Literature DB >> 17322011

Physiological monitoring for critically ill patients: testing a predictive model for the early detection of sepsis.

Karen K Giuliano1.   

Abstract

OBJECTIVE: To assess the predictive value for the early detection of sepsis of the physiological monitoring parameters currently recommended by the Surviving Sepsis Campaign.
METHODS: The Project IMPACT data set was used to assess whether the physiological parameters of heart rate, mean arterial pressure, body temperature, and respiratory rate can be used to distinguish between critically ill adult patients with and without sepsis in the first 24 hours of admission to an intensive care unit.
RESULTS: All predictor variables used in the analyses differed significantly between patients with sepsis and patients without sepsis. However, only 2 of the predictor variables, mean arterial pressure and high temperature, were independently associated with sepsis. In addition, the temperature mean for hypothermia was significantly lower in patients without sepsis. The odds ratio for having sepsis was 2.126 for patients with a temperature of 38 degrees C or higher, 3.874 for patients with a mean arterial blood pressure of less than 70 mm Hg, and 4.63 times greater for patients who had both of these conditions.
CONCLUSIONS: The results support the use of some of the guidelines of the Surviving Sepsis Campaign. However, the lowest mean temperature was significantly less for patients without sepsis than for patients with sepsis, a finding that calls into question the clinical usefulness of using hypothermia as an early predictor of sepsis. Alone the group of variables used is not sufficient for discriminating between critically ill patients with and without sepsis.

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Year:  2007        PMID: 17322011

Source DB:  PubMed          Journal:  Am J Crit Care        ISSN: 1062-3264            Impact factor:   2.228


  7 in total

1.  Hepatic Induction of Fatty Acid Binding Protein 4 Plays a Pathogenic Role in Sepsis in Mice.

Authors:  Bingfang Hu; Yujin Li; Li Gao; Yan Guo; Yiwen Zhang; Xiaojuan Chai; Meishu Xu; Jiong Yan; Peipei Lu; Songrong Ren; Su Zeng; Yulan Liu; Wen Xie; Min Huang
Journal:  Am J Pathol       Date:  2017-03-06       Impact factor: 4.307

2.  Developing predictive models using electronic medical records: challenges and pitfalls.

Authors:  Chris Paxton; Alexandru Niculescu-Mizil; Suchi Saria
Journal:  AMIA Annu Symp Proc       Date:  2013-11-16

3.  Patterns of unexpected in-hospital deaths: a root cause analysis.

Authors:  Lawrence A Lynn; J Paul Curry
Journal:  Patient Saf Surg       Date:  2011-02-11

4.  Clinical outcomes after utilizing surviving sepsis campaign in children with septic shock and prognostic value of initial plasma NT-proBNP.

Authors:  Rujipat Samransamruajkit; Rattapon Uppala; Khemmachart Pongsanon; Jitladda Deelodejanawong; Suchada Sritippayawan; Nuanchan Prapphal
Journal:  Indian J Crit Care Med       Date:  2014-02

5.  An Innovative Wearable Device For Monitoring Continuous Body Surface Temperature (HEARThermo): Instrument Validation Study.

Authors:  Chun-Yin Yeh; Yi-Ting Chung; Kun-Ta Chuang; Yu-Chen Shu; Hung-Yu Kao; Po-Lin Chen; Wen-Chien Ko; Nai-Ying Ko
Journal:  JMIR Mhealth Uhealth       Date:  2021-02-10       Impact factor: 4.773

Review 6.  Machine learning and artificial intelligence: applications in healthcare epidemiology.

Authors:  Alisa J Hamilton; Alexandra T Strauss; Diego A Martinez; Jeremiah S Hinson; Scott Levin; Gary Lin; Eili Y Klein
Journal:  Antimicrob Steward Healthc Epidemiol       Date:  2021-10-07

7.  DeepAISE - An interpretable and recurrent neural survival model for early prediction of sepsis.

Authors:  Supreeth P Shashikumar; Christopher S Josef; Ashish Sharma; Shamim Nemati
Journal:  Artif Intell Med       Date:  2021-02-13       Impact factor: 5.326

  7 in total

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