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The Bone & Joint Journal
Vol. 105-B, Issue 6 | Pages 702 - 710
1 Jun 2023
Yeramosu T Ahmad W Bashir A Wait J Bassett J Domson G

Aims

The aim of this study was to identify factors associated with five-year cancer-related mortality in patients with limb and trunk soft-tissue sarcoma (STS) and develop and validate machine learning algorithms in order to predict five-year cancer-related mortality in these patients.

Methods

Demographic, clinicopathological, and treatment variables of limb and trunk STS patients in the Surveillance, Epidemiology, and End Results Program (SEER) database from 2004 to 2017 were analyzed. Multivariable logistic regression was used to determine factors significantly associated with five-year cancer-related mortality. Various machine learning models were developed and compared using area under the curve (AUC), calibration, and decision curve analysis. The model that performed best on the SEER testing data was further assessed to determine the variables most important in its predictive capacity. This model was externally validated using our institutional dataset.


The Bone & Joint Journal
Vol. 104-B, Issue 12 | Pages 1292 - 1303
1 Dec 2022
Polisetty TS Jain S Pang M Karnuta JM Vigdorchik JM Nawabi DH Wyles CC Ramkumar PN

Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered.

Cite this article: Bone Joint J 2022;104-B(12):1292–1303.


The Bone & Joint Journal
Vol. 103-B, Issue 11 | Pages 1725 - 1730
1 Nov 2021
Baumber R Gerrand C Cooper M Aston W

Aims

The incidence of bone metastases is between 20% to 75% depending on the type of cancer. As treatment improves, the number of patients who need surgical intervention is increasing. Identifying patients with a shorter life expectancy would allow surgical intervention with more durable reconstructions to be targeted to those most likely to benefit. While previous scoring systems have focused on surgical and oncological factors, there is a need to consider comorbidities and the physiological state of the patient, as these will also affect outcome. The primary aim of this study was to create a scoring system to estimate survival time in patients with bony metastases and to determine which factors may adversely affect this.

Methods

This was a retrospective study which included all patients who had presented for surgery with metastatic bone disease. The data collected included patient, surgical, and oncological variables. Univariable and multivariable analysis identified which factors were associated with a survival time of less than six months and less than one year. A model to predict survival based on these factors was developed using Cox regression.


The Bone & Joint Journal
Vol. 103-B, Issue 9 | Pages 1442 - 1448
1 Sep 2021
McDonnell JM Evans SR McCarthy L Temperley H Waters C Ahern D Cunniffe G Morris S Synnott K Birch N Butler JS

In recent years, machine learning (ML) and artificial neural networks (ANNs), a particular subset of ML, have been adopted by various areas of healthcare. A number of diagnostic and prognostic algorithms have been designed and implemented across a range of orthopaedic sub-specialties to date, with many positive results. However, the methodology of many of these studies is flawed, and few compare the use of ML with the current approach in clinical practice. Spinal surgery has advanced rapidly over the past three decades, particularly in the areas of implant technology, advanced surgical techniques, biologics, and enhanced recovery protocols. It is therefore regarded an innovative field. Inevitably, spinal surgeons will wish to incorporate ML into their practice should models prove effective in diagnostic or prognostic terms. The purpose of this article is to review published studies that describe the application of neural networks to spinal surgery and which actively compare ANN models to contemporary clinical standards allowing evaluation of their efficacy, accuracy, and relatability. It also explores some of the limitations of the technology, which act to constrain the widespread adoption of neural networks for diagnostic and prognostic use in spinal care. Finally, it describes the necessary considerations should institutions wish to incorporate ANNs into their practices. In doing so, the aim of this review is to provide a practical approach for spinal surgeons to understand the relevant aspects of neural networks.

Cite this article: Bone Joint J 2021;103-B(9):1442–1448.


The Bone & Joint Journal
Vol. 103-B, Issue 5 | Pages 864 - 871
3 May 2021
Hunt LP Matharu GS Blom AW Howard PW Wilkinson JM Whitehouse MR

Aims

Debate remains whether the patella should be resurfaced during total knee replacement (TKR). For non-resurfaced TKRs, we estimated what the revision rate would have been if the patella had been resurfaced, and examined the risk of re-revision following secondary patellar resurfacing.

Methods

A retrospective observational study of the National Joint Registry (NJR) was performed. All primary TKRs for osteoarthritis alone performed between 1 April 2003 and 31 December 2016 were eligible (n = 842,072). Patellar resurfacing during TKR was performed in 36% (n = 305,844). The primary outcome was all-cause revision surgery. Secondary outcomes were the number of excess all-cause revisions associated with using TKRs without (versus with) patellar resurfacing, and the risk of re-revision after secondary patellar resurfacing.


The Bone & Joint Journal
Vol. 102-B, Issue 9 | Pages 1183 - 1193
14 Sep 2020
Anis HK Strnad GJ Klika AK Zajichek A Spindler KP Barsoum WK Higuera CA Piuzzi NS

Aims

The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors.

Methods

Data were prospectively collected on all patients who underwent total or unicompartmental knee arthroplasty at a between July 2015 and June 2018. Cohort 1 (n = 5,958) was utilized to develop models for LOS and 90 day readmission. Cohort 2 (n = 2,391, surgery date 2015 to 2017) was utilized to develop models for one-year improvements in Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score, KOOS function score, and KOOS quality of life (QOL) score. Model accuracies within the imputed data set were assessed through cross-validation with root mean square errors (RMSEs) and mean absolute errors (MAEs) for the LOS and PROMs models, and the index of prediction accuracy (IPA), and area under the curve (AUC) for the readmission models. Model accuracies in new patient data sets were assessed with AUC.


The Bone & Joint Journal
Vol. 102-B, Issue 1 | Pages 26 - 32
1 Jan 2020
Parikh S Singh H Devendra A Dheenadhayalan J Sethuraman AS Sabapathy R Rajasekaran S

Aims

Open fractures of the tibia are a heterogeneous group of injuries that can present a number of challenges to the treating surgeon. Consequently, few surgeons can reliably advise patients and relatives about the expected outcomes. The aim of this study was to determine whether these outcomes are predictable by using the Ganga Hospital Score (GHS). This has been shown to be a useful method of scoring open injuries to inform wound management and decide between limb salvage and amputation.

Methods

We collected data on 182 consecutive patients with a type II, IIIA, or IIIB open fracture of the tibia who presented to our hospital between July and December 2016. For the purposes of the study, the patients were jointly treated by experienced consultant orthopaedic and plastic surgeons who determined the type of treatment. Separately, the study team (SP, HS, AD, JD) independently calculated the GHS and prospectively collected data on six outcomes for each patient. These included time to bony union, number of admissions, length of hospital stay, total length of treatment, final functional score, and number of operations. Spearman’s correlation was used to compare GHS with each outcome. Forward stepwise linear regression was used to generate predictive models based on components of the GHS. Five-fold cross-validation was used to prevent models from over-fitting.


The Journal of Bone & Joint Surgery British Volume
Vol. 89-B, Issue 11 | Pages 1446 - 1451
1 Nov 2007
Biring GS Masri BA Greidanus NV Duncan CP Garbuz DS

A prospective cohort of 222 patients who underwent revision hip replacement between April 2001 and March 2004 was evaluated to determine predictors of function, pain and activity level between one and two years post-operatively, and to define quality of life outcomes using validated patient reported outcome tools. Predictive models were developed and proportional odds regression analyses were performed to identify factors that predict quality of life outcomes at one and two years post-operatively. The dependent outcome variables were the Western Ontario and McMaster Osteoarthritis Index (WOMAC) function and pain scores, and University of California Los Angeles activity scores. The independent variables included patient demographics, operative factors, and objective quality of life parameters, including pre-operative WOMAC, and the Short Form-12 mental component score. There was a significant improvement (t-test, p < 0.001) in all patient quality of life scores. In the predictive model, factors predictive of improved function (original regression analyses, p < 0.05) included a higher pre-operative WOMAC function score (p < 0.001), age between 60 and 70 years (p < 0.037), male gender (p = 0.017), lower Charnley class (p < 0.001) and aseptic loosening being the indication for revision (p < 0.003). Using the WOMAC pain score as an outcome variable, factors predictive of improvement included the pre-operative WOMAC function score (p = 0.001), age between 60 and 70 years (p = 0.004), male gender (p = 0.005), lower Charnley class (p = 0.001) and no previous revision procedure (p = 0.023). The pre-operative WOMAC function score (p = 0.001), the indication for the operation (p = 0.007), and the operating surgeon (p = 0.008) were significant predictors of the activity assessment at follow-up. Predictors of quality of life outcomes after revision hip replacement were established. Although some patient-specific and surgery-specific variables were important, age, gender, Charnley class and pre-operative WOMAC function score had the most robust associations with outcome