Trial Outcome Data Research Articles

Heart Failure and All-Cause Hospitalizations in Patients With Heart Failure

Heart failure (HF) hospitalization is a common end point in HF trials; however, how HF hospitalization is associated with all-cause hospitalization in terms of proportionality, correlation of treatment effects, and concomitant reporting has not been studied. To determine the ratio of HF to all-cause hospitalizations, whether reported treatment effects on HF hospitalization are associated with treatment effects on all-cause hospitalization, and how often all-cause hospitalization is reported alongside HF hospitalization. PubMed was searched from inception to September 2, 2024, for randomized clinical trials (RCTs) of HF treatments using MeSH (medical subject heading) terms and keywords associated with heart failure, ventricular failure, ventricular dysfunction, and cardiac failure, as well as the names of specific journals. RCTs of HF treatments and reporting on HF hospitalization published in 1 of 3 leading medical journals (New England Journal of Medicine, The Lancet, or JAMA). The PRISMA guidelines were followed. Data extraction was performed by 2 reviewers, and disagreements were resolved by consensus. Trial baseline characteristics and outcome data on HF and all-cause hospitalizations were extracted. The ratio of HF to all-cause hospitalizations was calculated. The association of HF hospitalization effects with all-cause hospitalization effects was evaluated using hierarchical bayesian models with weak priors. The posterior distribution was used to calculate the HF hospitalization treatment effects that would need to be observed before a high probability (97.5%) of a reduction in all-cause hospitalization could be achieved. The proportion of trials reporting all-cause hospitalization was calculated. HF and all-cause hospitalizations. Of 113 trials enrolling 261 068 patients (median proportion of female participants, 25.4% [IQR, 21.3%-34.2%]; median age, 66.2 [IQR, 62.8-70.0] years), 60 (53.1%) reported on all-cause hospitalization. The weighted median ratio of HF to all-cause hospitalizations was 45.9% (IQR, 30.7%-51.7%). This ratio was higher in trials with greater proportions of New York Heart Association class III or IV HF, with lower left ventricular ejection fractions, investigating nonpharmaceutical interventions, and that restricted recruitment to patients with HF and reduced ejection fraction. Reported effects on HF and all-cause hospitalizations were well-correlated (R2 = 90.1%; 95% credible interval, 62.3%-99.8%). In a large trial, the intervention would have to decrease the odds of HF hospitalization by 16% to ensure any reduction, 36% to ensure a 10% reduction, and 56% to ensure a 20% reduction in the odds of all-cause hospitalization with 97.5% probability. In this meta-analysis of HF trials, all-cause hospitalization was underreported despite a large burden of non-HF hospitalizations. Large reductions in HF hospitalization must be observed before clinically relevant reductions in all-cause hospitalization can be inferred.

Special Section on Patient-Reported Outcomes and Informatics: Collection of Patient-Reported Outcome Measures in Rural and Underserved Populations.

The NIH Pragmatic Trials Collaboratory supports the design and conduct of 31 embedded pragmatic clinical trials, and many of these trials use patient-reported outcome measures (PROMs) to provide valuable information about their patients' health and wellness. Often these trials enroll medically underserved patients, including people with incomes below the federal poverty threshold, racial or ethnically minoritized groups, or rural or frontier communities. In this series of trial case reports, we provide lessons learned about collecting PROMs in these populations. The unbiased collection of PROM data is critical to increase the generalizability of trial outcomes and to address health inequities. Use of electronic health records (EHRs) and other digital modes of PROM administration have gained traction. However, engagement with these modes is often low among disparities prone populations due to lessened digital proficiency, device access, and uptake of EHR portals and web interfaces. To maximize the completeness and representativeness of their trial outcome data, study teams tested a range of strategies to improve PROM response rates with emphasis on disparities prone and underserved patient groups. This manuscript describes the approaches, their implementation, and the targeted populations. Optimized PROM collection required hybrid approaches with multiple outreach modes, high-touch methods, creativity in promoting digital uptake, multi-modal participant engagement, and text messaging.

TWIN-GPT : Digital Twins for Clinical Trials via Large Language Model

Clinical trials are indispensable for medical research and the development of new treatments. However, clinical trials often involve thousands of participants and can span several years to complete, with a high probability of failure during the process. Recently, there has been a burgeoning interest in virtual clinical trials, which simulate real-world scenarios and hold the potential to significantly enhance patient safety, expedite development, reduce costs, and contribute to the broader scientific knowledge in healthcare. Existing research often focuses on leveraging electronic health records (EHRs) to support clinical trial outcome prediction. Yet, trained with limited clinical trial outcome data, existing approaches frequently struggle to perform accurate predictions. Some research has attempted to generate EHRs to augment model development but has fallen short in personalizing the generation for individual patient profiles. Recently, the emergence of large language models has illuminated new possibilities, as their embedded comprehensive clinical knowledge has proven beneficial in addressing medical issues. In this paper, we propose a large language model-based digital twin creation approach, called TWIN-GPT . TWIN-GPT can establish cross-dataset associations of medical information given limited data, generating unique personalized digital twins for different patients, thereby preserving individual patient characteristics. Comprehensive experiments show that using digital twins created by TWIN-GPT can boost the clinical trial outcome prediction, exceeding various previous prediction approaches. Besides, we also demonstrate that TWIN-GPT can generate high-fidelity trial data that closely approximates specific patients, aiding in more accurate result predictions in data-scarce situations. Moreover, our study provides practical evidence for the application of digital twins in healthcare, highlighting its potential significance.

Relationship between immune phenotype and treatment selection of Chemo-IO vs. IO-only in TPS-high NSCLC using hypothetical test-and-control group generation based on survival data extracted from phase III trials.

e13569 Background: Immunotherapy has become the treatment of choice for the first-line treatment of unresectable NSCLC with high PD-L1 tumor proportion score (TPS). However, the optimal treatment regimen, particularly when to add chemotherapy, remains unclear. To evaluate whether an immune microenvironment biomarker could help guide the treatment regimen, we have explored computational hypothetical analysis applying historical proportion of inflamed immune phenotype (IIP) to published outcome data of clinical trials. Methods: R package “IPDfromKM” was used to reconstruct raw time-to-event outcomes from Kaplan-Meier (KM) curves. Data from KN-189 and KN-407 for anti-PD1 with chemotherapy (Chemo-IO), and KN-024 and KN-042 for anti-PD1 (IO-only) in the first-line treatment of NSCLC were included for analysis. Overall survival (OS) data were computed from KM of pts with PD-L1 TPS of ≧ 50% from these studies (KN-189 n=132, KN-407 n=73, KN-024 n=154, KN-042 n=299). It was assumed that Lunit SCOPE IO classified pts into IIP or non-IIP at a ratio of 57% vs. 43%, based on previous analysis. One hundred hypothetical sets of Chemo-IO and IO-only groups satisfying hazard ratio (HR) of IIP to non-IIP as 0.81 and 0.61 (± 0.025) while maintaining the proportion of IIP were randomly generated. A total of 10,000 (100 x 100) combinations of cohorts were analyzed to test our hypothesis of superiority of Chemo-IO over IO-only in non-IIP as well as non-superiority of Chemo-IO over IO-only in IIP. Results: Patient-level OS data for a total of 205 pts with Chemo-IO and 453 pts with IO-only were reconstructed. The median OS was favorable in Chemo-IO vs IO-only (HR 0.73, 95% confidence interval [CI] 0.54-0.99, p = 0.045). From 10,000 hypothetical combinations of cohorts, the median HR of Chemo-IO compared to IO-only was 0.61 (interquartile range [IQR] 0.60-0.63) in non-IIP group, whereas that was 0.86 (IQR 0.84-0.89) in IIP group (Table). The upper 95% bound of HR was < 1 for 8,611 combinations of non-IIP groups, but > 1 for all possible combinations of IIP groups. Given a non-superiority margin of 1.37(=1/0.73), 8,306 combinations were positive for the upper 95% bound of HR < 1.37. Overall, 6,917 matched combinations supported our hypothesis. Conclusions: From the computational analysis, 69.17% cases of hypothetical combinations support that Chemo-IO would be superior to IO-only in non-IIP group, whereas Chemo-IO would not be superior to IO-only in IIP group, implying a potential value of confirmatory biomarker analysis in providing evidence for regimen selection. [Table: see text]

Gradient boosted decision trees reveal nuances of auditory discrimination behavior.

Animal psychophysics can generate rich behavioral datasets, often comprised of many 1000s of trials for an individual subject. Gradient-boosted models are a promising machine learning approach for analyzing such data, partly due to the tools that allow users to gain insight into how the model makes predictions. We trained ferrets to report a target word's presence, timing, and lateralization within a stream of consecutively presented non-target words. To assess the animals' ability to generalize across pitch, we manipulated the fundamental frequency (F0) of the speech stimuli across trials, and to assess the contribution of pitch to streaming, we roved the F0 from word token to token. We then implemented gradient-boosted regression and decision trees on the trial outcome and reaction time data to understand the behavioral factors behind the ferrets' decision-making. We visualized model contributions by implementing SHAPs feature importance and partial dependency plots. While ferrets could accurately perform the task across all pitch-shifted conditions, our models reveal subtle effects of shifting F0 on performance, with within-trial pitch shifting elevating false alarms and extending reaction times. Our models identified a subset of non-target words that animals commonly false alarmed to. Follow-up analysis demonstrated that the spectrotemporal similarity of target and non-target words rather than similarity in duration or amplitude waveform was the strongest predictor of the likelihood of false alarming. Finally, we compared the results with those obtained with traditional mixed effects models, revealing equivalent or better performance for the gradient-boosted models over these approaches.

Missing Outcome Data in Recent Perinatal and Neonatal Clinical Trials.

Missing outcome data in clinical trials may jeopardize the validity of the trial results and inferences for clinical practice. Although sick and preterm newborns are treated as a captive patient population during their stay in the NICUs, their long-term outcomes are often ascertained after discharge. This greatly increases the risk of attrition. We surveyed recently published perinatal and neonatal randomized trials in 7 high-impact general medical and pediatric journals to review the handling of missing primary outcome data and any choice of imputation methods. Of 87 eligible trials in this survey, 77 (89%) had incomplete primary outcome data. The missing outcome data were not discussed at all in 9 reports (12%). Most study teams restricted their main analysis to participants with complete information for the primary outcome (61 trials; 79%). Only 38 of the 77 teams (49%) performed sensitivity analyses using a variety of imputation methods. We conclude that the handling of missing primary outcome data was frequently inadequate in recent randomized perinatal and neonatal trials. To improve future approaches to missing outcome data, we discuss the strengths and limitations of different imputation methods, the appropriate estimation of sample size, and how to deal with data withdrawal. However, the best strategy to reduce bias from missing outcome data in perinatal and neonatal trials remains prevention. Investigators should anticipate and preempt missing data through careful study design, and closely monitor all incoming primary outcome data for completeness during the conduct of the trial.

The efficacy of psychological interventions for adult post-traumatic stress disorder following exposure to single versus multiple traumatic events: a meta-analysis of randomised controlled trials

Previous meta-analyses of psychological interventions for adult post-traumatic stress disorder (PTSD) did not investigate whether efficacy is diminished in individuals with PTSD related to multiple (vs single) traumatic events. We aimed to assess whether treatment efficacy would be lower in randomised controlled trials involving multiple-event-related PTSD versus single-event-related PTSD. For this meta-analysis, we searched PsycINFO, MEDLINE, Web of Science, and PTSDpubs from database inception to April 18, 2023. Randomised controlled trials involving adult clinical samples (≥70% meeting full PTSD criteria) with adequate size (≥10 participants per arm) were included. We extracted data on trial characteristics, demographics, and outcome data. Random-effects meta-analyses were run to summarise standardised mean differences (Hedges' g). Trials involving 100% of participants with single-event-related PTSD versus at least 50% of participants with multiple-event-related PTSD (ie, associated with ≥two traumatic events) were categorised. Quality of evidence was assessed using the Cochrane criteria. The review protocol was registered in PROSPERO (CRD42023407754). Overall, 137 (85%) of 161 randomised controlled trials were included in the quantitative synthesis, comprising 10 684 participants with baseline data and 9477 with post-treatment data. Of those randomly assigned, 5772 (54%) of 10 692 participants identified as female, 4917 (46%) as male, and three (<1%) as transgender or other. 34 (25%) of 137 trials exclusively involved women, 15 (11%) trials exclusively involved men, and the remainder were mixed samples. Mean age across the trials was 40·2 years (SD 9·0) ranging from 18·0 years to 65·4 years. 23 (17%) of 137 trials involved participants from low-income and middle-income countries (23 [17%] of 137). Data on ethnicity were not extracted. At treatment endpoint, psychological interventions were highly effective for PTSD when compared with passive control conditions in both samples with single-event-related PTSD (Hedges' g 1·04 [95% CI 0·77-1·31]; n=11; I2=43%) and multiple-event-related PTSD (Hedges' g 1·13 [0·90-1·35]; n=55, I2=87%), with no efficacy difference between these categories (p=0·48). Heterogeneity between studies was substantial but outlier-corrected analysis yielded similar results. Moderate-sized effects were found compared with active control conditions with no significant difference between single-trauma and multiple-trauma trials. Results were robust in various sensitivity analyses (eg, 90% cutoff for multiple-trauma trials) and analyses of follow-up data. The quality of evidence was moderate to high. Contrary to our hypothesis, we found strong evidence that psychological interventions are highly effective treatments for PTSD in patients with a history of multiple traumatic events. Results are encouraging for clinical practice and could counteract common misconceptions regarding treatment and treatment barriers. None.

Cost of digital technologies and family-observed DOT for a shorter MDR-TB regimen: a modelling study in Ethiopia, India and Uganda

BackgroundIn 2017, the WHO recommended the use of digital technologies, such as medication monitors and video observed treatment (VOT), for directly observed treatment (DOT) of drug-susceptible TB. The WHO’s 2020 guidelines extended these recommendations to multidrug-resistant tuberculosis (MDR-TB), based on low evidence. The impact of COVID on health systems and patients underscored the need to use digital technologies in the management of MDR-TB.MethodsA decision-tree model was developed to explore the costs of several potential DOT alternatives: VOT, 99DOTS (Directly-observed Treatment, Short-course) and family-observed DOT. Assuming a 9-month, all-oral regimen (as evaluated within the STREAM trial), we constructed base-case cost models for the standard-of-care DOTs in Ethiopia, India, and Uganda, as well as for the three alternative DOT approaches. The models were populated with STREAM Stage 2 clinical trial outcome and cost data, supplemented with market prices data for the digital DOT strategies. Sensitivity analyses were conducted on key parameters.ResultsModelling suggested that the standard-of-care DOT approach is the most expensive DOT strategy from a societal perspective in all three countries evaluated (Ethiopia, India, Uganda), with considerable direct- and indirect-costs incurred by patients. The second most expensive DOT approach is VOT, with high health-system costs, largely caused by up-front technology expenditure.Each of VOT, 99DOTS and family-observed DOT would reduce by more than 90% patients’ direct and indirect costs compared to standard of care DOT.Results were robust to the sensitivity analyses.ConclusionsWhile data on the costs and efficacy of alternative DOT approaches in the context of shorter MDR-TB treatment is limited, our modelling suggests alternative DOT approaches can significantly reduce patient costs in all three countries. Health system costs are higher for VOT and lower for 99DOTS and family-observed therapy when compared to standard of care DOT, as low smartphone penetration and internet availability requires the VOT health system to fund the cost of making them available to patients.

Best Practices for the Electronic Implementation and Migration of Patient-Reported Outcome Measures

While the use of electronic methods to collect patient-reported outcome data in clinical trials continues to increase, it remains the case that many patient-reported outcome measures (PROMs) have originally been developed and validated on paper. Careful consideration during the move from paper PROMs to electronic format is required to preserve the integrity of the measure and ensure a “faithful migration.” Relevant literature has long called out the importance of following migration best practices during this process; nevertheless, such best practices are distributed across multiple documents. This article consolidates and builds upon existing electronic PROM implementation best practice recommendations to provide a comprehensive, up-to-date, single point of reference. It reflects the current consensus based on the significant advances in technology capabilities and knowledge gleaned from the growing evidence base on electronic migration and implementation, to balance the need for maintaining the integrity of the measure while optimizing respondent usability. It also specifies whether the practice is rooted in evidence or expert consensus, to enable those using these best practices to make informed and considered decisions when conducting migration.

Exploring the perspectives of selectors and collecters of trial outcome data: an international qualitative study

IntroductionSelecting and collecting data to support appropriate primary and secondary outcomes is a critical step in designing trials that can change clinical practice. In this study, we aimed to investigate who contributes to the process of selecting and collecting trial outcomes, and how these people are involved. This work serves two main purposes: (1) it provides the trials community with evidence to demonstrate how outcomes are currently selected and collected, and (2) it allows people involved in trial design and conduct to pick apart these processes to consider how efficiencies and improvements can be made.MethodsOne-with-one semi-structured interviews, supported by a topic guide to ensure coverage of key content. The Framework approach was used for thematic analysis of data, and themes were linked through constant comparison of data both within and across participant groups. Interviews took place between July 2020 and January 2021. Participants were twenty-nine international trialists from various contributor groups, working primarily on designing and/or delivering phase III pragmatic effectiveness trials. Their experience spanned various funders, trial settings, clinical specialties, intervention types, and participant populations.ResultsWe identified three descriptive themes encompassing the process of primary and secondary outcome selection, collection, and the publication of outcome data. Within these themes, participants raised issues around the following: 1) Outcome selection: clarity of the research question; confidence in selecting trial outcomes and how confidence decreases with increased experience; interplay between different interested parties; how patients and the public are involved in outcome selection; perceived impact of poor outcome selection including poor recruitment and/or retention; and use of core outcome sets. 2) Outcome collection: disconnect between decisions made by outcome selectors and the practical work done by outcome collectors; potential impact of outcome measures on trial participants; potential impact on trial staff workload; and use of routinely collected data. 3) Publication of outcome data: difficulties in finding time to write and revise manuscripts for publication due to time and funding constraints. Participants overwhelmingly focused on the process of outcome selection, a topic they talked about unprompted. When prompted, participants do discuss outcome collection, but poor communication between selectors and collectors at the trial design stage means that outcome selection is rarely linked with the data collection workload it generates.DiscussionPeople involved in the design and conduct of trials fail to connect decisions around outcome selection with data collection workload. Publication of outcome data and effective dissemination of trial results are hindered due to the project-based culture of some academic clinical trial research.

Effectiveness Of Cassia alata L Leaf Extract Decrease Blood Glucose Level On Streptozotocin-Induced Male White Rats

Introduction: Diabetes mellitus (DM) is a condition in which the body either fails to produce enough insulin at the right time or fails to use it properly. Aims: The goal of this study is to show how Cassia alata L. leaf extract affects blood glucose levels in streptozotocin-induced diabetic rats. Methods: Cassia alata L. leaves therapy is an alternative treatment for DM. In this investigation, laboratory techniques were employed. Streptozotocin was administered intraperitoneally to mice at a dose of 40 mg/kg body weight initially. Except that they produce normal controls. Six groups of 30 rats each received treatment with Cassia alata L. leaf extract at doses of 500 mg/kg body weight, 600 mg/kg body weight, and 700 mg/kg body weight in addition to the usual control group. Trial outcome data were first examined using one-way ANOVA to confirm differences between treatments and then put through the DUNCAN trial. Result: As a result, it was discovered that secondary metabolites of alkaloids, flavonoids, saponins, and tannins were present in the ethanol extract of Cassia alata L. leaves. A dose of 700 mg/kg body weight is an effective blood sugar-lowering dose, with an average reduction of 121 mg/dl. Conclusion: Ethanol extract from Cassia alata L. leaves has this effect.

A powerful partnership: researchers and patients working together to develop a patient-facing summary of clinical trial outcome data.

Availability of easy-to-understand patient-reported outcome (PRO) trial data may help individuals make more informed healthcare decisions. Easily interpretable, patient-centric PRO data summaries and visualizations are therefore needed. This three-stage study explored graphical format preferences, understanding, and interpretability of clinical trial PRO data presented to people with prostate cancer (PC). A 7-day online survey exploring people with PC's preferences for different PRO data presentations (stage 1; n = 30) informed development of a draft plain-language resource sheet containing PRO data. After refining for clarity during cognitive debriefing interviews (stage 2; n = 18), the final resource sheet was circulated to people with PC for broader feedback (stage 3; n = 45). Although participants expressed preferences for certain graphical formats (pie charts and bar charts), preference did not always associate with interpretability and overall message clarity. Iterative development (stages 1 and 2) led to a final resource sheet, which 91.1% of participants in stage 3 considered useful and informative, and 88.9% expressed interest in receiving similar resources in the future. Findings demonstrate PRO data are relevant to people with PC and highlights that targeted resource sheets can support patient-clinician discussions. Appropriate graphical formatting and use of plain-language text is essential for conveying interpretable PRO data. Data visualization preferences are context dependent. Resource sheets summarizing clinical trial PRO data can be helpful for decision-making in PC. Researchers and patients can work together to develop clear, relevant, sensitive, and understandable resource sheets, which equally consider patient priorities as well as those of scientists.

MSR19 Missing Patient Reported Outcome Data in Clinical Trials: An Overview and Simulation Study

Missing data occur in clinical trials and can have a negative impact on the results. In this study, we (1) outlined concepts related to missing patient reported outcomes (PRO) data, including patterns of missingness, missing data types (missing completely at random [MCAR], missing at random [MAR], missing not at random [MNAR]), and commonly used analytic strategies that address missingness (multiple imputation [MI], mixed model repeated measures [MMRM]), and (2) investigated the potential impact of missing data on PRO results in clinical trial settings.

Registry randomized controlled trials: the future of surgical trials?

Registry randomized controlled trials: the future of surgical trials?

Promoting inclusion, diversity, and equity in pain science.

Promoting inclusion, diversity, and equity in pain science.

Assessing the collection and reporting of patient-reported outcome data in interventional cancer trials: a single institution, retrospective systematic evaluation

BackgroundTo understand our performance with respect to the collection and reporting of patient-reported outcome (PRO) measure (PROM) data, we examined the protocol content, data completeness and publication of PROs from interventional trials conducted at the Royal Marsden NHS Foundation Trust (RM) and explored factors associated with data missingness and PRO publication.DesignFrom local records, we identified closed, intervention trials sponsored by RM that opened after 1995 and collected PROMs as primary, secondary or exploratory outcomes. Protocol data were extracted by two researchers and scored against the SPIRIT-PRO (PRO protocol content checklist; score 0–100, higher scores indicate better completeness). For studies with locally held datasets, the information team summarized for each study, PRO completion defined as the number of expected (as per protocol) PRO measurements versus the number of actual (i.e. completed) PRO measurements captured in the study data set. Relevant publications were identified by searching three online databases and chief investigator request. Data were extracted and each publication scored against the CONSORT-PRO (PRO manuscript content checklist; scored as SPIRIT-PRO above). Descriptive statistics are presented with exploratory comparisons of point estimates and 95% confidence intervals.ResultsTwenty-six of 65 studies were included in the review. Nineteen studies had accessible datasets and 18 studies published at least one article. Fourteen studies published PRO results. Most studies had a clinical (rather than PRO) primary outcome (16/26). Across all studies, responses in respect of 35 of 69 PROMs were published. Trial protocols scored on average 46.7 (range 7.1–92.9) on the SPIRIT-PRO. Among studies with accessible data, half (10/19) had less than 25% missing measurements. Publications scored on average 80.9 (range 36–100%) on the CONSORT-PRO. Studies that published PRO results had somewhat fewer missing measurements (19% [7–32%] vs 60% [− 26 to 146%]). For individual PROMs within studies, missing measurements were lower for those that were published (17% [10–24%] vs 41% [18–63%]). Studies with higher SPIRIT-PRO scores and PROs as primary endpoints (13% [4–22%] vs 39% [10–58%]) had fewer missing measurements.ConclusionsMissing data may affect publication of PROs. Extent of inclusion of SPIRIT-PRO protocol items and PROs as primary endpoints may improve data completeness. Preliminary evidence from the study suggests a future larger study examining the relationship between PRO completion and publication is warranted.

Abstract P012: Agreement In Same Day Research-Quality And Casual Systolic Blood Pressure Measurements: Insights From BETTER-BP

Introduction: Prior studies suggest that BPs measured casually during routine clinic workflow are higher than research-grade measurements. However, BPs in these studies were typically collected on different days. We sought to compare the influence of day of assessment on this difference, within the context of an ongoing randomized trial. Methods: This is a cross-sectional interim analysis of the BETTER-BP trial, which examines the effect of behavioral economic incentives for medication adherence on BP. Our study focused on concordance between baseline BP measurements and did not analyze trial outcome data. We included the first 171 participants (1/1/20-3/31/22). Research BP was measured in a seated position after a 5 minute rest period using a standard cuff (Omron HEM 907-XL). Casual BP measurements (most recent recording prior to the research measurement) as well as baseline characteristics were abstracted from the EHR. Participants were divided into two groups based on whether their casual BP was measured on the same day (vs different day) as their research BP. The mean systolic BP (SBP) obtained for research versus casual measurements were compared, for the two separate groups, using paired T-tests. Results: Mean age was 55 years, 49.1% were female, 93.2% were nonwhite, and 48.5% were Spanish-speaking. Common comorbidities were diabetes (48.5%) and obesity (49.1%). Among the 132 patients with same day measurements, mean research SBP was 139.2 ± 20.4 mmHg and casual SBP was 137.9 ± 16.4 mmHg (mean within person difference=1.3 mmHg; P=0.259). Among the 39 patients with different days of measurement (median 14 [IQR 6-47] days), mean research SBP was 136.5 ± 23.3 mmHg and casual SBP was 144.2 ± 18.9 mmHg (mean within person difference= -7.7 mmHg; P=0.007). Casual SBP was higher among patients with measurements on different days compared to patients with same day measurements (P=0.043). Conclusion: SBP measured casually during clinical practice were concordant with research-quality measurements when obtained on the same day. SBP measurements differed significantly when obtained on different days. These findings suggest that prior reports showing casual BPs to be consistently higher than research BPs may have been influenced by temporal variability.

A data-driven approach finds RNA polymerase III antibody and tendon friction rubs as enrichment tools for early diffuse scleroderma trials.

Clinical trials in early diffuse SSc have consistently shown a placebo group response with a declining modified Rodnan skin score (mRSS), with negative outcomes. Our objective was to identify strategies using clinical characteristics or laboratory values to improve trial design. We identified early diffuse SSc patients first seen at the University of Pittsburgh from 1980-2015. Eligible patients had ≥3 visits, with at least two mRSS scores within the first year of follow-up. We performed Kaplan-Meier analyses, group-based trajectory analysis of mRSS scores, followed by multivariable regression analysis and classification tree analysis. We applied the results to the abatacept in early diffuse systemic sclerosis (ASSET) trial outcome data. We identified 403 patients with <18 months, and 514 with <36 months disease duration. The median number of mRSS follow-up scores was 14 (interquartile range 8, 25). All methodologic approaches identified skin thickness progression rate, RNA polymerase III (RNAP3) antibody positivity and presence of tendon friction rubs (TFR) as predictors of mRSS trajectory over 5 years of follow-up, and thereby as potential enrichment variables. When applied to the ASSET data, adjustment for both RNAP3 and TFR demonstrated reduction of the placebo mRSS response, particularly at 6 months. A significant difference in the ACR Composite Response Index in Systemic Sclerosis (CRISS) score was found with adjustment by RNAP3 at 6 months, and TFR or RNAP3 at 12 months. Adjustment for both RNAP3 and TFR predicts mRSS trajectory and diminished the mRSS decline in ASSET placebo group, and identified significant differences in CRISS. RNAP3, particularly, is a stratification or enrichment approach to improve early diffuse SSc trial design.

Evaluating a school-based intervention through routine local authority data and national school data: Challenges and opportunities.

ObjectivesThe Department for Education funded local authorities (LAs) in England to embed social workers into schools (SWIS) so that they can work more effectively with teachers, children and families. A two-arm cluster randomised-controlled trial is evaluating the effect of SWIS compared to schools without on referrals to Children’s Social Care. ApproachLAs collate information about all children who are referred to Children’s Social Care within their authority. A case report form has been developed by the trial team to collect school-level information on child protection enquiries, referrals, child in need assessments and numbers of days children spent in state care. Cost data are also included for an economic evaluation. LAs return a completed dataset each quarter, reporting aggregate numbers by school and year group. Data on attendance and attainment will be requested separately from the National Pupil Database (NPD) in 2023. ResultsQuarterly (n=6) data was supplied for 291 schools across 21 LAs. We will focus on this method of data collection and its challenges and opportunities. Relying on 21 external parties to provide all trial data in a timely manner brought risks to the collation of trial outcome data but has reduced the overall costs of data collection. LAs work with a number of different (and changing) IT systems, many of which are not compatible with each other. This, along with personnel changes in data teams, has led to delays. Receiving aggregate data limits the amount of quality control possible, therefore detailed guidance mitigated potential data input errors. Working with LAs to develop and refine these guidance materials at the outset paid dividends. ConclusionUsing routine LA data is a novel cost-efficient approach to collecting trial outcome data at scale. However, the disjointed and varying data architecture that exists in LAs presents practical challenges. This means we relied on good working relationships with the LAs, including regular communication and collaborative problem solving.

Multisite randomised controlled trial of trauma-focused cognitive behaviour therapy for psychosis to reduce post-traumatic stress symptoms in people with co-morbid post-traumatic stress disorder and psychosis, compared to treatment as usual: study protocol for the STAR (Study of Trauma And Recovery) trial

BackgroundPeople with psychosis have high rates of trauma, with a post-traumatic stress disorder (PTSD) prevalence rate of approximately 15%, which exacerbates psychotic symptoms such as delusions and hallucinations. Pilot studies have shown that trauma-focused (TF) psychological therapies can be safe and effective in such individuals. This trial, the largest to date, will evaluate the clinical effectiveness of a TF therapy integrated with cognitive behaviour therapy for psychosis (TF-CBTp) on post-traumatic stress symptoms in people with psychosis. The secondary aims are to compare groups on cost-effectiveness; ascertain whether TF-CBTp impacts on a range of other meaningful outcomes; determine whether therapy effects endure; and determine acceptability of the therapy in participants and therapists.MethodsRater-blind, parallel arm, pragmatic randomised controlled trial comparing TF-CBTp + treatment as usual (TAU) to TAU only. Adults (N = 300) with distressing post-traumatic stress and psychosis symptoms from five mental health Trusts (60 per site) will be randomised to the two groups. Therapy will be manualised, lasting 9 months (m) with trained therapists. We will assess PTSD symptom severity (primary outcome); percentage who show loss of PTSD diagnosis and clinically significant change; psychosis symptoms; emotional well-being; substance use; suicidal ideation; psychological recovery; social functioning; health-related quality of life; service use, a total of four times: before randomisation; 4 m (mid-therapy); 9 m (end of therapy; primary end point); 24 m (15 m after end of therapy) post-randomisation. Four 3-monthly phone calls will be made between 9 m and 24 m assessment points, to collect service use over the previous 3 months. Therapy acceptability will be assessed through qualitative interviews with participants (N = 35) and therapists (N = 5–10). An internal pilot will ensure integrity of trial recruitment and outcome data, as well as therapy protocol safety and adherence. Data will be analysed following intention-to-treat principles using generalised linear mixed models and reported according to Consolidated Standards of Reporting Trials-Social and Psychological Interventions Statement.DiscussionThe proposed intervention has the potential to provide significant patient benefit in terms of reductions in distressing symptoms of post-traumatic stress, psychosis, and emotional problems; enable clinicians to implement trauma-focused therapy confidently in this population; and be cost-effective compared to TAU through reduced service use.Trial registrationISRCTN93382525 (03/08/20)