Literature DB >> 28968279

Incidence of Artifacts and Deviating Values in Research Data Obtained from an Anesthesia Information Management System in Children.

Anne-Lee J Hoorweg1, Wietze Pasma, Leo van Wolfswinkel, Jurgen C de Graaff.   

Abstract

BACKGROUND: Vital parameter data collected in anesthesia information management systems are often used for clinical research. The validity of this type of research is dependent on the number of artifacts.
METHODS: In this prospective observational cohort study, the incidence of artifacts in anesthesia information management system data was investigated in children undergoing anesthesia for noncardiac procedures. Secondary outcomes included the incidence of artifacts among deviating and nondeviating values, among the anesthesia phases, and among different anesthetic techniques.
RESULTS: We included 136 anesthetics representing 10,236 min of anesthesia time. The incidence of artifacts was 0.5% for heart rate (95% CI: 0.4 to 0.7%), 1.3% for oxygen saturation (1.1 to 1.5%), 7.5% for end-tidal carbon dioxide (6.9 to 8.0%), 5.0% for noninvasive blood pressure (4.0 to 6.0%), and 7.3% for invasive blood pressure (5.9 to 8.8%). The incidence of artifacts among deviating values was 3.1% for heart rate (2.1 to 4.4%), 10.8% for oxygen saturation (7.6 to 14.8%), 14.1% for end-tidal carbon dioxide (13.0 to 15.2%), 14.4% for noninvasive blood pressure (10.3 to 19.4%), and 38.4% for invasive blood pressure (30.3 to 47.1%).
CONCLUSIONS: Not all values in anesthesia information management systems are valid. The incidence of artifacts stored in the present pediatric anesthesia practice was low for heart rate and oxygen saturation, whereas noninvasive and invasive blood pressure and end-tidal carbon dioxide had higher artifact incidences. Deviating values are more often artifacts than values in a normal range, and artifacts are associated with the phase of anesthesia and anesthetic technique. Development of (automatic) data validation systems or solutions to deal with artifacts in data is warranted.

Entities:  

Mesh:

Year:  2018        PMID: 28968279     DOI: 10.1097/ALN.0000000000001895

Source DB:  PubMed          Journal:  Anesthesiology        ISSN: 0003-3022            Impact factor:   7.892


  4 in total

1.  Automated anesthesia artifact analysis: can machines be trained to take out the garbage?

Authors:  Allan F Simpao; Olivia Nelson; Luis M Ahumada
Journal:  J Clin Monit Comput       Date:  2020-09-12       Impact factor: 2.502

2.  Hypotension and adverse neurodevelopmental outcomes among children with multiple exposures to general anesthesia: Subanalysis of the Mayo Anesthesia Safety in Kids (MASK) Study.

Authors:  Stephen J Gleich; Yu Shi; Randall Flick; Michael J Zaccariello; Darrell R Schroeder; Andrew C Hanson; David O Warner
Journal:  Paediatr Anaesth       Date:  2021-01-04       Impact factor: 2.129

3.  Completeness of manual data recording in the anaesthesia information management system: A retrospective audit of 1000 neurosurgical cases.

Authors:  Sangeetha R Palaniswamy; Vikyath Jain; Dhritiman Chakrabarti; Suparna Bharadwaj; Kamath Sriganesh
Journal:  Indian J Anaesth       Date:  2019-10-10

4.  Patient and anesthesia characteristics of children with low pre-incision blood pressure: A retrospective observational study.

Authors:  Wietze Pasma; Linda M Peelen; Stefanie van den Broek; Stef van Buuren; Wilton A van Klei; Jurgen C de Graaff
Journal:  Acta Anaesthesiol Scand       Date:  2019-12-22       Impact factor: 2.105

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.