Affiliations 

  • 1 Department of Medicine, Western University, London, ON, Canada; Population Health Research Institute, Hamilton, ON, Canada; Outcomes Research Consortium, Houston, TX, USA. Electronic address: proshano@uwo.ca
  • 2 Population Health Research Institute, Hamilton, ON, Canada; Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada; Department of Medicine, McMaster University, Hamilton, ON, Canada
  • 3 Department of Medicine, Western University, London, ON, Canada
  • 4 London Health Sciences Centre, London, ON, Canada
  • 5 Division of Transplantation and Nephrology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
  • 6 Division of Nephrology, Department of Medicine, University of Alberta, Edmonton, AB, Canada
  • 7 Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, NSW, Australia
  • 8 Department of Critical Care, Melbourne Medical School, University of Melbourne, Melbourne, VIC, Australia
  • 9 Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong, China
  • 10 Department of Medicine, McMaster University, Hamilton, ON, Canada
  • 11 Department of Anesthesiology, University of Malaya, Kuala Lumpur, Wilayah Persekutuan, Malaysia
  • 12 St John's Medical College, Bangalore, Karnataka, India; Division of Clinical Research and Training, St. John's Research Institute, St. John's National Academy of Health Sciences, Bangalore, Karnataka, India
  • 13 Outcomes Research Consortium, Department of Anesthesiology, Critical Care and Pain Medicine, University of Texas, Houston, TX, USA
  • 14 Center for Intensive Care and Perioperative Medicine, Jagiellonian University Medical College, Krakow, Małopolska, Poland
  • 15 Department of Anaesthesia and Intensive Care, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Copenhagen, Denmark
  • 16 Department of Surgery, University of Manitoba, Winnipeg, MB, Canada
  • 17 Numen Health, Bengaluru, Karnataka, India; Carmel Research, Bengaluru, Karnataka, India
  • 18 Centro de Investigaciones, Fundación Cardioinfantil - Instituto de Cardiología, Bogotá, Colombia; Facultad de Ciencias de la Salud, Universidad Autónoma de Bucaramanga, Bucaramanga, Santander, Colombia
  • 19 Westmead Applied Research Centre, University of Sydney, Sydney, NSW, Australia; Department of Cardiology, Westmead Hospital, Sydney, NSW, Australia
  • 20 Graduate Program in Epidemiology and Cardiovascular Science, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil; Institute for Health Technology Assessment, Porto Alegre, Rio Grande do Sul, Brazil
  • 21 Department of Medicine, University of Calgary, Calgary, AB, Canada; Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada; O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
  • 22 Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
  • 23 Department of Anesthesiology and Perioperative Medicine, University of Texas - MD Anderson Cancer Center, Houston, TX, USA
  • 24 Department of Anesthesiology and Perioperative Medicine, Queen's University and Kingston Health Sciences Centre, Kingston, ON, Canada
  • 25 Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
  • 26 Population Health Research Institute, Hamilton, ON, Canada; Department of Medicine, McMaster University, Hamilton, ON, Canada
Br J Anaesth, 2025 Feb;134(2):297-307.
PMID: 39753401 DOI: 10.1016/j.bja.2024.10.039

Abstract

BACKGROUND: Optimised use of kidney function information might improve cardiac risk prediction in noncardiac surgery.

METHODS: In 35,815 patients from the VISION cohort study and 9219 patients from the POISE-2 trial who were ≥45 yr old and underwent nonurgent inpatient noncardiac surgery, we examined (by age and sex) the association between continuous nonlinear preoperative estimated glomerular filtration rate (eGFR) and the composite of myocardial injury after noncardiac surgery, nonfatal cardiac arrest, or death owing to a cardiac cause within 30 days after surgery. We estimated contributions of predictive information, C-statistic, and net benefit from eGFR and other common patient and surgical characteristics to large multivariable models.

RESULTS: The primary composite occurred in 4725 (13.2%) patients in VISION and 1903 (20.6%) in POISE-2; in both studies cardiac events had a strong, graded association with lower preoperative eGFR that was attenuated by older age (Pinteraction<0.001 for VISION; Pinteraction=0.008 for POISE-2). For eGFR of 30 compared with 90 ml min-1 1.73 m-2, relative risk was 1.49 (95% confidence interval 1.26-1.78) at age 80 yr but 4.50 (2.84-7.13) at age 50 yr in female patients in VISION. This differed modestly (but not meaningfully) in men in VISION (Pinteraction=0.02) but not in POISE-2 (Pinteraction=0.79). eGFR contributed the most predictive information and mean net benefit of all predictors in both studies, most C-statistic in VISION, and third most C-statistic in POISE-2.

CONCLUSIONS: Continuous preoperative eGFR is among the best cardiac risk predictors in noncardiac surgery of the large set examined. Along with its interaction with age, preoperative eGFR would improve risk calculators.

CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT00512109 (VISION) and NCT01082874 (POISE-2).

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.