Skip to main content

Advertisement

SpringerLink
Log in

Can extant comorbidity indices identify patients who experience poor outcomes following total joint arthroplasty?

  • Orthopaedic Surgery
  • Published:
Archives of Orthopaedic and Trauma Surgery Aims and scope Submit manuscript

Abstract

Introduction

It is uncertain if generic comorbidity indices commonly used in orthopedics accurately predict outcomes after total hip (THA) or knee arthroplasty (TKA). The purpose of this study was to determine the predictive ability of such comorbidity indices for: (1) 30-day mortality; (2) 30-day rate of major and minor complications; (3) discharge disposition; and (4) extended length of stay (LOS).

Methods

The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was retrospectively reviewed for all patients who underwent elective THA (n = 202,488) or TKA (n = 230,823) from 2011 to 2019. The American Society of Anesthesiologists (ASA) physical status classification system score, modified Charlson Comorbidity Index (mCCI), Elixhauser Comorbidity Measure (ECM), and 5-Factor Modified Frailty Index (mFI-5) were calculated for each patient. Logistic regression models predicting 30-day mortality, discharge disposition, LOS greater than 1 day, and 30-day major and minor complications were fit for each index.

Results

The ASA classification (C-statistic = 0.773 for THA and TKA) and mCCI (THA: c-statistic = 0.781; TKA: C-statistic = 0.771) were good models for predicting 30-day mortality. However, ASA and mCCI were not predictive of major and minor complications, discharge disposition, or LOS. The ECM and mFI-5 did not reliably predict any outcomes of interest.

Conclusion

ASA and mCCI are good models for predicting 30-day mortality after THA and TKA. However, similar to ECM and mFI-5, these generic comorbidity risk-assessment tools do not adequately predict 30-day postoperative outcomes or in-hospital metrics. This highlights the need for an updated, data-driven approach for standardized comorbidity reporting and risk assessment in arthroplasty.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Ayers DC, Franklin PD, Ploutz-Snyder R, Boisvert CB (2005) Total knee replacement outcome and coexisting physical and emotional illness. Clinical orthopaedics and related research. Lippincott Williams and Wilkins, Philadelphia, pp 157–161

    Google Scholar 

  2. Taylor HD, Dennis DA, Crane HS, Szymanski A (1997) Relationship between mortality rates and hospital patient volume for Medicare patients undergoing major orthopaedic surgery of the hip, knee, spine, and femur. J Arthroplasty 12:235–242. https://doi.org/10.1016/S0883-5403(97)90018-8

    Article  CAS  PubMed  Google Scholar 

  3. Learmonth ID, Young C, Rorabeck C (2007) The operation of the century: total hip replacement. Lancet 370:1508–1519

    Article  PubMed  Google Scholar 

  4. Huddleston JI, Wang Y, Uquillas C et al (2012) Age and obesity are risk factors for adverse events after total hip arthroplasty. Clin Orthop Relat Res 470:490–496. https://doi.org/10.1007/s11999-011-1967-y

    Article  PubMed  Google Scholar 

  5. Sloan M, Premkumar A, Sheth NP (2018) Projected volume of primary total joint arthroplasty in the U.S., 2014 to 2030. J Bone Jt Surg 100:1455–1460. https://doi.org/10.2106/JBJS.17.01617

    Article  Google Scholar 

  6. Koroukian SM, Schiltz NK, Warner DF et al (2018) Older adults undergoing total hip or knee arthroplasty: chronicling changes in their multimorbidity profile in the last two decades. J Arthroplasty 33:976–982. https://doi.org/10.1016/j.arth.2017.11.014

    Article  PubMed  Google Scholar 

  7. Hilton ME, Gioe T, Noorbaloochi S, Singh JA (2016) Increasing comorbidity is associated with worsening physical function and pain after primary total knee arthroplasty. BMC Musculoskelet Disord 17:1–10. https://doi.org/10.1186/s12891-016-1261-y

    Article  Google Scholar 

  8. Oh C, Gold H, Slover J (2020) Diagnosis of depression and other patient factors impacts length of stay after total knee arthroplasty. Arthroplast Today 6:77–80. https://doi.org/10.1016/j.artd.2019.11.010

    Article  PubMed  PubMed Central  Google Scholar 

  9. Hofstede SN, Gademan MGJ, Vliet Vlieland TPM et al (2016) Preoperative predictors for outcomes after total hip replacement in patients with osteoarthritis: a systematic review. BMC Musculoskelet Disord 17:212

    Article  PubMed  PubMed Central  Google Scholar 

  10. Warren JA, George J, Anis HK et al (2020) Effects of estimated glomerular filtration rate on 30-day mortality and postoperative complications after total hip arthroplasty: a risk stratification instrument. J Arthroplasty 35:786–793. https://doi.org/10.1016/j.arth.2019.10.001

    Article  PubMed  Google Scholar 

  11. Sundaram K, Warren JA, Krebs OK et al (2021) Estimated glomerular filtration rate is a prognosticator of adverse outcomes after primary total knee arthroplasty among patients with chronic kidney disease and glomerular hyperfiltration. Knee 28:36–44. https://doi.org/10.1016/j.knee.2020.11.008

    Article  PubMed  Google Scholar 

  12. Scully W, Piuzzi NS, Sodhi N et al (2020) The effect of body mass index on 30-day complications after total hip arthroplasty. HIP Int 30:125–134. https://doi.org/10.1177/1120700019826482

    Article  PubMed  Google Scholar 

  13. George J, Piuzzi NS, Ng M et al (2018) Association between body mass index and thirty-day complications after total knee arthroplasty. J Arthroplasty 33:865–871. https://doi.org/10.1016/j.arth.2017.09.038

    Article  PubMed  Google Scholar 

  14. Higuera CA, Elsharkawy K, Klika AK et al (2011) 2010 Mid-America orthopaedic association physician in training award: predictors of early adverse outcomes after knee and hip arthroplasty in geriatric patients. Clin Orthop Relat Res 469:1391–1400. https://doi.org/10.1007/s11999-011-1804-3

    Article  PubMed  PubMed Central  Google Scholar 

  15. Charlson ME, Pompei P, Ales KL, MacKenzie R (1987) Charlson comorbidity index. J Chronic Dis. https://doi.org/10.1016/0021-9681/87

    Article  PubMed  Google Scholar 

  16. Elixhauser A, Steiner C, Harris DR, Coffey RM (1998) Comorbidity measures for use with administrative data. Med Care 36:8–27. https://doi.org/10.1097/00005650-199801000-00004

    Article  CAS  PubMed  Google Scholar 

  17. of MS-TJ of the AS, 1941 undefined Grading of patients for surgical procedures. pubs.asahq.org

  18. Subramaniam S, Aalberg JJ, Soriano RP, Divino CM (2018) New 5-Factor Modified Frailty Index Using American College of Surgeons NSQIP Data. J Am Coll Surg 226:173-181.e8. https://doi.org/10.1016/j.jamcollsurg.2017.11.005

    Article  PubMed  Google Scholar 

  19. Medical C (1987) A new method of classifying prognostic in longitudinal studies: development. J Chronic Dis 40:373–383

    Article  Google Scholar 

  20. Deyo RA, Cherkin DC, Ciol MA (1992) Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 45:613–619. https://doi.org/10.1016/0895-4356(92)90133-8

    Article  CAS  PubMed  Google Scholar 

  21. Romano PS, Rggs LL, Jollis G (1993) Adapting a clinical comorbidity index for use with ICD-9- CM administrative data: differing perspectives. J Clin Epldemiel 46:1075–1079

    Article  CAS  Google Scholar 

  22. Mt S, Aylin PFAU, Bottle A (2012) Systematic review of comorbidity indices for administrative data. PG - 1109–18 LID - 10.1097/MLR.0b013e31825f64d0 [doi]. Med Care 50:1109–1118

    Google Scholar 

  23. Moore BJ, White S, Washington R et al (2017) Identifying increased risk of readmission and in-hospital mortality using hospital administrative data: the AHRQ Elixhauser Comorbidity Index. Med Care 55:698–705. https://doi.org/10.1097/MLR.0000000000000735

    Article  PubMed  Google Scholar 

  24. Caetano SJ, Sonpavde G, Pond GR (2018) C-statistic: a brief explanation of its construction, interpretation and limitations. Eur J Cancer 90:130–132

    Article  CAS  PubMed  Google Scholar 

  25. Kim CY, Sivasundaram L, LaBelle MW et al (2018) Predicting adverse events, length of stay, and discharge disposition following shoulder arthroplasty: a comparison of the Elixhauser Comorbidity Measure and Charlson Comorbidity Index. J Shoulder Elb Surg 27:1748–1755. https://doi.org/10.1016/j.jse.2018.03.001

    Article  Google Scholar 

  26. Ondeck NT, Bohl DD, Bovonratwet P et al (2018) Discriminative ability of Elixhauser’s comorbidity measure is superior to other comorbidity scores for inpatient adverse outcomes after total hip arthroplasty. J Arthroplasty 33:250–257. https://doi.org/10.1016/j.arth.2017.08.032

    Article  PubMed  Google Scholar 

  27. Goltz DE, Ryan SP, Howell CB et al (2019) A weighted index of Elixhauser comorbidities for predicting 90-day readmission after total joint arthroplasty. J Arthroplasty 34:857–864. https://doi.org/10.1016/j.arth.2019.01.044

    Article  PubMed  Google Scholar 

  28. Zusmanovich M, Kester BS, Schwarzkopf R (2018) Postoperative complications of total joint arthroplasty in obese patients stratified by BMI. J Arthroplasty 33:856–864. https://doi.org/10.1016/j.arth.2017.09.067

    Article  PubMed  Google Scholar 

  29. Roth A, Khlopas A, George J et al (2019) The effect of body mass index on 30-day complications after revision total hip and knee arthroplasty. J Arthroplasty 34:S242–S248. https://doi.org/10.1016/j.arth.2019.02.005

    Article  PubMed  Google Scholar 

  30. Arias-De La Torre J, Smith K, Dregan A et al (2020) Impact of comorbidity on the short- and medium-term risk of revision in total hip and knee arthroplasty. BMC Musculoskelet Disord 21:447. https://doi.org/10.1186/s12891-020-03455-3

    Article  PubMed  PubMed Central  Google Scholar 

  31. Podmore B, Hutchings A, Van Der Meulen J et al (2018) Impact of comorbid conditions on outcomes of hip and knee replacement surgery: a systematic review and meta-analysis. BMJ Open 8:1–12. https://doi.org/10.1136/bmjopen-2018-021784

    Article  Google Scholar 

  32. Anis HK, Emara AK, Klika AK et al (2020) The potential effects of imposing a body mass index threshold on patient-reported outcomes after total knee arthroplasty. J Arthroplasty. https://doi.org/10.1016/j.arth.2020.08.060

    Article  PubMed  Google Scholar 

  33. Arnold N, Anis H, Barsoum WK et al (2020) Preoperative cut-off values for body mass index deny patients clinically significant improvements in patient-reported outcomes after total hip arthroplasty. Bone Jt J 102:683–692. https://doi.org/10.1302/0301-620X.102B6.BJJ-2019-1644.R1

    Article  Google Scholar 

  34. Giori NJ, Amanatullah DF, Gupta S et al (2018) Risk reduction compared with access to care: quantifying the trade-off of enforcing a body mass index eligibility criterion for joint replacement. J Bone Jt Surg 100:539–545

    Article  Google Scholar 

  35. Penna S, Bell KL, Kuo FC et al (2019) Impact of co-morbidities on the cost of care in primary elective joint arthroplasty. J Arthroplasty 34:834–838. https://doi.org/10.1016/j.arth.2019.01.038

    Article  PubMed  Google Scholar 

  36. Ramos NL, Karia RJ, Hutzler LH et al (2014) The effect of discharge disposition on 30-day readmission rates after total joint arthroplasty. J Arthroplasty 29:674–677. https://doi.org/10.1016/j.arth.2013.09.010

    Article  PubMed  Google Scholar 

  37. Federal Register: Medicare Program; Comprehensive Care for Joint Replacement Payment Model for Acute Care Hospitals Furnishing Lower Extremity Joint Replacement Services. https://www.federalregister.gov/documents/2015/11/24/2015-29438/medicare-program-comprehensive-care-for-joint-replacement-payment-model-for-acute-care-hospitals. Accessed 28 Jan 2021

  38. Iorio R (2016) The future is here: bundled payments and international statistical classification of diseases, 10th revision. J Arthroplasty 31:931

    Article  PubMed  Google Scholar 

  39. Bilimoria KY, Liu Y, Paruch JL et al (2013) Surgical risk calculator: a decision aide and informed consent tool for patients and surgeons. J Am Coll Surg 217(5):833-842.e3. https://doi.org/10.1016/j.jamcollsurg.2013.07.385.Development

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

  1. Case Western Reserve University School of Medicine, 9501 Euclid Avenue, Cleveland, OH, 44106, USA

    Kara M. McConaghy

  2. Department of Orthopaedic Surgery, Orthopaedic and Rheumatology Institute, Cleveland Clinic, 9500 Euclid Avenue, A41, Cleveland, OH, 44195, USA

    Melissa N. Orr, Ahmed K. Emara, SaTia T. Sinclair, Alison K. Klika & Nicolas S. Piuzzi

Authors
  1. Kara M. McConaghy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Melissa N. Orr
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmed K. Emara
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. SaTia T. Sinclair
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alison K. Klika
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nicolas S. Piuzzi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nicolas S. Piuzzi.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

This is an observational study. No ethical approval is required.

Informed consent

This is an observational study. Informed consent was not required.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file7 (DOCX 16 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

McConaghy, K.M., Orr, M.N., Emara, A.K. et al. Can extant comorbidity indices identify patients who experience poor outcomes following total joint arthroplasty?. Arch Orthop Trauma Surg 143, 1253–1263 (2023). https://doi.org/10.1007/s00402-021-04250-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00402-021-04250-y

Keywords

Search

Navigation