Updating and recalibrating causal probabilistic models on a new target population.

OBJECTIVE: Very often the performance of a Bayesian Network (BN) is affected when applied to a new target population. This is mainly because of differences in population characteristics. External validation of the model performance on different populations is a standard approach to test model's generalisability. However, a good predictive performance is not enough to show that the model represents the unique population characteristics and can be adopted in the new environment. METHODS: In this paper, we present a methodology for updating and recalibrating developed BN models - both their structure and parameters - to better account for the characteristics of the target population. Attention has been given on incorporating expert knowledge and recalibrating latent variables, which are usually omitted from data-driven models. RESULTS: The method is successfully applied to a clinical case study about the prediction of trauma-induced coagulopathy, where a BN has already been developed for civilian trauma patients and now it is recalibrated on combat casualties. CONCLUSION: The methodology proposed in this study is important for developing credible models that can demonstrate a good predictive performance when applied to a target population. Another advantage of the proposed methodology is that it is not limited to data-driven techniques and shows how expert knowledge can also be used when updating and recalibrating the model.

Inferring biological kinship in ancient datasets: comparing the response of ancient DNA-specific software packages to low coverage data.

BACKGROUND: The inference of biological relations between individuals is fundamental to understanding past human societies. Caregiving, resource sharing and sexual behaviours are often mediated by biological kinship and yet the identification and interpretation of kin relationships in prehistoric human groups is difficult. In recent years, the advent of archaeogenetic techniques have offered a fresh approach, and when combined with more traditional osteological and interpretive archaeological methods, allows for improved interpretation of the burial practices, cultural behaviours, and societal stratification in ancient societies. Although archaeogenetic techniques are developing at pace, questions remain as to their accuracy, particularly when applied to the low coverage datasets that results from the sequencing of DNA derived from highly degraded ancient material. RESULTS: The performance of six of the most commonly used kinship identifcation software methods was explored at a range of low and ultra low genome coverages. An asymmetrical response was observed across packages, with decreased genome coverage resulting in differences in both direction and degree of change of calculated kinship scores and thus pairwise relatedness estimates are dependant on both package used and genome coverage. Methods reliant upon genotype likelihoods methods (lcMLkin, NGSrelate and NGSremix) show a decreased level of prediction at coverage below 1x, although were consistent in the particular relationships identified at these coverages when compared to the pseudohaploid reliant methods tested (READ, the Kennett 2017 method and TKGWV2.0). The three pseudohaploid methods show predictive potential at coverages as low as 0.05x, although the accuracy of the relationships identified is questionable given the increase in the number of relationships identifIed at the low coverage (type I errors). CONCLUSION: Two pseudohaploid methods (READ and Kennett 2017) show relatively consistent inference of kin relationships at low coverage (0.5x), with READ only showing a significant performance drop off at ultralow coverages (< 0.2x). More generally, our results reveal asymmetrical kinship classifications in some software packages even at high coverages, highlighting the importance of applying multiple methods to authenticate kin relationships in ancient material, along with the continuing need to develop laboratory methods that maximise data output for downstream analyses.

mHealth apps for gestational diabetes mellitus that provide clinical decision support or artificial intelligence: A scoping review.

AIMS: Gestational diabetes (GDM) is the most common metabolic disorder of pregnancy, requiring complex management and empowerment of those affected. Mobile health (mHealth) applications (apps) are proposed for streamlining healthcare service delivery, extending care relationships into the community, and empowering those affected by prolonged medical disorders to be equal collaborators in their healthcare. This review investigates mHealth apps intended for use with GDM; specifically those powered by artificial intelligence (AI) or providing decision support. METHODS: A scoping review using the novel Survey Tool approach for collaborative literature Reviews (STaR) process was performed. RESULTS: From 18 papers, 11 discrete GDM-based mHealth apps were identified, but only 3 were reasonably mature with only one currently in use in a clinical setting. Two-thirds of the apps provided condition-relevant contextual user feedback that could aid in patient self care. However, although each app targeted one or more components of the GDM clinical pathway, no app addressed the entirety from diagnosis to postpartum. CONCLUSIONS: There are limited mHealth apps for GDM that incorporate AI or AI-based decision support. Many exist only to record patient information like blood glucose readings or diet, provide generic patient education or advice, or to reduce adverse events by providing medication or appointment alerts. Significant barriers remain that continue to limit the adoption of mHealth apps in clinical care settings. Further research and development are needed to deliver intelligent holistic mHealth apps using AI that can truly reduce healthcare resource use and improve outcomes by enabling patient self care in the community.

Reducing the question burden of patient reported outcome measures using Bayesian networks.

Patient Reported Outcome Measures (PROMs) are questionnaires completed by patients about aspects of their health status. They are a vital part of learning health systems as they are the primary source of information about important outcomes that are best assessed by patients such as pain, disability, anxiety and depression. The volume of questions can easily become burdensome. Previous techniques reduced this burden by dynamically selecting questions from question item banks which are specifically built for different latent constructs being measured. These techniques analyzed the information function between each question in the item bank and the measured construct based on item response theory then used this information function to dynamically select questions by computerized adaptive testing. Here we extend those ideas by using Bayesian Networks (BNs) to enable Computerized Adaptive Testing (CAT) for efficient and accurate question selection on widely-used existing PROMs. BNs offer more comprehensive probabilistic models of the connections between different PROM questions, allowing the use of information theoretic techniques to select the most informative questions. We tested our methods using five clinical PROM datasets, demonstrating that answering a small subset of questions selected with CAT has similar predictions and error to answering all questions in the PROM BN. Our results show that answering 30% - 75% questions selected with CAT had an average area under the receiver operating characteristic curve (AUC) of 0.92 (min: 0.8 - max: 0.98) for predicting the measured constructs. BNs outperformed alternative CAT approaches with a 5% (min: 0.01% - max: 9%) average increase in the accuracy of predicting the responses to unanswered question items.

Usability Testing of a Digital Assessment Routing Tool for Musculoskeletal Disorders: Iterative, Convergent Mixed Methods Study.

BACKGROUND: Musculoskeletal disorders negatively affect millions of patients worldwide, placing significant demand on health care systems. Digital technologies that improve clinical outcomes and efficiency across the care pathway are development priorities. We developed the musculoskeletal Digital Assessment Routing Tool (DART) to enable self-assessment and immediate direction to the right care. OBJECTIVE: We aimed to assess and resolve all serious DART usability issues to create a positive user experience and enhance system adoption before conducting randomized controlled trials for the integration of DART into musculoskeletal management pathways. METHODS: An iterative, convergent mixed methods design was used, with 22 adult participants assessing 50 different clinical presentations over 5 testing rounds across 4 DART iterations. Participants were recruited using purposive sampling, with quotas for age, habitual internet use, and English-language ability. Quantitative data collection was defined by the constructs within the International Organization for Standardization 9241-210-2019 standard, with user satisfaction measured by the System Usability Scale. Study end points were resolution of all grade 1 and 2 usability problems and a mean System Usability Scale score of ≥80 across a minimum of 3 user group sessions. RESULTS: All participants (mean age 48.6, SD 15.2; range 20-77 years) completed the study. Every assessment resulted in a recommendation with no DART system errors and a mean completion time of 5.2 (SD 4.44, range 1-18) minutes. Usability problems were reduced from 12 to 0, with trust and intention to act improving during the study. The relationship between eHealth literacy and age, as explored with a scatter plot and calculation of the Pearson correlation coefficient, was performed for all participants (r=-0.2; 20/22, 91%) and repeated with a potential outlier removed (r=-0.23), with no meaningful relationships observed or found for either. The mean satisfaction for daily internet users was highest (19/22, 86%; mean 86.5, SD 4.48; 90% confidence level [CL] 1.78 or -1.78), with nonnative English speakers (6/22, 27%; mean 78.1, SD 4.60; 90% CL 3.79 or -3.79) and infrequent internet users scoring the lowest (3/22, 14%; mean 70.8, SD 5.44; 90% CL 9.17 or -9.17), although the CIs overlap. The mean score across all groups was 84.3 (SD 4.67), corresponding to an excellent system, with qualitative data from all participants confirming that DART was simple to use. CONCLUSIONS: All serious DART usability issues were resolved, and a good level of satisfaction, trust, and willingness to act on the DART recommendation was demonstrated, thus allowing progression to randomized controlled trials that assess safety and effectiveness against usual care comparators. The iterative, convergent mixed methods design proved highly effective in fully evaluating DART from a user perspective and could provide a blueprint for other researchers of mobile health systems. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/27205.

Early Identification of Trauma-induced Coagulopathy: Development and Validation of a Multivariable Risk Prediction Model.

OBJECTIVE: The aim of this study was to develop and validate a risk prediction tool for trauma-induced coagulopathy (TIC), to support early therapeutic decision-making. BACKGROUND: TIC exacerbates hemorrhage and is associated with higher morbidity and mortality. Early and aggressive treatment of TIC improves outcome. However, injured patients that develop TIC can be difficult to identify, which may compromise effective treatment. METHODS: A Bayesian Network (BN) prediction model was developed using domain knowledge of the causal mechanisms of TIC, and trained using data from 600 patients recruited into the Activation of Coagulation and Inflammation in Trauma (ACIT) study. Performance (discrimination, calibration, and accuracy) was tested using 10-fold cross-validation and externally validated on data from new patients recruited at 3 trauma centers. RESULTS: Rates of TIC in the derivation and validation cohorts were 11.8% and 11.0%, respectively. Patients who developed TIC were significantly more likely to die (54.0% vs 5.5%, P < 0.0001), require a massive blood transfusion (43.5% vs 1.1%, P < 0.0001), or require damage control surgery (55.8% vs 3.4%, P < 0.0001), than those with normal coagulation. In the development dataset, the 14-predictor BN accurately predicted this high-risk patient group: area under the receiver operating characteristic curve (AUROC) 0.93, calibration slope (CS) 0.96, brier score (BS) 0.06, and brier skill score (BSS) 0.40. The model maintained excellent performance in the validation population: AUROC 0.95, CS 1.22, BS 0.05, and BSS 0.46. CONCLUSIONS: A BN (http://www.traumamodels.com) can accurately predict the risk of TIC in an individual patient from standard admission clinical variables. This information may support early, accurate, and efficient activation of hemostatic resuscitation protocols.

Predicting the Outcome of Limb Revascularization in Patients With Lower-extremity Arterial Trauma: Development and External Validation of a Supervised Machine-learning Algorithm to Support Surgical Decisions.

OBJECTIVES: Estimating the likely success of limb revascularization in patients with lower-extremity arterial trauma is central to decisions between attempting limb salvage and amputation. However, the projected outcome is often unclear at the time these decisions need to be made, making them difficult and threatening sound judgement. The objective of this study was to develop and validate a prediction model that can quantify an individual patient's risk of failed revascularization. METHODS: A BN prognostic model was developed using domain knowledge and data from the US joint trauma system. Performance (discrimination, calibration, and accuracy) was tested using ten-fold cross validation and externally validated on data from the UK Joint Theatre Trauma Registry. BN performance was compared to the mangled extremity severity score. RESULTS: Rates of amputation performed because of nonviable limb tissue were 12.2% and 19.6% in the US joint trauma system (n = 508) and UK Joint Theatre Trauma Registry (n = 51) populations respectively. A 10-predictor BN accurately predicted failed revascularization: area under the receiver operating characteristic curve (AUROC) 0.95, calibration slope 1.96, Brier score (BS) 0.05, and Brier skill score 0.50. The model maintained excellent performance in an external validation population: AUROC 0.97, calibration slope 1.72, Brier score 0.08, Brier skill score 0.58, and had significantly better performance than mangled extremity severity score at predicting the need for amputation [AUROC 0.95 (0.92-0.98) vs 0.74 (0.67-0.80); P < 0.0001]. CONCLUSIONS: A BN (https://www.traumamodels.com) can accurately predict the outcome of limb revascularization at the time of initial wound evaluation. This information may complement clinical judgement, support rational and shared treatment decisions, and establish sensible treatment expectations.

Evaluation of the National Institute for Health and Care Excellence Diagnostics Assessment Program Decisions: Incremental Cost-Effectiveness Ratio Thresholds and Decision-Modifying Factors.

OBJECTIVES: The National Institute for Health and Care Excellence (NICE) Diagnostics Assessment Programme (DAP) evaluates the cost-effectiveness of diagnostic technologies. A decision-making process benchmarking the incremental cost-effectiveness ratio (ICER) against a threshold while considering decision-modifying factors is common to NICE evaluations. This study investigated whether DAP decisions are consistent with the ICER thresholds described in the DAP manual, and to assess the impact of decision-modifying factors. METHODS: DAP evaluations published before March 2018 were reviewed, and the following items were extracted: diagnostic technologies evaluated, decision problems assessed, Diagnostics Advisory Committee (DAC) decisions, incremental quality-adjusted life years (QALYs), incremental costs, ICERs considered to be most plausible by the DAC, and decision justifications. RESULTS: All 30 evaluations were reviewed; 8 were excluded because the DAC concluded there was "insufficient evidence" for decision making. In the remaining 22 evaluations, 91 decision problems were identified for further analysis, of which 52, 15, and 24 received "recommended," "not recommended," and "not recommended-only in research" guidance, respectively. The overall consistency rate of the DAC decisions with the £20 000/QALY threshold was 73.6%. Diagnostic technologies that were not recommended, despite an ICER less than £20 000/QALY, were associated with a larger number of decision-modifying factors favoring the comparator, versus recommended diagnostic technologies with ICERs less than £20 000/QALY. For technologies with ICERs greater than £20 000/QALY, the number of decision-modifying factors was comparable for positive and negative recommendations. CONCLUSIONS: Most DAP decisions were consistent with the ICER threshold. However, cost-effectiveness was not the only determining factor in decision making; recommendations also considered patient- and healthcare-centric factors and uncertainty.

Medical idioms for clinical Bayesian network development.

Bayesian Networks (BNs) are graphical probabilistic models that have proven popular in medical applications. While numerous medical BNs have been published, most are presented fait accompli without explanation of how the network structure was developed or justification of why it represents the correct structure for the given medical application. This means that the process of building medical BNs from experts is typically ad hoc and offers little opportunity for methodological improvement. This paper proposes generally applicable and reusable medical reasoning patterns to aid those developing medical BNs. The proposed method complements and extends the idiom-based approach introduced by Neil, Fenton, and Nielsen in 2000. We propose instances of their generic idioms that are specific to medical BNs. We refer to the proposed medical reasoning patterns as medical idioms. In addition, we extend the use of idioms to represent interventional and counterfactual reasoning. We believe that the proposed medical idioms are logical reasoning patterns that can be combined, reused and applied generically to help develop medical BNs. All proposed medical idioms have been illustrated using medical examples on coronary artery disease. The method has also been applied to other ongoing BNs being developed with medical experts. Finally, we show that applying the proposed medical idioms to published BN models results in models with a clearer structure.

Genomic Characterization of Candidate Division LCP-89 Reveals an Atypical Cell Wall Structure, Microcompartment Production, and Dual Respiratory and Fermentative Capacities.

Recent experimental and bioinformatic advances enable the recovery of genomes belonging to yet-uncultured microbial lineages directly from environmental samples. Here, we report on the recovery and characterization of single amplified genomes (SAGs) and metagenome-assembled genomes (MAGs) representing candidate phylum LCP-89, previously defined based on 16S rRNA gene sequences. Analysis of LCP-89 genomes recovered from Zodletone Spring, an anoxic spring in Oklahoma, predicts slow-growing, rod-shaped organisms. LCP-89 genomes contain genes for cell wall lipopolysaccharide (LPS) production but lack the entire machinery for peptidoglycan biosynthesis, suggesting an atypical cell wall structure. The genomes, however, encode S-layer homology domain-containing proteins, as well as machinery for the biosynthesis of CMP-legionaminate, inferring the possession of an S-layer glycoprotein. A nearly complete chemotaxis machinery coupled to the absence of flagellar synthesis and assembly genes argues for the utilization of alternative types of motility. A strict anaerobic lifestyle is predicted, with dual respiratory (nitrite ammonification) and fermentative capacities. Predicted substrates include a wide range of sugars and sugar alcohols and a few amino acids. The capability of rhamnose metabolism is confirmed by the identification of bacterial microcompartment genes to sequester the toxic intermediates generated. Comparative genomic analysis identified differences in oxygen sensitivities, respiratory capabilities, substrate utilization preferences, and fermentation end products between LCP-89 genomes and those belonging to its four sister phyla (Calditrichota, SM32-31, AABM5-125-24, and KSB1) within the broader FCB (Fibrobacteres-Chlorobi-Bacteroidetes) superphylum. Our results provide a detailed characterization of members of the candidate division LCP-89 and highlight the importance of reconciling 16S rRNA-based and genome-based phylogenies.IMPORTANCE Our understanding of the metabolic capacities, physiological preferences, and ecological roles of yet-uncultured microbial phyla is expanding rapidly. Two distinct approaches are currently being utilized for characterizing microbial communities in nature: amplicon-based 16S rRNA gene surveys for community characterization and metagenomics/single-cell genomics for detailed metabolic reconstruction. The occurrence of multiple yet-uncultured bacterial phyla has been documented using 16S rRNA surveys, and obtaining genome representatives of these yet-uncultured lineages is critical to our understanding of the role of yet-uncultured organisms in nature. This study provides a genomics-based analysis highlighting the structural features and metabolic capacities of a yet-uncultured bacterial phylum (LCP-89) previously identified in 16S rRNA surveys for which no prior genomes have been described. Our analysis identifies several interesting structural features for members of this phylum, e.g., lack of peptidoglycan biosynthetic machinery and the ability to form bacterial microcompartments. Predicted metabolic capabilities include degradation of a wide range of sugars, anaerobic respiratory capacity, and fermentative capacities. In addition to the detailed structural and metabolic analysis provided for candidate division LCP-89, this effort represents an additional step toward a unified scheme for microbial taxonomy by reconciling 16S rRNA gene-based and genomics-based taxonomic outlines.

Focused Ultrasound Thalamotomy and Other Interventions for Medication-Refractory Essential Tremor: An Indirect Comparison of Short-Term Impact on Health-Related Quality of Life.

BACKGROUND: Up to 50% of essential tremor patients are refractory to medication and require alternative treatment to achieve tremor relief. This study aimed to identify and analyze evidence supporting the use of the emerging magnetic resonance-guided focused ultrasound (MRgFUS) compared to alternative stimulatory and ablative interventions for the treatment of medication-refractory essential tremor: radiofrequency thalamotomy, unilateral deep brain stimulation (DBS), and stereotactic radiosurgery. METHODS: A systematic literature review was conducted to identify clinical, health-related quality of life (HRQoL), and economic evidence for each intervention. Because of the lack of comparative evidence captured, a feasibility assessment was performed to determine possible comparisons between interventions, and newly established matching-adjusted indirect comparison and simulated treatment comparison techniques were used to conduct a comparison between unilateral DBS aggregate data and MRgFUS individual patient data. RESULTS: The systematic literature review identified 1,559 records, and screening yielded 46 relevant articles. The captured studies demonstrated that radiofrequency thalamotomy, DBS, stereotactic radiosurgery, and MRgFUS all exhibit clinical efficacy, with variation in onset and duration of tremor relief, and are each associated with a unique safety profile. The matching-adjusted indirect comparison and simulated treatment comparison results demonstrated no evidence of a difference in efficacy (measured by Clinical Rating Scale for Tremor Total) and HRQoL (measured by Clinical Rating Scale for Tremor Part C) outcomes between MRgFUS and unilateral DBS in the short term (≤12 months). CONCLUSIONS: This study provides preliminary evidence that MRgFUS could elicit similar short-term tremor- and HRQoL-related benefits to DBS, the current standard of care, and allowed for the first robust statistical comparison between these interventions.

Race and Prevalence of Large Bowel Polyps Among the Low-Income and Uninsured in South Carolina.

BACKGROUND: Compared to whites, blacks have higher colorectal cancer incidence and mortality rates and are at greater risk for early-onset disease. The reasons for this racial disparity are poorly understood, but one contributing factor could be differences in access to high-quality screening and medical care. AIMS: The present study was carried out to assess whether a racial difference in prevalence of large bowel polyps persists within a poor and uninsured population (n = 233, 124 blacks, 91 whites, 18 other) undergoing screening colonoscopy. METHODS: Eligible patients were uninsured, asymptomatic, had no personal history of colorectal neoplasia, and were between the ages 45-64 years (blacks) or 50-64 years (whites, other). We examined the prevalence of any adenoma (conventional, serrated) and then difference in adenoma/polyp type by race and age categories. RESULTS: Prevalence for ≥1 adenoma was 37 % (95 % CI 31-43 %) for all races combined and 36 % in blacks <50 years, 38 % in blacks ≥50 years, and 35 % in whites. When stratified by race, blacks had a higher prevalence of large conventional proximal neoplasia (8 %) compared to whites (2 %) (p value = 0.06) but a lower prevalence of any serrated-like (blacks 18 %, whites 32 %; p value = 0.02) and sessile serrated adenomas/polyps (blacks 2 %, whites 8 % Chi-square p value; p = 0.05). CONCLUSIONS: Within this uninsured population, the overall prevalence of adenomas was high and nearly equal by race, but the racial differences observed between serrated and conventional polyp types emphasize the importance of taking polyp type into account in future research on this topic.

Representation of impossible worlds in the cognitive map.

It is often assumed that humans represent large-scale spatial environments as cognitive maps, but the exact features of these representations are still unclear. We investigate the structure of this representation with the impossible worlds paradigm by testing whether the information provided by virtual environments (VEs) with arbitrary violations of geometrical rules is rectified ("distorted") to become compatible with a map-like structure. The experiments were conducted in virtual reality using a natural locomotion interface. The subjects' task was to explore possible and impossible VEs carefully to achieve a "full understanding". After each trial, they had to "blindly" reproduce the path through the environment from memory in a VE with impoverished visual cues. We have found no evidence for angular or configurational distortions or alterations in the blind reproductions of impossible VEs. Blind reproduction indicates that impossible VEs do not require a transformation into a "possible" format to make them fit into the cognitive map. This suggests that the representation may not be similar to a map in a bounded sense of interpretation but requires more generalized concepts for its understanding.

Not just data: a method for improving prediction with knowledge.

Many medical conditions are only indirectly observed through symptoms and tests. Developing predictive models for such conditions is challenging since they can be thought of as 'latent' variables. They are not present in the data and often get confused with measurements. As a result, building a model that fits data well is not the same as making a prediction that is useful for decision makers. In this paper, we present a methodology for developing Bayesian network (BN) models that predict and reason with latent variables, using a combination of expert knowledge and available data. The method is illustrated by a case study into the prediction of acute traumatic coagulopathy (ATC), a disorder of blood clotting that significantly increases the risk of death following traumatic injuries. There are several measurements for ATC and previous models have predicted one of these measurements instead of the state of ATC itself. Our case study illustrates the advantages of models that distinguish between an underlying latent condition and its measurements, and of a continuing dialogue between the modeller and the domain experts as the model is developed using knowledge as well as data.

What characteristics of clinical decision support system implementations lead to adoption for regular use? A scoping review.

INTRODUCTION: Digital healthcare innovation has yielded many prototype clinical decision support (CDS) systems, however, few are fully adopted into practice, despite successful research outcomes. We aimed to explore the characteristics of implementations in clinical practice to inform future innovation. METHODS: Web of Science, Trip Database, PubMed, NHS Digital and the BMA website were searched for examples of CDS systems in May 2022 and updated in June 2023. Papers were included if they reported on a CDS giving pathway advice to a clinician, adopted into regular clinical practice and had sufficient published information for analysis. Examples were excluded if they were only used in a research setting or intended for patients. Articles found in citation searches were assessed alongside a detailed hand search of the grey literature to gather all available information, including commercial information. Examples were excluded if there was insufficient information for analysis. The normalisation process theory (NPT) framework informed analysis. RESULTS: 22 implemented CDS projects were included, with 53 related publications or sources of information (40 peer-reviewed publications and 13 alternative sources). NPT framework analysis indicated organisational support was paramount to successful adoption of CDS. Ensuring that workflows were optimised for patient care alongside iterative, mixed-methods implementation was key to engaging clinicians. CONCLUSION: Extensive searches revealed few examples of CDS available for analysis, highlighting the implementation gap between research and healthcare innovation. Lessons from included projects include the need for organisational support, an underpinning mixed-methods implementation strategy and an iterative approach to address clinician feedback.

Evaluation of a Musculoskeletal Digital Assessment Routing Tool (DART): Crossover Noninferiority Randomized Pilot Trial.

BACKGROUND: Musculoskeletal conditions account for 16% of global disability, resulting in a negative effect on patients and increasing demand for health care use. Triage directing patients to appropriate level intervention improving health outcomes and efficiency has been prioritized. We developed a musculoskeletal digital assessment routing tool (DART) mobile health (mHealth) system, which requires evaluation prior to implementation. Such innovations are rarely rigorously tested in clinical trials-considered the gold standard for evaluating safety and efficacy. This pilot study is a precursor to a trial assessing DART performance with a physiotherapist-led triage assessment. OBJECTIVE: The study aims to evaluate trial design, assess procedures, and collect exploratory data to establish the feasibility of delivering an adequately powered, definitive randomized trial, assessing DART safety and efficacy in an NHS primary care setting. METHODS: A crossover, noninferiority pilot trial using an integrated knowledge translation approach within a National Health Service England primary care setting. Participants were patients seeking assessment for a musculoskeletal condition, completing a DART assessment and the history-taking element of a face-to-face physiotherapist-led triage in a randomized order. The primary outcome was agreement between DART and physiotherapist triage recommendation. Data allowed analysis of participant recruitment and retention, randomization, blinding, study burden, and potential barriers to intervention delivery. Participant satisfaction was measured using the System Usability Scale. RESULTS: Over 8 weeks, 129 patients were invited to participate. Of these, 92% (119/129) proceeded to eligibility assessment, with 60% (78/129) meeting the inclusion criteria and being randomized into each intervention arm (39/39). There were no dropouts and data were analyzed for all 78 participants. Agreement between physiotherapist and DART across all participants and all primary triage outcomes was 41% (32/78; 95% CI 22-45), intraclass correlation coefficient 0.37 (95% CI 0.16-0.55), indicating that the reliability of DART was poor to moderate. Feedback from the clinical service team led to an adjusted analysis yielding of 78% (61/78; 95% CI 47-78) and an intraclass correlation coefficient of 0.57 (95% CI 0.40-0.70). Participant satisfaction was measured quantitively using amalgamated System Usability Scale scores (n=78; mean score 84.0; 90% CI +2.94 to -2.94), equating to an "excellent" system. There were no study incidents, and the trial burden was acceptable. CONCLUSIONS: Physiotherapist-DART agreement of 78%, with no adverse triage decisions and high patient satisfaction, was sufficient to conclude DART had the potential to improve the musculoskeletal pathway. Study validity was enhanced by the recruitment of real-world patients and using an integrated knowledge translation approach. Completion of a context-specific consensus process is recommended to provide definitive definitions of safety criteria, range of appropriateness, noninferiority margin, and sample size. This pilot demonstrated an adequately powered definitive trial is feasible, which would provide evidence of DART safety and efficacy, ultimately informing potential for DART implementation. TRIAL REGISTRATION: ClinicalTrials.gov NCT04904029; http://clinicaltrials.gov/ct2/show/NCT04904029. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/31541.

Identification of major hemorrhage in trauma patients in the prehospital setting: diagnostic accuracy and impact on outcome.

Background: Hemorrhage is the most common cause of potentially preventable death after injury. Early identification of patients with major hemorrhage (MH) is important as treatments are time-critical. However, diagnosis can be difficult, even for expert clinicians. This study aimed to determine how accurate clinicians are at identifying patients with MH in the prehospital setting. A second aim was to analyze factors associated with missed and overdiagnosis of MH, and the impact on mortality. Methods: Retrospective evaluation of consecutive adult (≥16 years) patients injured in 2019-2020, assessed by expert trauma clinicians in a mature prehospital trauma system, and admitted to a major trauma center (MTC). Clinicians decided to activate the major hemorrhage protocol (MHPA) or not. This decision was compared with whether patients had MH in hospital, defined as the critical admission threshold (CAT+): administration of ≥3 U of red blood cells during any 60-minute period within 24 hours of injury. Multivariate logistical regression analyses were used to analyze factors associated with diagnostic accuracy and mortality. Results: Of the 947 patients included in this study, 138 (14.6%) had MH. MH was correctly diagnosed in 97 of 138 patients (sensitivity 70%) and correctly excluded in 764 of 809 patients (specificity 94%). Factors associated with missed diagnosis were penetrating mechanism (OR 2.4, 95% CI 1.2 to 4.7) and major abdominal injury (OR 4.0; 95% CI 1.7 to 8.7). Factors associated with overdiagnosis were hypotension (OR 0.99; 95% CI 0.98 to 0.99), polytrauma (OR 1.3, 95% CI 1.1 to 1.6), and diagnostic uncertainty (OR 3.7, 95% CI 1.8 to 7.3). When MH was missed in the prehospital setting, the risk of mortality increased threefold, despite being admitted to an MTC. Conclusion: Clinical assessment has only a moderate ability to identify MH in the prehospital setting. A missed diagnosis of MH increased the odds of mortality threefold. Understanding the limitations of clinical assessment and developing solutions to aid identification of MH are warranted. Level of evidence: Level III-Retrospective study with up to two negative criteria. Study type: Original research; diagnostic accuracy study.

Attitudes towards technology supported rheumatoid arthritis care: investigating patient- and clinician-perceived opportunities and barriers.

Objectives: Globally, demand outstrips capacity in rheumatology services, making Mobile Health (mHealth) attractive, with the potential to improve access, empower patient self-management and save costs. Existing mHealth interventions have poor uptake by end users. This study was designed to understand existing challenges, opportunities and barriers for computer technology in the RA care pathway. Methods: People with RA were recruited from Barts Health NHS Trust rheumatology clinics to complete paper questionnaires and clinicians were recruited from a variety of centres in the UK to complete an online questionnaire. Data collected included demographics, current technology use, challenges managing RA, RA medications and monitoring, clinic appointments, opportunities for technology and barriers to technology. Results: A total of 109 patient and 41 clinician questionnaires were completed. A total of 83.5% of patients and 93.5% of clinicians use smartphones daily. However, only 25% had ever used an arthritis app and only 5% had persisted with one. Both groups identified managing pain, flares and RA medications as areas of existing need. Access to care, medication support and disease education were mutually agreeable opportunities; however, discrepancies existed between groups with clinicians prioritizing education over access, likely due to concerns of data overwhelm (80.6% considered this a barrier). Conclusions: In spite of high technology use and willingness from both sides, our cohort did not utilize technology to support care, suggesting inadequacies in the existing software. The lack of an objective biomarker for RA disease activity, existing challenges in the healthcare system and the need for integration with existing technical systems were identified as the greatest barriers. Trial registration: Registered on the Clinical Research Network registry (IRAS ID: 264690).