World Journal of Surgery

, Volume 41, Issue 7, pp 1782–1789 | Cite as

Derivation and Validation of a Novel Physiological Emergency Surgery Acuity Score (PESAS)

  • Naveen F. Sangji
  • Jordan D. Bohnen
  • Elie P. Ramly
  • George C. Velmahos
  • David C. Chang
  • Haytham M. A. Kaafarani
Original Scientific Report



We present a novel and abbreviated Physiological Emergency Surgery Acuity Score (PESAS) that assesses the severity of disease at presentation in patients undergoing Emergency Surgery (ES).


Using the 2011 ACS-NSQIP database, we identified all patients who underwent “emergent” surgery. The following methodology was designed: (1) identification of independent predictors of 30-day mortality that are markers of acuity; (2) derivation of PESAS based on the relative impact (i.e., odds ratio) of each predictor; and (3) measurement of the c-statistic. The PESAS was validated using the 2012 ACS-NSQIP database.


From 24,702 ES cases, a 15-point score was derived. This included 10 components with a range of 0 and 15 points. Its c-statistic was 0.80. Mortality gradually increased from 1.7 to 40.6 to 100% at scores of 0, 8, and 15, respectively. In the validation phase, PESAS c-statistic remained stable.


PESAS is a novel score that assesses the acuity of disease at presentation in ES patients and strongly correlates with postoperative mortality risk. PESAS could prove useful for preoperative counseling and for risk-adjusted benchmarking.


Receiver Operating Curve Stepwise Logistic Regression Model Derivation Cohort Physiological Derangement Risk Calculator Tool 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Dr. Matthew Hutter (Department of Surgery, Massachusetts General Hospital and Director, Codman Center for Clinical Effectiveness in Surgery, Massachusetts General Hospital) and Ms. Donna Antonelli (Codman Center for Clinical Effectiveness in Surgery, Massachusetts General Hospital) for their guidance in utilizing the ACS-NSQIP database.

Authors contribution

Drs. NFS, JAB, EPR, DCC, and HMAK contributed to study design. Drs. NFS, JAB, EPR, DCC, and HMAK collected the data. Drs. NFS, JAB, EPR, DCC, and HMAK helped in data analysis/interpretation. Drs. NFS, JAB, EPR, DCC, and HMAK analyzed the data statistically. Literature search was performed by Dr. NFS. Drs. NFS, JAB, EPR, DCC, HMAK, and GCV wrote and critically revised the manuscript.

Compliance with ethical standards

Conflict of interest

We have no conflicts of interest to report.


  1. 1.
    Gale S, Shafi S, Dombrovskiy V et al (2014) The public health burden of emergency general surgery in the United States: a 10-year analysis of the Nationwide Inpatient Sample—2001 to 2010. J Trauma Acute Care Surg 77(2):202–208CrossRefPubMedGoogle Scholar
  2. 2.
    Havens J, Peetz A, Do W et al (2015) The excess morbidity and mortality of emergency general surgery. J Trauma Acute Care Surg 78(2):306–311CrossRefPubMedGoogle Scholar
  3. 3.
    Ingraham A, Cohen M, Milimoria K et al (2010) Comparison of 30-day outcomes after emergency general surgery procedures: potential for targeted improvement. Surgery 148(2):217–238CrossRefPubMedGoogle Scholar
  4. 4.
    Smith M, Hussain A, Xiao J et al (2013) The importance of improving the quality of emergency surgery for a regional quality collaborative. Ann Surg 257(4):596–602CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Ingraham A, Cohen M, Raval M et al (2011) Comparison of hospital performance in emergency versus elective general surgery operations at 198 hospitals. J Am Coll Surg 212(1):20–28 (e1) CrossRefPubMedGoogle Scholar
  6. 6.
    Ingraham A, Cohen M, Raval M et al (2011) Effect of trauma center status on 30-day outcomes after emergency general surgery. J Am Coll Surg 212(3):277–286CrossRefPubMedGoogle Scholar
  7. 7.
    Knaus W, Zimmerman J, Wagner D et al (1981) APACHE-acute physiology and chronic health evaluation: a physiologically based classification system. Crit Care Med 9(8):591–597CrossRefPubMedGoogle Scholar
  8. 8.
    Ranson J, Rifkind K, Roses D et al (1974) Prognostic signs and the role of operative management in acute pancreatitis. Surg Gynecol Obstet 139(1):69–81PubMedGoogle Scholar
  9. 9.
    Marshall J, Cook D, Christou N et al (1995) Multiple organ dysfunction score: a reliable descriptor of a complex clinical outcome. Crit Care Med 23(10):1638–1652CrossRefPubMedGoogle Scholar
  10. 10.
    Zimmerman J, Karamer A, McNair D et al (2006) Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today’s critically ill patients. Crit Care Med 34(5):1297–1310CrossRefPubMedGoogle Scholar
  11. 11.
    Wong C, Khin L, Heng K et al (2004) The LRINEC (Laboratory Risk Indicator for Necrotizing Fasciitis) score: a tool for distinguishing necrotizing fasciitis from other soft tissue infections. Crit Care Med 32(7):1535–1541CrossRefPubMedGoogle Scholar
  12. 12.
    Kwok A, Lipsitz S, Bader A et al (2011) Are targeted preoperative risk prediction tools more powerful? A test of models for emergency colon surgery in the very elderly. J Am Coll Surg 213(2):220–225CrossRefPubMedGoogle Scholar
  13. 13.
    Bilimoria K, Liu Y, Paruch J 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(5):833–842 (e1-3) CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Parikh P, Shiloach M, Choen M et al (2010) Pancreatectomy risk calculator: an ACS-NSQIP resource. HPB (Oxford) 12(7):488–497CrossRefGoogle Scholar
  15. 15.
    Cohen M, Bilimoria K, Karl Y 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(6):1009–1016CrossRefPubMedGoogle Scholar
  16. 16.
    Sutton R, Bann S, Brooks M et al (2002) The surgical risk scale as an improved tool for risk-adjusted analysis in comparative surgical audit. Br J Surg 89(6):763–768CrossRefPubMedGoogle Scholar
  17. 17.
    Saklad M (1941) Grading of patients for surgical procedures. Anesthesiology 2(3):281–284CrossRefGoogle Scholar
  18. 18.
    Charlson M, Pompei P, Ales K et al (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40(5):373–383CrossRefPubMedGoogle Scholar
  19. 19.
    Elixhauser A, Steiner C, Harris D et al (1998) Comorbidity measures for use with administrative data. Med Care 36(1):8–27CrossRefPubMedGoogle Scholar
  20. 20.
    ACS NSQIP (2012) User guide for the 2012 ACS NSQIP participant use data fileGoogle Scholar
  21. 21.
    ACS NSQIP (2011) User guide for the 2011 participant use data fileGoogle Scholar
  22. 22.
    ACS NSQIP, Data Collection, Analysis, and ReportingGoogle Scholar
  23. 23.
    ACS NSQIP, About ACS NSQIPGoogle Scholar
  24. 24.
    Hanley J, McNeil B (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29–36CrossRefPubMedGoogle Scholar
  25. 25.
    Easterlin M, Chang D, Wilson S (2013) A practical index to predict 30-day mortality after major amputation. Ann Vasc Surg 27(7):909–917CrossRefPubMedGoogle Scholar
  26. 26.
    Sangji N, Bohnen J, Ramly E et al (2016) Derivation and validation of a novel emergency surgery acuity score (ESAS). J Trauma Acute Care Surg 81(2):213–220CrossRefPubMedGoogle Scholar
  27. 27.
    Shah A, Haider A, Zogg C et al (2015) National estimates of predictors of outcomes for emergency general surgery. J Trauma Acute Care Surg 78(3):482–490 (discussion 490–491) CrossRefPubMedGoogle Scholar
  28. 28.
    Copeland G, Jones D, Walters M (1991) POSSUM: a scoring system for surgical audit. Br J Surg 78(3):355–360CrossRefPubMedGoogle Scholar
  29. 29.
    Whiteley M, Prytherch D, Higgins B et al (1996) An evaluation of the POSSUM surgical scoring system. Br J Surg 83(6):812–815CrossRefPubMedGoogle Scholar
  30. 30.
    Alvarado A (1986) A practical score for the early diagnosis of acute appendicitis. Ann Emerg Med 15(5):557–564CrossRefPubMedGoogle Scholar
  31. 31.
    Kamath P, Wiesner R, Malinchoc M et al (2001) A model to predict survival in patients with end-stage liver disease. Hepatology 33(2):464–470CrossRefPubMedGoogle Scholar
  32. 32.
    Gawande A, Kwaan M, Rogenbogen S et al (2007) An Apgar score for surgery. J Am Coll Surg 204(2):201–208CrossRefPubMedGoogle Scholar
  33. 33.
    Meredith J, Evans G, Kilgo P et al (2002) A comparison of the abilities of nine scoring algorithms in predicting mortality. J Trauma 53(4):621–628 (discussion 628–629) CrossRefPubMedGoogle Scholar
  34. 34.
    Ho K, Williams T, Harasheh Y et al (2016) Using patient admission characteristics alone to predict mortality of critically ill patients: a comparison of 3 prognostic scores. J Crit Care 31(1):21–25CrossRefPubMedGoogle Scholar
  35. 35.
    Neary W, Prytherch D, Foy C (2007) Comparison of different methods of risk stratification in urgent and emergency surgery. Br J Surg 94(10):1300–1305CrossRefPubMedGoogle Scholar
  36. 36.
    Crandall M, Agarwal S, Muskat P et al (2014) Application of a uniform anatomic grading system to measure disease severity in eight emergency general surgical illnesses. J Trauma Acute Care Surg 77(5):705–708CrossRefPubMedGoogle Scholar
  37. 37.
    Savage S, Klekar C, Priest E et al (2015) Validating a new grading scale for emergency general surgery diseases. J Surg Res 196(2):264–269CrossRefPubMedGoogle Scholar
  38. 38.
    Shafi S, Aboutanos M, Brown C et al (2014) Measuring anatomic severity of disease in emergency general surgery. J Trauma Acute Care Surg 76(3):884–887CrossRefPubMedGoogle Scholar
  39. 39.
    Shafi S, Aboutanos M, Agarwal S et al (2013) Emergency general surgery: definition and estimated burden of disease. J Trauma Acute Care Surg 74(4):1092–1097CrossRefPubMedGoogle Scholar
  40. 40.
    Bernat J, Peterson L (2006) Patient-centered informed consent in surgical practice. Arch Surg 141(1):86–92CrossRefPubMedGoogle Scholar

Copyright information

© Société Internationale de Chirurgie 2017

Authors and Affiliations

  • Naveen F. Sangji
    • 1
    • 2
  • Jordan D. Bohnen
    • 1
  • Elie P. Ramly
    • 1
  • George C. Velmahos
    • 1
  • David C. Chang
    • 2
  • Haytham M. A. Kaafarani
    • 1
    • 2
  1. 1.Department of Surgery, Division of Trauma, Emergency Surgery, and Surgical Critical CareMassachusetts General Hospital and Harvard Medical SchoolBostonUSA
  2. 2.Codman Center for Clinical Effectiveness in SurgeryMassachusetts General HospitalBostonUSA

Personalised recommendations