Aging Clinical and Experimental Research

, Volume 30, Issue 5, pp 419–431 | Cite as

Addition of biomarker panel improves prediction performance of American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) calculator for cardiac risk assessment of elderly patients preparing for major non-cardiac surgery: a pilot study

  • Danica Z. Marković
  • Tatjana Jevtović-Stoimenov
  • Vladan Ćosić
  • Biljana Stošić
  • Bojana Marković Živković
  • Radmilo J. Janković
Original Article



Number of elderly patients subjected to extensive surgical procedures in the presence of cardiovascular morbidities is increasing every year. Therefore, there is a need to make preoperative diagnostics more accurate.


To evaluate the usefulness of American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) calculator as a predictive tool in preoperative assessment of cardiovascular risk in elderly patients.


This prospective pilot study included 78 patients who were being prepared for extensive non-cardiac surgeries under general anaesthesia. Their data have been processed on the interactive ACS NSQIP calculator. Blood sampling has been performed 7 days prior to surgery, and serum has been separated. Clinical, novel, and experimental biomarkers [hsCRP, H-FABP, and Survivin (BIRC5)] have been measured in specialized laboratories.


Mean age of included patients was 71.35 ± 6.89 years. In the case of heart complications and mortality prediction, hsCRP and ACS NSQIP showed the highest specificity and sensitivity with AUC, respectively, 0.869 and 0.813 for heart complications and 0.883 and 0.813 for mortality. When combined with individual biomarkers AUC of ACS NSQIP raised, but if we combined all three biomarkers with ACS NSQIP, AUC reached as much as 0.920 for heart complications and 0.939 for mortality.


ACS NSQIP proved to reduce inaccuracy in preoperative assessment, but it cannot be used independently, which has already been proved by other authors.


Our results indicate that ACS NSQIP represents an accurate tool for preoperative assessment of elderly patients, especially if combined with cardiac biomarkers.


Period Preoperative; survivin protein Human; H-FABP Human; hsCRP Human 



We would like to thank Miodrag Krstić, Master Engineer of Electrical Engineering and Computer Science, for his assistance in statistical analyses of data.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in this study were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

All included patients signed the informed consent.


  1. 1.
    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) (2014) 2014 ESC/ESA Guidelines on non-cardiac surgery: cardiovascular assessment and management. Eur Heart J 35:2383–2431CrossRefGoogle Scholar
  2. 2.
    Kim SW, Han HS, Jung HW et al (2014) Multidimensional frailty score for the prediction of postoperative mortality risk. JAMA Surg 149:633–640Google Scholar
  3. 3.
    Fried LP, Ferrucci L, Darer J, Williamson JD, Anderson G (2004) Untangling the concepts of disability, frailty, and comorbidity: implications for improved targeting and care. J Gerontol A Biol Sci Med Sci 59:255–263CrossRefPubMedGoogle Scholar
  4. 4.
    Partridge JS, Harari D, Dhesi JK (2012) Frailty in the older surgical patient: a review. Age Ageing 41:142–147CrossRefPubMedGoogle Scholar
  5. 5.
    Saxton A, Velanovich V (2011) Preoperative frailty and quality of life as predictors of postoperative complications. Ann Surg 253:1223–1229CrossRefPubMedGoogle Scholar
  6. 6.
    Bollegala N, Jackson TD, Nguyen GC (2016) Increased postoperative mortality and complications among elderly patients with inflammatory bowel diseases: an analysis of the national surgical quality improvement program cohort. Clin Gastroenterol Hepatol 14:1274–1281CrossRefPubMedGoogle Scholar
  7. 7.
    Borson S, Scanlan JM, Lessig M et al (2010) Comorbidity in aging and dementia: scales differ, and the difference matters. Am J Geriatr Psychiatry 18:999–1006CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Kirkhus L, Jordhøy M, Šaltytė Benth J et al (2016) Comparing comorbidity scales: Attending physician score versus the Cumulative Illness Rating Scale for Geriatrics. J Geriatr Oncol 7:90–98Google Scholar
  9. 9.
    Goldman L (1983) Cardiac risk and complications of noncardiac surgery. Ann Intern Med 98:504–513CrossRefPubMedGoogle Scholar
  10. 10.
    Detsky AS, Abrams HB, Forbath N et al (1986) Cardiac assessment for patients undergoing noncardiac surgery. Arch Intern Med 146:2131CrossRefPubMedGoogle Scholar
  11. 11.
    Lee TH, Marcantonio ER, Mangione CM et al (1999) Derivation and prospective validation of a simple index for prediction of cardiac risk of major noncardiac surgery. Circulation 100:1043–1049CrossRefPubMedGoogle Scholar
  12. 12.
    Bilimoria KY, Liu Y, Paruch JL et al (2013) Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons. J Am Coll Surg 217:833–842CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Gupta PK, Gupta H, Sundaram A et al (2011) Development and validation of a risk calculator for prediction of cardiac risk after surgery. Circulation 124:381–387CrossRefPubMedGoogle Scholar
  14. 14.
    Cohen ME, Ko CY, Bilimoria KY et al (2013) Optimizing ACS NSQIP modeling for evaluation of surgical quality and risk: patient risk adjustment, procedure mix adjustment, shrinkage adjustment, and surgical focus. J Am Coll Surg 217:336–346CrossRefPubMedGoogle Scholar
  15. 15.
    Rahman MM, Alam MM, Jahan NA et al (2016) Prognostic role of multiple cardiac biomarkers in newly diagnosed acute coronary syndrome patients. Mymensingh Med J 25:326–333PubMedGoogle Scholar
  16. 16.
    Klingenberg R, Aghlmandi S, Räber L et al (2016) Improved risk stratification of patients with acute coronary syndromes using a combination of hsTnT, NT-proBNP and hsCRP with the GRACE score. Eur Heart J Acute Cardiovasc Care. doi: 10.1177/2048872616684678
  17. 17.
    Das UN (2016) Heart-type fatty acid-binding protein (H-FABP) and coronary heart disease. Indian Heart J 68:16–18CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Janković RJ, Marković DZ, Sokolović DT et al (2016) Clinical indices and biomarkers for perioperative cardiac risk stratification: an update. Minerva Anestesiol. doi: 10.23736/S0375-9393.16.11545-7
  19. 19.
    Sanhueza C, Wehinger S, Castillo Bennett J et al (2015) The twisted survivin connection to angiogenesis. Mol Cancer 14:198CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Lee PJH, Rudenko D, Kuliszewski MA et al (2014) Survivin gene therapy attenuates left ventricular systolic dysfunction in doxorubicin cardiomyopathy by reducing apoptosis and fibrosis. Cardiovsc Res 101:423–433Google Scholar
  21. 21.
    Markovic D, Djordjevic VB (2013) Apoptosis regulation by inhibitors of programmed cell death. J Med Biochem 32:207–213CrossRefGoogle Scholar
  22. 22.
    Marković D, Jevtović-Stoimenov T, Golubović M et al (2016) Significance of survivin (BIRC5) as a cardiac biomarker for the assessment of preoperative cardiovascular risk in nin-cardiac surgeries- surviving (BIRC5) as a novel cardiac biomarker. SJAIT 38:201–212Google Scholar
  23. 23.
    Hernandez AF, Newby LK, O’Connor CM (2004) Preoperative evaluation for major noncardiac surgery focusing on heart failure. Arch Intern Med 164:1729–1736CrossRefPubMedGoogle Scholar
  24. 24.
    Tomlinson JH, Ramani Moonesinghe S (2016) Risk assessment in anaesthesia. Anaesth Intensive Care Med 17:486–491CrossRefGoogle Scholar
  25. 25.
    Maddox TM (2005) Preoperative cardiovascular evaluation for noncardiac surgery. Mt Sinai J Med 72:185–192PubMedGoogle Scholar
  26. 26.
    Asouhidou I, Asteri T, Sountoulides P et al (2009) Early postoperative mortality in the elderly: a pilot study. BMC Res Notes 2:118CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Chou WC, Liu KH, Lu CH et al (2016) To Operate or Not: Prediction of 3-Month Postoperative Mortality in Geriatric Cancer Patients. J Cancer 7.1:14–21CrossRefGoogle Scholar
  28. 28.
    Mihajlović J, Pechlivanoglou P, Miladinov-Mikov M et al (2013) Cancer incidence and mortality in Serbia 1999–2009. BMC Cancer 13:18CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Ashford MW, Gottstein U (2000) The impact on civilians of the bombing of Kosovo and Serbia. Med Confl Surviv 16:267–280CrossRefPubMedGoogle Scholar
  30. 30.
    Marinkovic I, Radivojevic B (2016) Mortality trends and depopulation in Serbia. Geogr Pannonica 20:220–226Google Scholar
  31. 31.
    Urošević J, Odović G, Rapaić D et al (2015) Quality of life of the elderly in urban and rural areas in Serbia Kvalitet života starih u urbanoj i ruralnoj sredini u Srbiji. Vojnosanit Pregl 72: 968–974Google Scholar
  32. 32.
    Republic of Serbia, Ministry of Health (2014) Results of the national health survey of the Republic of Serbia 2013. Belgrade, Serbia, pp 91–93Google Scholar
  33. 33.
    Tzeng CWD, Cooper AB, Vauthey JN (2014) Predictors of morbidity and mortality after hepatectomy in elderly patients: analysis of 7621 NSQIP patients. HPB (Oxford) 16:459–468CrossRefGoogle Scholar
  34. 34.
    Latkauskas T, Rudinskaitė G, Kurtinaitis J et al (2005) The impact of age on post-operative outcomes of colorectal cancer patients undergoing surgical treatment. BMC Cancer 5:153CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Turrentine FE, Wang H, Simpson VB et al (2006) Surgical risk factors, morbidity, and mortality in elderly patients. J Am Coll Surg 203:865–877CrossRefPubMedGoogle Scholar
  36. 36.
    D’Apuzzo MR, Pao AW, Novicoff WM et al (2014) Age as an independent risk factor for postoperative morbidity and mortality after total joint arthroplasty in patients 90 years of age or older. J Arthroplasty 29:477–480CrossRefPubMedGoogle Scholar
  37. 37.
    Lees MC, Merani S, Tauh K et al (2015) Perioperative factors predicting poor outcome in elderly patients following emergency general surgery: a multivariate regression analysis. Can J Surg 58:312–317CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Hernandez AF, Whellan DJ, Stroud S et al (2014) Outcomes in heart failure patients after major noncardiac surgery. J Am Coll Cardiol 44:1446–1453CrossRefGoogle Scholar
  39. 39.
    Heriot AG, Tekkis PP, Smith JJ et al (2006) Prediction of postoperative mortality in elderly patients with colorectal cancer. Dis Colon Rectum 49:816–824CrossRefPubMedGoogle Scholar
  40. 40.
    Jakobson T, Karjagin J, Vipp L et al (2014) Postoperative complications and mortality after major gastrointestinal surgery. Medicina (B Aires) 50:111–117CrossRefGoogle Scholar
  41. 41.
    Yan AT, Yan RT, Tan M et al (2007) Risk scores for risk stratification in acute coronary syndromes: useful but simpler is not necessarily better. Eur Heart J 28:1072–1078CrossRefPubMedGoogle Scholar
  42. 42.
    Harris C, Kim S, Groban L (2015) How well does the NSQIP surgical calculator predict early adverse outcomes in plder non-cardiac surgical patients with self-reported limitations in mobility? Gerontologist 55:192Google Scholar
  43. 43.
    Marković D, Stošić B, Savić S et al (2016) Improtance of biomarkers in preoperative evaluation of cardiovascular risk. Acta Med Med 55:70–75Google Scholar
  44. 44.
    Cohen ME, Bilimoria KY, Ko CY et al (2009) Development of an American College of Surgeons National Surgery Quality Improvement Program: morbidity and mortality risk calculator for colorectal surgery. J Am Coll Surg 208:1009–1016CrossRefPubMedGoogle Scholar
  45. 45.
    Barnett S, Ramani Moonesinghe S (2011) Clinical risk scores to guide perioperative management. Postgrad Med J 87:535–541CrossRefPubMedGoogle Scholar
  46. 46.
    Graversen P, Abildstrøm SZ, Jespersen L et al (2016) Cardiovascular risk prediction: can systematic coronary risk evaluation (SCORE) be improved by adding simple risk markers? Results from the Copenhagen City Heart Study. Eur J Prev Cardiol 23:1546–1556CrossRefPubMedGoogle Scholar
  47. 47.
    Vaid S, Bell T, Grim R et al (2012) Predicting risk of death in general surgery patients on the basis of preoperative variables using American College of Surgeons National Surgical Quality Improvement Program Data. Perm J 16:10–17Google Scholar
  48. 48.
    Rivard C, Nahum R, Slagle E et al (2016) Evaluation of the performance of the ACS NSQIP surgical risk calculator in gynecologic oncology patients undergoing laparotomy. Gynecol Oncol 141:281–286CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Basta MN, Bauder AR, Kovach S et al (2016) Assessing the predictive accuracy of the ACS NSQIP surgical risk calculator in open ventral hernia repair. Plast Reconstr Surg Glob Open 4:115Google Scholar
  50. 50.
    Hyder JA, Reznor G, Wakeam E et al (2016) Risk prediction accuracy differs for emergency versus elective cases in the ACS-NSQIP. Ann Surg 264:959–965CrossRefPubMedGoogle Scholar
  51. 51.
    Madhavan S, Soong SL, Vishalkumar S et al (2016) A comparison, validation and improvisation of possum and ACS-NSQIP surgical risk calculator in patients undergoing hepatic resection. HPB (Oxford) 18:e157CrossRefGoogle Scholar
  52. 52.
    Chung PJ, Carter TI, Burack JH et al (2015) Predicting the risk of death following coronary artery bypass graft made simple: a retrospective study using the American College of Surgeons National Surgical Quality Improvement Program database. J Cardiothorac Surg 10:62CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Blankenberg S, McQueen MJ, Smieja M et al (2006) Comparative impact of multiple biomarkers and N-terminal pro-brain natriuretic peptide in the context of conventional risk factors for the prediction of recurrent cardiovascular events in the heart outcomes prevention evaluation (HOPE) study. Circulation 114:201–208CrossRefPubMedGoogle Scholar
  54. 54.
    Zethelius B, Berglund L, Sundström J et al (2008) Use of multiple biomarkers to improve the prediction of death from cardiovascular causes. N Engl J Med 358:2107–2116CrossRefPubMedGoogle Scholar
  55. 55.
    Ritt M, Ritt JI, Sieber CC et al (2017) Comparing the predictive accuracy of frailty, comorbidity, and disability for mortality: a 1-year follow-up in patients hospitalized in geriatric wards. Clin Interv Aging 12:293–304CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.General Surgery Clinic, Center for Anestesiology and ReanimatologyClinical Center in NišNisSerbia
  2. 2.Department for Biochemistry, Medical SchoolUniversity in NišNisSerbia
  3. 3.Center for Medical BiochemistryClinical Center in NišNisSerbia
  4. 4.Department for Emergency Medicine, Medical SchoolUniversity in NišNisSerbia
  5. 5.Medical High School ‘Dr. Milenko Hadžić’NisSerbia

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