Aims. This study aimed to compare the performance of survival
Aims. To develop and internally validate a preoperative clinical
Early and accurate prediction of hospital length-of-stay
(LOS) in patients undergoing knee replacement is important for economic
and operational reasons. Few studies have systematically developed
a multivariable model to predict LOS. We performed a retrospective
cohort study of 1609 patients aged ≥ 50 years who underwent elective,
primary total or unicompartmental knee replacements. Pre-operative
candidate predictors included patient demographics, knee function,
self-reported measures, surgical factors and discharge plans. In
order to develop the model, multivariable regression with bootstrap
internal validation was used. The median LOS for the sample was
four days (interquartile range 4 to 5). Statistically significant
predictors of longer stay included older age, greater number of comorbidities,
less knee flexion range of movement, frequent feelings of being
down and depressed, greater walking aid support required, total
(versus unicompartmental) knee replacement, bilateral
surgery, low-volume surgeon, absence of carer at home, and expectation
to receive step-down care. For ease of use, these ten variables were
used to construct a nomogram-based
This study demonstrates a significant correlation
between the American Knee Society (AKS) Clinical Rating System and
the Oxford Knee Score (OKS) and provides a validated prediction
tool to estimate score conversion. A total of 1022 patients were prospectively clinically assessed
five years after TKR and completed AKS assessments and an OKS questionnaire.
Multivariate regression analysis demonstrated significant correlations between
OKS and the AKS knee and function scores but a stronger correlation
(r = 0.68, p <
0.001) when using the sum of the AKS knee and
function scores. Addition of body mass index and age (other statistically
significant predictors of OKS) to the algorithm did not significantly
increase the predictive value. The simple regression model was used to predict the OKS in a
group of 236 patients who were clinically assessed nine to ten years
after TKR using the AKS system. The predicted OKS was compared with
actual OKS in the second group. Intra-class correlation demonstrated
excellent reliability (r = 0.81, 95% confidence intervals 0.75 to
0.85) for the combined knee and function score when used to predict
OKS. Our findings will facilitate comparison of outcome data from
studies and registries using either the OKS or the AKS scores and
may also be of value for those undertaking meta-analyses and systematic
reviews. Cite this article:
Aims. The aim of this study was to develop and internally validate a prognostic nomogram to predict the probability of gaining a functional range of motion (ROM ≥ 120°) after open arthrolysis of the elbow in patients with post-traumatic stiffness of the elbow. Methods. We developed the Shanghai
Aims. To develop and externally validate a parsimonious statistical
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
Aims. Heterotopic ossification (HO) is a common complication after elbow trauma and can cause severe upper limb disability. Although multiple prognostic factors have been reported to be associated with the development of post-traumatic HO, no model has yet been able to combine these predictors more succinctly to convey prognostic information and medical measures to patients. Therefore, this study aimed to identify prognostic factors leading to the formation of HO after surgery for elbow trauma, and to establish and validate a nomogram to predict the probability of HO formation in such particular injuries. Methods. This multicentre case-control study comprised 200 patients with post-traumatic elbow HO and 229 patients who had elbow trauma but without HO formation between July 2019 and December 2020. Features possibly associated with HO formation were obtained. The least absolute shrinkage and selection operator regression model was used to optimize feature selection. Multivariable logistic regression analysis was applied to build the new nomogram: the Shanghai post-Traumatic Elbow Heterotopic Ossification
Aims. The risk factors for recurrent instability (RI) following a primary traumatic anterior shoulder dislocation (PTASD) remain unclear. In this study, we aimed to determine the rate of RI in a large cohort of patients managed nonoperatively after PTASD and to develop a clinical
Aims. The aim of this study was to assess whether supine flexibility predicts the likelihood of curve progression in patients with adolescent idiopathic scoliosis (AIS) undergoing brace treatment. Methods. This was a retrospective analysis of patients with AIS prescribed with an underarm brace between September 2008 to April 2013 and followed up until 18 years of age or required surgery. Patients with structural proximal curves that preclude underarm bracing, those who were lost to follow-up, and those who had poor compliance to bracing (<16 hours a day) were excluded. The major curve Cobb angle, curve type, and location were measured on the pre-brace standing posteroanterior (PA) radiograph, supine whole spine radiograph, initial in-brace standing PA radiograph, and the post-brace weaning standing PA radiograph. Validation of the previous in-brace Cobb angle regression model was performed. The outcome of curve progression post-bracing was tested using a logistic regression model. The supine flexibility cut-off for curve progression was analyzed with receiver operating characteristic curve. Results. A total of 586 patients with mean age of 12.6 years (SD 1.2) remained for analysis after exclusion. The baseline Cobb angle was similar for thoracic major curves (31.6° (SD 3.8°)) and lumbar major curves (30.3° (SD 3.7°)). Curve progression was more common in the thoracic curves than lumbar curves with mean final Cobb angles of 40.5° (SD 12.5°) and 31.8° (SD 9.8°) respectively. This dataset matched the
Aims. The aim of this study was to determine the influence of developmental spinal stenosis (DSS) on the risk of re-operation at an adjacent level. Patients and Methods. This was a retrospective study of 235 consecutive patients who had undergone decompression-only surgery for lumbar spinal stenosis and had a minimum five-year follow-up. There were 106 female patients (45.1%) and 129 male patients (54.9%), with a mean age at surgery of 66.8 years (. sd. 11.3). We excluded those with adult deformity and spondylolisthesis. Presenting symptoms, levels operated on initially and at re-operation were studied. MRI measurements included the anteroposterior diameter of the bony spinal canal, the degree of disc degeneration, and the thickness of the ligamentum flavum. DSS was defined by comparative measurements of the bony spinal canal. Risk factors for re-operation at the adjacent level were determined and included in a multivariate stepwise logistic regression for
Aims. This study aims to assess first, whether mutations in the epidermal
growth factor receptor (EGFR) and Kirsten rat sarcoma (kRAS) genes
are associated with overall survival (OS) in patients who present
with symptomatic bone metastases from non-small cell lung cancer
(NSCLC) and secondly, whether mutation status should be incorporated into
prognostic models that are used when deciding on the appropriate
palliative treatment for symptomatic bone metastases. Patients and Methods. We studied 139 patients with NSCLC treated between 2007 and 2014
for symptomatic bone metastases and whose mutation status was known.
The association between mutation status and overall survival was
analysed and the results applied to a recently published prognostic
model to determine whether including the mutation status would improve
its discriminatory power. Results. The median OS was 3.9 months (95% confidence interval (CI) 2.1
to 5.7). Patients with EGFR (15%) or kRAS mutations (34%) had a
median OS of 17.3 months (95% CI 12.7 to 22.0) and 1.8 months (95%
CI 1.0 to 2.7), respectively. Compared with EGFR-positive patients,
EGFR-negative patients had a 2.5 times higher risk of death (95%
CI 1.5 to 4.2). Incorporating EGFR mutation status in the prognostic
model improved its discriminatory power. Conclusion. Survival
This study identified variables which influence the outcome of surgical management on 126 ununited scaphoid fractures managed by internal fixation and non-vascular bone grafting. The site of fracture was defined by a new method: the ratio of the length of the proximal fragment to the sum of the lengths of both fragments, calculated using specific views in the plain radiographs. Bone healing occurred in 71% (89) of cases. Only the site of nonunion (p = 1 × 10. −6. ) and the delay to surgery (p = 0.001) remained significant on multivariate analysis. The effect of surgical delay on the probability of union increased as the fracture site moved proximally. A
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. 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.Aims
Methods
Frailty greatly increases the risk of adverse outcome of trauma in older people. Frailty detection tools appear to be unsuitable for use in traumatically injured older patients. We therefore aimed to develop a method for detecting frailty in older people sustaining trauma using routinely collected clinical data. We analyzed prospectively collected registry data from 2,108 patients aged ≥ 65 years who were admitted to a single major trauma centre over five years (1 October 2015 to 31 July 2020). We divided the sample equally into two, creating derivation and validation samples. In the derivation sample, we performed univariate analyses followed by multivariate regression, starting with 27 clinical variables in the registry to predict Clinical Frailty Scale (CFS; range 1 to 9) scores. Bland-Altman analyses were performed in the validation cohort to evaluate any biases between the Nottingham Trauma Frailty Index (NTFI) and the CFS.Aims
Methods
To examine whether natural language processing (NLP) using a clinically based large language model (LLM) could be used to predict patient selection for total hip or total knee arthroplasty (THA/TKA) from routinely available free-text radiology reports. Data pre-processing and analyses were conducted according to the Artificial intelligence to Revolutionize the patient Care pathway in Hip and knEe aRthroplastY (ARCHERY) project protocol. This included use of de-identified Scottish regional clinical data of patients referred for consideration of THA/TKA, held in a secure data environment designed for artificial intelligence (AI) inference. Only preoperative radiology reports were included. NLP algorithms were based on the freely available GatorTron model, a LLM trained on over 82 billion words of de-identified clinical text. Two inference tasks were performed: assessment after model-fine tuning (50 Epochs and three cycles of k-fold cross validation), and external validation.Aims
Methods
There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for various aspects of Trauma & Orthopaedic surgery. However, correct interpretation of these models is difficult for those without specific knowledge of computing or health data science methodology. Lack of current reporting standards leads to the potential for significant heterogeneity in the design and quality of published studies. We provide an overview of machine-learning techniques for the lay individual, including key terminology and best practice reporting guidelines. Cite this article:
Prediction tools are instruments which are commonly used to estimate the prognosis in oncology and facilitate clinical decision-making in a more personalized manner. Their popularity is shown by the increasing numbers of prediction tools, which have been described in the medical literature. Many of these tools have been shown to be useful in the field of soft-tissue sarcoma of the extremities (eSTS). In this annotation, we aim to provide an overview of the available prediction tools for eSTS, provide an approach for clinicians to evaluate the performance and usefulness of the available tools for their own patients, and discuss their possible applications in the management of patients with an eSTS. Cite this article:
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: