External validation of the Revised Cardiac Risk Index and update of its renal variable to predict 30-day risk of major cardiac complications after non-cardiac surgery: rationale and plan for analyses of the VISION study

Pavel S Roshanov, Michael Walsh, P J Devereaux, S Danielle MacNeil, Ngan N Lam, Ainslie M Hildebrand, Rey R Acedillo, Marko Mrkobrada, Clara K Chow, Vincent W Lee, Lehana Thabane, Amit X Garg, Pavel S Roshanov, Michael Walsh, P J Devereaux, S Danielle MacNeil, Ngan N Lam, Ainslie M Hildebrand, Rey R Acedillo, Marko Mrkobrada, Clara K Chow, Vincent W Lee, Lehana Thabane, Amit X Garg

Abstract

Introduction: The Revised Cardiac Risk Index (RCRI) is a popular classification system to estimate patients' risk of postoperative cardiac complications based on preoperative risk factors. Renal impairment, defined as serum creatinine >2.0 mg/dL (177 µmol/L), is a component of the RCRI. The estimated glomerular filtration rate has become accepted as a more accurate indicator of renal function. We will externally validate the RCRI in a modern cohort of patients undergoing non-cardiac surgery and update its renal component.

Methods and analysis: The Vascular Events in Non-cardiac Surgery Patients Cohort Evaluation (VISION) study is an international prospective cohort study. In this prespecified secondary analysis of VISION, we will test the risk estimation performance of the RCRI in ∼34 000 participants who underwent elective non-cardiac surgery between 2007 and 2013 from 29 hospitals in 15 countries. Using data from the first 20 000 eligible participants (the derivation set), we will derive an optimal threshold for dichotomising preoperative renal function quantified using the Chronic Kidney Disease Epidemiology Collaboration (CKD-Epi) glomerular filtration rate estimating equation in a manner that preserves the original structure of the RCRI. We will also develop a continuous risk estimating equation integrating age and CKD-Epi with existing RCRI risk factors. In the remaining (approximately) 14 000 participants, we will compare the risk estimation for cardiac complications of the original RCRI to this modified version. Cardiac complications will include 30-day non-fatal myocardial infarction, non-fatal cardiac arrest and death due to cardiac causes. We have examined an early sample to estimate the number of events and the distribution of predictors and missing data, but have not seen the validation data at the time of writing.

Ethics and dissemination: The research ethics board at each site approved the VISION protocol prior to recruitment. We will publish our results and make our models available online at http://www.perioperativerisk.com.

Trial registration number: ClinicalTrials.gov NCT00512109.

Keywords: Risk prediction; SURGERY; cardiac events; perioperative medicine.

Conflict of interest statement

Conflicts of Interest: None declared.

Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

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References

    1. Weiser TG, Haynes AB, Molina G et al. . Estimate of the global volume of surgery in 2012: an assessment supporting improved health outcomes. Lancet 2015;385(Suppl 2):S11 10.1016/S0140-6736(15)60806-6
    1. Devereaux PJ, Goldman L, Cook DJ et al. . Perioperative cardiac events in patients undergoing noncardiac surgery: a review of the magnitude of the problem, the pathophysiology of the events and methods to estimate and communicate risk. CMAJ 2005;173:627–34. 10.1503/cmaj.050011
    1. Lee TH, Marcantonio ER, Mangione CM et al. . Derivation and prospective validation of a simple index for prediction of cardiac risk of major noncardiac surgery. Circulation 1999;100:1043–9. 10.1161/01.CIR.100.10.1043
    1. Levey AS, Stevens LA, Schmid CH et al. . A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150:604–12. 10.7326/0003-4819-150-9-200905050-00006
    1. Mooney JF, Ranasinghe I, Chow CK et al. . Preoperative estimates of glomerular filtration rate as predictors of outcome after surgery: a systematic review and meta-analysis. Anesthesiology 2013;118:809–24. 10.1097/ALN.0b013e318287b72c
    1. Thygesen K, Alpert JS, Jaffe AS et al. . Third universal definition of myocardial infarction. Circulation 2012;126:2020–35. 10.1161/CIR.0b013e31826e1058
    1. Landerman LR, Land KC, Pieper CF. An empirical evaluation of the predictive mean matching method for imputing missing values. Sociol Methods Res 1997;26:3–33. 10.1177/0049124197026001001
    1. Azur MJ, Stuart EA, Frangakis C, Leaf PJ. Multiple imputation by chained equations: What is it and how does it work?. Int J Methods Psychiatr Res 2011;20:40–9.
    1. Steyerberg EW. Clinical prediction models: a practical approach to development, validation, and updating. New York: Springer-Verlag, 2009.
    1. Moons KGM, Donders RART, Stijnen T et al. . Using the outcome for imputation of missing predictor values was preferred. J Clin Epidemiol 2006;59:1092–101. 10.1016/j.jclinepi.2006.01.009
    1. Stevens PE, Levin A, Kidney Disease: Improving Global Outcomes Chronic Kidney Disease Guideline Development Work Group Members. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med 2013;158:825–30. 10.7326/0003-4819-158-11-201306040-00007
    1. Steyerberg EW, Vickers AJ, Cook NR et al. . Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 2010;21:128–38. 10.1097/EDE.0b013e3181c30fb2
    1. Janes H, Pepe MS, Gu W. Assessing the value of risk predictions by using risk stratification tables. Ann Intern Med 2008;149:751–60. 10.7326/0003-4819-149-10-200811180-00009
    1. Rodseth RN, Biccard BM, Le Manach Y et al. . The Prognostic Value of Pre-Operative and Post-Operative B-Type Natriuretic Peptides in Patients Undergoing Noncardiac Surgery: B-type natriuretic peptide and N-terminal fragment of pro-B-type natriuretic peptide: a systematic review and individual patient data meta-analysis. J Am Coll Cardiol 2014;63:170–80. 10.1016/j.jacc.2013.08.1630
    1. Rodseth RN, Biccard BM, Chu R et al. . Postoperative B-type natriuretic peptide for prediction of major cardiac events in patients undergoing noncardiac surgery: systematic review and individual patient meta-analysis. Anesthesiology 2013;119:270–83. 10.1097/ALN.0b013e31829083f1
    1. Sheth T, Chan M, Butler C et al. . Prognostic capabilities of coronary computed tomographic angiography before non-cardiac surgery: prospective cohort study. BMJ 2015;350:h1907–h1907. 10.1136/bmj.h1907
    1. Kristensen SD, Knuuti J, Saraste A et al. . 2014 ESC/ESA Guidelines on non-cardiac surgery: cardiovascular assessment and management: the Joint Task Force on non-cardiac surgery: cardiovascular assessment and management of the European Society of Cardiology (ESC) and the European Society of Anaesthesiology (ESA). Eur J Anaesthesiol 2014;31:517–73. 10.1097/EJA.0000000000000150
    1. Cook NR, Ridker PM. Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Ann Intern Med 2009;150:795–802. 10.7326/0003-4819-150-11-200906020-00007
    1. Pencina MJ, D'Agostino RB, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med 2011;30:11–21. 10.1002/sim.4085
    1. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 2006;26:565–74. 10.1177/0272989X06295361
    1. Steyerberg EW, Vickers AJ. Decision curve analysis: a discussion. Med Decis Making 2008;28:146–9. 10.1177/0272989X07312725
    1. Janssen KJM, Moons KGM, Kalkman CJ et al. . Updating methods improved the performance of a clinical prediction model in new patients. J Clin Epidemiol 2008;61:76–86. 10.1016/j.jclinepi.2007.04.018
    1. Steyerberg EW, Borsboom GJJM, van Houwelingen HC et al. . Validation and updating of predictive logistic regression models: a study on sample size and shrinkage. Stat Med 2004;23:2567–86. 10.1002/sim.1844
    1. Vergouwe Y, Steyerberg EW, Eijkemans MJC et al. . Substantial effective sample sizes were required for external validation studies of predictive logistic regression models. J Clin Epidemiol 2005;58:475–83. 10.1016/j.jclinepi.2004.06.017
    1. Steyerberg EW, Eijkemans MJC, Habbema JDF. Application of shrinkage techniques in logistic regression analysis: a case study. Stat Neerl 2001;55:76–88. 10.1111/1467-9574.00157
    1. Ford MK, Beattie WS, Wijeysundera DN. Systematic review: prediction of perioperative cardiac complications and mortality by the revised cardiac risk index. Ann Intern Med 2010;152:26–35. 10.7326/0003-4819-152-1-201001050-00007
    1. Siddiqui NF, Coca SG, Devereaux PJ et al. . Secular trends in acute dialysis after elective major surgery—1995 to 2009. Can Med Assoc J 2012;184:1237–45. 10.1503/cmaj.110895
    1. Murthy K, Stevens LA, Stark PC et al. . Variation in the serum creatinine assay calibration: a practical application to glomerular filtration rate estimation. Kidney Int 2005;68:1884–7. 10.1111/j.1523-1755.2005.00608.x
    1. Stevens LA, Manzi J, Levey AS et al. . Impact of creatinine calibration on performance of GFR estimating equations in a pooled individual patient database. Am J Kidney Dis 2007;50:21–35. 10.1053/j.ajkd.2007.04.004
    1. Coresh J, Astor BC, McQuillan G et al. . Calibration and random variation of the serum creatinine assay as critical elements of using equations to estimate glomerular filtration rate. Am J Kidney Dis 2002;39:920–9. 10.1053/ajkd.2002.32765
    1. Fleisher LA, Fleischmann KE, Auerbach AD et al. . 2014 ACC/AHA guideline on perioperative cardiovascular evaluation and management of patients undergoing noncardiac surgery: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 2014;130:e278–333 10.1161/CIR.0000000000000106

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