The evidence base within trauma and orthopaedics has traditionally favoured quantitative research methodologies. Qualitative research can provide unique insights which illuminate patient experiences and perceptions of care. Qualitative methods reveal the subjective narratives of patients that are not captured by quantitative data, providing a more comprehensive understanding of patient-centred care. The aim of this study is to quantify the level of qualitative research within the orthopaedic literature. A bibliometric search of journals’ online archives and multiple databases was undertaken in March 2024, to identify articles using qualitative research methods in the top 12 trauma and orthopaedic journals based on the 2023 impact factor and SCImago rating. The bibliometric search was conducted and reported in accordance with the preliminary guideline for reporting bibliometric reviews of the biomedical literature (BIBLIO).Aims
Methods
Despite the vast quantities of published artificial intelligence (AI) algorithms that target trauma and orthopaedic applications, very few progress to inform clinical practice. One key reason for this is the lack of a clear pathway from development to deployment. In order to assist with this process, we have developed the Clinical Practice Integration of Artificial Intelligence (CPI-AI) framework – a five-stage approach to the clinical practice adoption of AI in the setting of trauma and orthopaedics, based on the IDEAL principles ( Cite this article:
Aims. Bacterial infection activates neutrophils to release neutrophil extracellular traps (NETs) in bacterial biofilms of periprosthetic joint infections (PJIs). The aim of this study was to evaluate the increase in NET activation and release (NETosis) and haemostasis markers in the plasma of patients with PJI, to evaluate whether such plasma induces the activation of neutrophils, to ascertain whether increased NETosis is also mediated by reduced DNaseI activity, to explore novel therapeutic interventions for NETosis in PJI in vitro, and to evaluate the potential diagnostic use of these markers. Methods. We prospectively recruited 107 patients in the preoperative period of prosthetic surgery, 71 with a suspicion of PJI and 36 who underwent arthroplasty for non-septic indications as controls, and obtained citrated plasma. PJI was confirmed in 50 patients. We measured NET markers, inflammation markers, DNaseI activity, haemostatic markers, and the thrombin generation test (TGT). We analyzed the ability of plasma from confirmed PJI and controls to induce NETosis and to degrade in vitro-generated NETs, and explored the therapeutic restoration of the impairment to degrade NETs of PJI plasma with recombinant human DNaseI. Finally, we assessed the contribution of these markers to the diagnosis of PJI. Results. Patients with confirmed PJI had significantly increased levels of NET markers (cfDNA (p < 0.001), calprotectin (p < 0.001), and neutrophil elastase (p = 0.022)) and inflammation markers (IL-6; p < 0.001) in plasma. Moreover, the plasma of patients with PJI induced significantly more neutrophil activation than the plasma of the controls (p < 0.001) independently of tumour necrosis factor alpha. Patients with PJI also had a reduced DNaseI activity in plasma (p < 0.001), leading to a significantly impaired degradation of NETs (p < 0.001). This could be therapeutically restored with recombinant human DNaseI to the level in the controls. We developed a model to improve the diagnosis of PJI with cfDNA, calprotectin, and the start tail of TGT as predictors, though cfDNA alone achieved a good
This study explored the shared genetic traits and molecular interactions between postmenopausal osteoporosis (POMP) and sarcopenia, both of which substantially degrade elderly health and quality of life. We hypothesized that these motor system diseases overlap in pathophysiology and regulatory mechanisms. We analyzed microarray data from the Gene Expression Omnibus (GEO) database using weighted gene co-expression network analysis (WGCNA), machine learning, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to identify common genetic factors between POMP and sarcopenia. Further validation was done via differential gene expression in a new cohort. Single-cell analysis identified high expression cell subsets, with mononuclear macrophages in osteoporosis and muscle stem cells in sarcopenia, among others. A competitive endogenous RNA network suggested regulatory elements for these genes.Aims
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The principles of evidence-based medicine (EBM) are the foundation of modern medical practice. Surgeons are familiar with the commonly used statistical techniques to test hypotheses, summarize findings, and provide answers within a specified range of probability. Based on this knowledge, they are able to critically evaluate research before deciding whether or not to adopt the findings into practice. Recently, there has been an increased use of artificial intelligence (AI) to analyze information and derive findings in orthopaedic research. These techniques use a set of statistical tools that are increasingly complex and may be unfamiliar to the orthopaedic surgeon. It is unclear if this shift towards less familiar techniques is widely accepted in the orthopaedic community. This study aimed to provide an exploration of understanding and acceptance of AI use in research among orthopaedic surgeons. Semi-structured in-depth interviews were carried out on a sample of 12 orthopaedic surgeons. Inductive thematic analysis was used to identify key themes.Aims
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Disorders of bone integrity carry a high global disease burden, frequently requiring intervention, but there is a paucity of methods capable of noninvasive real-time assessment. Here we show that miniaturized handheld near-infrared spectroscopy (NIRS) scans, operated via a smartphone, can assess structural human bone properties in under three seconds. A hand-held NIR spectrometer was used to scan bone samples from 20 patients and predict: bone volume fraction (BV/TV); and trabecular (Tb) and cortical (Ct) thickness (Th), porosity (Po), and spacing (Sp).Aims
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Patient-reported outcome measures have become an important part of routine care. The aim of this study was to determine if Patient-Reported Outcomes Measurement Information System (PROMIS) measures can be used to create patient subgroups for individuals seeking orthopaedic care. This was a cross-sectional study of patients from Duke University Department of Orthopaedic Surgery clinics (14 ambulatory and four hospital-based). There were two separate cohorts recruited by convenience sampling (i.e. patients were included in the analysis only if they completed PROMIS measures during a new patient visit). Cohort #1 (n = 12,141; December 2017 to December 2018,) included PROMIS short forms for eight domains (Physical Function, Pain Interference, Pain Intensity, Depression, Anxiety, Sleep Quality, Participation in Social Roles, and Fatigue) and Cohort #2 (n = 4,638; January 2019 to August 2019) included PROMIS Computer Adaptive Testing instruments for four domains (Physical Function, Pain Interference, Depression, and Sleep Quality). Cluster analysis (K-means method) empirically derived subgroups and subgroup differences in clinical and sociodemographic factors were identified with one-way analysis of variance.Aims
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Aims. The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments. Methods. Based on a clinical series of 479 primary total knees and 1,305 associated surgical decisions, various ML models were developed. These models identified the indicated surgical decision based on available, intra-operative alignment, and tibiofemoral load data. Results. With an associated area under the receiver-operator curve ranging between 0.75 and 0.98, the optimized ML models resulted in good to excellent
In order to assess current opinions on the long-term outcome after primary total hip replacement, we performed a multicentre, cross-sectional survey in 22 centres from 12 European countries. Different patient characteristics were categorised into ‘decreases chances’, ‘does not affect chances’, and ‘increases chances’ of a favourable long-term outcome, by 304 orthopaedic surgeons and 314 referring practitioners. The latter were less likely to associate age older than 80 years and obesity with a favourable outcome than orthopaedic surgeons (p <
0.001 and p = 0.006, respectively) and more likely to associate age younger than 50 years with a favourable outcome (p = 0.006). Comorbidity, rheumatoid arthritis, and poor bone quality were thought to be associated with a decreased chance of a favourable outcome. We found important differences in the opinions regarding long-term outcome after total hip replacement within and between referring practitioners and orthopaedic surgeons. These are likely to affect access to and the provision of total hip replacement.
We describe the development and validation of a scoring system for auditing orthopaedic surgery. It is a minor modification of the POSSUM scoring system widely used in general surgery. The orthopaedic POSSUM system which we have developed gives