Clinical Frailty Scale Predicts Outcomes Following Total Joint Arthroplasty

Overview

Journal Arthroplasty

Publisher Biomed Central

Date 2025 Mar 2

PMID 40025603

Authors

Benjamin J Wall

Matthias Wittauer

Karlia Dillon

Hannah Seymour

Piers J Yates

Christopher W Jones

Affiliations

Soon will be listed here.

Abstract

Background: As the population ages, the number of total joint arthroplasty (TJA) performed is rising, making early identification of patients at risk for adverse events essential to improving care and reducing healthcare costs. The aim of this study was to evaluate the association between Clinical Frailty Scale (CFS) and postoperative outcomes in elective total hip arthroplasty (THA) and total knee arthroplasty (TKA).

Methods: We conducted a retrospective study of prospectively collected data regarding 328 TKAs and 294 THAs at a single institution from February 2019 to February 2020. Patient demographic data were harvested, and the preoperative CFS scores were calculated for all patients and analyzed to identify their associations with the length of stay (LOS), the need for admission to an inpatient rehabilitation unit (IPRU), postoperative complications and patient-reported outcome measures (PROMs).

Results: Robust patients (CFS < 3) had a significantly shorter LOS than their non-robust (CFS > 3) counterparts in both the TKA and THA groups (3.7 vs. 5.2 days, P < 0.001, and 3.8 vs. 5.8 days, P < 0.001, respectively). IPRU admission rates were significantly higher in non-robust than in robust patients. Specifically, none of the robust TKA patients required IPRU admission, whereas up to 39 non-robust patients (11.9%) did (P < 0.001). Similarly, for THA, 9 robust (5.7%) and 30 non-robust (21.9%) patients were admitted to an IPRU (P < 0.001). Non-robust patients had a significantly higher complication rate for both THA (11.0% vs. 6.4%, P = 0.03) and TKA (8.7% vs. 2.6%, P = 0.11). Both cohorts showed significant improvements in PROMs post-surgery, with non-robust patients experiencing greater relative gains than robust patients.

Conclusions: CFS is a strong predictor of the hospital length of stay, IPRU admission, and complication rates following TJA. This study also highlighted the link between frailty and PROMs in joint replacement patients. The CFS may be a valuable tool in the preoperative assessment of elective THA and TKA.

References

El-Othmani M, Zalikha A, Shah R . Comparative Analysis of the Ability of Machine Learning Models in Predicting In-hospital Postoperative Outcomes After Total Hip Arthroplasty. J Am Acad Orthop Surg. 2022; 30(20):e1337-e1347. DOI: 10.5435/JAAOS-D-21-00987. View

Wang H, Fafard J, Ahern S, Vendittoli P, Hebert P . Frailty as a predictor of hospital length of stay after elective total joint replacements in elderly patients. BMC Musculoskelet Disord. 2018; 19(1):14. PMC: 5771036. DOI: 10.1186/s12891-018-1935-8. View

Pulok M, Theou O, van der Valk A, Rockwood K . The role of illness acuity on the association between frailty and mortality in emergency department patients referred to internal medicine. Age Ageing. 2020; 49(6):1071-1079. PMC: 7583513. DOI: 10.1093/ageing/afaa089. View

Cooper Z, Rogers Jr S, Ngo L, Guess J, Schmitt E, Jones R . Comparison of Frailty Measures as Predictors of Outcomes After Orthopedic Surgery. J Am Geriatr Soc. 2016; 64(12):2464-2471. PMC: 5173406. DOI: 10.1111/jgs.14387. View

Lin H, Watts J, Peel N, Hubbard R . Frailty and post-operative outcomes in older surgical patients: a systematic review. BMC Geriatr. 2016; 16(1):157. PMC: 5007853. DOI: 10.1186/s12877-016-0329-8. View

Rosas S, Luo T, Emory C, Krueger C, Huddleston J, Buller L . Dually Insured Medicare/Medicaid Patients Undergoing Primary TJA Have More Comorbidities, Higher Complication Rates, and Lower Reimbursements Compared to Privately Insured Patients. J Arthroplasty. 2022; 37(8S):S748-S752. DOI: 10.1016/j.arth.2022.02.056. View

Makary M, Segev D, Pronovost P, Syin D, Bandeen-Roche K, Patel P . 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. View

Patel I, Nham F, Zalikha A, El-Othmani M . Epidemiology of total hip arthroplasty: demographics, comorbidities and outcomes. Arthroplasty. 2023; 5(1):2. PMC: 9808997. DOI: 10.1186/s42836-022-00156-1. View

McGuckin D, Mufti S, Turner D, Bond C, Moonesinghe S . The association of peri-operative scores, including frailty, with outcomes after unscheduled surgery. Anaesthesia. 2018; 73(7):819-824. DOI: 10.1111/anae.14269. View

10.

Ganapathi A, Englum B, Hanna J, Schechter M, Gaca J, Hurwitz L . Frailty and risk in proximal aortic surgery. J Thorac Cardiovasc Surg. 2013; 147(1):186-191.e1. PMC: 4336171. DOI: 10.1016/j.jtcvs.2013.09.011. View

11.

WELLS V, Hearn T, McCaul K, Anderton S, Wigg A, Graves S . Changing incidence of primary total hip arthroplasty and total knee arthroplasty for primary osteoarthritis. J Arthroplasty. 2002; 17(3):267-73. DOI: 10.1054/arth.2002.30414. View

12.

Kua J, Ramason R, Rajamoney G, Chong M . Which frailty measure is a good predictor of early post-operative complications in elderly hip fracture patients?. Arch Orthop Trauma Surg. 2016; 136(5):639-47. DOI: 10.1007/s00402-016-2435-7. View

13.

Mancino F, Wall B, Bucher T, Prosser G, Yates P, Jones C . The Clinical Frailty Scale is a Strong Predictor of 1-Year Mortality in Surgically Managed Hip Periprosthetic Fracture: An Analysis From a High-Volume Institution. J Arthroplasty. 2023; 39(5):1157-1164. DOI: 10.1016/j.arth.2023.11.010. View

14.

Nham F, Patel I, Zalikha A, El-Othmani M . Epidemiology of primary and revision total knee arthroplasty: analysis of demographics, comorbidities and outcomes from the national inpatient sample. Arthroplasty. 2023; 5(1):18. PMC: 10068145. DOI: 10.1186/s42836-023-00175-6. View

15.

Rockwood K, Song X, MacKnight C, Bergman H, Hogan D, McDowell I . A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005; 173(5):489-95. PMC: 1188185. DOI: 10.1503/cmaj.050051. View

16.

Ikram A, Norrish A, Marson B, Craxford S, Gladman J, Ollivere B . Can the Clinical Frailty Scale on admission predict 30-day survival, postoperative complications, and institutionalization in patients with fragility hip fracture? : a cohort study of 1,255 patients. Bone Joint J. 2022; 104-B(8):980-986. PMC: 9948448. DOI: 10.1302/0301-620X.104B8.BJJ-2020-1835.R2. View

17.

Shaikh H, Botros M, Ramirez G, Thirukumaran C, Ricciardi B, Myers T . Comparable performance of machine learning algorithms in predicting readmission and complications following total joint arthroplasty with external validation. Arthroplasty. 2023; 5(1):58. PMC: 10631030. DOI: 10.1186/s42836-023-00208-0. View

18.

Sniderman J, Stark R, Schwartz C, Imam H, Finkelstein J, Nousiainen M . Patient Factors That Matter in Predicting Hip Arthroplasty Outcomes: A Machine-Learning Approach. J Arthroplasty. 2021; 36(6):2024-2032. DOI: 10.1016/j.arth.2020.12.038. View

19.

Momtaz D, Okpara S, Martinez A, Cushing T, Ghali A, Gonuguntla R . A modified frailty index predicts complication, readmission, and 30-day mortality following the revision total hip arthroplasty. Arthroplasty. 2024; 6(1):7. PMC: 10838456. DOI: 10.1186/s42836-024-00232-8. View

20.

Gould D, Bailey J, Spelman T, Bunzli S, Dowsey M, Choong P . Predicting 30-day readmission following total knee arthroplasty using machine learning and clinical expertise applied to clinical administrative and research registry data in an Australian cohort. Arthroplasty. 2023; 5(1):30. PMC: 10234041. DOI: 10.1186/s42836-023-00186-3. View