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Inpatient mortality after orthopaedic surgery

International Orthopaedics volume 39, pages 1307–1314 (2015)Cite this article

Abstract

Purpose

Adequate comorbidity risk adjustment is central for reliable outcome prediction and provider performance evaluation. The two most commonly employed risk-adjustment methods in orthopaedic surgery were not originally validated in this patient population. We sought (1) to develop a single numeric comorbidity score for predicting inpatient mortality in patients undergoing orthopaedic surgery by combining and reweighting the conditions included in the Charlson and Elixhauser measures, and to compare its predictive performance to each of the separate component scores. We also (2) evaluated the new score separately for spine surgery, adult reconstruction, hip fracture, and musculoskeletal oncology admissions.

Methods

Data from the National Hospital Discharge Survey for the years 1990 through 2007 were obtained. A comorbidity score for predicting inpatient mortality was developed by combining conditions from the Charlson and Elixhauser measures. Weights were derived from a random sample of 80 % of the cohort (n = 26,454,972), and the predictive ability of the new score was internally validated on the remaining 20 % (n = 6,739,169). Performance of scores was assessed and compared using the area under the receiver operating characteristic curve (AUC) derived from multivariable logistic regression models.

Results

The new combined comorbidity score (AUC = 0.858, 95 % CI 0.856–0.859) performed 58 % better than the Charlson score (AUC = 0.794, 95 % CI 0.792–0.796) and 12 % better than the Elixhauser score (AUC = 0.845, 95 % CI 0.844–0.847). Of the seven conditions that received the highest weights in the new combined score, only three of them were included in both the Charlson and the Elixhauser indices. The new combined score achieved higher discriminatory power for all orthopaedic admission subgroups.

Conclusion

A single numeric comorbidity score combining conditions from the Charlson and Elixhauser models provided better discrimination of inpatient mortality than either of its constituent scores. Future research should test this score in other populations and data settings.

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References

  1. Bhattacharyya T, Iorio R, Healy WL (2002) Rate of and risk factors for acute inpatient mortality after orthopaedic surgery. J Bone Joint Surg Am 84-A(4):562–572

    PubMed  Google Scholar 

  2. Escobar GJ, Greene JD, Scheirer P, Gardner MN, Draper D, Kipnis P (2008) Risk-adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases. Med Care 46(3):232–239. doi:10.1097/MLR.0b013e3181589bb6

    PubMed  Article  Google Scholar 

  3. Jiang HX, Majumdar SR, Dick DA, Moreau M, Raso J, Otto DD, Johnston DW (2005) Development and initial validation of a risk score for predicting in-hospital and 1-year mortality in patients with hip fractures. J Bone Miner Res 20(3):494–500. doi:10.1359/JBMR.041133

    CAS  PubMed  Article  Google Scholar 

  4. Neuhaus V, King J, Hageman MG, Ring DC (2013) Charlson comorbidity indices and in-hospital deaths in patients with hip fractures. Clin Orthop Relat Res 471(5):1712–1719. doi:10.1007/s11999-012-2705-9

    PubMed Central  PubMed  Article  Google Scholar 

  5. Streubel PN, Ricci WM, Wong A, Gardner MJ (2011) Mortality after distal femur fractures in elderly patients. Clin Orthop Relat Res 469(4):1188–1196. doi:10.1007/s11999-010-1530-2

    PubMed Central  PubMed  Article  Google Scholar 

  6. Thompson HJ, Rivara FP, Nathens A, Wang J, Jurkovich GJ, Mackenzie EJ (2010) Development and validation of the mortality risk for trauma comorbidity index. Ann Surg 252(2):370–375. doi:10.1097/SLA.0b013e3181df03d6

    PubMed Central  PubMed  Article  Google Scholar 

  7. Austin SR, Wong YN, Uzzo RG, Beck JR, Egleston BL (2013) Why summary comorbidity measures such as the Charlson comorbidity index and Elixhauser score work. Med Care. doi:10.1097/MLR.0b013e318297429c

    PubMed Central  Google Scholar 

  8. Iezzoni LI, Heeren T, Foley SM, Daley J, Hughes J, Coffman GA (1994) Chronic conditions and risk of in-hospital death. Health Serv Res 29(4):435–460

    CAS  PubMed Central  PubMed  Google Scholar 

  9. Nikkel LE, Fox EJ, Black KP, Davis C, Andersen L, Hollenbeak CS (2012) Impact of comorbidities on hospitalization costs following hip fracture. J Bone Joint Surg Am 94(1):9–17. doi:10.2106/JBJS.J.01077

    PubMed  Article  Google Scholar 

  10. Schneeweiss S, Wang PS, Avorn J, Glynn RJ (2003) Improved comorbidity adjustment for predicting mortality in Medicare populations. Health Serv Res 38(4):1103–1120

    PubMed Central  PubMed  Article  Google Scholar 

  11. Narins CR, Dozier AM, Ling FS, Zareba W (2005) The influence of public reporting of outcome data on medical decision making by physicians. Arch Intern Med 165(1):83–87. doi:10.1001/archinte.165.1.83

    PubMed  Article  Google Scholar 

  12. Pine M, Jordan HS, Elixhauser A, Fry DE, Hoaglin DC, Jones B, Meimban R, Warner D, Gonzales J (2007) Enhancement of claims data to improve risk adjustment of hospital mortality. JAMA 297(1):71–76. doi:10.1001/jama.297.1.71

    CAS  PubMed  Article  Google Scholar 

  13. Schneider EC, Epstein AM (1996) Influence of cardiac-surgery performance reports on referral practices and access to care. A survey of cardiovascular specialists. N Engl J Med 335(4):251–256. doi:10.1056/NEJM199607253350406

    CAS  PubMed  Article  Google Scholar 

  14. Gordon M, Stark A, Skoldenberg OG, Karrholm J, Garellick G (2013) The influence of comorbidity scores on re-operations following primary total hip replacement: comparison and validation of three comorbidity measures. Bone Joint J 95-B(9):1184–1191. doi:10.1302/0301-620X.95B9.31006

    CAS  PubMed  Article  Google Scholar 

  15. Jain NB, Guller U, Pietrobon R, Bond TK, Higgins LD (2005) Comorbidities increase complication rates in patients having arthroplasty. Clin Orthop Relat Res 435:232–238

    PubMed  Article  Google Scholar 

  16. Poultsides L, Memtsoudis S, Gonzalez Della Valle A, De Martino I, Do HT, Alexiades M, Sculco T (2014) Perioperative morbidity and mortality of same-day bilateral TKAs: incidence and risk factors. Clin Orthop Relat Res 472(1):111–120. doi:10.1007/s11999-013-3156-7

    PubMed Central  PubMed  Article  Google Scholar 

  17. Singh JA, Sperling JW, Cofield RH (2011) Ninety day mortality and its predictors after primary shoulder arthroplasty: an analysis of 4,019 patients from 1976–2008. BMC Musculoskelet Disord 12:231. doi:10.1186/1471-2474-12-231

    PubMed Central  PubMed  Article  Google Scholar 

  18. Voskuijl T, Hageman M, Ring D (2014) Higher Charlson comorbidity index scores are associated with readmission after orthopaedic surgery. Clin Orthop Relat Res 472(5):1638–1644. doi:10.1007/s11999-013-3394-8

  19. Yoshihara H, Yoneoka D (2014) Predictors of allogeneic blood transfusion in spinal fusion in the United States, 2004–2009. Spine (Phila Pa 1976) 39(4):304–310. doi:10.1097/BRS.0000000000000123

    Article  Google Scholar 

  20. Charlson ME, Pompei P, Ales KL, MacKenzie CR (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40(5):373–383

    CAS  PubMed  Article  Google Scholar 

  21. Deyo RA, Cherkin DC, Ciol MA (1992) Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 45(6):613–619

    CAS  PubMed  Article  Google Scholar 

  22. Elixhauser A, Steiner C, Harris DR, Coffey RM (1998) Comorbidity measures for use with administrative data. Med Care 36(1):8–27

    CAS  PubMed  Article  Google Scholar 

  23. van Walraven C, Austin PC, Jennings A, Quan H, Forster AJ (2009) A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Med Care 47(6):626–633. doi:10.1097/MLR.0b013e31819432e5

    PubMed  Article  Google Scholar 

  24. Dennison C, Pokras R (2000) Design and operation of the national hospital discharge survey: 1988 redesign. Vital Health Stat 1(39):1–42

    Google Scholar 

  25. Hall MJ, DeFrances CJ, Williams SN, Golosinskiy A, Schwartzman A (2010) National hospital discharge survey: 2007 summary. Natl Health Stat Report 24(29):1–20

    Google Scholar 

  26. Arowolaju A 2nd, Gillum RF (2013) A new decline in hospitalization with atrial fibrillation among the elderly. Am J Med 126(5):455–457. doi:10.1016/j.amjmed.2012.10.023

    PubMed  Article  Google Scholar 

  27. Mainous AG 3rd, Johnson SP, Saxena SK, Wright RU (2013) Inpatient bariatric surgery among eligible black and white men and women in the United States, 1999–2010. Am J Gastroenterol 108(8):1218–1223. doi:10.1038/ajg.2012.365

    PubMed  Article  Google Scholar 

  28. Menendez ME, Neuhaus V, Bot AG, Ring D, Cha TD (2014) Psychiatric disorders and major spine surgery: epidemiology and perioperative outcomes. Spine (Phila Pa 1976) 39(2):E111–E122. doi:10.1097/BRS.0000000000000064

    Article  Google Scholar 

  29. Simons JP, Hill JS, Ng SC, Shah SA, Zhou Z, Whalen GF, Tseng JF (2009) Perioperative mortality for management of hepatic neoplasm: a simple risk score. Ann Surg 250(6):929–934. doi:10.1097/SLA.0b013e3181bc9c2f

    PubMed  Article  Google Scholar 

  30. Simons JP, Ng SC, Hill JS, Shah SA, Bodnari A, Zhou Z, Tseng JF (2009) In-hospital mortality for liver resection for metastases: a simple risk score. J Surg Res 156(1):21–25. doi:10.1016/j.jss.2009.03.073

    PubMed  Article  Google Scholar 

  31. Gagne JJ, Glynn RJ, Avorn J, Levin R, Schneeweiss S (2011) A combined comorbidity score predicted mortality in elderly patients better than existing scores. J Clin Epidemiol 64(7):749–759. doi:10.1016/j.jclinepi.2010.10.004

    PubMed Central  PubMed  Article  Google Scholar 

  32. Harrell FE Jr, Lee KL, Califf RM, Pryor DB, Rosati RA (1984) Regression modelling strategies for improved prognostic prediction. Stat Med 3(2):143–152

    PubMed  Article  Google Scholar 

  33. Uno H, Cai T, Pencina MJ, D’Agostino RB, Wei LJ (2011) On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat Med 30(10):1105–1117. doi:10.1002/sim.4154

    PubMed Central  PubMed  Google Scholar 

  34. Quan H, Li B, Couris CM, Fushimi K, Graham P, Hider P, Januel JM, Sundararajan V (2011) Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol 173(6):676–682. doi:10.1093/aje/kwq433

    PubMed  Article  Google Scholar 

  35. Schneeweiss S, Maclure M (2000) Use of comorbidity scores for control of confounding in studies using administrative databases. Int J Epidemiol 29(5):891–898

    CAS  PubMed  Article  Google Scholar 

  36. Southern DA, Quan H, Ghali WA (2004) Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data. Med Care 42(4):355–360

    PubMed  Article  Google Scholar 

  37. St Germaine-Smith C, Liu M, Quan H, Wiebe S, Jette N (2011) Development of an epilepsy-specific risk adjustment comorbidity index. Epilepsia 52(12):2161–2167. doi:10.1111/j.1528-1167.2011.03292.x

    PubMed  Article  Google Scholar 

  38. Schneeweiss S, Wang PS, Avorn J, Maclure M, Levin R, Glynn RJ (2004) Consistency of performance ranking of comorbidity adjustment scores in Canadian and U.S. utilization data. J Gen Intern Med 19(5 Pt 1):444–450. doi:10.1111/j.1525-1497.2004.30109.x

    PubMed Central  PubMed  Article  Google Scholar 

  39. Fleischut PM, Mazumdar M, Memtsoudis SG (2013) Perioperative database research: possibilities and pitfalls. Br J Anaesth 111(4):532–534. doi:10.1093/bja/aet164

    CAS  PubMed  Article  Google Scholar 

  40. Memtsoudis SG (2009) Limitations associated with the analysis of data from administrative databases. Anesthesiology 111(2):449. doi:10.1097/ALN.0b013e3181adf739, author reply 450–441

    PubMed  Article  Google Scholar 

  41. Iezzoni LI, Foley SM, Daley J, Hughes J, Fisher ES, Heeren T (1992) Comorbidities, complications, and coding bias. Does the number of diagnosis codes matter in predicting in-hospital mortality? JAMA 267(16):2197–2203

    CAS  PubMed  Article  Google Scholar 

  42. Menendez ME, Neuhaus V, Bot AG, Vrahas MS, Ring D (2013) Do psychiatric comorbidities influence inpatient death, adverse events, and discharge after lower extremity fractures? Clin Orthop Relat Res 471(10):3336–3348. doi:10.1007/s11999-013-3138-9

    PubMed Central  PubMed  Article  Google Scholar 

  43. Wright JD, Ananth CV, Ojalvo L, Herzog TJ, Lewin SN, Lu YS, Neugut AI, Hershman DL (2013) Failure to rescue after major gynecologic surgery. Am J Obstet Gynecol 209(5):420 e421–428. doi:10.1016/j.ajog.2013.08.006

  44. Myers RP, Quan H, Hubbard JN, Shaheen AA, Kaplan GG (2009) Predicting in-hospital mortality in patients with cirrhosis: results differ across risk adjustment methods. Hepatology 49(2):568–577. doi:10.1002/hep.22676

    PubMed  Article  Google Scholar 

  45. Ghali WA, Quan H, Brant R (2001) Risk adjustment using administrative data: impact of a diagnosis-type indicator. J Gen Intern Med 16(8):519–524

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  46. Memtsoudis SG, Gonzalez Della Valle A, Besculides MC, Gaber L, Sculco TP (2008) In-hospital complications and mortality of unilateral, bilateral, and revision TKA: based on an estimate of 4,159,661 discharges. Clin Orthop Relat Res 466(11):2617–2627. doi:10.1007/s11999-008-0402-5

    PubMed Central  PubMed  Article  Google Scholar 

  47. Schneeweiss S, Seeger JD, Maclure M, Wang PS, Avorn J, Glynn RJ (2001) Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data. Am J Epidemiol 154(9):854–864

    CAS  PubMed  Article  Google Scholar 

  48. Dimick JB (2012) How should we risk-adjust hospital outcome comparisons? Arch Surg 147(2):135–136. doi:10.1001/archsurg.2011.1846

    PubMed  Article  Google Scholar 

  49. Menendez ME, Neuhaus V, van Dijk CN, Ring D (2014) The Elixhauser comorbidity method outperforms the Charlson index in predicting inpatient death after orthopaedic surgery. Clin Orthop Relat Res. doi:10.1007/s11999-014-3686-7

    Google Scholar 

  50. Memtsoudis SG, Ma Y, Chiu YL, Poultsides L, Gonzalez Della Valle A, Mazumdar M (2011) Bilateral total knee arthroplasty: risk factors for major morbidity and mortality. Anesth Analg 113(4):784–790. doi:10.1213/ANE.0b013e3182282953

    PubMed Central  PubMed  Google Scholar 

  51. Memtsoudis SG, Ma Y, Chiu YL, Walz JM, Voswinckel R, Mazumdar M (2010) Perioperative mortality in patients with pulmonary hypertension undergoing major joint replacement. Anesth Analg 111(5):1110–1116. doi:10.1213/ANE.0b013e3181f43149

    PubMed  Article  Google Scholar 

  52. Memtsoudis SG, Pumberger M, Ma Y, Chiu YL, Fritsch G, Gerner P, Poultsides L, Valle AG (2012) Epidemiology and risk factors for perioperative mortality after total hip and knee arthroplasty. J Orthop Res 30(11):1811–1821. doi:10.1002/jor.22139

    PubMed Central  PubMed  Article  Google Scholar 

  53. Pumberger M, Chiu YL, Ma Y, Girardi FP, Mazumdar M, Memtsoudis SG (2012) National in-hospital morbidity and mortality trends after lumbar fusion surgery between 1998 and 2008. J Bone Joint Surg Br 94(3):359–364. doi:10.1302/0301-620X.94B3.27825

    CAS  PubMed  Article  Google Scholar 

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Affiliations

  1. Department of Orthopaedic Surgery, Massachusetts General Hospital, 55 Fruit Street, Yawkey Center, Suite 2100, Boston, MA, 02114, USA

    Mariano E. Menendez, Valentin Neuhaus & David Ring

Authors
  1. Mariano E. Menendez
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  2. Valentin Neuhaus
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  3. David Ring
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Correspondence to Mariano E. Menendez.

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Menendez, M.E., Neuhaus, V. & Ring, D. Inpatient mortality after orthopaedic surgery. International Orthopaedics (SICOT) 39, 1307–1314 (2015). https://doi.org/10.1007/s00264-015-2702-1

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  • DOI: https://doi.org/10.1007/s00264-015-2702-1

Keywords

  • Mortality
  • Charlson
  • Elixhauser
  • Orthopaedic surgery
  • Risk adjustment
  • Outcome
  • Prediction