The Princess Grace Hospital

Robotic-assisted systems are being increasingly integrated into orthopaedic surgery, offering potential improvements in precision, reproducibility, and patient-reported outcome measures (PROMs) for unicompartmental knee arthroplasty (UKA), total knee arthroplasty (TKA), and total hip arthroplasty (THA). A review and summary of the early value assessment (EVA) undertaken by the National Institute for Health and Care Excellence (NICE) to evaluate the clinical effectiveness, cost-effectiveness, and implementation considerations of these technologies is presented. The EVA includes a review of the literature, an evidence gap analysis, and an evidence generation plan. Evidence from 28 studies and two registries suggested that robotic-assisted surgery was noninferior to conventional approaches in key outcomes, including PROMs, complications, hospital stay, and revisions, but it was associated with enhanced implant alignment. Short-term PROMs showed modest improvements, although most differences fell below currently defined clinically meaningful thresholds. Revision data were limited in follow-up, and ergonomic benefits for surgeons were uncertain. Economic evaluations suggested potential cost-effectiveness, particularly for THA, but assumptions regarding utilities, resource use, procedure volume, and implant costs introduced substantial uncertainty. The EVA highlighted the importance of training, equitable access, and national coordination for implementation. Evidence gaps were identified in long-term revision rates, health-related quality of life outcomes, productivity, sub-group effects, surgical team benefits, and learning curves. NICE recommended conditional adoption of six robotic platforms (ApolloKnee (Corin, UK), CORI (Smith & Nephew, UK), Mako (Stryker, USA), ROSA (Zimmer Biomet, USA), SkyWalker (MicroPort, China), and VELYS (DePuy Synthes, USA)) over a three-year evidence generation period, requiring annual reporting and adherence to regulatory standards. While robotic-assisted arthroplasty offers technical advantages, definitive clinical and economic benefits remain unproven. Surgeons, healthcare systems, and industry partners will need to work collaboratively to provide the required evidence in the next three years. A UK robotic registry could help address the evidence gaps more rapidly than individual centres by capturing large-scale national data.

With the increasing use of cementless implants for primary total knee arthroplasty (TKA), the aim of this systematic review and meta-analysis was to evaluate the clinical and radiological outcomes of contemporary cementless designs of TKA compared with cemented TKA. Adhering to the PRISMA guidelines, a review of medical databases including Cochrane Registry, MEDLINE, Embase, Web of Science, and grey literature was conducted. The search included studies published since 2010 and encompassed only the current generation of cementless implants. Quality assessment was performed using the ROB-2/ROBIN-I risk of bias tools. The search yielded 23 randomized controlled trials and 45 observational studies which met the inclusion criteria. A total of 31 outcome metrics, including clinical and functional outcomes, were analyzed. Subgroup analysis was based on the patient's age. Meta-analysis of all-cause and aseptic survival at specific timepoints between one and 19 years postopertaively showed no significant difference between the groups. Subgroup analysis for individual patients aged < 60 years showed significant differences favouring cementless implants up to 14 years. Overall migration, measured at one and five years, was significantly higher for cementless implants (p < 0.001), with comparable migration at ten years (p = 0.43). Meta-analysis of most clinical outcomes showed equivalence up to 20 years, though some metrics favoured cementless implants. Subscales of Knee Society Score function/pain revealed significant differences at two (p = 0.003) and five years (all p < 0.05) favouring cementless implants. Cementless groups also had significantly shorter operating times (p < 0.001) and lengths of stay in hospital (p = 0.003). Surgical complications were significantly more frequent in the cemented group (p < 0.001), as was the incidence of deep venous thrombosis (p = 0.002). This meta-analysis highlighted considerable variations in outcomes between cemented and cementless TKA, suggesting that newer designs of cementless implants may offer improved survival in patients aged < 60 years. There was early migration of cementless TKA with stabilization in the longer term and improvement in several clinical and functional outcomes favouring cementless over cemented implants.

Cluster of differentiation 84 (CD84), a member of the signalling lymphocytic activating molecule (SLAM) family, has emerged as a potential prognostic biomarker and therapeutic target in breast cancer. This study explored CD84 expression and its correlation with clinicopathological features in a well-characterised cohort. Using quantitative real-time PCR, mRNA expression levels of CD84 and related molecules, including PDL1, CD74, and other immune tolerance markers, were analysed. The findings reveal that elevated CD84 expression predicts poor overall survival, independent of conventional prognostic factors such as the Nottingham Prognostic Index (NPI). Notably, a combined signature of CD84, CD48, VAV1, and CTNNB1 demonstrated stronger prognostic power than individual markers. CD84 exhibited significant correlations with immunosuppressive molecules, including PDL1 and CD74, underscoring its role in fostering immune tolerance within the tumour microenvironment. CD84 may mediate an immunosuppressive phenotype, facilitating immune evasion in breast cancer. This highlights its potential as a therapeutic target, particularly in triple-negative breast cancer, to overcome immune resistance and enhance treatment efficacy.

We read with interest the recent study investigating a potential association between third-generation aromatase inhibitors (AIs) and dementia using VigiBase data [1]. While the authors present a compelling case for further exploration of this link, several key methodological limitations were either inadequately discussed or overlooked. First, pharmacovigilance databases like VigiBase are inherently prone to reporting bias. The underreporting of adverse drug reactions (ADRs) is well recognized, particularly for chronic, insidious conditions such as dementia. Conversely, heightened awareness of cognitive issues in breast cancer survivors undergoing AI therapy may contribute to selective overreporting, artificially inflating the observed signal. Without robust denominator data, disproportionality analyses alone cannot establish causality or even true incidence rates. Second, confounding variables were not sufficiently controlled. Age, baseline cognitive function, comorbidities (e.g., cerebrovascular disease, diabetes, depression), and concurrent medications (e.g., antihypertensives, statins, or other endocrine therapies) are critical contributors to dementia risk. The authors acknowledge this limitation but do not provide any sensitivity analyses or adjustments to mitigate these confounding effects. Third, indication bias remains a major concern. AIs are prescribed exclusively to postmenopausal women, a group inherently at increased risk for dementia due to estrogen decline and aging itself. The study compares AI users to all other drug users in the database rather than an appropriate control group of postmenopausal women not receiving AIs. Without such a comparator, the reported RORs may reflect background dementia risk rather than a drug effect. Moreover, the lack of longitudinal follow-up and missing data on treatment duration further weaken the conclusions. The study reports that in 86%–96% of cases, treatment duration was unknown, and in most reports, no information was available on whether cognitive symptoms improved after discontinuation. This is critical, as transient cognitive impairment related to cancer treatment (“chemo brain”) could be misclassified as early dementia. Finally, the absence of validation through large-scale epidemiological studies is a key gap. While pharmacovigilance signals are useful for hypothesis generation, they must be corroborated by prospective cohort studies or randomized trials before clinical implications can be drawn. Conversely, other studies have found no significant impact of AIs on cognitive function. Ilhan et al. reported that prolonged adjuvant treatment with AIs does not affect cognitive function in hormone receptor-positive breast cancer patients [2]. Additionally, some research indicates that AIs might have protective effects against neurodegenerative diseases. A study in JAMA Network Open found that tamoxifen and AIs were associated with significantly reduced relative risks for all neurodegenerative diseases, including Alzheimer's disease [3]. These findings directly challenge the conclusions drawn in the current study and highlight the need for caution in interpreting pharmacovigilance signals without robust epidemiological validation. Beyond methodological concerns, the clinical impact of such findings cannot be ignored. Breast cancer patients often face difficult treatment decisions, and undue alarm over unproven risks may deter women from taking life-saving endocrine therapy. Given the well-established benefits of AIs in reducing recurrence and improving survival, it is crucial that such studies provide a balanced interpretation of findings, emphasizing limitations to avoid unwarranted fear and nonadherence. In conclusion, while this study raises an important question regarding AI use and dementia risk, its methodological shortcomings significantly limit the strength of its conclusions. Future research should integrate prospective cognitive assessments, better-matched control groups, and confounder-adjusted analyses to clarify this potential relationship. Until then, patients and clinicians should interpret these findings with caution, ensuring that concerns about unproven risks do not compromise adherence to proven, life-extending treatments. K.M. conceptualization. K.M. writing – original draft preparation. M.R. and K.M. writing – review and editing. All authors have read and agreed to the published version of the manuscript. K.M. received research grant provided by Breast Cancer Charity, consulting fees from QMedical and Sebbin, and honoraria for offering academic and clinical advice to Merit Medical and QMedical corporations. He is a fractional shareholder of Datar Genetics stock. Furthermore, he owns shares in HCA Healthcare UK and OncoBotanica. The other author has no conflicts of interest to declare. The data presented in this study are publicly available and can be used without restrictions.

Breast cancer remains a global health challenge, requiring innovative strategies for early detection, diagnosis, treatment monitoring, and recurrence detection. Liquid biopsy-leveraging circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), microRNAs (miRNAs), exosomes, immune-based biomarkers, and tumor-educated platelets (TEPs)-has emerged as a promising tool to address these needs. CTCs and ctDNA provide critical insights into tumor heterogeneity, therapeutic targets, and resistance mechanisms, while miRNAs, exosomes, and other non-CTC-based markers reflect the tumor microenvironment and offer potential biomarkers for disease progression. Importantly, liquid biopsy offers distinct advantages in early detection and precise diagnosis, as well as in identifying therapeutic resistance in real time, allowing clinicians to adapt treatment strategies effectively. The non-invasive nature of liquid biopsy further enables real-time tumor monitoring, paving the way for personalized treatment approaches. However, several challenges hinder its routine clinical adoption, including technical complexity, economic constraints, and variations in detection sensitivity due to low biomarker abundance. Additionally, a lack of standardization in methodology and interpretation limits its widespread application. Rigorous standardization and clinical validation are essential to address these barriers, ensuring equitable access across diverse healthcare settings and transforming breast cancer care for millions worldwide. Future directions include integrating artificial intelligence and multi-omic approaches to enhance diagnostic accuracy and clinical utility.