Machine Learning Predicts Prolonged Acute Hypoxemic Respiratory Failure in Pediatric Severe Influenza

Background:. Influenza virus is a major cause of acute hypoxemic respiratory failure. Early identification of patients who will suffer severe complications can help stratify patients for clinical trials and plan for resource use in case of pandemic. Objective:. We aimed to identify which clinical va...

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Autores principales:	Michaël S. Sauthier, MD MBI, Philippe A. Jouvet, MD, PhDMBA, Margaret M. Newhams,, MPH, Adrienne G. Randolph, MD, MSc, for the Pediatric Acute Lung Injury and Sepsis Investigators (PALISI) Pediatric Intensive Care Influenza (PICFLU) Network Investigators, Michele Kong, Ronald C. Sanders, Jr, Olivia K. Irby, David Tellez, Katri Typpo, Barry Markovitz, Natalie Cvijanovich, Heidi Flori, Adam Schwarz, Nick Anas, Patrick McQuillen, Peter Mourani, John S. Giuliano, Jr., Gwenn McLaughlin, Matthew Paden, Keiko Tarquinio, Bria M. Coates, Neethi Pinto, Juliane Bubeck Wardenburg, Janice Sullivan, Vicki Montgomery, Adrienne G. Randolph, Anna A. Agan, Tanya Novak, Margaret M. Newhams, Melania Bembea, Sapna R. Kudchadkar, Stephen C. Kurachek, Mary E. Hartman, Edward J. Truemper, Sidharth Mahapatra, Sholeen Nett, Daniel L. Levin, Kate G. Ackerman, Ryan Nofziger, Steven L. Shein, Mark W. Hall, Neal Thomas, Scott L. Weiss, Julie Fitzgerald, Renee Higgerson, Laura L. Loftis, Rainer G. Gedeit
Formato:	article
Lenguaje:	EN
Publicado:	Wolters Kluwer 2020
Materias:	Medical emergencies. Critical care. Intensive care. First aid RC86-88.9
Acceso en línea:	https://doaj.org/article/ca6d7df3e8c14e91a9b1f188a01d97ae
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spelling	oai:doaj.org-article:ca6d7df3e8c14e91a9b1f188a01d97ae2021-11-25T07:51:49ZMachine Learning Predicts Prolonged Acute Hypoxemic Respiratory Failure in Pediatric Severe Influenza2639-802810.1097/CCE.0000000000000175https://doaj.org/article/ca6d7df3e8c14e91a9b1f188a01d97ae2020-08-01T00:00:00Zhttp://journals.lww.com/10.1097/CCE.0000000000000175https://doaj.org/toc/2639-8028Background:. Influenza virus is a major cause of acute hypoxemic respiratory failure. Early identification of patients who will suffer severe complications can help stratify patients for clinical trials and plan for resource use in case of pandemic. Objective:. We aimed to identify which clinical variables best predict prolonged acute hypoxemic respiratory failure in influenza-infected critically ill children. Acute hypoxemic respiratory failure was defined using hypoxemia cutoffs from international consensus definitions of acute respiratory distress syndrome in patients with ventilatory support. Prolonged acute hypoxemic respiratory failure was defined by acute hypoxemic respiratory failure criteria still present at PICU day 7. Derivation Cohort:. In this prospective multicenter study across 34 PICUs from November 2009 to April 2018, we included children (< 18 yr) without comorbid risk factors for severe disease. Validation Cohort:. We used a Monte Carlo cross validation method with N2 random train-test splits at a 70–30% proportion per model. Prediction Model:. Using clinical data at admission (day 1) and closest to 8 am on PICU day 2, we calculated the area under the receiver operating characteristic curve using random forests machine learning algorithms and logistic regression. Results:. We included 258 children (median age = 6.5 yr) and 11 (4.2%) died. By day 2, 65% (n = 165) had acute hypoxemic respiratory failure dropping to 26% (n = 67) with prolonged acute hypoxemic respiratory failure by day 7. Those with prolonged acute hypoxemic respiratory failure had a longer ICU stay (16.5 vs 4.0 d; p < 0.001) and higher mortality (13.4% vs 1.0%). A multivariable model using random forests with 10 admission and eight day 2 variables performed best (0.93 area under the receiver operating characteristic curve; 95 CI%: 0.90–0.95) where respiratory rate, Fio2, and pH on day 2 were the most important factors. Conclusions:. In this prospective multicentric study, most children with influenza virus–related respiratory failure with prolonged acute hypoxemic respiratory failure can be identified early in their hospital course applying machine learning onto routine clinical data. Further validation is needed prior to bedside implementation.Michaël S. Sauthier, MD MBIPhilippe A. Jouvet, MD, PhDMBAMargaret M. Newhams,, MPHAdrienne G. Randolph, MD, MScfor the Pediatric Acute Lung Injury and Sepsis Investigators (PALISI) Pediatric Intensive Care Influenza (PICFLU) Network InvestigatorsMichele KongRonald C. Sanders, JrOlivia K. IrbyDavid TellezKatri TyppoBarry MarkovitzNatalie CvijanovichHeidi FloriAdam SchwarzNick AnasPatrick McQuillenPeter MouraniJohn S. Giuliano, Jr.Gwenn McLaughlinMatthew PadenKeiko TarquinioBria M. CoatesNeethi PintoJuliane Bubeck WardenburgJanice SullivanVicki MontgomeryAdrienne G. RandolphAnna A. AganTanya NovakMargaret M. NewhamsMelania BembeaSapna R. KudchadkarStephen C. KurachekMary E. HartmanEdward J. TruemperSidharth MahapatraSholeen NettDaniel L. LevinKate G. AckermanRyan NofzigerSteven L. SheinMark W. HallNeal ThomasScott L. WeissJulie FitzgeraldRenee HiggersonLaura L. LoftisRainer G. GedeitWolters KluwerarticleMedical emergencies. Critical care. Intensive care. First aidRC86-88.9ENCritical Care Explorations, Vol 2, Iss 8, p e0175 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medical emergencies. Critical care. Intensive care. First aid RC86-88.9
spellingShingle	Medical emergencies. Critical care. Intensive care. First aid RC86-88.9 Michaël S. Sauthier, MD MBI Philippe A. Jouvet, MD, PhDMBA Margaret M. Newhams,, MPH Adrienne G. Randolph, MD, MSc for the Pediatric Acute Lung Injury and Sepsis Investigators (PALISI) Pediatric Intensive Care Influenza (PICFLU) Network Investigators Michele Kong Ronald C. Sanders, Jr Olivia K. Irby David Tellez Katri Typpo Barry Markovitz Natalie Cvijanovich Heidi Flori Adam Schwarz Nick Anas Patrick McQuillen Peter Mourani John S. Giuliano, Jr. Gwenn McLaughlin Matthew Paden Keiko Tarquinio Bria M. Coates Neethi Pinto Juliane Bubeck Wardenburg Janice Sullivan Vicki Montgomery Adrienne G. Randolph Anna A. Agan Tanya Novak Margaret M. Newhams Melania Bembea Sapna R. Kudchadkar Stephen C. Kurachek Mary E. Hartman Edward J. Truemper Sidharth Mahapatra Sholeen Nett Daniel L. Levin Kate G. Ackerman Ryan Nofziger Steven L. Shein Mark W. Hall Neal Thomas Scott L. Weiss Julie Fitzgerald Renee Higgerson Laura L. Loftis Rainer G. Gedeit Machine Learning Predicts Prolonged Acute Hypoxemic Respiratory Failure in Pediatric Severe Influenza
description	Background:. Influenza virus is a major cause of acute hypoxemic respiratory failure. Early identification of patients who will suffer severe complications can help stratify patients for clinical trials and plan for resource use in case of pandemic. Objective:. We aimed to identify which clinical variables best predict prolonged acute hypoxemic respiratory failure in influenza-infected critically ill children. Acute hypoxemic respiratory failure was defined using hypoxemia cutoffs from international consensus definitions of acute respiratory distress syndrome in patients with ventilatory support. Prolonged acute hypoxemic respiratory failure was defined by acute hypoxemic respiratory failure criteria still present at PICU day 7. Derivation Cohort:. In this prospective multicenter study across 34 PICUs from November 2009 to April 2018, we included children (< 18 yr) without comorbid risk factors for severe disease. Validation Cohort:. We used a Monte Carlo cross validation method with N2 random train-test splits at a 70–30% proportion per model. Prediction Model:. Using clinical data at admission (day 1) and closest to 8 am on PICU day 2, we calculated the area under the receiver operating characteristic curve using random forests machine learning algorithms and logistic regression. Results:. We included 258 children (median age = 6.5 yr) and 11 (4.2%) died. By day 2, 65% (n = 165) had acute hypoxemic respiratory failure dropping to 26% (n = 67) with prolonged acute hypoxemic respiratory failure by day 7. Those with prolonged acute hypoxemic respiratory failure had a longer ICU stay (16.5 vs 4.0 d; p < 0.001) and higher mortality (13.4% vs 1.0%). A multivariable model using random forests with 10 admission and eight day 2 variables performed best (0.93 area under the receiver operating characteristic curve; 95 CI%: 0.90–0.95) where respiratory rate, Fio2, and pH on day 2 were the most important factors. Conclusions:. In this prospective multicentric study, most children with influenza virus–related respiratory failure with prolonged acute hypoxemic respiratory failure can be identified early in their hospital course applying machine learning onto routine clinical data. Further validation is needed prior to bedside implementation.
format	article
author	Michaël S. Sauthier, MD MBI Philippe A. Jouvet, MD, PhDMBA Margaret M. Newhams,, MPH Adrienne G. Randolph, MD, MSc for the Pediatric Acute Lung Injury and Sepsis Investigators (PALISI) Pediatric Intensive Care Influenza (PICFLU) Network Investigators Michele Kong Ronald C. Sanders, Jr Olivia K. Irby David Tellez Katri Typpo Barry Markovitz Natalie Cvijanovich Heidi Flori Adam Schwarz Nick Anas Patrick McQuillen Peter Mourani John S. Giuliano, Jr. Gwenn McLaughlin Matthew Paden Keiko Tarquinio Bria M. Coates Neethi Pinto Juliane Bubeck Wardenburg Janice Sullivan Vicki Montgomery Adrienne G. Randolph Anna A. Agan Tanya Novak Margaret M. Newhams Melania Bembea Sapna R. Kudchadkar Stephen C. Kurachek Mary E. Hartman Edward J. Truemper Sidharth Mahapatra Sholeen Nett Daniel L. Levin Kate G. Ackerman Ryan Nofziger Steven L. Shein Mark W. Hall Neal Thomas Scott L. Weiss Julie Fitzgerald Renee Higgerson Laura L. Loftis Rainer G. Gedeit
author_facet	Michaël S. Sauthier, MD MBI Philippe A. Jouvet, MD, PhDMBA Margaret M. Newhams,, MPH Adrienne G. Randolph, MD, MSc for the Pediatric Acute Lung Injury and Sepsis Investigators (PALISI) Pediatric Intensive Care Influenza (PICFLU) Network Investigators Michele Kong Ronald C. Sanders, Jr Olivia K. Irby David Tellez Katri Typpo Barry Markovitz Natalie Cvijanovich Heidi Flori Adam Schwarz Nick Anas Patrick McQuillen Peter Mourani John S. Giuliano, Jr. Gwenn McLaughlin Matthew Paden Keiko Tarquinio Bria M. Coates Neethi Pinto Juliane Bubeck Wardenburg Janice Sullivan Vicki Montgomery Adrienne G. Randolph Anna A. Agan Tanya Novak Margaret M. Newhams Melania Bembea Sapna R. Kudchadkar Stephen C. Kurachek Mary E. Hartman Edward J. Truemper Sidharth Mahapatra Sholeen Nett Daniel L. Levin Kate G. Ackerman Ryan Nofziger Steven L. Shein Mark W. Hall Neal Thomas Scott L. Weiss Julie Fitzgerald Renee Higgerson Laura L. Loftis Rainer G. Gedeit
author_sort	Michaël S. Sauthier, MD MBI
title	Machine Learning Predicts Prolonged Acute Hypoxemic Respiratory Failure in Pediatric Severe Influenza
title_short	Machine Learning Predicts Prolonged Acute Hypoxemic Respiratory Failure in Pediatric Severe Influenza
title_full	Machine Learning Predicts Prolonged Acute Hypoxemic Respiratory Failure in Pediatric Severe Influenza
title_fullStr	Machine Learning Predicts Prolonged Acute Hypoxemic Respiratory Failure in Pediatric Severe Influenza
title_full_unstemmed	Machine Learning Predicts Prolonged Acute Hypoxemic Respiratory Failure in Pediatric Severe Influenza
title_sort	machine learning predicts prolonged acute hypoxemic respiratory failure in pediatric severe influenza
publisher	Wolters Kluwer
publishDate	2020
url	https://doaj.org/article/ca6d7df3e8c14e91a9b1f188a01d97ae
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Machine Learning Predicts Prolonged Acute Hypoxemic Respiratory Failure in Pediatric Severe Influenza

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