The Application of Comorbidity Indices to Predict Early Postoperative Outcomes After Laparoscopic Roux-en-Y Gastric Bypass: A Nationwide Comparative Analysis of Over 70,000 Cases



Patients undergoing laparoscopic Roux-en-Y gastric bypass (LRYGB) often have substantial comorbidities, which must be taken into account to appropriately assess expected postoperative outcomes. The Charlson/Deyo and Elixhauser indices are widely used comorbidity measures, both of which also have revised algorithms based on enhanced ICD-9-CM coding. It is currently unclear which of the existing comorbidity measures best predicts early postoperative outcomes following LRYGB.


Using the Nationwide Inpatient Sample, patients 18 years or older undergoing LRYGB for obesity between 2001 and 2008 were identified. Comorbidities were assessed according to the original and enhanced Charlson/Deyo and Elixhauser indices. Using multivariate logistic regression, the following early postoperative outcomes were assessed: overall postoperative complications, length of hospital stay, and conversion to open surgery. Model performance for the four comorbidity indices was assessed and compared using C-statistics and the Akaike’s information criterion (AIC).


A total of 70,287 patients were included. Mean age was 43.1 years (SD, 10.8), 81.6 % were female and 60.3 % were White. Both the original and enhanced Elixhauser indices modestly outperformed the Charlson/Deyo in predicting the surgical outcomes. All four models had similar C-statistics, but the original Elixhauser index was associated with the smallest AIC for all of the surgical outcomes.


The original Elixhauser index is the best predictor of early postoperative outcomes in our cohort of patients undergoing LRYGB. However, differences between the Charlson/Deyo and Elixhauser indices are modest, and each of these indices provides clinically relevant insight for predicting early postoperative outcomes in this high-risk patient population.

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  1. 1.

    Flegal KM, Carroll MD, Ogden CL, et al. Prevalence and trends in obesity among US adults, 1999–2008. JAMA. 2010;303:235–41.

    PubMed  Article  CAS  Google Scholar 

  2. 2.

    Fisher BL, Schauer P. Medical and surgical options in the treatment of severe obesity. Am J Surg. 2002;184:9S–16S.

    PubMed  Article  Google Scholar 

  3. 3.

    McTigue KM, Harris R, Hemphill B, et al. Screening and interventions for obesity in adults: summary of the evidence for the U.S. Preventive Services Task Force. Ann Intern Med. 2003;139:933–49.

    PubMed  Google Scholar 

  4. 4.

    Sjostrom L, Narbro K, Sjostrom CD, et al. Effects of bariatric surgery on mortality in Swedish obese subjects. N Engl J Med. 2007;357:741–52.

    PubMed  Article  Google Scholar 

  5. 5.

    Buchwald H, Avidor Y, Braunwald E, et al. Bariatric surgery: a systematic review and meta-analysis. JAMA. 2004;292:1724–37.

    PubMed  Article  CAS  Google Scholar 

  6. 6.

    Flum DR, Dellinger EP. Impact of gastric bypass operation on survival: a population-based analysis. J Am Coll Surg. 2004;199:543–51.

    PubMed  Article  Google Scholar 

  7. 7.

    Nguyen NT, Masoomi H, Magno CP, et al. Trends in use of bariatric surgery, 2003–2008. J Am Coll Surg. 2011;213:261–6.

    PubMed  Article  Google Scholar 

  8. 8.

    Santry HP, Gillen DL, Lauderdale DS. Trends in bariatric surgical procedures. JAMA. 2005;294:1909–17.

    PubMed  Article  CAS  Google Scholar 

  9. 9.

    DeMaria EJ, Portenier D, Wolfe L. Obesity surgery mortality risk score: proposal for a clinically useful score to predict mortality risk in patients undergoing gastric bypass. Surg Obes Relat Dis. 2007;3:134–40.

    PubMed  Article  Google Scholar 

  10. 10.

    Gupta PK, Franck C, Miller WJ, et al. Development and validation of a bariatric surgery morbidity risk calculator using the prospective, multicenter NSQIP dataset. J Am Coll Surg. 2011;212:301–9.

    PubMed  Article  Google Scholar 

  11. 11.

    Turner PL, Saager L, Dalton J, et al. A nomogram for predicting surgical complications in bariatric surgery patients. Obes Surg. 2011;21:655–62.

    PubMed  Article  Google Scholar 

  12. 12.

    Finks JF, Kole KL, Yenumula PR, et al. Predicting risk for serious complications with bariatric surgery: results from the Michigan Bariatric Surgery Collaborative. Ann Surg. 2011;254:633–40.

    PubMed  Article  Google Scholar 

  13. 13.

    Sjostrom LV. Morbidity of severely obese subjects. Am J Clin Nutr. 1992;55:508S–15S.

    PubMed  CAS  Google Scholar 

  14. 14.

    Flum DR, Belle SH, King WC, et al. Perioperative safety in the longitudinal assessment of bariatric surgery. N Engl J Med. 2009;361:445–54.

    PubMed  Article  Google Scholar 

  15. 15.

    Ballantyne GH, Svahn J, Capella RF, et al. Predictors of prolonged hospital stay following open and laparoscopic gastric bypass for morbid obesity: body mass index, length of surgery, sleep apnea, asthma, and the metabolic syndrome. Obes Surg. 2004;14:1042–50.

    PubMed  Article  Google Scholar 

  16. 16.

    Collazo-Clavell ML, Clark MM, McAlpine DE, et al. Assessment and preparation of patients for bariatric surgery. Mayo Clin Proc. 2006;81:S11–7.

    PubMed  Google Scholar 

  17. 17.

    Tang J, Wan JY, Bailey JE. Performance of comorbidity measures to predict stroke and death in a community-dwelling, hypertensive Medicaid population. Stroke. 2008;39:1938–44.

    PubMed  Article  Google Scholar 

  18. 18.

    Lieffers JR, Baracos VE, Winget M, et al. A comparison of Charlson and Elixhauser comorbidity measures to predict colorectal cancer survival using administrative health data. Cancer. 2011;117:1957–65.

    PubMed  Article  Google Scholar 

  19. 19.

    Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–83.

    PubMed  Article  CAS  Google Scholar 

  20. 20.

    Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45:613–9.

    PubMed  Article  CAS  Google Scholar 

  21. 21.

    Elixhauser A, Steiner C, Harris DR, et al. Comorbidity measures for use with administrative data. Med Care. 1998;36:8–27.

    PubMed  Article  CAS  Google Scholar 

  22. 22.

    Southern DA, Quan H, Ghali WA. Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data. Med Care. 2004;42:355–60.

    PubMed  Article  Google Scholar 

  23. 23.

    Stukenborg GJ, Wagner DP, Connors Jr AF. Comparison of the performance of two comorbidity measures, with and without information from prior hospitalizations. Med Care. 2001;39:727–39.

    PubMed  Article  CAS  Google Scholar 

  24. 24.

    Quan H, Li B, Saunders LD, et al. Assessing validity of ICD-9-CM and ICD-10 administrative data in recording clinical conditions in a unique dually coded database. Health Serv Res. 2008;43:1424–41.

    PubMed  Article  Google Scholar 

  25. 25.

    Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43:1130–9.

    PubMed  Article  Google Scholar 

  26. 26.

    Agency for Healthcare Research and Quality. Healthcare Cost and Utilization Project. Overview of the Nationwide Inpatients Sample(NIS). 2009. Accessed 22 Feb 2010.

  27. 27.

    Healthcare Cost and Utilization Project. Overview of the Nationwide Inpatients Sample (NIS). Accessed February 2010.

  28. 28.

    Whalen D, Houchens R, Elixhauser A. 2004 HCUP Nationwide Inpatient Sample (NIS) comparison report. HCUP Method Series Report No. 2007-03. Healthcare Cost and Utilization Project (HCUP) web site. 2006. Accessed February 2010.

  29. 29.

    Birkmeyer NJ, Dimick JB, Share D, et al. Hospital complication rates with bariatric surgery in Michigan. JAMA. 2010;304:435–42.

    PubMed  Article  CAS  Google Scholar 

  30. 30.

    Sokol DK, Wilson J. What is a surgical complication? World J Surg. 2008;32:942–4.

    PubMed  Article  Google Scholar 

  31. 31.

    Guller U, Hervey S, Purves H, et al. Laparoscopic versus open appendectomy: outcomes comparison based on a large administrative database. Ann Surg. 2004;239:43–52.

    PubMed  Article  Google Scholar 

  32. 32.

    Ricciardi R, Town RJ, Kellogg TA, et al. Outcomes after open versus laparoscopic gastric bypass. Surg Laparosc Endosc Percutan Tech. 2006;16:317–20.

    PubMed  Article  Google Scholar 

  33. 33.

    Weller WE, Rosati C. Comparing outcomes of laparoscopic versus open bariatric surgery. Ann Surg. 2008;248:10–5.

    PubMed  Article  Google Scholar 

  34. 34.

    Worni M, Guller U, Shah A, et al. Cholecystectomy concomitant with laparoscopic gastric bypass: a trend analysis of the nationwide inpatient sample from 2001 to 2008. Obes Surg. 2012;22:220–9.

    PubMed  Article  Google Scholar 

  35. 35.

    Myers RP, Quan H, Hubbard JN, et al. Predicting in-hospital mortality in patients with cirrhosis: results differ across risk adjustment methods. Hepatology. 2009;49:568–77.

    PubMed  Article  Google Scholar 

  36. 36.

    Thombs BD, Singh VA, Halonen J, et al. The effects of preexisting medical comorbidities on mortality and length of hospital stay in acute burn injury: evidence from a national sample of 31,338 adult patients. Ann Surg. 2007;245:629–34.

    PubMed  Article  Google Scholar 

  37. 37.

    Akaike H. A new look at the statistical model identification. IEEE Trans Autum Control. 1974;19:716–23.

    Article  Google Scholar 

  38. 38.

    Burnham KP, Anderson DR. Model selection and multimodel inference: a practical information-theoretic approach. New York: Springer; 2002.

    Google Scholar 

  39. 39.

    Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 1983;148:839–43.

    PubMed  CAS  Google Scholar 

  40. 40.

    Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29–36.

    PubMed  CAS  Google Scholar 

  41. 41.

    Erdreich LS, Lee ET. Use of relative operating characteristic analysis in epidemiology. A method for dealing with subjective judgement. Am J Epidemiol. 1981;114:649–62.

    PubMed  CAS  Google Scholar 

  42. 42.

    Grendar J, Shaheen AA, Myers RP, et al. Predicting in-hospital mortality in patients undergoing complex gastrointestinal surgery: determining the optimal risk adjustment method. Arch Surg. 2012;147:126–35.

    PubMed  Article  Google Scholar 

  43. 43.

    Ali MR, Maguire MB, Wolfe BM. Assessment of obesity-related comorbidities: a novel scheme for evaluating bariatric surgical patients. J Am Coll Surg. 2006;202:70–7.

    PubMed  Article  Google Scholar 

  44. 44.

    Szomstein S, Avital S, Brasesco O, et al. Laparoscopic gastric bypass in patients on thyroid replacement therapy for subnormal thyroid function—prevalence and short-term outcome. Obes Surg. 2004;14:95–7.

    PubMed  Article  Google Scholar 

  45. 45.

    Cunningham JL, Merrell CC, Sarr M, et al. Investigation of antidepressant medication usage after bariatric surgery. Obes Surg. 2012;22:530–5.

    PubMed  Article  Google Scholar 

  46. 46.

    Fernandez Jr AZ, Demaria EJ, Tichansky DS, et al. Multivariate analysis of risk factors for death following gastric bypass for treatment of morbid obesity. Ann Surg. 2004;239:698–702.

    PubMed  Article  Google Scholar 

  47. 47.

    Perugini RA, Mason R, Czerniach DR, et al. Predictors of complication and suboptimal weight loss after laparoscopic Roux-en-Y gastric bypass: a series of 188 patients. Arch Surg. 2003;138:541–5. discussion 545–546.

    PubMed  Article  Google Scholar 

  48. 48.

    Zhang W, Mason EE, Renquist KE, et al. Factors influencing survival following surgical treatment of obesity. Obes Surg. 2005;15:43–50.

    PubMed  Article  CAS  Google Scholar 

  49. 49.

    Wallace AE, Young-Xu Y, Hartley D, et al. Racial, socioeconomic, and rural–urban disparities in obesity-related bariatric surgery. Obes Surg. 2010;20:1354–60.

    PubMed  Article  Google Scholar 

  50. 50.

    Kohn GP, Galanko JA, Overby DW, et al. High case volumes and surgical fellowships are associated with improved outcomes for bariatric surgery patients: a justification of current credentialing initiatives for practice and training. J Am Coll Surg. 2010;210:909–18.

    PubMed  Article  Google Scholar 

  51. 51.

    Carbonell AM, Lincourt AE, Matthews BD, et al. National study of the effect of patient and hospital characteristics on bariatric surgery outcomes. Am Surg. 2005;71:308–14.

    PubMed  Google Scholar 

  52. 52.

    Poulose BK, Griffin MR, Zhu Y, et al. National analysis of adverse patient safety for events in bariatric surgery. Am Surg. 2005;71:406–13.

    PubMed  Google Scholar 

  53. 53.

    Poulose BK, Griffin MR, Moore DE, et al. Risk factors for post-operative mortality in bariatric surgery. J Surg Res. 2005;127:1–7.

    PubMed  Article  Google Scholar 

  54. 54.

    Livingston EH, Langert J. The impact of age and Medicare status on bariatric surgical outcomes. Arch Surg. 2006;141:1115–20. discussion 1121.

    PubMed  Article  Google Scholar 

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Conflicts of Interest

All contributing authors, including Jin Hee Shin, Mathias Worni, Anthony W. Castleberry, Ricardo Pietrobon, Philip A. Omotosho, Mina Silberberg, and Truls Østbye, declare that they have no conflicts of interest in relation to this manuscript.

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Correspondence to Truls Østbye.



  ICD-9-CM code
Postoperative complications
Mechanical wound complications
 Postoperative hematoma 998.12
 Postoperative seroma (noninfected) 998.13
 Disruption of operative wound 998.3
 Disruption of wound unspecified 998.30
 Disruption of internal operation (surgical) wound 998.31
 Disruption of external operation (surgical) wound 998.32
 Persistent postoperative fistula 998.6
 Delayed wound healing 998.83
 Postoperative infection 998.5
 Postoperative infected seroma 998.51
 Postoperative skin abscess/infection 998.59
 Postoperative septic wound complications 998.59
 Postoperative intraabdominal/subdiaphragmatic abscess 998.59
Urinary/renal complications
 Postoperative urinary retention 997.5
 Postoperative urinary tract infection 997.5
 Acute renal failure 997.5
Pulmonary complications
 Postoperative acute pneumothorax 512.1
 Postoperative pulmonary edema 518.4
 Adult respiratory distress syndrome following surgery 518.5
 Transfusion-related acute lung injury 518.7
 Postoperative atelectasis/pneumonia 997.3
 Mendelson syndrome resulting from a procedure 997.3
Gastrointestinal complications
 Postoperative vomiting 564.3
 Diarrhea following gastrointestinal surgery 564.4
 Postoperative small bowel obstruction/ileus (requiring nasogastric tube) 997.4
 Complication of anastomosis of gastrointestinal tract 997.4
Cardiovascular complications
 Postoperative hypotension 458.29
 Postoperative stroke 997.02
 Cardiac arrest/insufficiency during or resulting from a procedure 997.1
 Phlebitis or thrombophlebitis from procedure 997.2
Systemic complications
 Postoperative shock 998.0
 Postoperative fever 998.89
 Unspecified complication of procedure, not elsewhere classified 998.9

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Shin, J.H., Worni, M., Castleberry, A.W. et al. The Application of Comorbidity Indices to Predict Early Postoperative Outcomes After Laparoscopic Roux-en-Y Gastric Bypass: A Nationwide Comparative Analysis of Over 70,000 Cases. OBES SURG 23, 638–649 (2013).

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  • Laparoscopic Roux-en-Y gastric bypass
  • Comorbidity
  • Charlson/Deyo index
  • Elixhauser index
  • Postoperative outcomes