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Effects of pain, sedation and delirium monitoring on clinical and economic outcome: A retrospective study

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  • Marc Deffland
  • Claudia Spies
  • Bjoern Weiss
  • Niklas Keller
  • Mirjam Jenny
  • Jochen Kruppa
  • Felix Balzer

Abstract

Background: Significant improvements in clinical outcome can be achieved by implementing effective strategies to optimise pain management, reduce sedative exposure, and prevent and treat delirium in ICU patients. One important strategy is the monitoring of pain, agitation and delirium (PAD bundle). We hypothesised that there is no sufficient financial benefit to implement a monitoring strategy in a Diagnosis Related Group (DRG)-based reimbursement system, therefore we expected better clinical and decreased economic outcome for monitored patients. Methods: This is a retrospective observational study using routinely collected data. We used univariate and multiple linear analysis, machine-learning analysis and a novel correlation statistic (maximal information coefficient) to explore the association between monitoring adherence and resulting clinical and economic outcome. For univariate analysis we split patients in an adherence achieved and an adherence non-achieved group. Results: In total 1,323 adult patients from two campuses of a German tertiary medical centre, who spent at least one day in the ICU between admission and discharge between 1. January 2016 and 31. December 2016. Adherence to PAD monitoring was associated with shorter hospital LoS (e.g. pain monitoring 13 vs. 10 days; p

Suggested Citation

  • Marc Deffland & Claudia Spies & Bjoern Weiss & Niklas Keller & Mirjam Jenny & Jochen Kruppa & Felix Balzer, 2020. "Effects of pain, sedation and delirium monitoring on clinical and economic outcome: A retrospective study," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-14, September.
    • Handle: RePEc:plo:pone00:0234801
    DOI: 10.1371/journal.pone.0234801
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    References listed on IDEAS

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    2. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
    3. Paul T E Cusack, 2020. "On Pain," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 31(3), pages 24253-24254, October.
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