Literature DB >> 27631709

Automated Detection of Sepsis Using Electronic Medical Record Data: A Systematic Review.

Laurel A Despins.   

Abstract

Severe sepsis and septic shock are global issues with high mortality rates. Early recognition and intervention are essential to optimize patient outcomes. Automated detection using electronic medical record (EMR) data can assist this process. This review describes automated sepsis detection using EMR data. PubMed retrieved publications between January 1, 2005 and January 31, 2015. Thirteen studies met study criteria: described an automated detection approach with the potential to detect sepsis or sepsis-related deterioration in real or near-real time; focused on emergency department and hospitalized neonatal, pediatric, or adult patients; and provided performance measures or results indicating the impact of automated sepsis detection. Detection algorithms incorporated systemic inflammatory response and organ dysfunction criteria. Systems in nine studies generated study or care team alerts. Care team alerts did not consistently lead to earlier interventions. Earlier interventions did not consistently translate to improved patient outcomes. Performance measures were inconsistent. Automated sepsis detection is potentially a means to enable early sepsis-related therapy but current performance variability highlights the need for further research.

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Year:  2017        PMID: 27631709     DOI: 10.1097/JHQ.0000000000000066

Source DB:  PubMed          Journal:  J Healthc Qual        ISSN: 1062-2551            Impact factor:   1.095


  8 in total

1.  Design, Implementation, and Validation of a Pediatric ICU Sepsis Prediction Tool as Clinical Decision Support.

Authors:  Maya Dewan; Rhea Vidrine; Matthew Zackoff; Zachary Paff; Brandy Seger; Stephen Pfeiffer; Philip Hagedorn; Erika L Stalets
Journal:  Appl Clin Inform       Date:  2020-03-25       Impact factor: 2.342

2.  Iterative User Interface Design for Automated Sequential Organ Failure Assessment Score Calculator in Sepsis Detection.

Authors:  Christopher Ansel Aakre; Jaben E Kitson; Man Li; Vitaly Herasevich
Journal:  JMIR Hum Factors       Date:  2017-05-18

3.  Predicted Economic Benefits of a Novel Biomarker for Earlier Sepsis Identification and Treatment: A Counterfactual Analysis.

Authors:  Carly J Paoli; Mark A Reynolds; Courtney Coles; Matthew Gitlin; Elliott Crouser
Journal:  Crit Care Explor       Date:  2019-08-07

4.  Sepsis surveillance: an examination of parameter sensitivity and alert reliability.

Authors:  Robert C Amland; Mark Burghart; J Marc Overhage
Journal:  JAMIA Open       Date:  2019-06-11

5.  Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review.

Authors:  Melissa Y Yan; Lise Tuset Gustad; Øystein Nytrø
Journal:  J Am Med Inform Assoc       Date:  2022-01-29       Impact factor: 4.497

Review 6.  Computerized Clinical Decision Support Systems for the Early Detection of Sepsis Among Adult Inpatients: Scoping Review.

Authors:  Khalia Ackermann; Jannah Baker; Malcolm Green; Mary Fullick; Hilal Varinli; Johanna Westbrook; Ling Li
Journal:  J Med Internet Res       Date:  2022-02-23       Impact factor: 7.076

7.  Multidisciplinary Kaizen Event to Improve Adherence to a Sepsis Clinical Care Guideline.

Authors:  Kimberly S Denicolo; Jacqueline B Corboy; Norma-Jean E Simon; Kate J Balsley; Daniel J Skarzynski; Emily C Roben; Elizabeth R Alpern
Journal:  Pediatr Qual Saf       Date:  2021-06-23

8.  The Use of Patient Monitoring Systems to Improve Sepsis Recognition and Outcomes: A Systematic Review.

Authors:  Bryan M Gale; Kendall K Hall
Journal:  J Patient Saf       Date:  2020-09       Impact factor: 2.243

  8 in total

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