Comparative evaluation of scales for predicting an unfavorable postoperative outcome: Preliminary results of the multicenter study “The role of concomitant diseases in the stratification of the risk of postoperative complications in abdominal surgery STOPRISK”
#3 2022
PDF_2022-3_27-44 (Русский)
HTML_2022-3_27-44 (Русский)


Risk Assessment
Postoperative Complications
Treatment Outcome
Fatal Outcome
Hospital Mortality

How to Cite

Zabolotskikh IB, Trembach NV, Magomedov MA, Krasnov VG, Chernienko LY, Shevyrev SN, Popov AS, Tyutyunova EV, Vatutin SN, Malyshev YP, Popov EA, Smolin AA, Kitiashvili IZ, Dmitriev AA, Grigoryev EV, Kameneva EA, Fisher VV, Volkov EV, Yatsuk IV, Levit DA, Sharipov AM, Khoronenko VE, Shemetova MM, Kokhno VN, Polovnikov EV, Spasova AP, Mironov AV, Davydova VR, Shapovalov KG, Gritsan AI, Sorsunov SV, Lebedinskii KM, Dunts PV, Rudnov VA, Stadler VV, Bayalieva AZ, Prigorodov MV, Antonov VF, Voroshin DG, Ovezov AM, Pivovarova AA, Martynov DV, Batigyan OA, Zamyatin MN, Voskanyan SE, Astakhov AA, Khoteev AZ, Protsenko DN, Arikan NG, Zakharchenko IA, Matveev AS, Trembach IA, Musaeva TS Comparative evaluation of scales for predicting an unfavorable postoperative outcome: Preliminary results of the multicenter study “The role of concomitant diseases in the stratification of the risk of postoperative complications in abdominal surgery STOPRISK”. Annals of Critical Care. 2022;(3):27–44. doi:10.21320/1818-474X-2022-3-27-44.


Abstract Views: 43
PDF_2022-3_27-44 (Русский) Downloads: 5
HTML_2022-3_27-44 (Русский) Downloads: 3
Plum Analytics


English Русский

Social Networks




INTRODUCTION. The need for accurate risk stratification is obvious. Modern methods are quite cumbersome, which can cause difficulties when applied in routine practice, and therefore relatively simple but accurate forecasting methods have become very popular, which, however, have not been validated in Russia: SORT (Surgical Outcome Risk Tool), SRS (Surgical Risk Scale), POSPOM (Preoperative Score to Predict Postoperative Mortality), NZRISK (New Zealand RISK), SMPM (Surgical Mortality Probability Model). OBJECTIVES. The aim of this work is to determine the prognostic value of risk assessment scales in predicting an unfavorable postoperative outcome based on the analysis of data obtained in the STOPRISK study in patients undergoing open abdominal surgery. MATERIALS AND METHODS. The analysis of data on perioperative parameters of 1,179 patients who underwent open abdominal surgery is presented. RESULTS. The fatal outcome was recorded in 14 patients (1.18 %). A total of 135 complications were registered in 92 patients (7.8 %). All scales demonstrated satisfactory prognostic value in assessing the risk of complications (the area under the operating characteristic curve (AUROC) for the Physical Status Scale of the American Society of Anesthesiologists (ASA) was 0.714 (0.687–0.739), for the Surgical Risk Scale (SRS) — 0.727 (0.701–0.753), for the Surgical Outcome Risk Scale (SORT) — 0.738 (0.712–0.763), for the New Zealand Risk Scale (NZRISK) — 0.763 (0.738–0.787), for the Surgical Mortality Probability Scale (SMPM) — 0.732 (0.706–0.757), for the Preoperative Postoperative mortality Prediction Scale (POSPOM) — 0.764 (0.738–0.788)) and good in assessing the risk of death (AUROC for the ASA scale was 0.82 (0.804–0.843), for the SRS scale — 0.860 (0.838–0.879), for the SORT scale — 0.860 (0.838–0.879), for the NZRISK scale — 0.807 (0.783–0.829), for the SMPM scale — 0.852 (0.831–0.872), for the POSPOM scale — 0.811 (0.788–0.833)). CONCLUSIONS. All the studied scales have good prognostic value in assessing the risk of 30-day mortality after major abdominal surgery. The NZRISK and POSPOM scales demonstrate good prognostic value for cardiovascular complications, POSPOM and SRS scales — for acute renal injury. POSPOM and NZRISK scales showed an excellent prognostic value in relation to the risk of postoperative delirium.
PDF_2022-3_27-44 (Русский)
HTML_2022-3_27-44 (Русский)


  1. Meara J.G., Leather A.J., Hagander L., et al. Global Surgery 2030: evidence and solutions for achieving health, welfare, and economic development. Int J Obstet Anesth. 2016; 25: 75–8. DOI: 10.1016/j.ijoa.2015.09.006
  2. Ganesh R., Kebede E., Mueller M., et al. Perioperative Cardiac Risk Reduction in Noncardiac Surgery. Mayo Clin Proc. 2021; 96(8): 2260–76. DOI: 10.1016/j.mayocp.2021.03.014
  3. Levine G.N., O’Gara P.T., Beckman J.A., et al. Recent Innovations, Modifications, and Evolution of ACC/AHA Clinical Practice Guidelines: An Update for Our Constituencies: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines [published correction appears in Circulation. 2020 Jan 14; 141(2): e34]. Circulation. 2019; 139(17): e879– DOI: 10.1161/CIR.0000000000000651
  4. Guarracino F., Baldassarri R., Priebe H.J. Revised ESC/ESA Guidelines on non-cardiac surgery: cardiovascular assessment and management. Implications for preoperative clinical evaluation. Minerva Anestesiol. 2015; 81(2): 226–
  5. Biccard B.M., Madiba T.E., Kluyts H.L., et al. Perioperative patient outcomes in the African Surgical Outcomes Study: a 7-day prospective observational cohort study. Lancet. 2018; 391(10130): 1589–98. DOI: 10.1016/S0140-6736(18)30001-1
  6. Ghaferi A.A., Birkmeyer J.D., Dimick J.B. Complications, failure to rescue, and mortality with major inpatient surgery in medicare patients. Ann Surg. 2009; 250(6): 1029–34. DOI: 10.1097/sla.0b013e3181bef697
  7. Wong D.J.N., Harris S.K., Moonesinghe S.R., et al. Cancelled operations: a 7-day cohort study of planned adult inpatient surgery in 245 UK National Health Service hospitals. Br J Anaesth. 2018; 121(4): 730–8. DOI: 10.1016/j.bja.2018.07.002
  8. Weiser T.G., Haynes A.B., 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. DOI: 10.1016/S0140-6736(15)60806-6
  9. Moonesinghe S.R., Harris S., Mythen M.G., et al. Survival after postoperative morbidity: a longitudinal observational cohort study. Br J Anaesth. 2014; 113(6): 977–84. DOI: 10.1093/bja/aeu224
  10. Bilimoria K.Y., Liu Y., Paruch J.L., et al. 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. 2013; 217(5): 833-42.e1–3. DOI: 10.1016/j.jamcollsurg.2013.07.385
  11. Abbott T.E.F., Fowler A.J., Dobbs T.D., et al. Frequency of surgical treatment and related hospital procedures in the UK: a national ecological study using hospital episode statistics. Br J Anaesth. 2017; 119(2): 249–57. DOI: 10.1093/bja/aex137
  12. Khuri S.F., Henderson W.G., DePalma R.G., et al. Determinants of long-term survival after major surgery and the adverse effect of postoperative complications. Ann Surg. 2005; 242(3): 326–43. DOI: 10.1097/01.sla.0000179621.33268.83
  13. Toner A., Hamilton M. The long-term effects of postoperative complications. Curr Opin Crit Care. 2013; 19(4): 364–8. DOI: 10.1097/MCC.0b013e3283632f77
  14. Partridge J.S., Harari D., Dhesi J.K. Frailty in the older surgical patient: a review. Age Ageing. 2012; 41(2): 142– DOI: 10.1093/ageing/afr182
  15. Trembach N., Zabolotskikh I. The pathophysiology of complications after laparoscopic colorectal surgery: Role of baroreflex and chemoreflex impairment. 2019; 26(2): 115–20. DOI: 10.1016/j.pathophys.2019.05.004
  16. Moonesinghe S.R., Mythen M.G., Das P., et al. Risk stratification tools for predicting morbidity and mortality in adult patients undergoing major surgery: qualitative systematic review. Anesthesiology. 2013; 119(4): 959–81. DOI: 10.1097/ALN.0b013e3182a4e94d
  17. Peden C.J., Stephens T., Martin G., et al. Effectiveness of a national quality improvement programme to improve survival after emergency abdominal surgery (EPOCH): a stepped-wedge cluster-randomised trial. Lancet. 2019; 393(10187): 2213–21. DOI: 10.1016/S0140-6736(18)32521-2
  18. Wijeysundera D.N., Pearse R.M., Shulman M.A., et al. Assessment of functional capacity before major non-cardiac surgery: an international, prospective cohort study. Lancet. 2018; 391: 2631–40. DOI: 10.1016/S0140-6736(18)31131-0
  19. Mudumbai S.C., Pershing S., Bowe T., et al. Development and validation of a predictive model for American Society of Anesthesiologists Physical Status. BMC Health Serv Res. 2019; 19(1): 859. DOI: 10.1186/s12913-019-4640-x
  20. Jammer I., Wickboldt N., Sander M., et al. Standards for definitions and use of outcome measures for clinical effectiveness research in perioperative medicine: European Perioperative Clinical Outcome (EPCO) definitions: a statement from the ESA-ESICM joint taskforce on perioperative outcome measures. Eur J Anaesthesiol. 2015; 32(2): 88–105. DOI: 10.1097/EJA.0000000000000118
  21. Saklad M. Grading of patients for surgical procedures. Anesthesiology 1941; 2: 281–4.
  22. Sutton R., Bann S., Brooks M., Sarin S. The Surgical Risk Scale as an improved tool for risk-adjusted analysis in comparative surgical audit. Br J Surg. 2002; 89(6): 763–8. DOI: 10.1046/j.1365-2168.2002.02080.x
  23. Protopapa K.L., Simpson J.C., Smith N.C., Moonesinghe S.R. Development and validation of the Surgical Outcome Risk Tool (SORT). Br J Surg. 2014; 101(13): 1774–83. DOI: 10.1002/bjs.9638
  24. Campbell D., Boyle L., Soakell-Ho M., et al. National risk prediction model for perioperative mortality in non-cardiac surgery. Br J Surg. 2019; 106(11): 1549–57. DOI: 10.1002/bjs.11232
  25. Glance L.G., Lustik S.J., Hannan E.L., et al. The Surgical Mortality Probability Model: derivation and validation of a simple risk prediction rule for noncardiac surgery. Ann Surg. 2012; 255(4): 696– DOI: 10.1097/SLA.0b013e31824b45af
  26. Le Manach Y., Collins G., Rodseth R., et al. Preoperative Score to Predict Postoperative Mortality (POSPOM): Derivation and Validation. Anesthesiology. 2016; 124(3): 570–9. DOI: 10.1097/ALN.0000000000000972
  27. International Surgical Outcomes Study group. Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries [published correction appears in Br J Anaesth. 2017 Sep 1; 119(3): Br J Anaesth. 2016; 117(5): 601–9. DOI: 10.1093/bja/aew316
  28. Kim M., Wall M.M., Li G. Risk Stratification for Major Postoperative Complications in Patients Undergoing Intra-abdominal General Surgery Using Latent Class Analysis. Anesth Analg. 2018; 126(3): 848–57. DOI: 10.1213/ANE.0000000000002345
  29. Заболотских И.Б., Трембач Н.В., Магомедов М.А. и др. Возможности предоперационной оценки риска неблагоприятного исхода абдоминальных операций: предварительные результаты многоцентрового исследования STOPRISK. Вестник интенсивной терапии им. А.И. Салтанова. 2020; 4: 12–27. DOI: 21320/1818-474X-2020-4-12-27 [Zabolotskikh I.B., Trembach N.V., Magomedov M.A., et al. Possibilities of preoperative assessment of the risk of an adverse outcomes after abdominal surgery: preliminary results of the multicenter STOPRISK study. Ann Crit Care. 2020; 4: 12–27. DOI: 10.21320/1818-474X-2020-4-12-27 (In Russ)]
  30. Goffi L., Saba V., Ghiselli R., et al. Preoperative APACHE II and ASA scores in patients having major general surgical operations: prognostic value and potential clinical applications. Eur J Surg. 1999; 165(8): 730–5. DOI: 10.1080/11024159950189483
  31. Davenport D.L., Bowe E.A., Henderson W.G., et al. National Surgical Quality Improvement Program (NSQIP) risk factors can be used to validate American Society of Anesthesiologists Physical Status Classification (ASA PS) levels. Ann Surg. 2006; 243(5): 636–44. DOI: 10.1097/
  32. Hightower C.E., Riedel B.J., Feig B.W., et al. A pilot study evaluating predictors of postoperative outcomes after major abdominal surgery: Physiological capacity compared with the ASA physical status classification system. Br J Anaesth. 2010; 104(4): 465– DOI: 10.1093/bja/aeq034
  33. Makary M.A., Segev D.L., Pronovost P.J., et al. Frailty as a predictor of surgical outcomes in older patients. J Am Coll Surg. 2010; 210(6): 901–8. DOI: 10.1016/j.jamcollsurg.2010.01.028
  34. Brooks M.J., Sutton R., Sarin S. Comparison of Surgical Risk Score, POSSUM and p-POSSUM in higher-risk surgical patients. Br J Surg. 2005; 92(10): 1288–92. DOI: 10.1002/bjs.5058
  35. Neary W.D., Prytherch D., Foy C., et al. Comparison of different methods of risk stratification in urgent and emergency surgery. Br J Surg. 2007; 94(10): 1300–5. DOI: 10.1002/bjs.5809
  36. Magouliotis D.E., Walker D., Baloyiannis I., et al. Validation of the Surgical Outcome Risk Tool (SORT) for Predicting Postoperative Mortality in Colorectal Cancer Patients Undergoing Surgery and Subgroup Analysis. World J Surg. 2021; 45(6): 1940–8. DOI: 10.1007/s00268-021-06006-6
  37. Wong D.J.N., Harris S., Sahni A., et al. Developing and validating subjective and objective risk-assessment measures for predicting mortality after major surgery: An international prospective cohort study. PLoS Med. 2020; 17(10): e1003253. DOI: 10.1371/journal.pmed.1003253
  38. Ul Huda A., Khan A.Z., Memon A.S., et al. Is the SORT score reliable in predicting postoperative 30-day mortality after a nonemergency surgery in Saudi population? Saudi J Anaesth. 2021; 15(4): 387–9. DOI: 10.4103/sja.sja_105_21
  39. Wong D.J.N., Oliver C.M., Moonesinghe S.R. Predicting postoperative morbidity in adult elective surgical patients using the Surgical Outcome Risk Tool (SORT). Br J Anaesth. 2017; 119(1): 95–105. DOI: 10.1093/bja/aex117
  40. Wickboldt N., Haller G., Delhumeau C., Walder B. A low observed-to-expected postoperative mortality ratio in a Swiss high-standard peri-operative care environment — an observational study. Swiss Med Wkly. 2015; 145: DOI: 10.4414/smw.2015.14205
  41. Kazimierczak S., Rybicka A., Strauss J., et al. External Validation Of The Surgical Mortality Probability Model (S-MPM) In Patients Undergoing Non-Cardiac Surgery. Ther Clin Risk Manag. 2019; 15: 1173–82. DOI: 10.2147/TCRM.S212308
  42. Niessen R., Bihin B., Gourdin M., et al. Prediction of postoperative mortality in elderly patient with hip fractures: a single-centre, retrospective cohort study. BMC Anesthesiol. 2018; 18(1): 183. DOI: 10.1186/s12871-018-0646-x
  43. Froehner M., Koch R., Hübler M., et al. Validation of the Preoperative Score to Predict Postoperative Mortality in Patients Undergoing Radical Cystectomy. Eur Urol Focus. 2019; 5(2): 197–200. DOI: 10.1016/j.euf.2017.05.003
  44. Hill B.L., Brown R., Gabel E., et al. An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data. Br J Anaesth. 2019; 123(6): 877– DOI: 10.1016/j.bja.2019.07.030
  45. Stolze A., van de Garde E.M.W., Posthuma L.M., et al. Validation of the PreOperative Score to predict Post-Operative Mortality (POSPOM) in Dutch non-cardiac surgery patients. BMC Anesthesiol. 2022; 22(1): 58. DOI: 10.1186/s12871-022-01564-1
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Copyright (c) 2022 ANNALS OF CRITICAL CARE


Download data is not yet available.