module root features and 23 derived features. CONCLUSION: The SSEW model for hematological or oncological pediatric patients could help clinicians to predict the risk of septic shock in patients with fever or neutropenia 24 h in advance. Further prospective studies on clinical application scenarios are needed to determine the clinical utility of this AI model. |
format | Online Article Text |
id | pubmed-8240637 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82406372021-06-30 Machine Learning for Early Warning of Septic Shock in Children With Hematological Malignancies Accompanied by Fever or Neutropenia: A Single Center Retrospective Study Xiang, Long Wang, Hansong Fan, Shujun Zhang, Wenlan Lu, Hua Dong, Bin Liu, Shijian Chen, Yiwei Wang, Ying Zhao, Liebin Fu, Lijun Front Oncol Oncology OBJECTIVES: The purpose of this article was to establish and validate clinically applicable septic shock early warning model (SSEW model) that can identify septic shock in hospitalized children with onco-hematological malignancies accompanied with fever or neutropenia. METHODS: Data from EMRs were collected from hospitalized pediatric patients with hematological and oncological disease at Shanghai Children’s Medical Center. Medical records of patients (>30 days and <19 years old) with fever (≥38°C) or absolute neutrophil count (ANC) below 1.0 × 10(9)/L hospitalized with hematological or oncological disease between January 1, 2017 and August 1, 2019 were considered. Patients in whom septic shock was diagnosed during the observation period formed the septic shock group, whereas non-septic-shock group was the control group. In the septic shock group, the time points at 4, 8, 12, and 24 hours prior to septic shock were taken as observation points, and corresponding observation points were obtained in the control group after matching. We employed machine learning artificial intelligence (AI) to filter features and used XGBoost algorithm to build SSEW model. Area under the ROC curve (AU-ROC) was used to compare the effectiveness among the SSEW Model, logistic regression model, and pediatric sequential organ failure score (pSOFA) for early warning of septic shock. MAIN RESULTS: A total of 64 observation periods in the septic shock group and 2191 in the control group were included. AU-ROC of the SSEW model had higher predictive value for septic shock compared with the pSOFA score (0.93 vs. 0.76, Z = −2.73, P = 0.006). Further analysis showed that the AU-ROC of the SSEW model was superior to the pSOFA score at the observation points 4, 8, 12, and 24 h before septic shock. At the 24 h observation point, the SSEW model incorporated 14 module root features and 23 derived features. CONCLUSION: The SSEW model for hematological or oncological pediatric patients could help clinicians to predict the risk of septic shock in patients with fever or neutropenia 24 h in advance. Further prospective studies on clinical application scenarios are needed to determine the clinical utility of this AI model. Frontiers Media S.A. 2021-06-15 /pmc/articles/PMC8240637/ /pubmed/34211848 http://dx.doi.org/10.3389/fonc.2021.678743 Text en Copyright © 2021 Xiang, Wang, Fan, Zhang, Lu, Dong, Liu, Chen, Wang, Zhao and Fu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Xiang, Long Wang, Hansong Fan, Shujun Zhang, Wenlan Lu, Hua Dong, Bin Liu, Shijian Chen, Yiwei Wang, Ying Zhao, Liebin Fu, Lijun Machine Learning for Early Warning of Septic Shock in Children With Hematological Malignancies Accompanied by Fever or Neutropenia: A Single Center Retrospective Study |
title | Machine Learning for Early Warning of Septic Shock in Children With Hematological Malignancies Accompanied by Fever or Neutropenia: A Single Center Retrospective Study |
title_full | Machine Learning for Early Warning of Septic Shock in Children With Hematological Malignancies Accompanied by Fever or Neutropenia: A Single Center Retrospective Study |
title_fullStr | Machine Learning for Early Warning of Septic Shock in Children With Hematological Malignancies Accompanied by Fever or Neutropenia: A Single Center Retrospective Study |
title_full_unstemmed | Machine Learning for Early Warning of Septic Shock in Children With Hematological Malignancies Accompanied by Fever or Neutropenia: A Single Center Retrospective Study |
title_short | Machine Learning for Early Warning of Septic Shock in Children With Hematological Malignancies Accompanied by Fever or Neutropenia: A Single Center Retrospective Study |
title_sort | machine learning for early warning of septic shock in children with hematological malignancies accompanied by fever or neutropenia: a single center retrospective study |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8240637/ https://www.ncbi.nlm.nih.gov/pubmed/34211848 http://dx.doi.org/10.3389/fonc.2021.678743 |
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