European Geriatric Medicine

, Volume 9, Issue 1, pp 51–59 | Cite as

Addition of clinical risk scores improves prediction performance of American Society of Anesthesiologists (ASA) physical status classification for postoperative mortality in older patients: a pilot study

  • Danica MarkovicEmail author
  • Tatjana Jevtovic-Stoimenov
  • Milena Stojanovic
  • Anita Vukovic
  • Vesna Dinic
  • Bojana Markovic-Zivkovic
  • Radmilo J. Jankovic
Research Paper



Many methods for preoperative risk stratifications used in everyday practice do not take into account all of the comorbidities and complex physiological status of older patients. Therefore, anaesthesiologists and surgeons must consider multiple ways of preoperative diagnostics. Determining which of the preoperative clinical risk scores [Revised Lee score, the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) calculator and Surgical Outcome Risk Tool (SORT)] best improves routinely used American Society of Anaesthesiologists (ASA) physical status classification.


The prospective pilot study included 78 patients who were being prepared for extensive non-cardiac surgeries under general anaesthesia. Preoperatively, anaesthesiologist determined ASA score according to guidelines. Then, the data of patients have been processed on the interactive calculators of Revised Lee score, ACS NSQIP and SORT.


Mean age of included patients was 71.4 ± 6.9 years. When it comes to postoperative mortality prediction, three risk scores (ASA, ACS NSQIP and SORT) have been statistically significant, respectively, P = 0.016, P < 0.0001, P < 0.0001. Results showed that AUC being higher in ACS NSQIP and SORT (0.813; 0.797). Out of all three additional risk scores, ACS NSQIP showed to add the most to the specificity and sensitivity of ASA score, with combined AUC = 0.841.


ACS NSQIP and SORT increase the accuracy of ASA score. Revised Lee score cannot be considered a good indicator of postoperative mortality risk since it is primarily the score which indicates risk for cardiovascular complications. Further studies, with a greater number of patients, are needed.


Care Preoperative Mortality In Hospital ACS-NSQIP ASA SORT 



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

Informed consent was obtained from all individual participants included in the study.


  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 149:633–640CrossRefPubMedGoogle Scholar
  3. 3.
    Partridge JS, Harari D, Dhesi JK (2012) Frailty in the older surgical patient: a review. Age Ageing 41:142–147CrossRefPubMedGoogle Scholar
  4. 4.
    Saxton A, Velanovich V (2011) Preoperative frailty and quality of life as predictors of postoperative complications. Ann Surg 253:1223–1229CrossRefPubMedGoogle Scholar
  5. 5.
    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
  6. 6.
    Detsky AS, Abrams HB, Forbath N, Scott JG, Hilliard JR (1986) Cardiac assessment for patients undergoing noncardiac surgery. Arch Intern Med 146:2131CrossRefPubMedGoogle Scholar
  7. 7.
    Lee TH, Marcantonio ER, Mangione CM, Thomas EJ, Polanczyk CA, Cook EF 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
  8. 8.
    Goldman L (1983) Cardiac risk and complications of noncardiac surgery. Ann Intern Med 98:504–513CrossRefPubMedGoogle Scholar
  9. 9.
    Daabiss M (2011) American Society of Anaesthesiologists physical status classification. Indian J Anaesth 55(2):111–115CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Saklad M (1941) Grading of patients for surgical procedures. Anesthesiology 2:281–284CrossRefGoogle Scholar
  11. 11.
    Bilimoria KY, Liu Y, Paruch JL, Zhou L, Kniecik TE, Ko CY 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
  12. 12.
    Gupta PK, Gupta H, Sundaram A, Kaushnik M, Fang X, Miller WJ et al (2011) Development and validation of a risk calculator for prediction of cardiac risk after surgery. Circulation 124:381–387CrossRefPubMedGoogle Scholar
  13. 13.
    Markovic D, Jevtovic-Stoimenov T, Cosic V, Stosic B, Markovic-Zivkovic B, Jankovic RJ. 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. Aging Clin Exp Res (Article in print) Google Scholar
  14. 14.
    Protopapa KL, Simpson JC, Smith NCE, Moonesinghe SR (2014) Development and validation of the Surgical Outcome Risk Tool (SORT). BJS 101(13):1774–1783CrossRefGoogle Scholar
  15. 15.
    Tomlinson JH, Ramani Moonesinghe S (2016) Risk assessment in anaesthesia. Anaesth Intensive Care Med 17:486–491CrossRefGoogle Scholar
  16. 16.
    Asouhidou I, Asteri T, Sountoulides P, Natsis K, Georgiadis G (2009) Early postoperative mortality in the elderly: a pilot study. BMC Res Notes 2:118CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Evans LA, Goeteyn J, Carter B, Greig M, Tay HS, McCormack C et al (2017) Preoperative kidney function linked to mortality and readmission outcomes at day 90 and 30 in older emergency surgical patients. Eur Geriatr Med 8(3):216–220CrossRefGoogle Scholar
  18. 18.
    Petrakis E, Andreou AG, Venianaki MV, Xenaki SA, Lasithiotakis KG, Chalkiadakis GE (2014) Outcome following colorectal surgery in elderly patients: our experience using the preoperative comprehensive geriatric assessment. Eur Geriatr Med 5(1):S227–S228CrossRefGoogle Scholar
  19. 19.
    de Saint-Hubert M, Jamart J, Gabriel L, Gourdin M, Mitchell J, Michaux I (2013) Assessment of frailty in older patients before cardiac surgery. Eur Geriatr Med 4(1):S194CrossRefGoogle Scholar
  20. 20.
    Maddox TM (2005) Preoperative cardiovascular evaluation for noncardiac surgery. Mt Sinai J Med 72:185–192PubMedGoogle Scholar
  21. 21.
    Tzeng CWD, Cooper AB, Vauthey JN, Curley SA, Aloia TA (2014) Predictors of morbidity and mortality after hepatectomy in elderly patients: analysis of 7621 NSQIP patients. HPB 16:459–468CrossRefPubMedGoogle Scholar
  22. 22.
    Latkauskas T, Rudinskaitė G, Kurtinaitis J, Janciauskiene R, Tamelis A, Saladzinskas Z et al (2005) The impact of age on post-operative outcomes of colorectal cancer patients undergoing surgical treatment. BMC Cancer 5:153CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Turrentine FE, Wang H, Simpson VB, Jones RS (2006) Surgical risk factors, morbidity, and mortality in elderly patients. J Am Coll Surg 203:865–877CrossRefPubMedGoogle Scholar
  24. 24.
    D’Apuzzo MR, Pao AW, Novicoff WM, Browne JA (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 Arthroplast 29:477–480CrossRefGoogle Scholar
  25. 25.
    Chou WC, Liu KH, Lu CH, Hung YS, Chen MF, Cheng YF et al (2016) To operate or not: prediction of 3-month postoperative mortality in geriatric cancer patients. J Cancer 7(1):14–21CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Lees MC, Merani S, Tauh K, Khadaroo RG (2015) Perioperative factors predicting poor outcome in elderly patients following emergency general surgery: a multivariate regression analysis. Can J Surg 58:312–317CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Hernandez AF, Whellan DJ, Stroud S, Sun JL, O’Connor CM, Jollis JG (2014) Outcomes in heart failure patients after major noncardiac surgery. J Am Coll Cardiol 44:1446–1453CrossRefGoogle Scholar
  28. 28.
    Heriot AG, Tekkis PP, Smith JJ, Cohen CR, Montgomery A, Audisio RA, Thompson MR, Stamatakis JD (2006) Prediction of postoperative mortality in elderly patients with colorectal cancer. Dis Colon Rectum 49:816–824CrossRefPubMedGoogle Scholar
  29. 29.
    Jakobson T, Karjagin J, Vipp L, Padar M, Parik AH, Starkopf L, Kern H, Tammik O, Starkopf J (2014) Postoperative complications and mortality after major gastrointestinal surgery. Medicina 50:111–117CrossRefPubMedGoogle Scholar
  30. 30.
    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
  31. 31.
    Marković D, Stošić B, Savić S, Veselinović I, Dinić V, Djindjić B et al (2016) Improtance of biomarkers in preoperative evaluation of cardiovascular risk. Acta Medica Medianae 55:70–75CrossRefGoogle Scholar
  32. 32.
    Cohen ME, Bilimoria KY, Ko CY, Hall BL (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
  33. 33.
    Barnett S, Ramani Moonesinghe S (2011) Clinical risk scores to guide perioperative management. Postgrad Med J 87:535–541CrossRefPubMedGoogle Scholar
  34. 34.
    Vaid S, Bell T, Grim R, Ahuja V (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 16:10–17CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Rivard C, Nahum R, Slagle E, Duininck M, Vogel RI, Teoh D (2016) Evaluation of the performance of the ACS NSQIP surgical risk calculator in gynecologic oncology patients undergoing laparotomy. Gynecol Oncol 141:281–286CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Basta MN, Bauder AR, Kovach S, Fischer JP (2016) Assessing the predictive accuracy of the ACS NSQIP surgical risk calculator in open ventral hernia repair. Plast Reconstr Surg Glob Open 4:p115Google Scholar
  37. 37.
    Hyder JA, Reznor G, Wakeam E, Nguyen LL, Lipsitz SR, Havens JM (2016) Risk prediction accuracy differs for emergency versus elective cases in the ACS-NSQIP. Ann Surg 264:959–965CrossRefPubMedGoogle Scholar
  38. 38.
    Madhavan S, Soong SL, Vishalkumar S, Woon WWL, Low JK, Bei W et al (2016) A comparison, validation and improvisation of possum and ACS-NSQIP surgical risk calculator in patients undergoing hepatic resection. HPB 18:e157CrossRefGoogle Scholar
  39. 39.
    Chung PJ, Carter TI, Burack JH, Tam S, Alfonso A, Sugiyama G (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
  40. 40.
    Jankovic RJ, Markovic DZ, Sokolovic DT, Zdravkovic I, Sorbello M (2017) Clinical indices and biomarkers for perioperative cardiac risk stratification: an update. Minerva Anestesiol 83(4):392–401PubMedGoogle Scholar
  41. 41.
    Janković R, Marković D, Savić N, Dinić V (2015) Beyond the limits: clinical utility of novel cardiac biomarkers. BioMed Res Int 2015:187384PubMedPubMedCentralGoogle Scholar
  42. 42.
    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
  43. 43.
    Wozniak SE, Coleman JA, Katlic MR (2016) Preoperative evaluation of the older patient. J Perioper Crit Intensive Care Nurs 2:1Google Scholar
  44. 44.
    Kirkhus L, Jordhøy M, Šaltytė Benth J, Rostoft S, Selbæk G, Jensen Hjermstad M et al (2016) Comparing comorbidity scales: attending physician score versus the Cumulative Illness Rating Scale for Geriatrics. J Geriatr Oncol 7:90–98CrossRefPubMedGoogle Scholar
  45. 45.
    Chow WB, Rosenthal RA, Merkow RP, Ko CY, Esnaola NF (2012) Optimal preoperative assessment of the geriatric surgical patient: a best practices guideline from the American College of Surgeons National Surgical Quality Improvement Program and the American Geriatrics Society. J Am Coll Surg 215(4):453–466CrossRefPubMedGoogle Scholar
  46. 46.
    Borson S, Scanlan JM, Lessig M, DeMers S (2010) Comorbidity in aging and dementia: scales differ, and the difference matters. Am J Geriatr Psychiatry 18:999–1006CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© European Geriatric Medicine Society 2017

Authors and Affiliations

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

Personalised recommendations