CompletedNot Applicable

Machine Learning Modeling of Intraoperative Hemodynamic Predictors of Postoperative Outcomes

35,000 participantsStarted 2013-01-01

Plain-language summary

With population aging and limited resources, strategies to improve outcomes after surgery are ever more important. There is a limited understanding of what ranges of hemodynamic variables under anesthesia are associated with better outcomes. This retrospective cohort study will analyze how hemodynamic variables during surgeries predict mortality, morbidity, Intensive Care Unit admission, length of hospital stay, and hospital readmission. The use of machine learning in a large, broad surgery population dataset could detect new relationships and strategies that may inform current practice, and generate ideas for future research.

Who can participate

Age range45 Years

SexALL

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Inclusion Criteria: * All patients ages ≥ 45 receiving their index (i.e. first) non-cardiac surgery with an overnight stay at the Nova Scotia Health Authority Queen Elizabeth II (QEII) hospitals (Victoria General and Halifax Infirmary) Halifax, Canada, from January 1, 2013 to December 1, 2017. * For patients who had multiple surgeries, only the first non-cardiac surgery with an overnight stay at QEII will be included to avoid confounding from previous surgical admissions (i.e. one surgical admission per patient). Exclusion Criteria: * No intraoperative anesthetic records * Cardiac surgery patients * Deceased organ donation

What they're measuring

Mortality

Timeframe: 30 days after date of surgery

Trial details

NCT IDNCT04014010

SponsorJanny Xue Chen Ke

Sponsor typeOTHER

Study typeOBSERVATIONAL

Primary completion2017-12-31

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