Beyond Unplanned ICU Transfers: Linking a Revised Definition of Deterioration to Patient Outcomes

Thomas F. Byrd*, Tom A. Phelan, Nicholas E. Ingraham, Benjamin W. Langworthy, Ajay Bhasin, Abhinab Kc, Genevieve B. Melton-Meaux, Christopher J. Tignanelli

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

OBJECTIVES: To develop an electronic descriptor of clinical deterioration for hospitalized patients that predicts short-term mortality and identifies patient deterioration earlier than current standard definitions. DESIGN: A retrospective study using exploratory record review, quantitative analysis, and regression analyses. SETTING: Twelve-hospital community-academic health system. PATIENTS: All adult patients with an acute hospital encounter between January 1, 2018, and December 31, 2022. INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: Clinical trigger events were selected and used to create a revised electronic definition of deterioration, encompassing signals of respiratory failure, bleeding, and hypotension occurring in proximity to ICU transfer. Patients meeting the revised definition were 12.5 times more likely to die within 7 days (adjusted odds ratio 12.5; 95% CI, 8.9–17.4) and had a 95.3% longer length of stay (95% CI, 88.6–102.3%) compared with those who were transferred to the ICU or died regardless of meeting the revised definition. Among the 1812 patients who met the revised definition of deterioration before ICU transfer (52.4%), the median detection time was 157.0min earlier (interquartile range 64.0–363.5min). CONCLUSIONS: The revised definition of deterioration establishes an electronic descriptor of clinical deterioration that is strongly associated with short-term mortality and length of stay and identifies deterioration over 2.5 hours earlier than ICU transfer. Incorporating the revised definition of deterioration into the training and validation of early warning system algorithms may enhance their timeliness and clinical accuracy.

Original languageEnglish (US)
Pages (from-to)e439-e449
JournalCritical care medicine
Volume52
Issue number9
DOIs
StatePublished - Sep 1 2024

Funding

This study was supported by grant UL1TR002494 from the National Center for Advancing Translational Sciences of the National Institutes of Health (NIH; Dr. Langworthy), grant K23HL166783 from the National Heart Lung and Blood Institute of the NIH (Dr. Ingraham), and grant P30HS029744 from the Agency for Healthcare Research and Quality (AHRQ) and Patient-Centered Outcomes Research Institute (PCORI; Dr. Byrd). Additional support for the Minnesota Learning Health System (MN-LHS) Mentored Career Development Program is offered by the University of Minnesota Office of Academic Clinical Affairs, Clinical Translational Science Institute, and Center for Learning Health System Sciences. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH, AHRQ, PCORI, or MN-LHS. The remaining authors have disclosed that they do not have any potential conflicts of interest.

Keywords

  • artificial intelligence
  • clinical
  • decision support systems
  • hemorrhage
  • hypotension
  • respiratory insufficiency

ASJC Scopus subject areas

  • Critical Care and Intensive Care Medicine

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