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
Aims. Machine-learning (ML)
Aims. To develop
Aims. The aims of this study were to assess mapping models to predict the three-level version of EuroQoL five-dimension utility index (EQ-5D-3L) from the Oxford Knee Score (OKS) and validate these before and after total knee arthroplasty (TKA). Methods. A retrospective cohort of 5,857 patients was used to create the
The February 2023 Children’s orthopaedics Roundup. 360. looks at: Trends in management of paediatric distal radius buckle fractures; Pelvic osteotomy in patients with previous sacral-alar-iliac fixation; Sacral-alar-iliac fixation in patients with previous pelvic osteotomy; Idiopathic toe walking: an update on natural history, diagnosis, and treatment; A
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. No predictive model has been published to forecast operating time for total knee arthroplasty (TKA). The aims of this study were to design and validate a predictive model to estimate operating time for robotic-assisted TKA based on demographic data, and evaluate the added predictive power of CT scan-based predictors and their impact on the accuracy of the predictive model. Methods. A retrospective study was conducted on 1,061 TKAs performed from January 2016 to December 2019 with an image-based robotic-assisted system. Demographic data included age, sex, height, and weight. The femoral and tibial mechanical axis and the osteophyte volume were calculated from CT scans. These inputs were used to develop a predictive model aimed to predict operating time based on demographic data only, and demographic and 3D patient anatomy data. Results. The key factors for predicting operating time were the surgeon and patient weight, followed by 12 anatomical parameters derived from CT scans. The predictive model based only on demographic data showed that 90% of predictions were within 15 minutes of actual operating time, with 73% within ten minutes. The predictive model including demographic data and CT scans showed that 94% of predictions were within 15 minutes of actual operating time and 88% within ten minutes. Conclusion. The primary factors for predicting robotic-assisted TKA operating time were surgeon, patient weight, and osteophyte volume. This study demonstrates that incorporating 3D patient-specific data can improve operating time
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. Advances in treatment have extended the life expectancy of patients with metastatic bone disease (MBD). Patients could experience more skeletal-related events (SREs) as a result of this progress. Those who have already experienced a SRE could encounter another local management for a subsequent SRE, which is not part of the treatment for the initial SRE. However, there is a noted gap in research on the rate and characteristics of subsequent SREs requiring further localized treatment, obligating clinicians to extrapolate from experiences with initial SREs when confronting subsequent ones. This study aimed to investigate the proportion of MBD patients developing subsequent SREs requiring local treatment, examine if there are prognostic differences at the initial treatment between those with single versus subsequent SREs, and determine if clinical, oncological, and prognostic features differ between initial and subsequent SRE treatments. Methods. This retrospective study included 3,814 adult patients who received local treatment – surgery and/or radiotherapy – for bone metastasis between 1 January 2010 and 31 December 2019. All included patients had at least one SRE requiring local treatment. A subsequent SRE was defined as a second SRE requiring local treatment. Clinical, oncological, and prognostic features were compared between single SREs and subsequent SREs using Mann-Whitney U test, Fisher’s exact test, and Kaplan–Meier curve. Results. Of the 3,814 patients with SREs, 3,159 (83%) patients had a single SRE and 655 (17%) patients developed a subsequent SRE. Patients who developed subsequent SREs generally had characteristics that favoured longer survival, such as higher BMI, higher albumin levels, fewer comorbidities, or lower neutrophil count. Once the patient got to the point of subsequent SRE, their clinical and oncological characteristics and one-year survival (28%) were not as good as those with only a single SRE (35%; p < 0.001), indicating that clinicians’ experiences when treating the initial SRE are not similar when treating a subsequent SRE. Conclusion. This study found that 17% of patients required treatments for a second, subsequent SRE, and the current clinical guideline did not provide a specific approach to this clinical condition. We observed that referencing the initial treatment, patients in the subsequent SRE group had longer six-week, 90-day, and one-year median survival than patients in the single SRE group. Once patients develop a subsequent SRE, they have a worse one-year survival rate than those who receive treatment for a single SRE. Future research should identify prognostic factors and assess the applicability of existing survival
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 number of convolutional neural networks (CNN) available for fracture detection and classification is rapidly increasing. External validation of a CNN on a temporally separate (separated by time) or geographically separate (separated by location) dataset is crucial to assess generalizability of the CNN before application to clinical practice in other institutions. We aimed to answer the following questions: are current CNNs for fracture recognition externally valid?; which methods are applied for external validation (EV)?; and, what are reported performances of the EV sets compared to the internal validation (IV) sets of these CNNs?. Methods. The PubMed and Embase databases were systematically searched from January 2010 to October 2020 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The type of EV, characteristics of the external dataset, and diagnostic performance characteristics on the IV and EV datasets were collected and compared. Quality assessment was conducted using a seven-item checklist based on a modified Methodologic Index for NOn-Randomized Studies instrument (MINORS). Results. Out of 1,349 studies, 36 reported development of a CNN for fracture detection and/or classification. Of these, only four (11%) reported a form of EV. One study used temporal EV, one conducted both temporal and geographical EV, and two used geographical EV. When comparing the CNN’s performance on the IV set versus the EV set, the following were found: AUCs of 0.967 (IV) versus 0.975 (EV), 0.976 (IV) versus 0.985 to 0.992 (EV), 0.93 to 0.96 (IV) versus 0.80 to 0.89 (EV), and F1-scores of 0.856 to 0.863 (IV) versus 0.757 to 0.840 (EV). Conclusion. The number of externally validated CNNs in orthopaedic trauma for fracture recognition is still scarce. This greatly limits the potential for transfer of these CNNs from the developing institute to another hospital to achieve similar diagnostic performance. We recommend the use of geographical EV and statements such as the Consolidated Standards of Reporting Trials–Artificial Intelligence (CONSORT-AI), the Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence (SPIRIT-AI) and the Transparent Reporting of a multivariable
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