Literature DB >> 28104273

Physiologically-based, predictive analytics using the heart-rate-to-Systolic-Ratio significantly improves the timeliness and accuracy of sepsis prediction compared to SIRS.

Omar K Danner1, Sandra Hendren2, Ethel Santiago3, Brittany Nye3, Prasad Abraham3.   

Abstract

BACKGROUND: Enhancing the efficiency of diagnosis and treatment of severe sepsis by using physiologically-based, predictive analytical strategies has not been fully explored. We hypothesize assessment of heart-rate-to-systolic-ratio significantly increases the timeliness and accuracy of sepsis prediction after emergency department (ED) presentation.
METHODS: We evaluated the records of 53,313 ED patients from a large, urban teaching hospital between January and June 2015. The HR-to-systolic ratio was compared to SIRS criteria for sepsis prediction. There were 884 patients with discharge diagnoses of sepsis, severe sepsis, and/or septic shock.
RESULTS: Variations in three presenting variables, heart rate, systolic BP and temperature were determined to be primary early predictors of sepsis with a 74% (654/884) accuracy compared to 34% (304/884) using SIRS criteria (p < 0.0001)in confirmed septic patients.
CONCLUSION: Physiologically-based predictive analytics improved the accuracy and expediency of sepsis identification via detection of variations in HR-to-systolic ratio. This approach may lead to earlier sepsis workup and life-saving interventions.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Big data; Heart rate to systolic ratio; Machine learning; Predictive analytics; Sepsis; Sepsis predictive probability index; Severe sepsis

Mesh:

Year:  2017        PMID: 28104273     DOI: 10.1016/j.amjsurg.2017.01.006

Source DB:  PubMed          Journal:  Am J Surg        ISSN: 0002-9610            Impact factor:   2.565


  6 in total

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Review 3.  Can Prehospital Data Improve Early Identification of Sepsis in Emergency Department? An Integrative Review of Machine Learning Approaches.

Authors:  Manushi D Desai; Mohammad S Tootooni; Kathleen L Bobay
Journal:  Appl Clin Inform       Date:  2022-02-02       Impact factor: 2.342

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Authors:  Caroline M Ruminski; Matthew T Clark; Douglas E Lake; Rebecca R Kitzmiller; Jessica Keim-Malpass; Matthew P Robertson; Theresa R Simons; J Randall Moorman; J Forrest Calland
Journal:  J Clin Monit Comput       Date:  2018-08-18       Impact factor: 1.977

5.  Using Predictive Analytics to Identify Children at High Risk of Defaulting From a Routine Immunization Program: Feasibility Study.

Authors:  Subhash Chandir; Danya Arif Siddiqi; Owais Ahmed Hussain; Tahira Niazi; Mubarak Taighoon Shah; Vijay Kumar Dharma; Ali Habib; Aamir Javed Khan
Journal:  JMIR Public Health Surveill       Date:  2018-09-04

6.  Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction.

Authors:  Eli Bloch; Tammy Rotem; Jonathan Cohen; Pierre Singer; Yehudit Aperstein
Journal:  J Healthc Eng       Date:  2019-11-03       Impact factor: 2.682

  6 in total

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