Demographic and clinical characteristics associated with advanced stage colorectal cancer: a registry-based cohort study in Saudi Arabia.

BACKGROUND: In Saudi Arabia, approximately one-third of colorectal cancer (CRC) patients are diagnosed at an advanced stage. Late diagnosis is often associated with a worse prognosis. Understanding the risk factors for late-stage presentation of CRC is crucial for developing targeted interventions enabling earlier detection and improved patient outcomes. METHODS: We conducted a retrospective cohort study on 17,541 CRC patients from the Saudi Cancer Registry (1997-2017). We defined distant CRCs as late-stage and localized and regional CRCs as early-stage. To assess risk factors for late-stage CRC, we first used multivariable logistic regression, then developed a decision tree to segment regions by late-stage CRC risk, and finally used stratified logistic regression models to examine geographical and sex variations in risk factors. RESULTS: Of all cases, 29% had a late-stage diagnosis, and 71% had early-stage CRC. Young (< 50 years) and unmarried women had an increased risk of late-stage CRC, overall and in some regions. Regional risk variations by sex were observed. Sex-related differences in late-stage rectosigmoid cancer risk were observed in specific regions but not in the overall population. Patients diagnosed after 2001 had increased risks of late-stage presentation. CONCLUSION: Our study identified risk factors for late-stage CRC that can guide targeted early detection efforts. Further research is warranted to fully understand these relationships and develop and evaluate effective prevention strategies.

Comparison of trueness and repeatability of facial prosthesis design using a 3D morphable model approach, traditional computer-aided design methods, and conventional manual sculpting techniques.

STATEMENT OF PROBLEM: Manually sculpting a wax pattern of a facial prosthesis is a time-, skill-, and resource-intensive process. Computer-aided design (CAD) methods have been proposed as a substitute for manual sculpting, but these techniques can still require high technical or artistic abilities. Three-dimensional morphable models (3DMMs) could semi-automate facial prosthesis CAD. Systematic comparisons of different design approaches are needed. PURPOSE: The purpose of this study was to compare the trueness and repeatability of replacing facial features with 3 methods of facial prosthesis design involving 3DMM, traditional CAD, and conventional manual sculpting techniques. MATERIAL AND METHODS: Fifteen participants without facial defects were scanned with a structured light scanner. The facial meshes were manipulated to generate artificial orbital, nasal, or combined defects. Three methods of facial prosthesis design were compared for the 15 participants and repeated to produce 5 of each design for 2 participants. For the 3DMM approach, the Leeds face model informed the designs in a statistically meaningful way. For the traditional CAD methods, designs were created by using mirroring techniques or from a nose model database. For the conventional manual sculpting techniques, wax patterns were manually created on 3D printed full face baseplates. For analysis, the unedited facial feature was the standard. The unsigned distance was calculated from each of the several thousand vertices on the unedited facial feature to the closest point on the external surface of the prosthesis prototype. The mean absolute error was calculated, and a Friedman test was performed (α=.05). RESULTS: The median mean absolute error was 1.13 mm for the 3DMM group, 1.54 mm for the traditional CAD group, and 1.49 mm for the manual sculpting group, with no statistically significant differences among groups (P=.549). Boxplots showed substantial differences in the distribution of mean absolute error among groups, with the 3DMM group showing the greatest consistency. The 3DMM approach produced repeat designs with the lowest coefficient of variation. CONCLUSIONS: The 3DMM approach shows potential as a semi-automated method of CAD. Further clinical research is planned to explore the 3DMM approach in a feasibility trial.

B-cell capacity for expansion and differentiation into plasma cells are altered in osteoarthritis.

OBJECTIVE: Autoantibody (autoAbs) production in osteoarthritis (OA), coupled with evidence of disturbed B-cell homoeostasis, suggest a potential role for B-cells in OA. B-cells can differentiate with T-cell help (T-dep) or using alternative Toll like recptor (TLR) co-stimulation (TLR-dep). We analysed the capacity for differentiation of B-cells in OA versus age-matched healthy controls (HCs) and compared the capacity of OA synovitis-derived stromal cells to provide support for plasma cell (PC) maturation. METHODS: B-cells were isolated from OA and HC. Standardised in vitro models of B-cell differentiation were used comparing T-dep (CD40 (cluster of differentiation-40/BCR (B-cell receptor)-ligation) versus TLR-dep (TLR7/BCR-activation). Differentiation marker expression was analysed by flow-cytometry; antibody secretion (immunnoglobulins IgM/IgA/IgG) by ELISA (enzyme-linked immunosorbent assay), gene expression by qPCR (quantitative polymerase chain reaction). RESULTS: Compared to HC, circulating OA B-cells showed an overall more mature phenotype. The gene expression profile of synovial OA B-cells resembled that of PCs. Circulating B-cells differentiated under both TLR-dep and T-dep, however OA B-cells executed differentiation faster in terms of change in surface marker and secreted more antibody at Day 6, while resulting in similar PC numbers at Day 13, with an altered phenotype at Day 13 in OA. The main difference was reduced early B-cells expansion in OA (notably in TLR-dep) and reduced cell death. Stromal cells support from OA-synovitis allowed better PC survival compared to bone marrow, with an additional population of cells and higher Ig-secretion. CONCLUSION: Our findings suggest that OA B-cells present an altered capacity for proliferation and differentiation while remaining able to produce antibodies, notably in synovium. These findings may partly contribute to autoAbs development as recently observed in OA synovial fluids.

Lymphocyte subset phenotyping for the prediction of progression to inflammatory arthritis in anti-citrullinated-peptide antibody-positive at-risk individuals.

OBJECTIVES: Inflammatory arthritis (IA) is considered the last stage of a disease continuum, where features of systemic autoimmunity can appear years before clinical synovitis. Time to progression to IA varies considerably between at-risk individuals, therefore the identification of biomarkers predictive of progression is of major importance. We previously reported on the value of three CD4+T-cell subsets as biomarkers of progression. Here, we aim to establish the value of 18 lymphocyte subsets (LS) for predicting progression to IA. METHODS: Participants were recruited based on a new musculoskeletal complaint and being positive for anti-citrullinated-peptide Antibody. Progression (over 10 years) was defined as the development of clinical synovitis. LS analysis was performed for lymphocyte lineages, naïve/memory subsets, inflammation-related cells (IRC), and regulatory cells (Treg/B-reg). Modelling used Logistic/Cox regressions. RESULTS: Of 210 patients included, 93 (44%) progressed to IA, 41/93 (44%) within 12 months (rapid progressors). 5/18 LS were associated with progression (Treg/CD4-naïve/IRC (adjusted p < 0.0001), CD8 (p = 0.021), B-reg (p = 0.015)) and 3 trends (NK-cells/memory-B-cells/plasmablasts).Unsupervised hierarchical clustering using these 8 subsets segregated 3 clusters of patients, one cluster being enriched (63/109(58%)) and one poor (10/45(22%)) in progressors.Combining all clinical and LS variables, forward logistic regression predicted progression with accuracy=85.7% and AUC=0.911, selecting smoking/Rheumatoid-Factor/HLA-Shared-Epitope/Tender-Joint-Count-78 and Treg/CD4-naive/CD8/NK-cells/B-reg/plasmablasts.To predict rapid progression, a Cox regression was performed resulting in a model combining smoking/rheumatoid factor and IRC/CD4-naïve/Treg/NK-cells/CD8+T-cells (AUC=0.794). CONCLUSION: Overall, progression was predicted by specific LS, suggesting potential triggers for events leading to the development of IA, while rapid progression was associated with a different set of subsets.

Association of circulating lymphocyte subsets with response to IL17i and TNFi in axial spondyloarthritis.

OBJECTIVES: Biologic disease-modifying anti-rheumatic drugs (b-DMARDs) have qualitatively improved the management of axial spondyloarthritis (axSpA), but up to 30-40% of patients do not respond. Although lymphocytes are clearly implicated in the pathology of SpA, circulating lymphocyte subsets (LS) dynamics has been poorly studied. The objective of this pilot study was to comprehensively analyse circulating LS abnormalities in axSpA, and to determine their potential association with response to b-DMARDs. METHODS: Sixty-nine patients with axSpA and 141 control subjects (HC) were included. The clinical features were measured at baseline, and additionally at 6 months in a subgroup of patients who received TNFi (n=36) or IL17i (n=26). Clinical response was defined as a 50% reduction of BASDAI or decrease in ASDAS of 1.1 point. CD4/CD8 T-cells, B-cells and NK-cells and their subsets were analysed by flow cytometry at inclusion. RESULTS: At baseline, alterations in LS were observed in axSpA with reduced/increased frequencies of 10/27 subsets (p<0.003 after correction) and trends for another 5. There was no association of response to bDMARDs with clinical data. Response to IL17i (61% cases) was associated with a higher frequency of NK-cells (p=0.003), trends for change in naïve/memory-CD8+T-cells (p<0.055) and increased expression of KIR3DL2 on Th17-cells (p=0.052). No LS was associated with response to TNFi (69% cases) although trends were observed (CD4+T-cells subsets, higher IL-6R on CD4+/CD8+T-cells). CONCLUSIONS: This pilot work demonstrated a dysregulation of LS in axSpA. The association observed between several LS and clinical response to IL17i (NK/CD8 subsets/Th17-KIR3DL2) was very different to that observed for TNFi (CD4/IL-6R).

Professional practice and awareness of child abuse among radiologists and radiologic technologists: results from Saudi Arabia.

BACKGROUND: The knowledge, awareness and professionalism of health care providers in the field of child protection are crucial in identifying and reporting suspected child abuse. Radiologic technologists and radiologists play a vital role in the diagnosis of suspected physical child abuse. OBJECTIVE: To assess current practice, knowledge and awareness of child abuse among radiologic technologists and radiologists in Saudi Arabia. MATERIALS AND METHODS: We distributed an internet-based questionnaire to radiologic technologists and radiologists working in Saudi Arabia via national radiology societies and social media channels over a 6-week period (27 October to 8 December 2021). Survey questions covered knowledge regarding child abuse, professional practice in radiology departments in Saudi Arabia in cases of suspected physical abuse (SPA), and knowledge of the national legislation and reporting and acting procedures in child abuse. RESULTS: A total of 315 respondents (224 radiologic technologists and 91 radiologists) participated in this study. The median score for knowledge of abuse was higher amongst radiologists (4.8) than radiologic technologists (4.0); P < 0.001. In total, 210 (93.8%) radiologic technologists and 61 (67.0%) radiologists reported that there was no protocol (i.e. skeletal survey) at their hospital for imaging children with SPA. Most radiologic technologists had no training in paediatric radiology (165/224, 73.7%) and most radiologists had received no training in evaluating imaging performed for SPA (73/91, 80.2%). More than half of respondents - 131 (58.5%) radiologic technologists and 44 (48.4%) radiologists - were not familiar with the reporting and acting procedures at their hospitals in cases of child abuse. CONCLUSION: Although radiologic technologists and radiologists in Saudi Arabia have good knowledge and awareness of child abuse in general, they lack specific knowledge of the reporting and acting procedures at their hospitals in cases of suspected child abuse. National imaging guidelines and training courses are needed to develop appropriate skills in the recognition, imaging and reporting of SPA in infants and young children in Saudi Arabia.

A systematic review of methods to estimate colorectal cancer incidence using population-based cancer registries.

BACKGROUND: Epidemiological studies of incidence play an essential role in quantifying disease burden, resource planning, and informing public health policies. A variety of measures for estimating cancer incidence have been used. Appropriate reporting of incidence calculations is essential to enable clear interpretation. This review uses colorectal cancer (CRC) as an exemplar to summarize and describe variation in commonly employed incidence measures and evaluate the quality of reporting incidence methods. METHODS: We searched four databases for CRC incidence studies published between January 2010 and May 2020. Two independent reviewers screened all titles and abstracts. Eligible studies were population-based cancer registry studies evaluating CRC incidence. We extracted data on study characteristics and author-defined criteria for assessing the quality of reporting incidence. We used descriptive statistics to summarize the information. RESULTS: This review retrieved 165 relevant articles. The age-standardized incidence rate (ASR) (80%) was the most commonly reported incidence measure, and the 2000 U.S. standard population the most commonly used reference population (39%). Slightly more than half (54%) of the studies reported CRC incidence stratified by anatomical site. The quality of reporting incidence methods was suboptimal. Of all included studies: 45 (27%) failed to report the classification system used to define CRC; 63 (38%) did not report CRC codes; and only 20 (12%) documented excluding certain CRC cases from the numerator. Concerning the denominator estimation: 61% of studies failed to state the source of population data; 24 (15%) indicated census years; 10 (6%) reported the method used to estimate yearly population counts; and only 5 (3%) explicitly explained the population size estimation procedure to calculate the overall average incidence rate. Thirty-three (20%) studies reported the confidence interval for incidence, and only 7 (4%) documented methods for dealing with missing data. CONCLUSION: This review identified variations in incidence calculation and inadequate reporting of methods. We outlined recommendations to optimize incidence estimation and reporting practices. There is a need to establish clear guidelines for incidence reporting to facilitate assessment of the validity and interpretation of reported incidence.

The Effectiveness of Supervised Machine Learning in Screening and Diagnosing Voice Disorders: Systematic Review and Meta-analysis.

BACKGROUND: When investigating voice disorders a series of processes are used when including voice screening and diagnosis. Both methods have limited standardized tests, which are affected by the clinician's experience and subjective judgment. Machine learning (ML) algorithms have been used as an objective tool in screening or diagnosing voice disorders. However, the effectiveness of ML algorithms in assessing and diagnosing voice disorders has not received sufficient scholarly attention. OBJECTIVE: This systematic review aimed to assess the effectiveness of ML algorithms in screening and diagnosing voice disorders. METHODS: An electronic search was conducted in 5 databases. Studies that examined the performance (accuracy, sensitivity, and specificity) of any ML algorithm in detecting pathological voice samples were included. Two reviewers independently selected the studies, extracted data from the included studies, and assessed the risk of bias. The methodological quality of each study was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool via RevMan 5 software (Cochrane Library). The characteristics of studies, population, and index tests were extracted, and meta-analyses were conducted to pool the accuracy, sensitivity, and specificity of ML techniques. The issue of heterogeneity was addressed by discussing possible sources and excluding studies when necessary. RESULTS: Of the 1409 records retrieved, 13 studies and 4079 participants were included in this review. A total of 13 ML techniques were used in the included studies, with the most common technique being least squares support vector machine. The pooled accuracy, sensitivity, and specificity of ML techniques in screening voice disorders were 93%, 96%, and 93%, respectively. Least squares support vector machine had the highest accuracy (99%), while the K-nearest neighbor algorithm had the highest sensitivity (98%) and specificity (98%). Quadric discriminant analysis achieved the lowest accuracy (91%), sensitivity (89%), and specificity (89%). CONCLUSIONS: ML showed promising findings in the screening of voice disorders. However, the findings were not conclusive in diagnosing voice disorders owing to the limited number of studies that used ML for diagnostic purposes; thus, more investigations are needed. While it might not be possible to use ML alone as a substitute for current diagnostic tools, it may be used as a decision support tool for clinicians to assess their patients, which could improve the management process for assessment. TRIAL REGISTRATION: PROSPERO CRD42020214438; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=214438.

The diagnostic performance of chest computed tomography in the detection of rib fractures in children investigated for suspected physical abuse: a systematic review and meta-analysis.

OBJECTIVES: To assess the diagnostic performance of chest CT in the detection of rib fractures in children investigated for suspected physical abuse (SPA). METHODS: Medline, Web of Science and Cochrane databases were searched from January 1980 to April 2020. The QUADAS-2 tool was used to assess the quality of the eligible English-only studies following which a formal narrative synthesis was constructed. Studies reporting true-positive, false-positive, true-negative, and false-negative results were included in the meta-analysis. Overall sensitivity and specificity of chest CT for rib fracture detection were calculated, irrespective of fracture location, and were pooled using a univariate random-effects meta-analysis. The diagnostic accuracy of specific locations along the rib arc (anterior, lateral or posterior) was assessed separately. RESULTS: Of 242 identified studies, 4 met the inclusion criteria. Of these, 2 were included in the meta-analysis. Chest CT identified 142 rib fractures compared to 79 detected by initial skeletal survey chest radiographs in live children with SPA. Post-mortem CT (PMCT) has low sensitivity (34%) but high specificity (99%) in the detection of rib fractures when compared to the autopsy reference standard. PMCT has low sensitivity (45%, 21% and 42%) but high specificity (99%, 97% and 99%) at anterior, lateral and posterior rib locations, respectively. CONCLUSIONS: Chest CT detects more rib fractures than initial skeletal survey chest radiographs in live children with SPA. PMCT has low sensitivity but high specificity for detecting rib fractures in children investigated for SPA. KEY POINTS: • PMCT has low sensitivity (34%) but high specificity (99%) in the detection of rib fractures; extrapolation to CT in live children is difficult. • No studies have compared chest CT with the current accepted practice of initial and follow-up skeletal survey chest radiographs in the detection of rib fractures in live children investigated for SPA.

What is the relationship between indoor air quality parameters and airborne microorganisms in hospital environments? A systematic review and meta-analysis.

Airborne microorganisms in hospitals have been associated with several hospital-acquired infections (HAIs), and various measures of indoor air quality (IAQ) parameters such as temperature, relative humidity, carbon dioxide (CO2 ), particle mass concentration, and particle size have been linked to pathogen survival or mitigation of pathogen spread. To investigate whether there are quantitative relationships between the concentration of airborne microorganisms and the IAQ in the hospital environment. Web of Science, Scopus and PubMed databases were searched for studies reporting airborne microbial levels and any IAQ parameter(s) in hospital environments, from database inception to October 2020. Pooled effect estimates were determined via random-effects models. Seventeen of 654 studies were eligible for the meta-analysis. The concentration of airborne microbial measured as aerobic colony count (ACC) was significantly correlated with temperature (r = 0.25 [95% CI = 0.06-0.42], p = 0.01), CO2 concentration (r = 0.53 [95% CI = 0.40-0.64], p Ë‚ 0.001), particle mass concentration (≤5 µg/m3 ; r = 0.40 [95% CI = 0.04-0.66], p = 0.03), and particle size (≤5 and ˃5 µm), (r = 0.51 [95% CI = 0.12-0.77], p = 0.01 and r = 0.55 [95% CI = 0.20-0.78], p = 0.003), respectively, while not being significantly correlated with relative humidity or particulate matter of size >5 µm. Conversely, airborne total fungi (TF) were not significantly correlated with temperature, relative humidity, or CO2 level. However, there was a significant weak correlation between ACC and TF (r = 0.31 [95% CI = 0.07-0.52], p = 0.013). Although significant correlations exist between ACC and IAQ parameters, the relationship is not definitive; the IAQ parameters may affect the microorganisms but are not responsible for the presence of airborne microorganisms. Environmental parameters could be related to the generating source, survival, dispersion, and deposition rate of microorganisms. Future studies should record IAQ parameters and factors such as healthcare worker presence and the activities carried out such as cleaning, sanitizing, and disinfection protocols. Foot traffic would influence both the generation of microorganisms and their deposition rate onto surfaces in the hospital environment. These data would inform models to improve the understanding of the likely concentration of airborne microorganisms and provide an alternative approach for real-time monitoring of the healthcare environment.

Stratifying tumour subtypes based on copy number alteration profiles using next-generation sequence data.

MOTIVATION: The role of personalized medicine and target treatment in the clinical management of cancer patients has become increasingly important in recent years. This has made the task of precise histological substratification of cancers crucial. Increasingly, genomic data are being seen as a valuable classifier. Specifically, copy number alteration (CNA) profiles generated by next-generation sequencing (NGS) can become a determinant for tumours subtyping. The principle purpose of this study is to devise a model with good prediction capability for the tumours histological subtypes as a function of both the patients covariates and their genome-wide CNA profiles from NGS data. RESULTS: We investigate a logistic regression for modelling tumour histological subtypes as a function of the patients' covariates and their CNA profiles, in a mixed model framework. The covariates, such as age and gender, are considered as fixed predictors and the genome-wide CNA profiles are considered as random predictors. We illustrate the application of this model in lung and oral cancer datasets, and the results indicate that the tumour histological subtypes can be modelled with a good fit. Our cross-validation indicates that the logistic regression exhibits the best prediction relative to other classification methods we considered in this study. The model also exhibits the best agreement in the prediction between smooth-segmented and circular binary-segmented CNA profiles. AVAILABILITY AND IMPLEMENTATION: An R package to run a logistic regression is available in http://www1.maths.leeds.ac.uk/~arief/R/CNALR/. CONTACT: a.gusnanto@leeds.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Translation, cultural adaptation and validation of Patient Satisfaction with Pharmacist Services Questionnaire (PSPSQ) 2.0 into the Arabic language among people with diabetes.

BACKGROUND: Understanding patient satisfaction is key to advancing pharmacy services and improving health outcomes. There is a lack of a translated and psychometrically validated tool in the Arabic language to measure patient satisfaction with pharmacy services. OBJECTIVE: To translate the English version of the PSPSQ 2.0 into Arabic language, culturally adapt, and verify its reliability and validity. SETTING: A community pharmacy in Riyadh, Saudi Arabia. METHOD: A cross-sectional study was conducted between April 2021 and June 2022 among patients with diabetes attending a community pharmacy. The International Society for Pharmacoeconomics and Outcomes Research good practice guidelines for linguistic translation and cultural adaptation were used to translate and culturally adapt the English version of PSPSQ 2.0 into Arabic. The Arabic version of PSPSQ 2.0 was subjected to factor analysis using principal component analysis with varimax rotation to evaluate its validity and Cronbach's alpha was used to assess the reliability of PSPSQ 2.0. RESULTS: A total of 129 (68.2% male, and mean age 50 (SD: 11.9) years) patients with diabetes participated in the study. The analysis was undertaken for the items in each of the three domains of PSPSQ 2.0: quality of care, interprofessional relationship and overall care. Exploratory factor analysis revealed validity of 92.7%, 80.5% and 96.2%, respectively. The Arabic version of PSPSQ 2.0 had high internal consistency with Cronbach's alpha scores 0.99, 0.95 and 0.98 for the three measured domains, respectively. The sample adequacy was 0.924. CONCLUSION: The PSPSQ 2.0 was successfully translated and culturally adapted into the Arabic language and had acceptable validity and reliability to measure patient satisfaction with services provided by pharmacists in community pharmacies.

Predicting incident heart failure from population-based nationwide electronic health records: protocol for a model development and validation study.

INTRODUCTION: Heart failure (HF) is increasingly common and associated with excess morbidity, mortality, and healthcare costs. Treatment of HF can alter the disease trajectory and reduce clinical events in HF. However, many cases of HF remain undetected until presentation with more advanced symptoms, often requiring hospitalisation. Predicting incident HF is challenging and statistical models are limited by performance and scalability in routine clinical practice. An HF prediction model implementable in nationwide electronic health records (EHRs) could enable targeted diagnostics to enable earlier identification of HF. METHODS AND ANALYSIS: We will investigate a range of development techniques (including logistic regression and supervised machine learning methods) on routinely collected primary care EHRs to predict risk of new-onset HF over 1, 5 and 10 years prediction horizons. The Clinical Practice Research Datalink (CPRD)-GOLD dataset will be used for derivation (training and testing) and the CPRD-AURUM dataset for external validation. Both comprise large cohorts of patients, representative of the population of England in terms of age, sex and ethnicity. Primary care records are linked at patient level to secondary care and mortality data. The performance of the prediction model will be assessed by discrimination, calibration and clinical utility. We will only use variables routinely accessible in primary care. ETHICS AND DISSEMINATION: Permissions for CPRD-GOLD and CPRD-AURUM datasets were obtained from CPRD (ref no: 21_000324). The CPRD ethical approval committee approved the study. The results will be submitted as a research paper for publication to a peer-reviewed journal and presented at peer-reviewed conferences. TRIAL REGISTRATION DETAILS: The study was registered on Clinical Trials.gov (NCT05756127). A systematic review for the project was registered on PROSPERO (registration number: CRD42022380892).

Risk factors for periprosthetic femoral fracture risk around a cemented polished taper-slip stem using an osteoporotic composite bone model.

This biomechanical study aimed to determine if variations in stem material, stem geometry, stem offset and cement viscosity affect mechanical resistance to postoperative periprosthetic fracture (PFF) after hip arthroplasty with a commonly used cemented polished taper-slip (PTS) stem (CPT, Zimmer Biomet) in a novel osteoporotic composite femoral bone model. Thirty-six osteoporotic composite femoral models were tested using a standardised in-vitro loading technique to simulate a typical PFF. Outcome measures were torque to failure (N), fracture energy (N/m2) and rotation to failure (°). Comparisons were made by stem material (cobalt chrome vs stainless steel), stem geometry (CPT stem vs Exeter stem), stem offset (standard offset vs extra extended offset) and cement viscosity (high viscosity vs low viscosity). Statistical comparisons were carried out with significance set at p < 0.05. All tested samples produced clinically representative fracture patterns with varying degrees of bone and cement comminution. There was no statistically significant difference in torque to failure, fracture energy or rotation to failure between any of the compared variables (all p > 0.05). This is the first biomechanical study on mechanical resistance to PFF using osteoporotic composite bone models. For the CPT stem, it confirms that stem material, stem offset, stem geometry and cement viscosity do not affect mechanical resistance to PFF in an osteoporotic bone model.

Linkage of routinely collected NHS data to evaluate liaison mental health services: challenges and lessons learned.

Background: Liaison mental health services provide mental health care to patients in acute hospital settings. Evaluation of liaison services is challenging due to their heterogeneous organisation and delivery, high case throughput and varied patient case mix. We aimed to link routinely collected National Health Service data from secondary care settings, chosen for their service characteristics, to data from primary care to evaluate hospital-based liaison mental health services in England. Methods: We planned to compare patients referred to hospital-based liaison services with comparable patients in the same hospital not referred to liaison services and comparable patients in hospitals without any liaison services. We designed and enacted a methodology to link data from: (1) Hospital Episode Statistics, a database controlled by the National Health Service Digital and (2) ResearchOne, a primary care database controlled by The Phoenix Partnership. Results: Obtaining approvals for the steps prespecified in the methodological protocol took 907 days. Enactment following approvals took 385 days. Data supplied from Hospital Episode Statistics contained 181,063 patients from 6 hospitals (mean = 30,177, standard deviation = 28,875.86) who matched the inclusion and exclusion criteria. Data supplied from ResearchOne contained 33,666 (18.6%) of these patients from the 6 hospitals (mean = 5611, standard deviation = 5206.59). Discussion: Time required for approvals and enactment was attributable to slowness of data handling processes within each data holder and to resolution of technical and organisational queries between them. Variation in number of patients for which data was supplied between databases and between hospitals was attributable to coding inconsistencies and to the limited intersection of patient populations between databases and variation in recording practices between hospitals. Conclusion: Although it is technically feasible to link primary and secondary care data, the current system is challenging, complicated, unnecessarily bureaucratic, time consuming and costly. This limits the number of studies that could be conducted with these rich data sources. Funding: This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme as award number 13/58/08.

Liaison mental health services are based in acute general hospitals and provide assessment and treatment for people who have both physical and mental health problems. Our aim was to use routinely collected National Health Service data to find out whether general hospital patients referred to liaison mental health services have improved outcomes, compared with patients not referred to liaison services, and comparable patients in hospitals where there are no liaison services. The main outcomes were less time spent in hospital and fewer re-admissions to hospital following discharge. We tried to link data from routine National Health Service sources for hospital and primary care, to compare patients referred to liaison mental health services with similar people in each hospital who had not been referred, and similar people in hospitals without any liaison services at all. We planned to find out how long these people stayed in hospital, whether they were re-admitted and how much was their healthcare cost was. We experienced significant difficulties in being able to link the National Health Service data from the different organisations we approached. The whole process was extremely complex, and a delay in one part of the process resulted delays in other parts. We eventually had to abandon the research without obtaining any meaningful data, although the lessons we learnt will be useful for other researchers, so they can avoid experiencing similar problems. Routinely collected National Health Service data from primary care and secondary care can be linked using the approaches we tried, but we were unable to complete the process within the time frame of the research programme, even with time extensions. Current processes need to be streamlined and standardised with designated clear response times for the different organisations.

Partial least squares and logistic regression random-effects estimates for gene selection in supervised classification of gene expression data.

Our main interest in supervised classification of gene expression data is to infer whether the expressions can discriminate biological characteristics of samples. With thousands of gene expressions to consider, a gene selection has been advocated to decrease classification by including only the discriminating genes. We propose to make the gene selection based on partial least squares and logistic regression random-effects (RE) estimates before the selected genes are evaluated in classification models. We compare the selection with that based on the two-sample t-statistics, a current practice, and modified t-statistics. The results indicate that gene selection based on logistic regression RE estimates is recommended in a general situation, while the selection based on the PLS estimates is recommended when the number of samples is low. Gene selection based on the modified t-statistics performs well when the genes exhibit moderate-to-high variability with moderate group separation. Respecting the characteristics of the data is a key aspect to consider in gene selection.

The impact of long-term conditions on the progression of frailty.

OBJECTIVE: To explore longitudinally the impact of multiple long-term conditions (LTCs) on frailty progression separately for males and females. METHODS: A functional frailty measure (FFM) was used to examine putative determinants of frailty progression among participants aged 65 to 90 in the English Longitudinal Study of Ageing (ELSA), across nine waves (18 years) of data collection. A multilevel growth model was fitted to measure the FFM progression over 18 years, grouped by LTC categories (zero, one, two and more). RESULTS: There were 2396 male participants at wave 1, of whom 742 (31.0%) had 1 LTC and 1147 (47.9%) had ≥2 LTCs. There were 2965 females at wave 1 of whom 881 (29.7%) had one LTC and 1584 (53.4%) had ≥2 LTCs. The FFM increased 4% each 10 years for the male participants with no LTCs, while it increased 6% per decade in females. The FFM increased with the number of LTCs, for males and females. The acceleration of FMM increases for males with one long-term health condition or more; however in females the acceleration of FMM increases when they have two LTCs or more. CONCLUSION: Frailty progression accelerates in males with one LTCs and females with two LTCs or more. Health providers should be aware of planning a suitable intervention once the elderly have two or more health conditions.

A systematic review demonstrating correlation of MRI compositional parameters with clinical outcomes following articular cartilage repair interventions in the knee.

Objective: Compositional-MRI parameters enable the assessment of cartilage ultrastructure. Correlation of these parameters with clinical outcomes is unclear. This systematic review investigated the correlation of various compositional- MRI parameters with clinical outcome measures following cartilage repair or regeneration interventions in the knee. Design: This study was registered with PROSPERO and reported in accordance with PRISMA. PubMed, Institute of Science Index, Scopus, Cochrane Central Register of Controlled Trials, and Embase databases were searched. All studies, regardless of type, that presented correlation of compositional- MRI parameters with clinical outcome measures were included. Two researchers independently performed data extraction and QUADAS-2 analysis. Compositional-MRI parameter change following intervention and correlation with clinical outcome measures were evaluated. Results: 19 studies were included. Risk of bias was generally low. 5 different compositional parameters were observed from the included studies. However, due to the significant variability in the reporting of compositional-MRI parameters across studies, meta-analyses were possible only for T2 values and T2 index values (T2 value of repair cartilage relative to normal cartilage). Correlation of T2 values of repair cartilage with clinical outcome score was r â€‹= â€‹0.33 [0.15, 0.52]. Correlation of T2 index with clinical outcome score was r â€‹= â€‹0.52 [0.32, 0.77]. Conclusions: Correlation between T2 values and clinical outcome scores following knee cartilage repair were found. The heterogeneity of the correlations extracted from the included studies limited the scope for the meta-analysis. Thus, standardised, high-quality studies are required for better assessment of correlation between compositional MRI parameters and clinical outcome measures after cartilage repair. Registration number: PROSPERO CRD42021287364.Study protocol available on PROSPERO website.

Progression of frailty as measured by a cumulative deficit index: A systematic review.

BACKGROUND: Frailty is a risk factor for adverse health outcomes. There is a paucity of literature on frailty progression defined by a cumulative deficit model among community dwelling older people. The objective of this review was to synthesise evidence on these changes in health and mortality among community-dwelling older people. METHODS: Six databases (Medline, Embase, CINAHL, Cochrane, PsycInfo, Web of Science) and a clinical trials registry were searched in July 2021. The inclusion criteria were studies using a frailty index and providing information on transition between frailty states or to death in community-dwelling older people aged ≥ 50. Exclusion criteria were studies examining specific health conditions, conference abstracts and non-English studies. To standardise the follow-up period and facilitate comparison, we converted the transition probabilities to annual transition rates. RESULTS: Two reviewers independently screened 5078 studies and 61 studies were included for analysis. Of these, only three used the same frailty state cut-points to facilitate cross-cohort comparison. This review found that frailty tends to increase with time, people who are frail at baseline have greater likelihood to progress in frailty and die, and the main factor that accelerates frailty progression is age. Other risk factors for progression are having chronic disease, smoking, obesity, low-income or/and low-education levels. A frailty index is an accurate predictor of adverse outcomes and death. DISCUSSION: This systematic review demonstrated that worsening in frailty was a common frailty transition, and older people who are frail at baseline are more likely to die. A frailty index has significant power to predict adverse health outcomes. It is a useful tool for within-cohort comparison but there are challenges comparing different cohorts due to dependence of frailty progression on age and differences in how frailty index is defined and measured.

Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression.

Given the limitations of traditional approaches, wearable artificial intelligence (AI) is one of the technologies that have been exploited to detect or predict depression. The current review aimed at examining the performance of wearable AI in detecting and predicting depression. The search sources in this systematic review were 8 electronic databases. Study selection, data extraction, and risk of bias assessment were carried out by two reviewers independently. The extracted results were synthesized narratively and statistically. Of the 1314 citations retrieved from the databases, 54 studies were included in this review. The pooled mean of the highest accuracy, sensitivity, specificity, and root mean square error (RMSE) was 0.89, 0.87, 0.93, and 4.55, respectively. The pooled mean of lowest accuracy, sensitivity, specificity, and RMSE was 0.70, 0.61, 0.73, and 3.76, respectively. Subgroup analyses revealed that there is a statistically significant difference in the highest accuracy, lowest accuracy, highest sensitivity, highest specificity, and lowest specificity between algorithms, and there is a statistically significant difference in the lowest sensitivity and lowest specificity between wearable devices. Wearable AI is a promising tool for depression detection and prediction although it is in its infancy and not ready for use in clinical practice. Until further research improve its performance, wearable AI should be used in conjunction with other methods for diagnosing and predicting depression. Further studies are needed to examine the performance of wearable AI based on a combination of wearable device data and neuroimaging data and to distinguish patients with depression from those with other diseases.