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:
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. 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.Aims
Methods
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. 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.Aims
Methods
Previously, we showed that case-specific non-linear
finite element (FE) models are better at predicting the load to failure
of metastatic femora than experienced clinicians. In this study
we improved our FE modelling and increased the number of femora
and characteristics of the lesions. We retested the robustness of
the FE predictions and assessed why clinicians have difficulty in
estimating the load to failure of metastatic femora. A total of
20 femora with and without artificial metastases were mechanically
loaded until failure. These experiments were simulated using case-specific
FE models. Six clinicians ranked the femora on load to failure and
reported their ranking strategies. The experimental load to failure
for intact and metastatic femora was well predicted by the FE models (R2 =
0.90 and R2 = 0.93, respectively). Ranking metastatic
femora on load to failure was well performed by the FE models (τ =
0.87), but not by the clinicians (0.11 <
τ <
0.42). Both the
FE models and the clinicians allowed for the characteristics of
the lesions, but only the FE models incorporated the initial bone
strength, which is essential for accurately predicting the risk
of fracture. Accurate prediction of the risk of fracture should
be made possible for clinicians by further developing FE models.
We performed a clinical and radiological study to determine the rate of failure of the Charnley Elite-Plus femoral component. Our aim was to confirm or refute the predictions of a previous roentgen stereophotogrammetric analysis study in which 20% of the Charnley Elite-Plus stems had shown rapid posterior head migration. It was predicted that this device would have a high early rate of failure. We examined 118 patients at a mean of nine years after hip replacement, including the 19 patients from the original roentgen stereophotogrammetric study. The number of revision procedures was recorded and clinical and radiological examinations were performed. The rate of survival of the femoral stems at ten years was 83% when revision alone was considered to be a failure. It decreased to 59% when a radiologically loose stem was also considered to be a failure. All the patients previously shown in the roentgen stereophotogrammetric study to have high posterior head migration went on to failure. There was a highly significant difference (p = 0.002) in posterior head migration measured at two years after operation between failed and non-failed femoral stems, but there was no significant difference in subsidence between these two groups. Our study has shown that the Charnley Elite-Plus femoral component has an unacceptably high rate of failure. It confirms that early evaluation of new components is important and that roentgen stereophotogrammetric is a good tool for this. Our findings have also shown that rapid posterior head migration is predictive of premature loosening and a better predictor than subsidence.
