The Michigan Emergency Department Improvement Collaborative: A novel model for implementing large scale practice change in pediatric emergency care.

Nypaver, Michele M.; Macy, Michelle L.; Pribble, James M.; Uren, Brad; Kocher, Keith E.; Levine, Greg

doi:10.1542/peds.142.1ma2.105

Cited by 2 publications

(1 citation statement)

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“…The University of Michigan Health System (or Michigan Medicine), as one of the primary regional centers managing the care of patients with COVID-19 during the pandemic, has collected a wealth of X-ray image data, in addition to demographic and clinical data, via the Electronic Health Record (EHR) 15 , 16 . Portable chest X-ray, with its availability and ease of use, has been routinely used for monitoring patients in need of urgent care at Michigan Medicine, even prior to the pandemic 17 . However, analysis of chest X-ray images is complicated by the data structure, particularly in the COVID-19 setting 18 .…”

Section: Introductionmentioning

confidence: 99%

Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality

Sun

Salerno

et al. 2023

Sci Rep

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As portable chest X-rays are an efficient means of triaging emergent cases, their use has raised the question as to whether imaging carries additional prognostic utility for survival among patients with COVID-19. This study assessed the importance of known risk factors on in-hospital mortality and investigated the predictive utility of radiomic texture features using various machine learning approaches. We detected incremental improvements in survival prognostication utilizing texture features derived from emergent chest X-rays, particularly among older patients or those with a higher comorbidity burden. Important features included age, oxygen saturation, blood pressure, and certain comorbid conditions, as well as image features related to the intensity and variability of pixel distribution. Thus, widely available chest X-rays, in conjunction with clinical information, may be predictive of survival outcomes of patients with COVID-19, especially older, sicker patients, and can aid in disease management by providing additional information.

show abstract

Section: Introductionmentioning

confidence: 99%

Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality

Sun

Salerno

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

Machine Learning to Assess the Prognostic Utility of Radiomic Features for In-hospital COVID-19 Mortality

Sun

Salerno

et al. 2022

Preprint

View full text Add to dashboard Cite

As portable chest X-rays are an efficient means of triaging emergent cases, their increased use has raised the question as to whether imaging carries additional prognostic utility for survival among patients with COVID-19. This study assessed the importance of known risk factors on in-hospital mortality and to investigate the predictive utility of radiomic texture features using various machine learning approaches. We detected incremental improvements in survival prognostication utilizing texture features derived from emergent chest X-rays, particularly among older patients or those with higher comorbidity burden. Important features included age, oxygen saturation, blood pressure, and certain comorbid conditions, as well as image features related to the intensity and variability of the pixel distribution. Thus, widely available chest X-rays, in conjunction with clinical information, may be predictive of survival outcomes of patients with COVID-19, especially older, sicker patients, and can aid in disease management by providing additional information.

show abstract

The Michigan Emergency Department Improvement Collaborative: A novel model for implementing large scale practice change in pediatric emergency care.

Cited by 2 publications

References 0 publications

Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality

Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality

Machine Learning to Assess the Prognostic Utility of Radiomic Features for In-hospital COVID-19 Mortality

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