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Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning

Background: High flow nasal cannula (HFNC) is commonly used as non-invasive respiratory support in critically ill children. There are limited data to inform consensus on optimal device parameters, determinants of successful patient response, and indications for escalation of support. Clinical scores...

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Autores principales: Krachman, Joshua A., Patricoski, Jessica A., Le, Christopher T., Park, Jina, Zhang, Ruijing, Gong, Kirby D., Gangan, Indranuj, Winslow, Raimond L., Greenstein, Joseph L., Fackler, James, Sochet, Anthony A., Bergmann, Jules P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606666/
https://www.ncbi.nlm.nih.gov/pubmed/34820341
http://dx.doi.org/10.3389/fped.2021.734753
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author Krachman, Joshua A.
Patricoski, Jessica A.
Le, Christopher T.
Park, Jina
Zhang, Ruijing
Gong, Kirby D.
Gangan, Indranuj
Winslow, Raimond L.
Greenstein, Joseph L.
Fackler, James
Sochet, Anthony A.
Bergmann, Jules P.
author_facet Krachman, Joshua A.
Patricoski, Jessica A.
Le, Christopher T.
Park, Jina
Zhang, Ruijing
Gong, Kirby D.
Gangan, Indranuj
Winslow, Raimond L.
Greenstein, Joseph L.
Fackler, James
Sochet, Anthony A.
Bergmann, Jules P.
author_sort Krachman, Joshua A.
collection PubMed
description Background: High flow nasal cannula (HFNC) is commonly used as non-invasive respiratory support in critically ill children. There are limited data to inform consensus on optimal device parameters, determinants of successful patient response, and indications for escalation of support. Clinical scores, such as the respiratory rate-oxygenation (ROX) index, have been described as a means to predict HFNC non-response, but are limited to evaluating for escalations to invasive mechanical ventilation (MV). In the presence of apparent HFNC non-response, a clinician may choose to increase the HFNC flow rate to hypothetically prevent further respiratory deterioration, transition to an alternative non-invasive interface, or intubation for MV. To date, no models have been assessed to predict subsequent escalations of HFNC flow rates after HFNC initiation. Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations. Methods: We performed a retrospective, cohort study assessing children admitted for acute respiratory failure under 24 months of age placed on HFNC in the Johns Hopkins Children's Center pediatric intensive care unit from January 2019 through January 2020. We excluded encounters with gaps in recorded clinical data, encounters in which MV treatment occurred prior to HFNC, and cases electively intubated in the operating room. The primary study outcome was discriminatory capacity of generated machine learning algorithms to predict HFNC flow rate escalations as compared to each other and ROX indices using area under the receiver operating characteristic (AUROC) analyses. In an exploratory fashion, model feature importance rankings were assessed by comparing Shapley values. Results: Our gradient boosting model with a time window of 8 h and lead time of 1 h before HFNC flow rate escalation achieved an AUROC with a 95% confidence interval of 0.810 ± 0.003. In comparison, the ROX index achieved an AUROC of 0.525 ± 0.000. Conclusion: In this single-center, retrospective cohort study assessing children under 24 months of age receiving HFNC for acute respiratory failure, tree-based machine learning models outperformed the ROX index in predicting subsequent flow rate escalations. Further validation studies are needed to ensure generalizability for bedside application.
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spelling pubmed-86066662021-11-23 Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning Krachman, Joshua A. Patricoski, Jessica A. Le, Christopher T. Park, Jina Zhang, Ruijing Gong, Kirby D. Gangan, Indranuj Winslow, Raimond L. Greenstein, Joseph L. Fackler, James Sochet, Anthony A. Bergmann, Jules P. Front Pediatr Pediatrics Background: High flow nasal cannula (HFNC) is commonly used as non-invasive respiratory support in critically ill children. There are limited data to inform consensus on optimal device parameters, determinants of successful patient response, and indications for escalation of support. Clinical scores, such as the respiratory rate-oxygenation (ROX) index, have been described as a means to predict HFNC non-response, but are limited to evaluating for escalations to invasive mechanical ventilation (MV). In the presence of apparent HFNC non-response, a clinician may choose to increase the HFNC flow rate to hypothetically prevent further respiratory deterioration, transition to an alternative non-invasive interface, or intubation for MV. To date, no models have been assessed to predict subsequent escalations of HFNC flow rates after HFNC initiation. Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations. Methods: We performed a retrospective, cohort study assessing children admitted for acute respiratory failure under 24 months of age placed on HFNC in the Johns Hopkins Children's Center pediatric intensive care unit from January 2019 through January 2020. We excluded encounters with gaps in recorded clinical data, encounters in which MV treatment occurred prior to HFNC, and cases electively intubated in the operating room. The primary study outcome was discriminatory capacity of generated machine learning algorithms to predict HFNC flow rate escalations as compared to each other and ROX indices using area under the receiver operating characteristic (AUROC) analyses. In an exploratory fashion, model feature importance rankings were assessed by comparing Shapley values. Results: Our gradient boosting model with a time window of 8 h and lead time of 1 h before HFNC flow rate escalation achieved an AUROC with a 95% confidence interval of 0.810 ± 0.003. In comparison, the ROX index achieved an AUROC of 0.525 ± 0.000. Conclusion: In this single-center, retrospective cohort study assessing children under 24 months of age receiving HFNC for acute respiratory failure, tree-based machine learning models outperformed the ROX index in predicting subsequent flow rate escalations. Further validation studies are needed to ensure generalizability for bedside application. Frontiers Media S.A. 2021-11-08 /pmc/articles/PMC8606666/ /pubmed/34820341 http://dx.doi.org/10.3389/fped.2021.734753 Text en Copyright © 2021 Krachman, Patricoski, Le, Park, Zhang, Gong, Gangan, Winslow, Greenstein, Fackler, Sochet and Bergmann. 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 Pediatrics
Krachman, Joshua A.
Patricoski, Jessica A.
Le, Christopher T.
Park, Jina
Zhang, Ruijing
Gong, Kirby D.
Gangan, Indranuj
Winslow, Raimond L.
Greenstein, Joseph L.
Fackler, James
Sochet, Anthony A.
Bergmann, Jules P.
Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning
title Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning
title_full Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning
title_fullStr Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning
title_full_unstemmed Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning
title_short Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning
title_sort predicting flow rate escalation for pediatric patients on high flow nasal cannula using machine learning
topic Pediatrics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606666/
https://www.ncbi.nlm.nih.gov/pubmed/34820341
http://dx.doi.org/10.3389/fped.2021.734753
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