Clinical factors associated with rapid treatment of sepsis.

<h4>Purpose</h4>To understand what clinical presenting features of sepsis patients are historically associated with rapid treatment involving antibiotics and fluids, as appropriate.<h4>Design</h4>This was a retrospective, observational cohort study using a machine-learning mo...

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Autores principales:	Xing Song, Mei Liu, Lemuel R Waitman, Anurag Patel, Steven Q Simpson
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/17dae5e0e89d496c969f7e7c9347a25e
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spelling	oai:doaj.org-article:17dae5e0e89d496c969f7e7c9347a25e2021-11-25T05:54:17ZClinical factors associated with rapid treatment of sepsis.1932-620310.1371/journal.pone.0250923https://doaj.org/article/17dae5e0e89d496c969f7e7c9347a25e2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0250923https://doaj.org/toc/1932-6203<h4>Purpose</h4>To understand what clinical presenting features of sepsis patients are historically associated with rapid treatment involving antibiotics and fluids, as appropriate.<h4>Design</h4>This was a retrospective, observational cohort study using a machine-learning model with an embedded feature selection mechanism (gradient boosting machine).<h4>Methods</h4>For adult patients (age ≥ 18 years) who were admitted through Emergency Department (ED) meeting clinical criteria of severe sepsis from 11/2007 to 05/2018 at an urban tertiary academic medical center, we developed gradient boosting models (GBMs) using a total of 760 original and derived variables, including demographic variables, laboratory values, vital signs, infection diagnosis present on admission, and historical comorbidities. We identified the most impactful factors having strong association with rapid treatment, and further applied the Shapley Additive exPlanation (SHAP) values to examine the marginal effects for each factor.<h4>Results</h4>For the subgroups with or without fluid bolus treatment component, the models achieved high accuracy of area-under-receiver-operating-curve of 0.91 [95% CI, 0.86-0.95] and 0.84 [95% CI, 0.81-0.86], and sensitivity of 0.81[95% CI, 0.72-0.87] and 0.91 [95% CI, 0.81-0.97], respectively. We identified the 20 most impactful factors associated with rapid treatment for each subgroup. In the non-hypotensive subgroup, initial physiological values were the most impactful to the model, while in the fluid bolus subgroup, value minima and maxima tended to be the most impactful.<h4>Conclusion</h4>These machine learning methods identified factors associated with rapid treatment of severe sepsis patients from a large volume of high-dimensional clinical data. The results provide insight into differences in the rapid provision of treatment among patients with sepsis.Xing SongMei LiuLemuel R WaitmanAnurag PatelSteven Q SimpsonPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0250923 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xing Song Mei Liu Lemuel R Waitman Anurag Patel Steven Q Simpson Clinical factors associated with rapid treatment of sepsis.
description	<h4>Purpose</h4>To understand what clinical presenting features of sepsis patients are historically associated with rapid treatment involving antibiotics and fluids, as appropriate.<h4>Design</h4>This was a retrospective, observational cohort study using a machine-learning model with an embedded feature selection mechanism (gradient boosting machine).<h4>Methods</h4>For adult patients (age ≥ 18 years) who were admitted through Emergency Department (ED) meeting clinical criteria of severe sepsis from 11/2007 to 05/2018 at an urban tertiary academic medical center, we developed gradient boosting models (GBMs) using a total of 760 original and derived variables, including demographic variables, laboratory values, vital signs, infection diagnosis present on admission, and historical comorbidities. We identified the most impactful factors having strong association with rapid treatment, and further applied the Shapley Additive exPlanation (SHAP) values to examine the marginal effects for each factor.<h4>Results</h4>For the subgroups with or without fluid bolus treatment component, the models achieved high accuracy of area-under-receiver-operating-curve of 0.91 [95% CI, 0.86-0.95] and 0.84 [95% CI, 0.81-0.86], and sensitivity of 0.81[95% CI, 0.72-0.87] and 0.91 [95% CI, 0.81-0.97], respectively. We identified the 20 most impactful factors associated with rapid treatment for each subgroup. In the non-hypotensive subgroup, initial physiological values were the most impactful to the model, while in the fluid bolus subgroup, value minima and maxima tended to be the most impactful.<h4>Conclusion</h4>These machine learning methods identified factors associated with rapid treatment of severe sepsis patients from a large volume of high-dimensional clinical data. The results provide insight into differences in the rapid provision of treatment among patients with sepsis.
format	article
author	Xing Song Mei Liu Lemuel R Waitman Anurag Patel Steven Q Simpson
author_facet	Xing Song Mei Liu Lemuel R Waitman Anurag Patel Steven Q Simpson
author_sort	Xing Song
title	Clinical factors associated with rapid treatment of sepsis.
title_short	Clinical factors associated with rapid treatment of sepsis.
title_full	Clinical factors associated with rapid treatment of sepsis.
title_fullStr	Clinical factors associated with rapid treatment of sepsis.
title_full_unstemmed	Clinical factors associated with rapid treatment of sepsis.
title_sort	clinical factors associated with rapid treatment of sepsis.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/17dae5e0e89d496c969f7e7c9347a25e
work_keys_str_mv	AT xingsong clinicalfactorsassociatedwithrapidtreatmentofsepsis AT meiliu clinicalfactorsassociatedwithrapidtreatmentofsepsis AT lemuelrwaitman clinicalfactorsassociatedwithrapidtreatmentofsepsis AT anuragpatel clinicalfactorsassociatedwithrapidtreatmentofsepsis AT stevenqsimpson clinicalfactorsassociatedwithrapidtreatmentofsepsis
_version_	1718414384906108928

Clinical factors associated with rapid treatment of sepsis.

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