Outcome measuresWe Research Articles

Endoscopic Fat Graft Myringoplasty Augmented With Hyaluronic Acid for Managing Large‐Sized Eardrum Perforations; A Prospective Comparative Randomised Study

ABSTRACTObjectivesWe aimed to assess the outcomes of fat graft myringoplasty augmented with hyaluronic acid in closing large‐sized eardrum perforations compared to the traditional underlay cartilage‐perichondrium composite myringoplasty (CPCM).Study DesignIt was a prospective randomised comparative study.SettingsIt was held in tertiary referral institutions between May 2020 and April 2022.ParticipantsWe included 100 patients with a large‐sized eardrum perforation (50%–75% of the eardrum surface area). Using the endoscopic transcanal approach, 50 patients were managed by fat graft myringoplasty augmented with hyaluronic acid, while CPCM managed the other 50 patients.Main Outcome MeasuresWe evaluated the closure rates 1, 6 months and 1 year after surgery. Also, we assessed the audiological performance of the patients with a successful closure before and 1 year after the operation.ResultsFat graft myringoplasty operation was statistically shorter than the CPCM. The closure rate 1 year after surgery was 92% in the first group and 86% in the second group, without a statistically significant difference between both groups. Successful air‐bone gap closure to less than 10 dB occurred in 93.5% of group A and 81.4% of group B without a statistically significant difference. The mean postoperative air‐bone gap was 5.3 ± 3.95 dB in the first group and 7.95 ± 5.17 dB in the second group, with a statistically significant difference.ConclusionsFat graft myringoplasty augmented with hyaluronic acid was a reliable, safe, simple, and effective manoeuvre to close large‐sized eardrum perforations compared to the conventional CPCM.

Exploratory analysis of factors influencing hospital preferences among the Lebanese population: a cross-sectional study

ObjectivesThe primary aim of this research is to uncover the underlying factors that shape hospital selection criteria among individuals in Lebanon.DesignCross-sectional study.SettingA survey was circulated across social media platforms and...

Physical Activity Variability in Patellofemoral Pain: Relationships with Clinical and Psychological Outcomes

ObjectivesTo (1) compare physical activity variability between individuals with patellofemoral pain (PFP) and pain-free individuals and (2) evaluate the relationships of physical activity variability with pain severity, symptom duration, disability, and pain catastrophizing in PFP cohorts. DesignCross-sectional case-control study. SettingUniversity research laboratory. ParticipantsIndividuals with PFP (N=34) and pain-free individuals (N=34) aged 18 to 40 years. InterventionsNot applicable. Main Outcome MeasuresWe assessed physical activity (daily steps and moderate-to-vigorous physical activity [MVPA]) over a period of 14 consecutive days using a triaxial accelerometer. Variability (coefficient of variation) for daily steps and MVPA was calculated, where higher CV values indicate greater physical activity variability. We evaluated pain severity (numeric pain rating scale), symptom duration (months), disability (Knee Injury and Osteoarthritis Outcome Score-Patellofemoral subscale), and pain catastrophizing (Pain Catastrophizing Scale). Independent t-tests or Mann-Whitney U tests evaluated group differences in physical activity variability. Spearman ρ correlation coefficients were calculated to determine the relationships of physical activity variability with pain severity, symptom duration, disability, and pain catastrophizing in PFP cohorts. Correlation coefficients were interpreted as weak (<0.40), moderate (0.40–0.70), and strong (>0.70). ResultsAge, height, and mass did not differ between individuals with PFP and pain-free individuals (P>.05). Individuals with PFP displayed greater variability in daily steps (P<.001) and MVPA (P=.001) compared to pain-free individuals. In individuals with PFP, greater variability in daily steps was moderately related to higher pain severity (ρ=0.41, P=.016), while greater variability in MVPA was weakly related to higher pain severity (ρ=0.36, P=.037). ConclusionsIndividuals with PFP demonstrated greater physical activity variability compared to pain-free individuals, which positively related to more severe pain. Future PFP research may explore the underlying factors contributing to increased physical activity variability and their potential implications for pain management.

Major financial problems, low mental well-being and reduced HIV/STI testing among sex workers in the Netherlands during the COVID-19 pandemic: a repeated cross-sectional survey

ObjectivesTo determine associations between the banning of sex work during the COVID-19 pandemic, and work, financial problems, mental well-being and HIV/sexually transmittable infection (STI) testing among sex workers in the...

Privacy Preserving Technology using Federated Learning and Blockchain in protecting against Adversarial Attacks for Retinal Imaging

PurposeCollaboration provides valuable data for robust artificial intelligence (AI) model development. Federated learning (FL) is a privacy enhancing technology that allows collaboration while respecting privacy via the development of models without raw data transfer. However state-of-the-art FL models still face challenges in non-independent and identically distributed (non-i.i.d.) health care settings and remain susceptible to privacy breaches. We propose a novel FL framework coupled with blockchain technology to address these challenges. DesignRetrospective multicohort study Subjects and Methods27,145 images from Singapore, China and Taiwan were used to design a novel FL aggregation method for the detection of myopic macular degeneration (MMD) from fundus photographs and macular disease from optical coherence tomography (OCT) scans in feature distribution skew and label distribution imbalance scenarios. We further performed adversarial attacks (label flipping and clean label). As proof of concept, blockchain was incorporated into FL to demonstrate secure transfer of model updates across collaborating sites. Main Outcome MeasuresWe evaluated our FL model performance in MMD and OCT classification and compared our model against state-of the-art FL and centralized models. ResultsOur FL model showed robust performance with areas under the receiving operating characteristic curves (AUC) of 0.868±0.009 for MMD detection and 0.970±0.012 for OCT macular disease classification. In label flipping attack, our FL model had an AUC of 0.861±0.019, similar to the centralized model (AUC 0.856± 0.015) and higher than other FL models (AUC 0.578–0.819) In clean label attack, our FL model had an AUC of 0.878±0.006 which was comparable to the centralized model (AUC 0.878±0.001) and superior to other state-of-the-art FL models with AUC of 0.529-0.838. Simulation showed that the additional time with blockchain in one global epoch was around 5 seconds. The addition of blockchain to the FL framework was feasible with a minimal impact on model development time. ConclusionsOur proposed FL algorithm overcomes the shortcoming of the traditional FL in non i.i.d. situations and remains robust to against adversarial attacks. The integration of blockchain adds further security during the transfer of model updates. Blockchain-enabled FL can be a trusted platform for collaborative health AI research.

Is cardiovascular risk profiling from UK Biobank retinal images using explicit deep learning estimates of traditional risk factors equivalent to actual risk measurements? A prospective cohort study design

ObjectiveDespite extensive exploration of potential biomarkers of cardiovascular diseases (CVDs) derived from retinal images, it remains unclear how retinal images contribute to CVD risk profiling and how the results can...

Combining electronic health records data from a clinical research network with registry data to examine long-term outcomes of interventions and devices: an observational cohort study

ObjectivesTo assess the feasibility of assessing long-term outcomes of peripheral vascular intervention (PVI) by linking data from a clinical registry to electronic health records (EHR) data from a clinical research...

Identifying markers of health-seeking behaviour and healthcare access in UK electronic health records

ObjectiveTo assess the feasibility of identifying markers of health-seeking behaviour and healthcare access in UK electronic health records (EHR), for identifying populations at risk of poor health outcomes and adjusting...

Multinational trends in sepsis mortality between 1985 and 2019: a temporal analysis of the WHO Mortality Database

ObjectivesUnderstanding the burden of disease of sepsis is essential for monitoring the effectiveness of international strategies to improve sepsis care. Our objective was to describe the multinational trend of sepsis-related...

Quantitative sensory testing and chronic pain syndromes: a cross-sectional study from TwinsUK

ObjectiveThe chronic pain syndromes (CPS) include syndromes such as chronic widespread pain (CWP), dry eye disease (DED) and irritable bowel syndrome (IBS). Highly prevalent and lacking pathognomonic biomarkers, the CPS...

Gender and ethnicity intersect to reduce participation at a large European hybrid HIV conference

ObjectiveTo evaluate how gender and ethnicity of panel members intersect to effect audience participation at a large European hybrid conference.DesignAn observational cross-sectional study design was used to collect data at...

Schizophrenia, antipsychotic treatment adherence and driver responsibility for motor vehicle crash: a population-based retrospective study in British Columbia, Canada

ObjectiveTo examine the relationship between schizophrenia, antipsychotic medication adherence and driver responsibility for motor vehicle crash.DesignRetrospective observational cohort study using 20 years of population-based administrative health and driving data.SettingBritish Columbia,...

Cost-effectiveness of hospital-based treatment compared with community treatment centres: an analysis of Korea National Health Insurance claims data

ObjectivesWe compared the cost-effectiveness of hospital-based treatment and that of community treatment centres (CTCs).DesignWe performed statistical analysis to compare the expenses incurred by COVID-19 patients who received hospital care with...

Childhood neurodivergent traits, inflammation and chronic disabling fatigue in adolescence: a longitudinal case–control study

ObjectivesTo test whether inflammatory processes link the expression of childhood neurodivergent traits to chronic disabling fatigue in adolescence.DesignLongitudinal case–control study.SettingWe analysed data from The Avon Longitudinal Study of Parents and...

Associations of hypertension, diabetes and heart disease risk with body mass index in older Chinese adults: a population-based cohort study

ObjectiveObesity is a well-established risk factor for disease. Controversy exists regarding the relative risk of morbidity and mortality in individuals who are overweight or underweight compared with individuals with a...

Registry cost description of carpal tunnel release in Finland in 2011–2015

ObjectivesIn this study, we evaluated the amount of public funds spent on the operative treatment of carpal tunnel syndrome (CTS) in Finland in 2011–2015.DesignA registry-based cost burden study.SettingThe data were...

Vitamin D levels in couples undergoing in vitro fertilization treatment: lack of association with embryo quality or pregnancy rates

ObjectiveTo investigate the potential impact of vitamin D serum levels of couples going through in vitro fertilization in terms of embryo quality and pregnancy rates. DesignRetrospective cohort study SettingFertipraxis, private human reproduction center on Rio de Janeiro, Brazil. Subjects267 couples who underwent intracytoplasmic sperm injection between January 2017 and March 2019. ExposureThe couples were categorized into four groups based on 25OH vitamin D levels measured at the beginning of the stimulation protocol: Group 1 with levels ≥ 30 ng/mL for both women and men; Group 2 with levels < 30 ng/mL for both; Group 3 with women < 30 ng/mL and men ≥ 30 ng/mL; and Group 4 with women ≥ 30 ng/mL and men < 30 ng/mL. Main Outcome MeasuresWe consider quantity and quality of embryos during the cleavage and blastocyst stages as primary outcomes. Correspondingly, clinical pregnancy rate was regarded as a secondary outcome. ResultsOur findings revealed no significant correlations between the studied VD groups and the evaluated outcomes. This includes quantity and quality of embryos during the cleavage and blastocyst stages, as well as clinical pregnancy rate. Primary analysis revealed a small but statistically significant difference in the duration of controlled ovarian stimulation between group 1 and group 2 (p=0.035; CI=0.07 – 3.04) and between group 1 and group 3 (p=0.040; CI=0.05 – 3.23). ConclusionThe present study found no correlation between the studied VD levels and quantity and quality of cleavage or blastocyst stage embryos, nor did it show any impact on clinical pregnancy rates. Further well designed, prospective studies are warranted to determine whether and how vitamin D affects reproductive outcomes.

Inequalities in lifespan and mortality risk in the US, 2015–2019: a cross-sectional analysis of subpopulations by social determinants of health

ObjectiveTo quantify inequalities in lifespan across multiple social determinants of health, how they act in tandem with one another, and to create a scoring system that can accurately identify subgroups...

Prediction Model for Delayed Behavior of Early Ambulation After Surgery for Varicose Veins of the Lower Extremity: A Prospective Case-Control Study

ObjectiveTo analyze influencing factors and establish a prediction model for delayed behavior of early ambulation after surgery for varicose veins of the lower extremity (VVLE). DesignA prospective case-control study. SettingPatients with VVLE were recruited from 2 local hospitals. ParticipantsIn total, 498 patients with VVLE were selected using convenience sampling and divided into a training set and a test set. InterventionsNot applicable. Main Outcome MeasuresWe collected information from the selected participants before surgery and followed up until the day after surgery, then divided them into a normal and delayed ambulation group. Propensity score matching was applied to all participants by type of surgery and anesthesia. All the characteristics in the 2 groups were compared using logistic regression, back propagation neural network (BPNN), and decision tree models. The accuracy, sensitivity, specificity, and area under the curve (AUC) values of the 3 models were compared to determine the optimal model. ResultsA total of 406 participants were included after propensity score matching. The AUC values for the training sets of logistic regression, BPNN, and decision tree models were 0.850, 0.932, and 0.757, respectively. The AUC values for the test sets were 0.928, 0.984, and 0.776, respectively. A BPNN was the optimal model. Social Support Rating Scale score, preoperative 30-second sit-stand test score, Clinical-Etiology-Anatomy-Pathophysiology (CEAP) grade, Medical Coping Modes Questionnaire score, and whether you know the need for early ambulation, in descending order of the result of a BPNN model. A probability value greater than 0.56 indicated delayed behavior of early ambulation. ConclusionsClinicians should pay more attention to those with lower Social Support Rating Scale scores, poor lower limb strength, a higher CEAP grade, and poor medical coping ability, and make patients aware of the necessity and importance of early ambulation, thereby assisting decision-making regarding postoperative rehabilitation. Further research is needed to improve the method, add more variables, and transform the model into a scale to screen and intervene in the delayed behavior of early ambulation of VVLE in advance.

Deep Learning–Based Clustering of OCT Images for Biomarker Discovery in Age-Related Macular Degeneration (PINNACLE Study Report 4)

PurposeWe introduce a deep-learning-based biomarker proposal system for the purpose of accelerating biomarker discovery in age-related macular degeneration (AMD). DesignRetrospective analysis of a large dataset of retinal optical coherence tomography (OCT) images. ParticipantsA total of 3,456 adults aged between 51 and 102 years of whom OCT images were collected under the PINNACLE project. MethodsOur system proposes candidates for novel AMD imaging biomarkers in OCT. It works by first training a neural network using self-supervised contrastive learning to discover, without any clinical annotations, features relating to both known and unknown AMD biomarkers present in 46,496 retinal OCT images. To interpret the learned biomarkers, we partition the images into 30 subsets, termed clusters, that contain similar features. We conduct two parallel 1.5-hour semi-structured interviews with two independent teams of retinal specialists to assign descriptions in clinical language to each cluster. Descriptions of clusters achieving consensus can potentially inform new biomarker candidates. Main Outcome MeasuresWe checked if each cluster showed clear features comprehensible to retinal specialists, if they related to AMD and how many described established biomarkers used in grading systems as opposed to recently proposed or potentially new biomarkers. We also compared their prognostic value for late stage wet and dry AMD against an established clinical grading system and a demographic baseline model. ResultsOverall, both teams independently identified clearly distinct characteristics in 27 of 30 clusters, of which 23 were related to AMD. Seven were recognised as known biomarkers used in established grading systems and 16 depicted biomarker combinations or subtypes that are either not yet used in grading systems, were only recently proposed, or were unknown. Clusters separated incomplete from complete retinal atrophy, intraretinal from subretinal fluid and thick from thin choroids, and in simulation outperformed clinically-used grading systems in prognostic value. ConclusionsUsing self-supervised deep learning we were able to automatically propose AMD biomarkers going beyond the set used in clinically established grading systems. Without any clinical annotations, contrastive learning discovered subtle differences between fine-grained biomarkers. Ultimately, we envision that equipping clinicians with discovery-oriented deep-learning tools can accelerate discovery of novel prognostic biomarkers.