Literature DB >> 28737573

Investigating the Impact of Different Suspicion of Infection Criteria on the Accuracy of Quick Sepsis-Related Organ Failure Assessment, Systemic Inflammatory Response Syndrome, and Early Warning Scores.

Matthew M Churpek1, Ashley Snyder, Sarah Sokol, Natasha N Pettit, Dana P Edelson.   

Abstract

OBJECTIVE: Studies in sepsis are limited by heterogeneity regarding what constitutes suspicion of infection. We sought to compare potential suspicion criteria using antibiotic and culture order combinations in terms of patient characteristics and outcomes. We further sought to determine the impact of differing criteria on the accuracy of sepsis screening tools and early warning scores.
DESIGN: Observational cohort study.
SETTING: Academic center from November 2008 to January 2016. PATIENTS: Hospitalized patients outside the ICU.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: Six criteria were investigated: 1) any culture, 2) blood culture, 3) any culture plus IV antibiotics, 4) blood culture plus IV antibiotics, 5) any culture plus IV antibiotics for at least 4 of 7 days, and 6) blood culture plus IV antibiotics for at least 4 of 7 days. Accuracy of the quick Sepsis-related Organ Failure Assessment score, Sepsis-related Organ Failure Assessment score, systemic inflammatory response syndrome criteria, the National and Modified Early Warning Score, and the electronic Cardiac Arrest Risk Triage score were calculated for predicting ICU transfer or death within 48 hours of meeting suspicion criteria. A total of 53,849 patients met at least one infection criteria. Mortality increased from 3% for group 1 to 9% for group 6 and percentage meeting Angus sepsis criteria increased from 20% to 40%. Across all criteria, score discrimination was lowest for systemic inflammatory response syndrome (median area under the receiver operating characteristic curve, 0.60) and Sepsis-related Organ Failure Assessment score (median area under the receiver operating characteristic curve, 0.62), intermediate for quick Sepsis-related Organ Failure Assessment (median area under the receiver operating characteristic curve, 0.65) and Modified Early Warning Score (median area under the receiver operating characteristic curve 0.67), and highest for National Early Warning Score (median area under the receiver operating characteristic curve 0.71) and electronic Cardiac Arrest Risk Triage (median area under the receiver operating characteristic curve 0.73).
CONCLUSIONS: The choice of criteria to define a potentially infected population significantly impacts prevalence of mortality but has little impact on accuracy. Systemic inflammatory response syndrome was the least predictive and electronic Cardiac Arrest Risk Triage the most predictive regardless of how infection was defined.

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Year:  2017        PMID: 28737573      PMCID: PMC5640476          DOI: 10.1097/CCM.0000000000002648

Source DB:  PubMed          Journal:  Crit Care Med        ISSN: 0090-3493            Impact factor:   7.598


  22 in total

1.  Incidence and Prognostic Value of the Systemic Inflammatory Response Syndrome and Organ Dysfunctions in Ward Patients.

Authors:  Matthew M Churpek; Frank J Zadravecz; Christopher Winslow; Michael D Howell; Dana P Edelson
Journal:  Am J Respir Crit Care Med       Date:  2015-10-15       Impact factor: 21.405

2.  Assessment of Clinical Criteria for Sepsis: For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3).

Authors:  Christopher W Seymour; Vincent X Liu; Theodore J Iwashyna; Frank M Brunkhorst; Thomas D Rea; André Scherag; Gordon Rubenfeld; Jeremy M Kahn; Manu Shankar-Hari; Mervyn Singer; Clifford S Deutschman; Gabriel J Escobar; Derek C Angus
Journal:  JAMA       Date:  2016-02-23       Impact factor: 56.272

3.  Development, implementation, and impact of an automated early warning and response system for sepsis.

Authors:  Craig A Umscheid; Joel Betesh; Christine VanZandbergen; Asaf Hanish; Gordon Tait; Mark E Mikkelsen; Benjamin French; Barry D Fuchs
Journal:  J Hosp Med       Date:  2014-09-26       Impact factor: 2.960

4.  Hospital deaths in patients with sepsis from 2 independent cohorts.

Authors:  Vincent Liu; Gabriel J Escobar; John D Greene; Jay Soule; Alan Whippy; Derek C Angus; Theodore J Iwashyna
Journal:  JAMA       Date:  2014-07-02       Impact factor: 56.272

5.  Reduction in time to first action as a result of electronic alerts for early sepsis recognition.

Authors:  Lisa Kurczewski; Michael Sweet; Richard McKnight; Kevin Halbritter
Journal:  Crit Care Nurs Q       Date:  2015 Apr-Jun

6.  Severe sepsis cohorts derived from claims-based strategies appear to be biased toward a more severely ill patient population.

Authors:  Stacey-Ann Whittaker; Mark E Mikkelsen; David F Gaieski; Sherine Koshy; Craig Kean; Barry D Fuchs
Journal:  Crit Care Med       Date:  2013-04       Impact factor: 7.598

7.  Identifying patients with severe sepsis using administrative claims: patient-level validation of the angus implementation of the international consensus conference definition of severe sepsis.

Authors:  Theodore J Iwashyna; Andrew Odden; Jeffrey Rohde; Catherine Bonham; Latoya Kuhn; Preeti Malani; Lena Chen; Scott Flanders
Journal:  Med Care       Date:  2014-06       Impact factor: 2.983

8.  Multicenter development and validation of a risk stratification tool for ward patients.

Authors:  Matthew M Churpek; Trevor C Yuen; Christopher Winslow; Ari A Robicsek; David O Meltzer; Robert D Gibbons; Dana P Edelson
Journal:  Am J Respir Crit Care Med       Date:  2014-09-15       Impact factor: 21.405

9.  Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards.

Authors:  Matthew M Churpek; Trevor C Yuen; Christopher Winslow; David O Meltzer; Michael W Kattan; Dana P Edelson
Journal:  Crit Care Med       Date:  2016-02       Impact factor: 7.598

10.  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

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  28 in total

Review 1.  SIRS or qSOFA? Is that the question? Clinical and methodological observations from a meta-analysis and critical review on the prognostication of patients with suspected sepsis outside the ICU.

Authors:  Stefano Franchini; Luca Scarallo; Michele Carlucci; Luca Cabrini; Moreno Tresoldi
Journal:  Intern Emerg Med       Date:  2018-10-15       Impact factor: 3.397

2.  Risk factors for infection and evaluation of Sepsis-3 in patients with trauma.

Authors:  Emanuel Eguia; Adrienne N Cobb; Marshall S Baker; Cara Joyce; Emily Gilbert; Richard Gonzalez; Majid Afshar; Matthew M Churpek
Journal:  Am J Surg       Date:  2019-03-08       Impact factor: 2.565

3.  Sepsis Surveillance Using Adult Sepsis Events Simplified eSOFA Criteria Versus Sepsis-3 Sequential Organ Failure Assessment Criteria.

Authors:  Chanu Rhee; Zilu Zhang; Sameer S Kadri; David J Murphy; Greg S Martin; Elizabeth Overton; Christopher W Seymour; Derek C Angus; Raymund Dantes; Lauren Epstein; David Fram; Richard Schaaf; Rui Wang; Michael Klompas
Journal:  Crit Care Med       Date:  2019-03       Impact factor: 7.598

4.  Allergic Immune Diseases and the Risk of Mortality Among Patients Hospitalized for Acute Infection.

Authors:  Philip A Verhoef; Sivasubramanium V Bhavani; Kyle A Carey; Matthew M Churpek
Journal:  Crit Care Med       Date:  2019-12       Impact factor: 7.598

5.  To catch a killer: electronic sepsis alert tools reaching a fever pitch?

Authors:  Halley Ruppel; Vincent Liu
Journal:  BMJ Qual Saf       Date:  2019-04-23       Impact factor: 7.035

6.  National Trends of Organ Dysfunctions in Sepsis:An 11-Year Longitudinal Population-Based Cohort Study.

Authors:  Chia-Hung Yo; Chih-Cheng Lai; Tzu-Chun Hsu; Cheng-Yi Wang; Alvaro E Galvis; Debra Yen; Wan-Ting Hsu; Jason Wang; Chien-Chang Lee
Journal:  J Acute Med       Date:  2019-12-01

7.  IRIS: A Modular Platform for Continuous Monitoring and Caretaker Notification in the Intensive Care Unit.

Authors:  Steven N Baldassano; Shawniqua Williams Roberson; Ramani Balu; Brittany Scheid; John M Bernabei; Jay Pathmanathan; Brian Oommen; Damien Leri; Javier Echauz; Michael Gelfand; Paulomi Kadakia Bhalla; Chloe E Hill; Amanda Christini; Joost B Wagenaar; Brian Litt
Journal:  IEEE J Biomed Health Inform       Date:  2020-01-13       Impact factor: 5.772

8.  Machine Learning and Sepsis: On the Road to Revolution.

Authors:  Vincent X Liu; Allan J Walkey
Journal:  Crit Care Med       Date:  2017-11       Impact factor: 7.598

9.  Increased Risk of Spontaneous Bacterial Peritonitis in Cirrhotic Patients Using Proton Pump Inhibitors.

Authors:  Abdel-Naser Elzouki; Nadia Neffati; Fatma A Rasoul; Ali Abdallah; Muftah Othman; Abdelkarim Waness
Journal:  GE Port J Gastroenterol       Date:  2018-06-08

10.  On classifying sepsis heterogeneity in the ICU: insight using machine learning.

Authors:  Zina M Ibrahim; Honghan Wu; Ahmed Hamoud; Lukas Stappen; Richard J B Dobson; Andrea Agarossi
Journal:  J Am Med Inform Assoc       Date:  2020-03-01       Impact factor: 4.497

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