Using the Revised Cardiac Risk Index to predict major postoperative events for people with kidney failure : An external validation and update

Funding Information: T.G.H. is supported by a Kidney Research Scientist Core Education and National Training Program postdoctoral fellowship (cosponsored by the Kidney Foundation of Canada and Canadian Institutes of Health Research) and the Clinician Investigator Program at the University of Calgary. These funding sources had no role in study design, data collection, analysis, reporting, or the decision to submit for publication. Funding Information: Ethics Statement: We followed the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist19 for prediction-model validation (Supplemental Table S1) and were granted ethics approval by the University of Calgary and the University of Alberta.Preoperative risk-prediction tools that are used to predict risk of perioperative death and CV events, and are supported by North American guidelines, include the revised cardiac risk index (RCRI),5 the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) tool,6,7 and the National Surgical Quality Improvement Program Myocardial Infarction or Cardiac Arrest (NSQIP MICA) tool.8 The RCRI has been recommended over others for use in Canada for all adults over the age of 45 years, and for those aged 18-45 years with CV disease, who are undergoing elective, noncardiac surgery.3 The RCRI incorporates 6 criteria based on surgical and comorbidity characteristics of the patient and derives an estimated probability of postoperative myocardial infarction, cardiac arrest, or death.5 Additionally, the RCRI is used to guide perioperative decision-making.3The Alberta Kidney Disease Network database includes person-level linkages of administrative health data, laboratory data, prescription information, and kidney disease-specific data from the province of Alberta, Canada.17 Alberta has approximately 4.4 million residents, and with universal public health insurance, health data capture is near complete.17,18 From this database, we derived a retrospective cohort of adults with kidney failure who underwent ambulatory or inpatient surgery. We used this cohort to externally validate and examine the performance of the RCRI for this population. We followed the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist19 for prediction-model validation (Supplemental Table S1) and were granted ethics approval by the University of Calgary and the University of Alberta.Peer reviewedPublisher PD