Download PDF
Thorac Cardiovasc Surg 2024; 72(01): 029-039
DOI: 10.1055/s-0043-1760747
Review Article

Risk Prediction Models for Long-Term Survival after Cardiac Surgery: A Systematic Review

Leah Argus 
1   The University of Manchester, Manchester, United Kingdom
,
Marcus Taylor 
2   Department of Cardiothoracic Surgery, Manchester University NHS Foundation Trust, Manchester, United Kingdom
,
Maral Ouzounian
3   Division of Cardiovascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
,
Rajamiyer Venkateswaran
2   Department of Cardiothoracic Surgery, Manchester University NHS Foundation Trust, Manchester, United Kingdom
,
Stuart W. Grant
4   Division of Cardiovascular Sciences, University of Manchester, Manchester, United Kingdom
5   Academic Cardiovascular Unit, South Tees Hospitals NHS Foundation Trust, Middlesborough, United Kingdom
› Author Affiliations
› Further Information
   Permissions and Reprints All articles of this category

Abstract

Background The reporting of alternative postoperative measures of quality after cardiac surgery is becoming increasingly important as in-hospital mortality rates continue to decline. This study aims to systematically review and assess risk models designed to predict long-term outcomes after cardiac surgery.

Methods The MEDLINE and Embase databases were searched for articles published between 1990 and 2020. Studies developing or validating risk prediction models for long-term outcomes after cardiac surgery were included. Data were extracted using checklists for critical appraisal and systematic review of prediction modeling studies.

Results Eleven studies were identified for inclusion in the review, of which nine studies described the development of long-term risk prediction models after cardiac surgery and two were external validation studies. A total of 70 predictors were included across the nine models. The most frequently used predictors were age (n = 9), peripheral vascular disease (n = 8), renal disease (n = 8), and pulmonary disease (n = 8). Despite all models demonstrating acceptable performance on internal validation, only two models underwent external validation, both of which performed poorly.

Conclusion Nine risk prediction models predicting long-term mortality after cardiac surgery have been identified in this review. Statistical issues with model development, limited inclusion of outcomes beyond 5 years of follow-up, and a lack of external validation studies means that none of the models identified can be recommended for use in contemporary cardiac surgery. Further work is needed either to successfully externally validate existing models or to develop new models. Newly developed models should aim to use standardized long-term specific reproducible outcome measures.

Keyword

Cardiac - outcomes - surgery - complications - statistics

Authors' Contribution

L.A., M.T., and S.W.G. designed the study. L.A. and M.T. collected the data. L.A., M.T., and S.W.G analyzed and interpreted the data. L.A.,M.T., M.O., R.V. and S.W.G. wrote the manuscript.


Supplementary Material



Publication History

Received: 23 June 2022

Accepted: 24 November 2022

Article published online:
07 February 2023

© 2023. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Bridgewater B. Society for Cardiothoracic Surgery in GB and Ireland. Cardiac registers: the adult cardiac surgery register. Heart 2010; 96 (18) 1441-1443
    CrossrefPubMedGoogle Scholar
  • 2 Grant SW, Kendall S, Goodwin AT. et al. Trends and outcomes for cardiac surgery in the United Kingdom from 2002 to 2016. JTCVS Open 2021; 7: 259-269
    CrossrefPubMedGoogle Scholar
  • 3 National Institute for Cardiovascular Outcomes Research (NICOR). National Adult Cardiac Surgery Audit (NACSA) 2020 Summary Report 2016/17–2018/19 Data. (2020). Accessed June 01, 2021 at: https://www.nicor.org.uk/wp-content/uploads/2020/12/National-Adult-Cardiac-Surgery-Audit-NACSA-FINAL.pdf
    PubMed
  • 4 Leon MB, Smith CR, Mack M. et al; PARTNER Trial Investigators. Transcatheter aortic-valve implantation for aortic stenosis in patients who cannot undergo surgery. N Engl J Med 2010; 363 (17) 1597-1607
    CrossrefPubMedGoogle Scholar
  • 5 Adams DH, Popma JJ, Reardon MJ. et al; U.S. CoreValve Clinical Investigators. Transcatheter aortic-valve replacement with a self-expanding prosthesis. N Engl J Med 2014; 370 (19) 1790-1798
    CrossrefPubMedGoogle Scholar
  • 6 Stone GW, Lindenfeld J, Abraham WT. et al; COAPT Investigators. Transcatheter mitral-valve repair in patients with heart failure. N Engl J Med 2018; 379 (24) 2307-2318
    CrossrefPubMedGoogle Scholar
  • 7 Nashef SAM, Roques F, Michel P, Gauducheau E, Lemeshow S, Salamon R. European System for Cardiac Operative Risk Evaluation (EuroSCORE). Eur J Cardiothorac Surg 1999; 16 (01) 9-13
    CrossrefPubMedGoogle Scholar
  • 8 Nashef SAM, Roques F, Sharples LD. et al. EuroSCORE II. Eur J Cardiothorac Surg 2012; 41 (04) 734-744 , discussion 744–745
    CrossrefPubMedGoogle Scholar
  • 9 Jin R, Furnary AP, Fine SC, Blackstone EH, Grunkemeier GL. Using Society of Thoracic Surgeons risk models for risk-adjusting cardiac surgery results. Ann Thorac Surg 2010; 89 (03) 677-682
    CrossrefPubMedGoogle Scholar
  • 10 Geersing G-J, Bouwmeester W, Zuithoff P, Spijker R, Leeflang M, Moons KG. Search filters for finding prognostic and diagnostic prediction studies in Medline to enhance systematic reviews. PLoS One 2012; 7 (02) e32844
    CrossrefPubMedGoogle Scholar
  • 11 Moons KGM, de Groot JAH, Bouwmeester W. et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med 2014; 11 (10) e1001744
    CrossrefPubMedGoogle Scholar
  • 12 Moons KG, Altman DG, Reitsma JB. et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 2015; 162 (01) W1-73
    CrossrefPubMedGoogle Scholar
  • 13 Slim K, Nini E, Forestier D, Kwiatkowski F, Panis Y, Chipponi J. Methodological index for non-randomized studies (minors): development and validation of a new instrument. ANZ J Surg 2003; 73 (09) 712-716
    CrossrefPubMedGoogle Scholar
  • 14 Wolff RF, Moons KGM, Riley RD. et al; PROBAST Group†. PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Ann Intern Med 2019; 170 (01) 51-58
    CrossrefPubMedGoogle Scholar
  • 15 Aktuerk D, McNulty D, Ray D. et al. National administrative data produces an accurate and stable risk prediction model for short-term and 1-year mortality following cardiac surgery. Int J Cardiol 2016; 203: 196-203
    CrossrefPubMedGoogle Scholar
  • 16 Karim MN, Reid CM, Huq M. et al. Predicting long-term survival after coronary artery bypass graft surgery. Interact Cardiovasc Thorac Surg 2018; 26 (02) 257-263
    CrossrefPubMedGoogle Scholar
  • 17 Tanaka S, Sakata R, Marui A, Furukawa Y, Kita T, Kimura T. CREDO-Kyoto Investigators. Predicting long-term mortality after first coronary revascularization: – the Kyoto model –. Circ J 2012; 76 (02) 328-334
    CrossrefPubMedGoogle Scholar
  • 18 MacKenzie TA, Malenka DJ, Olmstead EM. et al; Northern New England Cardiovascular Disease Study Group. Prediction of survival after coronary revascularization: modeling short-term, mid-term, and long-term survival. Ann Thorac Surg 2009; 87 (02) 463-472
    CrossrefPubMedGoogle Scholar
  • 19 Kilpin M, Talwar A, Meneguzzi J, Tran L, Reid C, Hayward P. Two long-term mortality risk models for coronary artery bypass graft surgery produced in American Populations validated in an Australian population. Heart Lung Circ 2018; 27 (01) 79-88
    CrossrefPubMedGoogle Scholar
  • 20 Shahian DM, O'Brien SM, Sheng S. et al. Predictors of long-term survival after coronary artery bypass grafting surgery: results from the Society of Thoracic Surgeons Adult Cardiac Surgery Database (the ASCERT study). Circulation 2012; 125 (12) 1491-1500
    CrossrefPubMedGoogle Scholar
  • 21 Wu C, Camacho FT, Wechsler AS. et al. Risk score for predicting long-term mortality after coronary artery bypass graft surgery. Circulation 2012; 125 (20) 2423-2430
    CrossrefPubMedGoogle Scholar
  • 22 Carr BM, Romeiser J, Ruan J. et al. Long-term post-CABG survival: performance of clinical risk models versus actuarial predictions. J Card Surg 2016; 31 (01) 23-30
    CrossrefPubMedGoogle Scholar
  • 23 Ziv-Baran T, Mohr R, Pevni D, Ben-Gal Y. A simple-to-use nomogram to predict long term survival of patients undergoing coronary artery bypass grafting (CABG) using bilateral internal thoracic artery grafting technique. PLoS One 2019; 14 (10) e0224310
    CrossrefPubMedGoogle Scholar
  • 24 McDonald B, van Walraven C, McIsaac DI. Predicting 1-year mortality after cardiac surgery complicated by prolonged critical illness: derivation and validation of a population-based risk model. J Cardiothorac Vasc Anesth 2020; 34 (10) 2628-2637
    CrossrefPubMedGoogle Scholar
  • 25 Toumpoulis IK, Anagnostopoulos CE, Ioannidis JP. et al. The importance of independent risk-factors for long-term mortality prediction after cardiac surgery. Eur J Clin Invest 2006; 36 (09) 599-607
    CrossrefPubMedGoogle Scholar
  • 26 Steyerberg EW, Vickers AJ, Cook NR. et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 2010; 21 (01) 128-138
    CrossrefPubMedGoogle Scholar
  • 27 Grant SW, Grayson AD, Mitchell DC, McCollum CN. Evaluation of five risk prediction models for elective abdominal aortic aneurysm repair using the UK National Vascular Database. Br J Surg 2012; 99 (05) 673-679
    CrossrefPubMedGoogle Scholar
  • 28 Moons KGM, Kengne AP, Grobbee DE. et al. Risk prediction models: II. External validation, model updating, and impact assessment. Heart 2012; 98 (09) 691-698
    CrossrefPubMedGoogle Scholar
  • 29 Debray TPA, Vergouwe Y, Koffijberg H, Nieboer D, Steyerberg EW, Moons KG. A new framework to enhance the interpretation of external validation studies of clinical prediction models. J Clin Epidemiol 2015; 68 (03) 279-289
    CrossrefPubMedGoogle Scholar
  • 30 Bleeker SE, Moll HA, Steyerberg EW. et al. External validation is necessary in prediction research: a clinical example. J Clin Epidemiol 2003; 56 (09) 826-832
    CrossrefPubMedGoogle Scholar
  • 31 Steyerberg EW, Harrell Jr FE. Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol 2016; 69: 245-247
    CrossrefPubMedGoogle Scholar
  • 32 Hickey GL, Grant SW, Murphy GJ. et al. Dynamic trends in cardiac surgery: why the logistic EuroSCORE is no longer suitable for contemporary cardiac surgery and implications for future risk models. Eur J Cardiothorac Surg 2013; 43 (06) 1146-1152
    CrossrefPubMedGoogle Scholar
  • 33 Moons KGM, Royston P, Vergouwe Y, Grobbee DE, Altman DG. Prognosis and prognostic research: what, why, and how?. BMJ 2009; 338: b375
    CrossrefPubMedGoogle Scholar
  • 34 Chen L. Overview of clinical prediction models. Ann Transl Med 2020; 8 (04) 71
    CrossrefPubMedGoogle Scholar
  • 35 Hickey GL, Grant SW, Cosgriff R. et al. Clinical registries: governance, management, analysis and applications. Eur J Cardiothorac Surg 2013; 44 (04) 605-614
    CrossrefPubMedGoogle Scholar
  • 36 Peric V, Stolic R, Jovanovic A. et al. Predictors of quality of life improvement after 2 years of coronary artery bypass surgery. Ann Thorac Cardiovasc Surg 2017; 23 (05) 233-238
    CrossrefPubMedGoogle Scholar
  • 37 Herlitz J, Brandrup-Wognsen G, Caidahl K. et al. Determinants for an impaired quality of life 10 years after coronary artery bypass surgery. Int J Cardiol 2005; 98 (03) 447-452
    CrossrefPubMedGoogle Scholar
  • 38 Pačarić S, Turk T, Erić I. et al. Assessment of the quality of life in patients before and after coronary artery bypass grafting (CABG): a prospective study. Int J Environ Res Public Health 2020; 17 (04) 1417
    CrossrefPubMedGoogle Scholar
  • 39 Stone GW, Kappetein AP, Sabik JF. et al; EXCEL Trial Investigators. Five-year outcomes after PCI or CABG for left main coronary disease. N Engl J Med 2019; 381 (19) 1820-1830
    CrossrefPubMedGoogle Scholar
  • 40 Head SJ, Davierwala PM, Serruys PW. et al. Coronary artery bypass grafting vs. percutaneous coronary intervention for patients with three-vessel disease: final five-year follow-up of the SYNTAX trial. Eur Heart J 2014; 35 (40) 2821-2830
    CrossrefPubMedGoogle Scholar
  • 41 Holm NR, Mäkikallio T, Lindsay MM. et al; NOBLE investigators. Percutaneous coronary angioplasty versus coronary artery bypass grafting in the treatment of unprotected left main stenosis: updated 5-year outcomes from the randomised, non-inferiority NOBLE trial. Lancet 2020; 395 (10219): 191-199
    CrossrefPubMedGoogle Scholar
  • 42 Ahn J-M, Roh J-H, Kim Y-H. et al. Randomized trial of stents versus bypass surgery for left main coronary artery disease: 5-year outcomes of the PRECOMBAT study. J Am Coll Cardiol 2015; 65 (20) 2198-2206
    CrossrefPubMedGoogle Scholar
  • 43 Garcia-Garcia HM, McFadden EP, Farb A. et al; Academic Research Consortium. Standardized end point definitions for coronary intervention trials: the Academic Research Consortium-2 Consensus Document. Circulation 2018; 137 (24) 2635-2650
    CrossrefPubMedGoogle Scholar
  • 44 Kappetein AP, Head SJ, Généreux P. et al; Valve Academic Research Consortium-2. Updated standardized endpoint definitions for transcatheter aortic valve implantation: the Valve Academic Research Consortium-2 consensus document. J Thorac Cardiovasc Surg 2013; 145 (01) 6-23
    CrossrefPubMedGoogle Scholar
  • 45 Siregar S, Groenwold RHH, de Mol BA. et al. Evaluation of cardiac surgery mortality rates: 30-day mortality or longer follow-up?. Eur J Cardiothorac Surg 2013; 44 (05) 875-883
    CrossrefPubMedGoogle Scholar
  • 46 Puskas JD, Kilgo PD, Thourani VH. et al. The society of thoracic surgeons 30-day predicted risk of mortality score also predicts long-term survival. Ann Thorac Surg 2012; 93 (01) 26-33 , discussion 33–35
    CrossrefPubMedGoogle Scholar
  • 47 Nilsson J, Algotsson L, Höglund P, Lührs C, Brandt J. Comparison of 19 pre-operative risk stratification models in open-heart surgery. Eur Heart J 2006; 27 (07) 867-874
    CrossrefPubMedGoogle Scholar
  • 48 Habib AM, Dhanji AR, Mansour SA, Wood A, Awad WI. The EuroSCORE: a neglected measure of medium-term survival following cardiac surgery. Interact Cardiovasc Thorac Surg 2015; 21 (04) 427-434
    CrossrefPubMedGoogle Scholar
  • 49 Nonaka M, Komiya T, Shimamoto T, Matsuo T. Comparison of clinical outcomes after coronary artery bypass grafting using stratified SYNTAX scores. Gen Thorac Cardiovasc Surg 2020; 68 (11) 1270-1277
    CrossrefPubMedGoogle Scholar
  • 50 Sotomi Y, Cavalcante R, van Klaveren D. et al. Individual long-term mortality prediction following either coronary stenting or bypass surgery in patients with multivessel and/or unprotected left main disease: an external validation of the SYNTAX score II model in the 1,480 patients of the BEST and PRECOMBAT randomized controlled trials. JACC Cardiovasc Interv 2016; 9 (15) 1564-1572
    CrossrefPubMedGoogle Scholar
  • 51 Hoogerduijn JG, de Rooij SE, Grobbee DE, Schuurmans MJ. Predicting functional decline in older patients undergoing cardiac surgery. Age Ageing 2014; 43 (02) 218-221
    CrossrefPubMedGoogle Scholar
  • 52 Paul M, Raz A, Leibovici L, Madar H, Holinger R, Rubinovitch B. Sternal wound infection after coronary artery bypass graft surgery: validation of existing risk scores. J Thorac Cardiovasc Surg 2007; 133 (02) 397-403
    CrossrefPubMedGoogle Scholar
  • 53 Biancari F, Asim Mahar MA, Kangasniemi OP. CHADS2 and CHA2DS2-VASc scores for prediction of immediate and late stroke after coronary artery bypass graft surgery. J Stroke Cerebrovasc Dis 2013; 22 (08) 1304-1311
    CrossrefPubMedGoogle Scholar
  • 54 Sündermann S, Dademasch A, Rastan A. et al. One-year follow-up of patients undergoing elective cardiac surgery assessed with the comprehensive assessment of frailty test and its simplified form. Interact Cardiovasc Thorac Surg 2011; 13 (02) 119-123 , discussion 123
    CrossrefPubMedGoogle Scholar