Literature DB >> 33861547

Determining the Electronic Signature of Infection in Electronic Health Record Data.

Matthew M Churpek1, Jay Dumanian2, Nicole Dussault2, Sivasubramanium V Bhavani3, Kyle A Carey3, Emily R Gilbert4, Erum Arain4, Chen Ye4, Christopher J Winslow5, Nirav S Shah5, Majid Afshar4, Dana P Edelson2.   

Abstract

OBJECTIVES: Recent sepsis studies have defined patients as "infected" using a combination of culture and antibiotic orders rather than billing data. However, the accuracy of these definitions is unclear. We aimed to compare the accuracy of different established criteria for identifying infected patients using detailed chart review.
DESIGN: Retrospective observational study.
SETTING: Six hospitals from three health systems in Illinois. PATIENTS: Adult admissions with blood culture or antibiotic orders, or Angus International Classification of Diseases infection codes and death were eligible for study inclusion as potentially infected patients. Nine-hundred to 1,000 of these admissions were randomly selected from each health system for chart review, and a proportional number of patients who did not meet chart review eligibility criteria were also included and deemed not infected.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: The accuracy of published billing code criteria by Angus et al and electronic health record criteria by Rhee et al and Seymour et al (Sepsis-3) was determined using the manual chart review results as the gold standard. A total of 5,215 patients were included, with 2,874 encounters analyzed via chart review and a proportional 2,341 added who did not meet chart review eligibility criteria. In the study cohort, 27.5% of admissions had at least one infection. This was most similar to the percentage of admissions with blood culture orders (26.8%), Angus infection criteria (28.7%), and the Sepsis-3 criteria (30.4%). Sepsis-3 criteria was the most sensitive (81%), followed by Angus (77%) and Rhee (52%), while Rhee (97%) and Angus (90%) were more specific than the Sepsis-3 criteria (89%). Results were similar for patients with organ dysfunction during their admission.
CONCLUSIONS: Published criteria have a wide range of accuracy for identifying infected patients, with the Sepsis-3 criteria being the most sensitive and Rhee criteria being the most specific. These findings have important implications for studies investigating the burden of sepsis on a local and national level.
Copyright © 2021 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

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Year:  2021        PMID: 33861547      PMCID: PMC8217098          DOI: 10.1097/CCM.0000000000004968

Source DB:  PubMed          Journal:  Crit Care Med        ISSN: 0090-3493            Impact factor:   9.296


  21 in total

1.  Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

Authors:  Paul A Harris; Robert Taylor; Robert Thielke; Jonathon Payne; Nathaniel Gonzalez; Jose G Conde
Journal:  J Biomed Inform       Date:  2008-09-30       Impact factor: 6.317

2.  Trends in the incidence and mortality of patients with community-acquired septic shock 2003-2016.

Authors:  Jordi Valles; Dionisia Fontanals; Joan Carles Oliva; Melcior Martínez; Ana Navas; Jaume Mesquida; Guillem Gruartmoner; Candelaria de Haro; Jaume Mestre; Consuelo Guía; Andrey Rodriguez; Eva Torrents; Cristina Espinal; Ana Ochagavía; Emili Diaz
Journal:  J Crit Care       Date:  2019-06-04       Impact factor: 3.425

3.  Quick Sepsis-related Organ Failure Assessment, Systemic Inflammatory Response Syndrome, and Early Warning Scores for Detecting Clinical Deterioration in Infected Patients outside the Intensive Care Unit.

Authors:  Matthew M Churpek; Ashley Snyder; Xuan Han; Sarah Sokol; Natasha Pettit; Michael D Howell; Dana P Edelson
Journal:  Am J Respir Crit Care Med       Date:  2017-04-01       Impact factor: 21.405

4.  Time to Treatment and Mortality during Mandated Emergency Care for Sepsis.

Authors:  Christopher W Seymour; Foster Gesten; Hallie C Prescott; Marcus E Friedrich; Theodore J Iwashyna; Gary S Phillips; Stanley Lemeshow; Tiffany Osborn; Kathleen M Terry; Mitchell M Levy
Journal:  N Engl J Med       Date:  2017-05-21       Impact factor: 91.245

5.  Validation the performance of New York Sepsis Severity Score compared with Sepsis Severity Score in predicting hospital mortality among sepsis patients.

Authors:  Natthaka Sathaporn; Bodin Khwannimit
Journal:  J Crit Care       Date:  2019-06-19       Impact factor: 3.425

6.  Incidence and Trends of Sepsis in US Hospitals Using Clinical vs Claims Data, 2009-2014.

Authors:  Chanu Rhee; Raymund Dantes; Lauren Epstein; David J Murphy; Christopher W Seymour; Theodore J Iwashyna; Sameer S Kadri; Derek C Angus; Robert L Danner; Anthony E Fiore; John A Jernigan; Greg S Martin; Edward Septimus; David K Warren; Anita Karcz; Christina Chan; John T Menchaca; Rui Wang; Susan Gruber; Michael Klompas
Journal:  JAMA       Date:  2017-10-03       Impact factor: 56.272

7.  Early prediction of septic shock in hospitalized patients.

Authors:  Steven W Thiel; Jamie M Rosini; William Shannon; Joshua A Doherty; Scott T Micek; Marin H Kollef
Journal:  J Hosp Med       Date:  2010-01       Impact factor: 2.960

8.  A targeted real-time early warning score (TREWScore) for septic shock.

Authors:  Katharine E Henry; David N Hager; Peter J Pronovost; Suchi Saria
Journal:  Sci Transl Med       Date:  2015-08-05       Impact factor: 17.956

9.  Comparison of Early Warning Scoring Systems for Hospitalized Patients With and Without Infection at Risk for In-Hospital Mortality and Transfer to the Intensive Care Unit.

Authors:  Vincent X Liu; Yun Lu; Kyle A Carey; Emily R Gilbert; Majid Afshar; Mary Akel; Nirav S Shah; John Dolan; Christopher Winslow; Patricia Kipnis; Dana P Edelson; Gabriel J Escobar; Matthew M Churpek
Journal:  JAMA Netw Open       Date:  2020-05-01

10.  Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study.

Authors:  Kristina E Rudd; Sarah Charlotte Johnson; Kareha M Agesa; Katya Anne Shackelford; Derrick Tsoi; Daniel Rhodes Kievlan; Danny V Colombara; Kevin S Ikuta; Niranjan Kissoon; Simon Finfer; Carolin Fleischmann-Struzek; Flavia R Machado; Konrad K Reinhart; Kathryn Rowan; Christopher W Seymour; R Scott Watson; T Eoin West; Fatima Marinho; Simon I Hay; Rafael Lozano; Alan D Lopez; Derek C Angus; Christopher J L Murray; Mohsen Naghavi
Journal:  Lancet       Date:  2020-01-18       Impact factor: 202.731

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  3 in total

1.  Identifying infected patients using semi-supervised and transfer learning.

Authors:  Fereshteh S Bashiri; John R Caskey; Anoop Mayampurath; Nicole Dussault; Jay Dumanian; Sivasubramanium V Bhavani; Kyle A Carey; Emily R Gilbert; Christopher J Winslow; Nirav S Shah; Dana P Edelson; Majid Afshar; Matthew M Churpek
Journal:  J Am Med Inform Assoc       Date:  2022-09-12       Impact factor: 7.942

2.  Identification of Clinical Phenotypes in Septic Patients Presenting With Hypotension or Elevated Lactate.

Authors:  Zachary T Aldewereld; Li Ang Zhang; Alisa Urbano; Robert S Parker; David Swigon; Ipsita Banerjee; Hernando Gómez; Gilles Clermont
Journal:  Front Med (Lausanne)       Date:  2022-05-19

3.  Comparison of early warning scores for predicting clinical deterioration and infection in obstetric patients.

Authors:  David E Arnolds; Kyle A Carey; Lena Braginsky; Roxane Holt; Dana P Edelson; Barbara M Scavone; Matthew Churpek
Journal:  BMC Pregnancy Childbirth       Date:  2022-04-06       Impact factor: 3.007

  3 in total

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