Chinese Public Attitudes Toward Epilepsy (PATE) scale: translation and psychometric evaluation

None of the quantitative scale for public attitudes toward epilepsy was translated to Chinese language. This study aimed to translate and test the validity and reliability of a Chinese version of the Public Attitudes Toward Epilepsy (PATE) scale. Methods: The translation was performed according to standard principles and tested in 140 Chinese-speaking adults aged more than 18 years for psychometric validation. Results: The items in each domain had similar standard deviations (equal item variance), ranged from 0.85-0.95 in personal domain and 0.75-1.04 in general domain. The correlation between an item and its domain was 0.4 and above for all, and higher than the correlation with the other domain. Multitrait analysis showed the Chinese PATE had a similar variance, floor and ceiling effects, and relative relationship between the domains, as the original PATE. The Chinese PATE scale showed a similar correlation with almost all demographic variable except age. Item means were generally clustered in the factor analysis as hypothesized. The Cronbach’s α values was within acceptable range (0.773) in the personal domain and satisfactory range (0.693) in the general domain. Conclusion: The Chinese PATE scale is a validated and reliable translated version in measuring the public attitudes toward epilepsy

Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke.

Abstract Background Better phenotyping of routinely collected coded data would be useful for research and health improvement. For example, the precision of coded data for hemorrhagic stroke (intracerebral hemorrhage [ICH] and subarachnoid hemorrhage [SAH]) may be as poor as < 50%. This work aimed to investigate the feasibility and added value of automated methods applied to clinical radiology reports to improve stroke subtyping. Methods From a sub-population of 17,249 Scottish UK Biobank participants, we ascertained those with an incident stroke code in hospital, death record or primary care administrative data by September 2015, and ≥ 1 clinical brain scan report. We used a combination of natural language processing and clinical knowledge inference on brain scan reports to assign a stroke subtype (ischemic vs ICH vs SAH) for each participant and assessed performance by precision and recall at entity and patient levels. Results Of 225 participants with an incident stroke code, 207 had a relevant brain scan report and were included in this study. Entity level precision and recall ranged from 78 to 100%. Automated methods showed precision and recall at patient level that were very good for ICH (both 89%), good for SAH (both 82%), but, as expected, lower for ischemic stroke (73%, and 64%, respectively), suggesting coded data remains the preferred method for identifying the latter stroke subtype. Conclusions Our automated method applied to radiology reports provides a feasible, scalable and accurate solution to improve disease subtyping when used in conjunction with administrative coded health data. Future research should validate these findings in a different population setting

Benchmarking network-based gene prioritization methods for cerebral small vessel disease

Network-based gene prioritization algorithms are designed to prioritize disease-associated genes based on known ones using biological networks of protein interactions, gene disease associations and other relationships between biological entities. Various algorithms have been developed based on different mechanisms, but it is not obvious which algorithm is optimal for a specific disease. To address this issue, we benchmarked multiple algorithms for their application in cerebral small vessel disease (cSVD). We curated protein-gene interactions (PGI) and gene-disease associations (GDA) from databases and assembled PGI networks and disease-gene heterogenous networks. A screening of algorithms resulted in seven representative algorithms to be benchmarked. Performance of algorithms was assessed using both leave-one-out cross-validation (LOOCV) and external validation with MEGASTROKE genome-wide association study (GWAS). We found that random walk with restart on the heterogeneous network (RWRH) showed best LOOCV performance, with median LOOCV rediscovery rank of 185.5 (out of 19,463 genes). The GenePanda algorithm had most GWAS-confirmable genes in top 200 predictions, while RWRH had best ranks for small vessel stroke associated genes confirmed in GWAS. In conclusion, RWRH has overall better performance for application in cSVD despite its susceptibility to bias caused by degree centrality. Choice of algorithms should be determined before applying to specific disease. Current pure network-based gene prioritization algorithms are unlikely to find novel disease-associated genes that are not associated with known ones. The tools for implementing and benchmarking algorithms have been made available and can be generalized for other diseases

Incontinence pessaries: size, POPQ measures, and successful fitting

The aim of the study was to determine whether successful incontinence pessary fitting or pessary size can be predicted by specific POPQ measurements in women without advanced pelvic organ prolapse. In a multicenter study, women with stress urinary incontinence (SUI) and POPQ stage ≤2 were randomized to three treatment arms: (1) incontinence pessary, (2) behavioral therapy, or (3) both. This study evaluates incontinence pessary size, POPQ measures, and successful fitting in the 266 women assigned to treatment arms 1 and 3. Two hundred thirty-five women (92%) were successfully fitted with an incontinence ring (n = 122) or dish (n = 113). Hysterectomy, genital hiatus (GH), and GH/total vaginal length (TVL) ratios did not predict unsuccessful fitting (p &gt; 0.05). However, mean TVL was greater in women successfully fitted (9.6 vs. 8.8 cm, p &lt; 0.01). Final pessary diameter was not predicted by TVL, point D, or point C (p &gt; 0.05). The vast majority of women with SUI can be successfully fitted with an incontinence pessary, but specific POPQ measures were not helpful in determining incontinence pessary size

Knowledge Driven Phenotyping

Extracting patient phenotypes from routinely collected health data (such as Electronic Health Records) requires translating clinically-sound phenotype definitions into queries/computations executable on the underlying data sources by clinical researchers. This requires significant knowledge and skills to deal with heterogeneous and often imperfect data. Translations are time-consuming, error-prone and, most importantly, hard to share and reproduce across different settings. This paper proposes a knowledge driven framework that (1) decouples the specification of phenotype semantics from underlying data sources; (2) can automatically populate and conduct phenotype computations on heterogeneous data spaces. We report preliminary results of deploying this framework on five Scottish health datasets

Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke

Background Better phenotyping of routinely collected coded data would be useful for research and health improvement. For example, the precision of coded data for hemorrhagic stroke (intracerebral hemorrhage [ICH] and subarachnoid hemorrhage [SAH]) may be as poor as &lt; 50%. This work aimed to investigate the feasibility and added value of automated methods applied to clinical radiology reports to improve stroke subtyping. Methods From a sub-population of 17,249 Scottish UK Biobank participants, we ascertained those with an incident stroke code in hospital, death record or primary care administrative data by September 2015, and ≥ 1 clinical brain scan report. We used a combination of natural language processing and clinical knowledge inference on brain scan reports to assign a stroke subtype (ischemic vs ICH vs SAH) for each participant and assessed performance by precision and recall at entity and patient levels. Results Of 225 participants with an incident stroke code, 207 had a relevant brain scan report and were included in this study. Entity level precision and recall ranged from 78 to 100%. Automated methods showed precision and recall at patient level that were very good for ICH (both 89%), good for SAH (both 82%), but, as expected, lower for ischemic stroke (73%, and 64%, respectively), suggesting coded data remains the preferred method for identifying the latter stroke subtype. Conclusions Our automated method applied to radiology reports provides a feasible, scalable and accurate solution to improve disease subtyping when used in conjunction with administrative coded health data. Future research should validate these findings in a different population setting

Benchmarking network-based gene prioritization methods for cerebral small vessel disease

Network-based gene prioritization algorithms are designed to prioritize disease-associated genes based on known ones using biological networks of protein interactions, gene–disease associations (GDAs) and other relationships between biological entities. Various algorithms have been developed based on different mechanisms, but it is not obvious which algorithm is optimal for a specific disease. To address this issue, we benchmarked multiple algorithms for their application in cerebral small vessel disease (cSVD). We curated protein–gene interactions (PGIs) and GDAs from databases and assembled PGI networks and disease–gene heterogeneous networks. A screening of algorithms resulted in seven representative algorithms to be benchmarked. Performance of algorithms was assessed using both leave-one-out cross-validation (LOOCV) and external validation with MEGASTROKE genome-wide association study (GWAS). We found that random walk with restart on the heterogeneous network (RWRH) showed best LOOCV performance, with median LOOCV rediscovery rank of 185.5 (out of 19 463 genes). The GenePanda algorithm had most GWAS-confirmable genes in top 200 predictions, while RWRH had best ranks for small vessel stroke-associated genes confirmed in GWAS. In conclusion, RWRH has overall better performance for application in cSVD despite its susceptibility to bias caused by degree centrality. Choice of algorithms should be determined before applying to specific disease. Current pure network-based gene prioritization algorithms are unlikely to find novel disease-associated genes that are not associated with known ones. The tools for implementing and benchmarking algorithms have been made available and can be generalized for other diseases

Feasibility pilot trial for the Trajectories of Recovery after Intravenous propofol versus inhaled VolatilE anesthesia (THRIVE) pragmatic randomised controlled trial

INTRODUCTION: Millions of patients receive general anaesthesia for surgery annually. Crucial gaps in evidence exist regarding which technique, propofol total intravenous anaesthesia (TIVA) or inhaled volatile anaesthesia (INVA), yields superior patient experience, safety and outcomes. The aim of this pilot study is to assess the feasibility of conducting a large comparative effectiveness trial assessing patient experiences and outcomes after receiving propofol TIVA or INVA. METHODS AND ANALYSIS: This protocol was cocreated by a diverse team, including patient partners with personal experience of TIVA or INVA. The design is a 300-patient, two-centre, randomised, feasibility pilot trial. Patients 18 years of age or older, undergoing elective non-cardiac surgery requiring general anaesthesia with a tracheal tube or laryngeal mask airway will be eligible. Patients will be randomised 1:1 to propofol TIVA or INVA, stratified by centre and procedural complexity. The feasibility endpoints include: (1) proportion of patients approached who agree to participate; (2) proportion of patients who receive their assigned randomised treatment; (3) completeness of outcomes data collection and (4) feasibility of data management procedures. Proportions and 95% CIs will be calculated to assess whether prespecified thresholds are met for the feasibility parameters. If the lower bounds of the 95% CI are above the thresholds of 10% for the proportion of patients agreeing to participate among those approached and 80% for compliance with treatment allocation for each randomised treatment group, this will suggest that our planned pragmatic 12 500-patient comparative effectiveness trial can likely be conducted successfully. Other feasibility outcomes and adverse events will be described. ETHICS AND DISSEMINATION: This study is approved by the ethics board at Washington University (IRB# 202205053), serving as the single Institutional Review Board for both participating sites. Recruitment began in September 2022. Dissemination plans include presentations at scientific conferences, scientific publications, internet-based educational materials and mass media. TRIAL REGISTRATION NUMBER: NCT05346588

Efficient Reuse of Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: A Phenotype Embedding Approach.

Background: Many efforts have been put into the use of automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records to construct comprehensive patient profiles for delivering better health-care. Reusing NLP models in new settings, however, remains cumbersome - requiring validation and/or retraining on new data iteratively to achieve convergent results. Objective: The aim of this work is to minimize the effort involved in reusing NLP models on free-text medical records. Methods: We formally define and analyse the model adaptation problem in phenotype-mention identification tasks. We identify "duplicate waste" and "imbalance waste", which collectively impede efficient model reuse. We propose a phenotype embedding based approach to minimize these sources of waste without the need for labelled data from new settings. Results: We conduct experiments on data from a large mental health registry to reuse NLP models in four phenotype-mention identification tasks. The proposed approach can choose the best model for a new task, identifying up to 76% (duplicate waste), i.e. phenotype mentions without the need for validation and model retraining, and with very good performance (93-97% accuracy). It can also provide guidance for validating and retraining the selected model for novel language patterns in new tasks, saving around 80% (imbalance waste), i.e. the effort required in "blind" model-adaptation approaches. Conclusions: Adapting pre-trained NLP models for new tasks can be more efficient and effective if the language pattern landscapes of old settings and new settings can be made explicit and comparable. Our experiments show that the phenotype-mention embedding approach is an effective way to model language patterns for phenotype-mention identification tasks and that its use can guide efficient NLP model reuse