Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Systematic Reviews
Published in: Systematic Reviews 1/2022

Open Access 01-12-2022 | Knee Osteoarthritis | Protocol

Evaluating methodological quality of prognostic prediction models on patient reported outcome measurements after total hip replacement and total knee replacement surgery: a systematic review protocol

Authors: Wei-Ju Chang, Justine Naylor, Pragadesh Natarajan, Victor Liu, Sam Adie

Published in: Systematic Reviews | Issue 1/2022

Login to get access

Abstract

Background

Prediction models for poor patient-reported surgical outcomes after total hip replacement (THR) and total knee replacement (TKR) may provide a method for improving appropriate surgical care for hip and knee osteoarthritis. There are concerns about methodological issues and the risk of bias of studies producing prediction models. A critical evaluation of the methodological quality of prediction modelling studies in THR and TKR is needed to ensure their clinical usefulness. This systematic review aims to (1) evaluate and report the quality of risk stratification and prediction modelling studies that predict patient-reported outcomes after THR and TKR; (2) identify areas of methodological deficit and provide recommendations for future research; and (3) synthesise the evidence on prediction models associated with post-operative patient-reported outcomes after THR and TKR surgeries.

Methods

MEDLINE, EMBASE, and CINAHL electronic databases will be searched to identify relevant studies. Title and abstract and full-text screening will be performed by two independent reviewers. We will include (1) prediction model development studies without external validation; (2) prediction model development studies with external validation of independent data; (3) external model validation studies; and (4) studies updating a previously developed prediction model. Data extraction spreadsheets will be developed based on the CHARMS checklist and TRIPOD statement and piloted on two relevant studies. Study quality and risk of bias will be assessed using the PROBAST tool. Prediction models will be summarised qualitatively. Meta-analyses on the predictive performance of included models will be conducted if appropriate. A narrative review will be used to synthesis the evidence if there are insufficient data to perform meta-analyses.

Discussion

This systematic review will evaluate the methodological quality and usefulness of prediction models for poor outcomes after THR or TKR. This information is essential to provide evidence-based healthcare for end-stage hip and knee osteoarthritis. Findings of this review will contribute to the identification of key areas for improvement in conducting prognostic research in this field and facilitate the progress in evidence-based tailored treatments for hip and knee osteoarthritis.

Systematic review registration

PROSPERO registration number CRD42021271828.
Appendix
Available only for authorised users
Literature
1.
Health AIo, Welfare. Osteoarthritis. Canberra: AIHW; 2020.
2.
Goodman SM, Mehta B, Mirza SZ, Figgie MP, Alexiades M, Rodriguez J, et al. Patients’ perspectives of outcomes after total knee and total hip arthroplasty: a nominal group study. BMC Rheumatol. 2020;4(1):3.PubMedPubMedCentralCrossRef
3.
Gandhi R, Davey JR, Mahomed NN. Predicting patient dissatisfaction following joint replacement surgery. J Rheumatol. 2008;35(12):2415–8.PubMedCrossRef
4.
Dowsey MM, Spelman T, Choong PF. Development of a prognostic nomogram for predicting the probability of nonresponse to total knee arthroplasty 1 year after surgery. J Arthroplasty. 2016;31(8):1654–60.PubMedCrossRef
5.
Singh JA, Lewallen DG. Predictors of activity limitation and dependence on walking aids after primary total hip arthroplasty. J Am Geriatr Soc. 2010;58(12):2387–93.PubMedCrossRef
6.
Maradit Kremers H, Kremers WK, Berry DJ, Lewallen DG. Patient-reported outcomes can be used to identify patients at risk for total knee arthroplasty revision and potentially individualize postsurgery follow-up. J Arthroplasty. 2017;32(11):3304–7.PubMedCrossRef
7.
Dalury DF, Pomeroy DL, Gorab RS, Adams MJ. Why are total knee arthroplasties being revised? J Arthroplasty. 2013;28(8 Suppl):120–1.PubMedCrossRef
8.
Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD StatementThe TRIPOD Statement. Ann Intern Med. 2015;162(1):55–63.PubMedCrossRef
9.
Moons KGM, Altman DG, Reitsma JB, Ioannidis JPA, Macaskill P, Steyerberg EW, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): explanation and elaboration. The TRIPOD statement: explanation and elaboration. Ann Intern Med. 2015;162(1):W1–W73.PubMedCrossRef
10.
Moons KG, de Groot JA, Bouwmeester W, Vergouwe Y, Mallett S, Altman DG, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med. 2014;11(10):e1001744.PubMedPubMedCentralCrossRef
11.
12.
Echouffo-Tcheugui JB, Kengne AP. Risk models to predict chronic kidney disease and its progression: a systematic review. PLoS Med. 2012;9(11):e1001344.PubMedPubMedCentralCrossRef
13.
Souwer ET, Bastiaannet E, Steyerberg EW, Dekker J-WT, van den Bos F, Portielje JE. Risk prediction models for postoperative outcomes of colorectal cancer surgery in the older population-a systematic review. J Geriatr Oncol. 2020;11(8):1217–28.PubMedCrossRef
14.
Deliu N, Cottone F, Collins GS, Anota A, Efficace F. Evaluating methodological quality of Prognostic models Including Patient-reported HeAlth outcomes iN oncologY (EPIPHANY): a systematic review protocol. BMJ Open. 2018;8(10):e025054.PubMedPubMedCentralCrossRef
15.
Adie S, Harris I, Chuan A, Lewis P, Naylor JM. Selecting and optimising patients for total knee arthroplasty. Med J Aust. 2019;210(3):135–41.PubMedCrossRef
16.
Romine LB, May RG, Taylor HD, Chimento GF. Accuracy and clinical utility of a peri-operative risk calculator for total knee arthroplasty. J Arthroplasty. 2013;28(3):445–8.PubMedCrossRef
17.
Wuerz TH, Kent DM, Malchau H, Rubash HE. A nomogram to predict major complications after hip and knee arthroplasty. J Arthroplasty. 2014;29(7):1457–62.PubMedCrossRef
18.
Harris AH, Kuo AC, Bowe T, Gupta S, Nordin D, Giori NJ. Prediction models for 30-day mortality and complications after total knee and hip arthroplasties for veteran health administration patients with osteoarthritis. J Arthroplasty. 2018;33(5):1539–45.PubMedCrossRef
19.
Oldmeadow LB, McBurney H, Robertson VJ. Predicting risk of extended inpatient rehabilitation after hip or knee arthroplasty. J Arthroplasty. 2003;18(6):775–9.PubMedCrossRef
20.
Shim J, Mclernon DJ, Hamilton D, Simpson HA, Beasley M, Macfarlane GJ. Development of a clinical risk score for pain and function following total knee arthroplasty: results from the TRIO study. Rheumatol Adv Pract. 2018;2(2):rky021.PubMedPubMedCentralCrossRef
21.
Sanchez-Santos M, Garriga C, Judge A, Batra R, Price A, Liddle A, et al. Development and validation of a clinical prediction model for patient-reported pain and function after primary total knee replacement surgery. Sci Rep. 2018;8(1):1–9.CrossRef
22.
Mu Y, Edwards JR, Horan TC, Berrios-Torres SI, Fridkin SK. Improving risk-adjusted measures of surgical site infection for the National Healthcare Safely Network. Infect Control Hosp Epidemiol. 2011;32(10):970–86.PubMedCrossRef
23.
Berbari EF, Osmon DR, Lahr B, Eckel-Passow JE, Tsaras G, Hanssen AD, et al. The Mayo prosthetic joint infection risk score: implication for surgical site infection reporting and risk stratification. Infect Control Hosp Epidemiol. 2012;33(8):774–81.PubMedCrossRef
24.
Bozic KJ, Ong K, Lau E, Berry DJ, Vail TP, Kurtz SM, et al. Estimating risk in Medicare patients with THA: an electronic risk calculator for periprosthetic joint infection and mortality. Clin Orthop Relat Res. 2013;471(2):574–83.PubMedCrossRef
25.
Kunutsor S, Whitehouse M, Blom A, Beswick A. Systematic review of risk prediction scores for surgical site infection or periprosthetic joint infection following joint arthroplasty. Epidemiol Infect. 2017;145(9):1738–49.PubMedCrossRef
26.
Mesko NW, Bachmann KR, Kovacevic D, LoGrasso ME, O’Rourke C, Froimson MI. Thirty-day readmission following total hip and knee arthroplasty–a preliminary single institution predictive model. J Arthroplasty. 2014;29(8):1532–8.PubMedCrossRef
27.
Paxton EW, Inacio MC, Khatod M, Yue E, Funahashi T, Barber T. Risk calculators predict failures of knee and hip arthroplasties: findings from a large health maintenance organization. Clin Orthop Relat Res. 2015;473(12):3965–73.PubMedPubMedCentralCrossRef
28.
Lungu E, Desmeules F, Dionne CE, Belzile ÉL, Vendittoli P-A. Prediction of poor outcomes six months following total knee arthroplasty in patients awaiting surgery. BMC Musculoskelet Disord. 2014;15(1):299.PubMedPubMedCentralCrossRef
29.
Huber M, Kurz C, Leidl R. Predicting patient-reported outcomes following hip and knee replacement surgery using supervised machine learning. BMC Med Inform Decis Mak. 2019;19(1):3.PubMedPubMedCentralCrossRef
30.
Pua Y-H, Poon CL-L, Seah FJ-T, Thumboo J, Clark RA, Tan M-H, et al. Predicting individual knee range of motion, knee pain, and walking limitation outcomes following total knee arthroplasty. Acta Orthop. 2019;90(2):179–86.PubMedPubMedCentralCrossRef
31.
Van Onsem S, Van Der Straeten C, Arnout N, Deprez P, Van Damme G, Victor J. A new prediction model for patient satisfaction after total knee arthroplasty. J Arthroplasty. 2016;31(12):2660–7.e1.PubMedCrossRef
32.
Khatib Y, Madan A, Naylor JM, Harris IA. Do psychological factors predict poor outcome in patients undergoing TKA? A systematic review. Clin Orthop Relat Res. 2015;473(8):2630–8.PubMedPubMedCentralCrossRef
33.
Vincent HK, Horodyski M, Gearen P, Vlasak R, Seay AN, Conrad BP, et al. Obesity and long term functional outcomes following elective total hip replacement. J Orthop Surg Res. 2012;7(1):16.PubMedPubMedCentralCrossRef
34.
Nanjayan SK, Swamy GN, Yellu S, Yallappa S, Abuzakuk T, Straw R. In-hospital complications following primary total hip and knee arthroplasty in octogenarian and nonagenarian patients. J Orthop Traumatol. 2014;15(1):29–33.PubMedCrossRef
35.
Manning DW, Edelstein AI, Alvi HM. Risk prediction tools for hip and knee arthroplasty. J Am Acad Orthop Surg. 2016;24(1):19–27.PubMedCrossRef
36.
Schwartz FH, Lange J. Factors that affect outcome following total joint arthroplasty: a review of the recent literature. Curr Rev Musculoskelet Med. 2017;10(3):346–55.PubMedPubMedCentralCrossRef
37.
Thuraisingam S, Dowsey M, Manski-Nankervis J-A, Spelman T, Choong P, Gunn J, et al. Developing prediction models for total knee replacement surgery in patients with osteoarthritis: statistical analysis plan. Osteoarthr Cartil Open. 2020;2(4):100126.CrossRef
38.
Buirs LD, Van Beers LWAH, Scholtes VAB, Pastoors T, Sprague S, Poolman RW. Predictors of physical functioning after total hip arthroplasty: a systematic review. BMJ Open. 2016;6(9):e010725.PubMedPubMedCentralCrossRef
39.
Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4(1):1.PubMedPubMedCentralCrossRef
40.
Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ. 2015;349:g7647.CrossRef
41.
Debray TPA, Damen JAAG, Snell KIE, Ensor J, Hooft L, Reitsma JB, et al. A guide to systematic review and meta-analysis of prediction model performance. BMJ. 2017;356:i6460.PubMedCrossRef
42.
Harris K, Dawson J, Gibbons E, Lim CR, Beard DJ, Fitzpatrick R, et al. Systematic review of measurement properties of patient-reported outcome measures used in patients undergoing hip and knee arthroplasty. Patient Relat Outcome Meas. 2016;7:101.PubMedPubMedCentralCrossRef
43.
Rolfson O, Bohm E, Franklin P, Lyman S, Denissen G, Dawson J, et al. Patient-reported outcome measures in arthroplasty registries: report of the patient-reported outcome measures working group of the International Society of Arthroplasty Registries Part II. Recommendations for selection, administration, and analysis. Acta Orthop. 2016;87(sup1):9–23.PubMedPubMedCentralCrossRef
44.
Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097.PubMedPubMedCentralCrossRef
45.
Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Br J Surg. 2015;102(3):148–58.PubMedCrossRef
46.
Moons KGM, Altman DG, Reitsma JB, Ioannidis JPA, Macaskill P, Steyerberg EW, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162(1):W1–W73.PubMedCrossRef
47.
Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21(1):128–38.PubMedPubMedCentralCrossRef
48.
Moons KGM, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med. 2019;170(1):W1–W33.PubMedCrossRef
49.
Core Team R. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2013.
50.
Snell KI, Hua H, Debray TP, Ensor J, Look MP, Moons KG, et al. Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model. J Clin Epidemiol. 2016;69:40–50.PubMedPubMedCentralCrossRef
51.
Huedo-Medina TB, Sánchez-Meca J, Marín-Martínez F, Botella J. Assessing heterogeneity in meta-analysis: Q statistic or I2 index? Psychol Methods. 2006;11(2):193–206.PubMedCrossRef
52.
Macaskill P. Empirical Bayes estimates generated in a hierarchical summary ROC analysis agreed closely with those of a full Bayesian analysis. J Clin Epidemiol. 2004;57(9):925–32.PubMedCrossRef
53.
Deeks JJ, Macaskill P, Irwig L. The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. J Clin Epidemiol. 2005;58(9):882–93.PubMedCrossRef
54.
55.
Duval S, Tweedie R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000;56(2):455–63.PubMedCrossRef
56.
Song F, Khan KS, Dinnes J, Sutton AJ. Asymmetric funnel plots and publication bias in meta-analyses of diagnostic accuracy. Int J Epidemiol. 2002;31(1):88–95.PubMedCrossRef
57.
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.PubMedPubMedCentralCrossRef
58.
Iorio A, Spencer FA, Falavigna M, Alba C, Lang E, Burnand B, et al. Use of GRADE for assessment of evidence about prognosis: rating confidence in estimates of event rates in broad categories of patients. BMJ. 2015;350:h870.PubMedCrossRef
Metadata
Title
Evaluating methodological quality of prognostic prediction models on patient reported outcome measurements after total hip replacement and total knee replacement surgery: a systematic review protocol
Authors
Wei-Ju Chang
Justine Naylor
Pragadesh Natarajan
Victor Liu
Sam Adie
Publication date
01-12-2022
Publisher
BioMed Central
Published in
Systematic Reviews / Issue 1/2022
Electronic ISSN: 2046-4053
DOI
https://doi.org/10.1186/s13643-022-02039-7

Other articles of this Issue 1/2022

Systematic Reviews 1/2022 Go to the issue

Research

Single-fraction stereotactic radiosurgery versus microsurgical resection for the treatment of vestibular schwannoma: a systematic review and meta-analysis

Protocol

All-cause mortality after major gastrointestinal bleeding among patients receiving direct oral anticoagulants: a protocol for a systematic review and meta-analysis

Research

The hemostatic effect of hot saline irrigation in endoscopic sinus surgery: a systematic review and meta-analysis

Letter

Guidance on review type selection for health technology assessments: key factors and considerations for deciding when to conduct a de novo systematic review, an update of a systematic review, or an overview of systematic reviews

Protocol

Male involvement in family planning use and its determinants in Ethiopia: a systematic review and meta-analysis protocol

Protocol

Screening for prostate cancer: protocol for updating multiple systematic reviews to inform a Canadian Task Force on Preventive Health Care guideline update