Clinical and Translational Science

Bayesian borrowing of external information can improve trial efficiency, particularly in pediatric and rare disease settings where patient populations are limited, but may introduce bias and inflate the Type~I error rate when the trial differs from external studies. Recent U.S. Food and Drug Administration (FDA) draft Bayesian guidance emphasizes careful evaluation of external information, prior specification, and assessment of operating characteristics. This paper compares three meta-analytic-predictive (MAP)-based methods for Bayesian borrowing: the MAP prior, robust MAP (RMAP) prior, and self-adapting mixture (SAM) prior. An adaptive platform trial design in Rett syndrome is used as a case study. Simulation studies evaluate frequentist operating characteristics under varying prior--data conflict, between-study heterogeneity, treatment effects, and clinically significant differences (CSDs) for the SAM prior. The MAP prior achieved the greatest efficiency when external and current data were compatible but exhibited the largest bias under substantial prior--data conflict. The RMAP priors improved robustness through fixed robust-component weights, whereas the SAM prior adaptively adjusted borrowing and was less sensitive to prior--data conflict while retaining efficiency gains when the data were compatible. Although the CSD influenced the degree of adaptive borrowing, as reflected by effective sample size, it had only a modest impact on frequentist operating characteristics. Sensitivity analyses using a skeptical robust component yielded similar qualitative conclusions, while accentuating the differences between the MAP and RMAP priors. These findings provide guidance for evaluating and selecting MAP-based borrowing strategies before trial implementation, particularly in rare disease settings, consistent with current FDA recommendations.

mRNA-based therapeutics have emerged as a transformative class of medicines, yet their translation beyond infectious disease vaccines remains challenged by the absence of an integrated pharmacological framework accounting for the tri-component nature of these therapies - the lipid nanoparticle, the mRNA, and the expressed protein. Here, we present a modular, multiscale computational platform integrating two complementary mechanistic models covering the full pharmacological cascade of mRNA-based immunotherapies. The first is a Quantitative Systems Pharmacology (QSP) model describing the immunological response to mRNA vaccines, from antigen expression in antigen-presenting cells through B cell activation and circulating antibody production. The second is a Physiologically Based Pharmacokinetic (PBPK) model tracking whole-body disposition of mRNA-encoded therapeutic antibodies, incorporating a molecular layer resolving LNP uptake, endosomal mRNA escape, and intracellular translation. Both models are informed by a machine learning pipeline that maps IVT-mRNA nucleotide sequences directly onto kinetic parameters, enabling product-specific model simulations. We propose this platform as a step toward the quantitative pharmacological framework that mRNA therapeutics currently lack, and as a practical tool for model-informed design and development of this therapeutic class.

Purpose: To evaluate whether initiation of GLP-1 receptor agonists (GLP-1RAs) is associated with anti-VEGF treatment burden in type 2 diabetes patients with diabetic macular edema (DME) in the IRIS(R) Registry (Intelligent Research in Sight). Methods: Incident GLP-1RA initiators were matched 1:1 with controls via Mahalanobis distance matching (9,896 pairs; N=19,792) on sociodemographics, DME risk factors, and factors influencing GLP-1RA prescription including hypertension, obesity, chronic kidney disease. A longitudinal mixed-effects event-study model evaluated monthly anti-VEGF injection frequency over a 36-month window (12 months before through 24 months after initiation), adjusting for DME duration. Visual acuity (VA) and central subfield thickness (CST) were secondary outcomes. Results: Following GLP-1RA initiation, anti-VEGF injection trajectories did not significantly differ between the matched GLP-1RA and control cohorts (interaction coefficients -0.18 to 1.59, P>0.05). Likewise, no differences in VA were observed between cohorts (-0.05 to 0.04 logMAR, P>0.05) or CST (-14.12 to 33.58 {micro}m, P>0.05). Conclusion: In these matched cohorts, GLP-1RA initiation was not associated with the trajectory of anti-VEGF use or changes in VA or CST. Precis We used the American Academy of Ophthalmology IRIS(R) Registry (Intelligent Research in Sight) to identify patients with DME. In 19,792 matched patients, there was no significant reduction in injection frequency post GLP1-RA initiation and no significant change in VA or CST.

Background Treatment of Raynaud phenomenon(RP) with oral vasodilators(calcium channel block-ers and phosphodiesterase type 5 inhibitors) has shown moderate efficacy, may not benefit to all patients, and adverse effects often compromise long-term treatment. In addition, a large placebo effect may jeopardize the assessment of treatment benefits. Pharmaconutritional strategies aiming at increasing nitric oxide bioavailability (beet-root juice and L-citrulline) may be promising alternatives, and we further hypothesized that patient preference for a treatment could be a driver of the response. Methods This study consisted of a series of randomized, double-blind, N-of-1 trials conducted in outpa-tients with primary or secondary RP. Each patient underwent a multiple crossover design with repeated blocks of randomized treatments periods: 2 weeks of placebo, 2 weeks of active treat-ments, and 1 week of washout. Outcomes included the Raynaud Condition Score(RCS), fre-quency and daily duration of attacks. Each patient prespecified its preferred primary outcome, efficacy threshold and preferred treatment, which was used for stratified randomization. Gener-alized linear mixed-effects models were used to determine individual and aggregated efficacy. Results Twenty-one patients completed 2 to 8 treatment blocks. Seventeen patients tested L-citrulline, 17 beetroot juice and 13 both treatments. Ten patients selected RCS as a primary outcome, 6 patients the number of attacks and 5 the duration of attacks. Me-dian threshold for considering treatment efficacy chosen by patients was 50% (min-max 20% to 75%) reduction of symptoms. Using individual criteria to define efficacy neither L-citrulline nor beetroot juice showed significant efficacy compared to baseline. Based on the aggregated data, our results show no significant difference between L-citrulline and the L-citrulline-based placebo, nor between beetroot juice and nitrate-depleted beetroot juice, with the exception of the daily duration of RP attacks with beetroot juice (p=0.002). Finally, there was a marked placebo response, notably when patients received their preferred treatment. Conclusions: Our study did not show significant beetroot juice or L-citrulline efficacy in RP. However, we found that individual preference for one treatment over another maximizes responses to both placebo and active treatments, particularly with regard to the frequency and duration of RP attacks, thus suggesting that a real and modifiable placebo effect exists in RP.

Background. Long COVID affects an estimated 400 million people worldwide, and is associated with low quality of life. Nearly all completed Long COVID clinical trials reported no benefit, and most required participants to travel to study sites. This requirement systematically excludes severely affected patients. Because there are numerous candidate therapeutics with established safety profiles and regulatory approvals for other indications, scaled, efficient evaluation of therapeutics is needed. Methods. We designed and are conducting a double-blind, placebo-controlled, phase two trial of tirzepatide for Long COVID fatigue, using an entirely remote infrastructure. Design elements included electronic consent, identity and diagnosis verification through document upload, cold-chain delivery of an injectable study drug through a central pharmacy, shared decision-making for dose titration, repeated at-home capillary blood collection in a biospecimen subcohort, weekly participant touch points through study application, wrist-worn wearable monitoring, and clinical support. The trial is operating under FDA Investigational New Drug authorization. Results. This trial enrolled 1,058 participants in 73 days, at least double the rate of any other Long COVID trial. Mean baseline metrics include mean Fatigue Severity Scale of 59.3 (standard deviation [SD] 4.9), daily step count of 3,611 (SD 2,706, general population reference mean 7,731), EQ-5D-5L of 0.6 (SD 0.2), and FUNCAP27 4.0 (SD 1.0), which was a more severely affected population than other clinical trials that collected comparable data. Study processes are working as designed. Participants use existing advocacy and support channels to gather and communicate. Conclusions. A direct-to-participant, siteless infrastructure can support a double-blind placebo-controlled trial of an injectable drug at scale, accelerate accrual, and reach severely affected participants who are routinely excluded by site-based designs. Modernizing drug distribution and regulatory pathways is needed to realize the full potential of decentralized infrastructure for drug repurposing clinical trials.

Abstract Background: Every non-invasive continuous glucose monitoring (NI-CGM) technology introduced into the landscape faces the same skeptical question, from regulators, clinicians, and competing developers alike: is the candidate signal actually specific to glucose, or does an apparently reasonable accuracy figure simply reflect a model fitting to motion, temperature, calibration offset, or trial-duration artifact? Existing evaluation practice does not answer this question directly. NI-CGM performance is instead reported almost exclusively with metrics inherited from minimally invasive, subcutaneous CGM, the Mean Absolute Relative Difference (MARD), Clarke/Parkes error grids, and ISO 15197-style agreement rates, which were designed for sensors whose glucose specificity is already chemically established and which therefore take specificity as a premise rather than treating it as a result to be demonstrated. Methods: We present a methodology for demonstrating NI-CGM technology glucose specificity during the algorithm-development phase, and illustrate it with a case study based on a quantum-cascade-laser (QCL) photoacoustic NI-CGM device (Neogly) evaluated in the SKAMo-2 free-living clinical trial (eight participants with type 1 diabetes). The methodology combines a white-noise control, a constant-glycemia control, a sensor-ablation control that removes the candidate physical signal while retaining auxiliary covariates, and explicit reporting of the train/test generalization level, so that a reported MARD can be read as evidence of specificity rather than taken on faith. Results: Removing the mid-infrared photoacoustic (PA) signal from the model while retaining all auxiliary sensors (accelerometer, skin temperature, hygrometry, PPG) degraded performance at every generalization level tested, inter-patient MARD rose from 35.0% with the PA signal to 43.1% without it, and intra-experimentation MARD rose from 22.5% to 23.9%, providing direct, internal evidence that the PA channel itself, and not merely the auxiliary covariates, carries glucose-specific information. At the same time, an algorithm trained on pure Gaussian noise produced a MARD of 25% over short test windows, and a trivial constant-glycemia predictor outperformed every machine-learning model tested when generalization was extended from a single recording to an unseen patient (MARD 55% for the naive constant model versus 37% for a deep neural network on inter-patient splits). Reported in isolation, any of these MARD values is uninterpretable; reported against one another, they jointly demonstrate that the signal is specific to glucose while also bounding how much of the headline accuracy figure that specificity currently explains. Conclusions: We propose a specificity-demonstration methodology for NI-CGM technology development, comprising (1) signal quality gating prior to any algorithm benchmarking, (2) a white-noise control to test for genuine information content, (3) a constant-glycemia control to expose trial-duration bias, (4) a sensor-ablation control that isolates the contribution of the candidate physical signal from auxiliary covariates, (5) explicit reporting of the data-splitting generalization level (intra-experimentation, intra-patient, inter-patient). This methodology answers a question that precedes clinical accuracy reporting and that recognized clinical frameworks such as the IFCC Working Group on CGM's Dynamic Glucose Regions guideline are not designed to answer: not how accurate is the device, but is the device measuring glucose at all. We argue that without these controls, MARD and error-grid values for NI-CGM are not comparable across studies and may either overstate clinical readiness or undermine promising technologies. We recommend that this specificity methodology be applied routinely once a candidate NI-CGM sensor reaches algorithm-development stage, alongside and as a deliberate complement to IFCC-style clinical accuracy reporting once the device is mature enough for that evaluation. Keywords: non-invasive continuous glucose monitoring; glucose specificity; algorithm validation; MARD; benchmarking; machine learning; photoacoustic spectroscopy; sensor ablation; Clarke error grid

Long-term in vivo transcriptomic time courses are costly, limiting assessment of chronic molecular responses from short studies. We developed a pharmacokinetics-informed transcriptomic ordinary differential equation model (PKT-ODE) that links an oral pharmacokinetic profile and Hill drug-effect function to first-order turnover of co-expression modules. The model was fitted to rat liver responses to fenofibrate at three doses in Open TG-GATEs through day 8. At the held-out day-29 endpoint, PKT-ODE achieved Pearson r = 0.960 and mean squared error (MSE) = 0.148. In this dataset, these values achieved lower prediction error and higher correlation than four statistical baselines and validation-selected linear and multilayer-perceptron transition models. Literature-curated peroxisome proliferator-activated receptor target genes occurred only in modules with positive fitted drug effects. These results provide a proof of concept for pharmacokinetics-informed transcriptomic extrapolation; cross-compound, cross-organ and alternative-regimen performance remain to be tested.

Accurate prediction of individual medical outcomes is essential for optimizing treatment allocation amid rising costs, coverage denials, and limited clinical resources. Traditional predictive models, including regression and neural networks, rely on average effects and cannot tailor predictions to the specific circumstances of individual cases. We present relevance-based prediction (RBP), a model-free method that predicts outcomes as weighted averages of observed cases, with weights determined by a rigorously defined measure of relevance. Unlike model-based methods that rely on fixed calibrated parameters, RBP revisits the original data for each prediction and customizes both the cases and variables used. Applied to opioid treatment, RBP provides case-specific insights unavailable from conventional models, including how each prior case informs a prediction, how each variable affects its reliability and value, and how reliable the prediction is before it is made. These individualized insights may prevent misleading average-based decisions and reduce harmful or suboptimal treatment.

Objective: To evaluate the clinical efficacy of non-invasive electrical pulsed radiofrequency (PRF) stimulation on diagnostic thresholds and subjective pain in chronic, pedal diabetic peripheral neuropathy (DPN). Methods: A randomized, single-blind, placebo-controlled trial (ClinicalTrials.gov: NCT07725419) enrolled 92 patients with pedal DPN naive to PRF and scoring [&ge;] 4/10 on the Douleur Neuropathique 4 (DN4) test. Participants received either active PRF stimulation (n = 46) or a non-stimulating placebo (n = 46) applied bilaterally to the sciatic nerve in the popliteal fossa for 10 minutes per limb, once weekly for three weeks. The primary outcome was clinical neuropathic resolution (DN4 < 4). Secondary outcomes included subjective pain tracking via the Brief Pain Inventory-Short Form (BPI-SF) Worst Pain scale over a 6-month follow-up window. Missing data were handled via Non-Responder Imputation (NRI). Longitudinal continuous trajectories were modeled using Linear Mixed-Effects Models (LMMs) adjusted for age, gender, and baseline medication use. Results: In the Intention-to-Treat population (N = 92), a significant diagnostic responder effect occurred at 3 months, with 39.1% of active patients dropping below the diagnostic threshold for neuropathy (DN4 < 4) versus 19.6% of placebo controls (p = 0.039). For subjective pain, 47.7% of active patients achieved a Minimally Clinically Important Difference ([&ge;] 3-point reduction) in BPI Worst Pain at 1 month compared to 19.4% of placebo controls (p = 0.008). Multivariable logistic regression identified active treatment as a significant independent predictor of clinical response (Adjusted OR = 4.86; 95% CI: 1.56 to 17.53; p = 0.010). Continuous LMM tracking confirmed a statistically significant treatment-by-timepoint interaction for BPI Worst Pain at 1 month (p = 0.046). Conclusion: A brief, three-week course of non-invasive PRF stimulation serves as a safe, effective, non-pharmacological adjunct that aids in managing the diagnostic presentation of neuropathic pain and mitigates worst pain experiences in patients suffering from pedal DPN.

Background. Cardiovascular-outcomes trials are lengthy, costly, and associated with substantial uncertainty prior to readout. In-silico trial simulation using real-world data (RWD) has emerged as a potential tool to support earlier decision-making; however, evidence of prospective predictive validity, generated prior to trial result disclosure, remains limited. Methods. We applied a semi-mechanistic machine learning framework integrating real-world patient data with biologically informed drug representations to prospectively simulate the VESALIUS-CV trial evaluating evolocumab versus placebo. The simulation model was trained on a combination of patient-level real-world data and a drug-centric knowledge graph and validated for both patient-level and trial-level retrospective predictive performance. The model was then used to simulate VESALIUS-CV before public disclosure of trial results, using a locked model and prespecified eligibility criteria and primary endpoint aligned with the clinical protocol. A patient-level time-to-event model was used to generate virtual trial arms, from which cumulative incidence curves, hazard ratios, confidence intervals, and p-values for major adverse cardiovascular events (MACE) were estimated. Results. In retrospective validation, the model demonstrated strong patient-level discrimination, with time-dependent ROC-AUC values ranging from 0.80 to 0.90 across follow-up horizons. For trial-level validation, 22 randomized cardiovascular-outcomes trials were simulated, and hazard ratios for 3-point MACE across 24 between-arm comparisons showed consistent directional agreement and quantitative correlation with published results such that the model accurately predicted trial success, achieving an F1 score of 0.83, with precision of 0.79 and sensitivity of 0.89. In a fully prospective application, the simulation predicted a statistically significant reduction in 3-point MACE with evolocumab versus placebo, estimating a hazard ratio of 0.78 (95% CI, 0.70-0.87) at 54 months. These predictions were consistent with the subsequently reported VESALIUS-CV results, which demonstrated a hazard ratio of 0.75 (95% CI, 0.65-0.86) at 55 months of median follow-up. Conclusions. In a fully prospective setting, a RWD-driven, AI-based simulation accurately predicted the direction, magnitude, and temporal dynamics of treatment effects observed in the VESALIUS-CV trial. These results demonstrate that in-silico trial simulation can anticipate clinical outcomes in the prospective setting, supporting its use as a complementary tool for early decision-making, trial design optimization, and de-risking in cardiovascular drug development.

Clinical relevance: Validated virtual control arms may provide population-level estimates of treatment effect and reduce reliance on untreated control allocations in myopia trials. Background: Untreated single-vision control arms in paediatric myopia efficacy trials are increasingly difficult to justify and retain. Several published models predict untreated childhood axial elongation by region or ethnicity. Here they are implemented unchanged in an open-source tool and validated against an untreated multi-ethnic cohort. Methods: Five published models predicted untreated elongation from baseline age, cycloplegic spherical equivalent, sex, and ethnicity, anchored at baseline axial length (AL) and evaluated at actual follow-up. Predictions were compared with 242 untreated myopic children (Chinese, Vietnamese, Indian) with AL measured at approximately 6 and 12 months, assessing bias, root-mean-square error, and prediction-interval coverage against pre-specified thresholds (bias <0.03 mm; coverage greater than or equal to 0.90). Results: The regional generalised estimating equation (GEE) and meta-regression models reproduced mean East Asian elongation without meaningful bias at 6 months (GEE bias -0.013 mm; equivalence to plus-or-minus 0.03 mm, p = 0.014) and at 12 months (-0.004 mm), although equivalence was not established at 12 months in an underpowered subgroup (n = 71, all Vietnamese; p = 0.068). Older age-only models under-predicted by 0.07 to 0.12 mm. Published individual prediction intervals were too narrow (coverage 0.77): the means were accurate, the individual uncertainty was not. Indian elongation fell between strata and was matched by no existing model. Conclusions: The models reproduce mean untreated East Asian elongation at 6 months, conditional on cohort independence; South Asian children remain unserved by any existing stratum. The tool is a group-level instrument, not an individual predictor, and a transparent unification of published models in open-source code. Its value for estimating treatment effect awaits back-testing against a trial with a known untreated arm, ideally over 24 to 36 months.

Background: Recruitment of diverse participants remains a challenge in cardiovascular clinical trials. Little is known about how recruitment efficiency and advertising costs with web-based tools vary across US communities. We evaluated an online recruitment platform and examined the cost of acquiring both all-comers and diverse participants in relation to community-level income. Methods: The Heartbeat Study evaluated a digital recruitment strategy to identify US participants for the ongoing Phase 3 LIBREXIA-AF trial. Online advertisements directed individuals with atrial fibrillation to a pre-screening website, where demographic and health data were collected. Advertising impressions, clicks, and costs were recorded. Participant ZIP codes were linked to Core Based Statistical Areas (CBSAs) and CBSA-level income. We measured recruits from underrepresented groups (women, African Americans, Latinos) completing online registration per $100,000 in advertising expenses. Click-weighted linear regression evaluated associations between CBSA income and advertising efficiency. Results: A total of 1,406 recruits completed online registration, with 1,319 participants from 260 CBSAs included in the geographic analysis. Participants were 73 years old on average; 547 (41.5%) were women, 59 (4.5%) African American, and 44 (3.3%) Latino. A total of $163,949.13 was spent on 82,681,711 impressions and 454,750 clicks. Recruits per $100,000 in advertising spend were 334 for women, 36 for African Americans, and 27 for Latinos. CBSA-level income was modestly inversely associated with cost per impression (R2=0.058; p<0.001) and cost per click (R2=0.038; p=0.005), but not recruitment yield for African Americans (p=0.99), Latinos (p=0.37), or women (p=0.21) (R2 range, 0.000-0.13). Conclusions: In this national analysis, online advertising enabled broad engagement across diverse US communities, but income was not associated with recruitment yield among women, African American, or Latino participants. Minority representation remained limited, suggesting digital recruitment alone may be insufficient to improve trial diversity. Targeted, culturally and linguistically tailored strategies may be needed to enhance diverse recruitment.

Purpose The direct effect of iStent inject on intraocular pressure (IOP) in patients with glaucoma is difficult to quantify in pragmatic trials where rates of post-surgical IOP-lowering therapy differ between intervention groups. We aimed to quantify the causal effect of iStent inject on unmedicated IOP at 12- and 24-months post-surgery. Methods Adults with mild-to-moderate glaucoma were 1:1 randomised to receive cataract surgery with iStent inject or cataract surgery alone at an Australian hospital (2017-2020, NCT03106181). IOP-lowering medications were prescribed as per clinician discretion. An exploratory analysis was used to estimate the controlled direct effect of iStent inject on IOP, analogous to the effect expected if all participants had undergone medication washout prior to assessment. Results Ninety-five eyes from 80 people were included (67.4% male, mean age 73.0 years, mean baseline IOP 17.1 mmHg). IOP-lowering medication was required for 53% of eyes in each group at 12 months (n=76); at 24 months (n=86) it was required for 43% and 64% in the active and control groups, respectively. Mean IOP was similar between intervention groups at each outcome visit. The controlled direct effect favoured the iStent inject group at 12 months (-2.1-mmHg difference, 95% CI -4.0,-0.3) but was attenuated at 24 months (-0.5 mmHg-difference, 95% CI -2.6,1.6). Conclusion Although the iStent inject was estimated to have an effect on lowering unmedicated IOP at 12 months, this effect had largely disappeared by 24 months. Medication washout is recommended when safe and practical in future trials to estimate these direct effects with more certainty.

PURPOSE: To assess the predictive performance of panel protein biomarkers as well as an established algorithm that considers repeat biomarker testing for risk prediction of PDAC among a prospective cohort of patients with New-onset diabetes. PATIENTS AND METHODS: A panel of protein biomarkers (CA19-9, CA125, CEA, LRG1, REG3A and TIMP1) were assayed in 6,516 serially collected pre-diagnostic plasma samples from 2,121 NOD patients from the Consortium of Chronic Pancreatitis Diabetes and Pancreatic Cancer (CPDPC)-initiated NOD study who completed the 3-year study follow-up period. The specimen set included 25 pre-diagnostic samples from the 12 PDAC cases diagnosed during study follow-up. We applied a single threshold (ST) method, which considers biomarker levels at a single time point, as well as a previously established parametrical empirical Bayes (PEB) algorithm, which considers prior biomarker measurements, with case calls made based on pre-specified cutoffs corresponding to 1% 1-year risk. Resultant biomarker data as well as case calls were provided to the EDRN Data Management and Coordinating Center as part of a Prospective-sample-collection-Retrospective-Blinded-Evaluation (ProBE)-compliant Phase 3 biomarker validation study. Area under the Receiver Operating Characteristic Curves (AUC), sensitivity, specificity, population-level positive predictive value (PPV), and negative predictive value (NPV) are reported. RESULTS: The 3-year incidence of PDAC in the NOD cohort was 0.57%. When considering PDAC vs non-cancer controls, respective AUCs of individual protein biomarkers ranged from 0.52-0.94, with CA19-9 achieving the highest overall performance of 0.94 (95% CI: 0.86-1.00). At the pre-defined 1% 1-year risk threshold, CA19-9 yielded sensitivity of 83.3% at 97.2% specificity. Additional markers CEA, CA125, and TIMP1 demonstrated sensitivity of 33.3%, 41.7%, and 8.3%, respectively. In a subset of patients, CA19-9 first tested positive at a median (interquartile range [IQR]) of 7 months (4 to 14 months) prior to clinical PDAC diagnosis. Of the two PDAC cases missed by CA19-9 using the ST method, one (diagnosed with stage III PDAC) was detected using the PEBCA19-9 algorithm. CONCLUSION: In the setting of adult new onset diabetes, CA19-9 is a readily available and promising biomarker that can be leveraged for earlier detection of an underlying pancreatic cancer. Additional protein biomarkers may improve sensitivity for earlier detection of PDAC among cases with low CA19-9.

Background: Parametric time-to-event models require specification of a baseline hazard function, which may influence prediction when the underlying hazard shape is uncertain. This study compared conventional joint longitudinal time-to-event models with mechanistic Multi-Task Logistic Regression, which directly models the survival distribution without selecting a continuous parametric hazard family. Methods: A simulated dataset of 100 individuals with longitudinal sum of longest diameters and event outcomes was analyzed using a shared mechanistic tumor shrinkage regrowth model. Event submodels comprised exponential, Gompertz, Weibull, log-normal, log-logistic, and circadian hazards, mechanistic Multi-Task Logistic Regression, and a hybrid neural-mechanistic extension. All models were estimated jointly using shared patient-specific random effects and longitudinal data. Models were evaluated using longitudinal goodness-of-fit, visual predictive checks, five-fold cross-validated inverse-probability-of-censoring-weighted dynamic area under the curve and Brier scores, integrated Brier score, calibration, and event-interval negative log score. Results: Longitudinal parameter estimates and diagnostics were comparable across models. All conventional hazard models produced identical dynamic area under the curve values within prediction windows, although probabilistic accuracy differed. The log-normal hazard achieved the lowest overall integrated Brier score (0.1928). Mechanistic Multi-Task Logistic Regression achieved the highest later landmark discrimination (area under the curve 0.867 versus 0.798 for all hazard models) and the lowest mean event-interval negative log score (2.362). The hybrid model improved intermediate-landmark discrimination but not overall probabilistic accuracy. Conclusions: Mechanistic Multi-Task Logistic Regression provided competitive joint time-to-event prediction while avoiding baseline hazard-family selection. It represents a practical complementary alternative to parametric hazard modeling, particularly when hazard shape is uncertain and dynamic discrimination is important.

Background: Warfarin therapy requires repetitive dose adjustments based on INR (International Normalised Ratio) monitoring. We evaluated the long-term real-world performance of Forsante Warfarin Advisor (WA), a CE-marked class IIb guideline-based decision support and care automation medical device used in anticoagulation management. Methods: Retrospective real-world data from routine clinical use between 2016 and 2026 were analysed. Treatment quality was assessed using Time in Therapeutic Range (TTR). Recommendation performance was evaluated by comparing achievement of target INR after clinician acceptance or modification of Warfarin Advisor recommendations. Results: Among 1348 patients in March 2026 median TTR was 83%, compared with 70% in March 2016. Dosages congruent with Warfarin Advisor recommendations were strongly associated with achieving target INR at follow-up in INR target ranges of 2.0-3.0 and 2.5-3.5. Treatment quality remained consistently high across years of deployment. No serious device-attributable safety incidents, regulatory incident reports, or CAPA cases were identified during 12 calendar years and 82,709 patient years of routine use. Conclusions: The findings provide real-world long-term evidence that a guideline-based warfarin dosing and care automation system can support sustained high-quality anticoagulation control in routine clinical practice. The findings support the feasibility of deploying workflow-integrated execution of selected guideline-driven clinical processes, while the causal effects on clinical outcomes require prospective confirmation. Keywords: Clinical decision support systems, Guideline execution, Real-world evidence, Warfarin, Anticoagulation

We present our Stage-1 Registered Report as a full clinical trial article with all methods in past tense and including mock results, table and figures for the primary analysis. To remind the reader that this Stage-1 article is written before data collection, we highlight in color that these mock results are only for illustrative purposes and will be replaced by the actual results in the Stage-2 Registered Report. Background In patients experiencing out-of-hospital cardiac arrest, optimization of oxygen delivery during cardiopulmonary resuscitation is a critical. Although both positive end-expiratory pressure (PEEP) and zero end-expiratory pressure (ZEEP) are employed during CPR, their respective impacts on clinically relevant outcomes is yet to be clearly established. Methods This investigator-initiated, pragmatic, registry-based, multicenter, triple-blind randomized controlled superiority trial evaluates whether applying 8 cm H2O PEEP during cardiopulmonary resuscitation improves outcomes compared with ZEEP in adults with non-traumatic, non-drowning out-of-hospital cardiac arrest. Pre-randomized CPR kits (1:1 PEEP vs. sham) were used by ambulance sites during manual ventilation throughout the resuscitation process. The primary analysis was conducted in the principal stratum of patients who received either a supraglottic airway or endotracheal tube. The primary outcome was neurological status at hospital discharge measured by a utility-weighted score on the modified Rankin Scale. Secondary outcomes included prehospital return of spontaneous circulation, 30-day survival, and 6-month quality of life. The primary safety outcome was clinically significant pneumothorax.

Background Optimal timing for resuming direct oral anticoagulants (DOACs) after high-risk endoscopic procedures remains uncertain, and existing recommendations derive largely from expert opinion. The objective of this study was to characterize practice patterns and perceptions among endoscopists and outcome prioritization among patients with atrial fibrillation, in order to inform the design of the planned RESUME randomized trial. Methods We conducted parallel, cross-sectional surveys of practicing endoscopists and patients with atrial fibrillation using electronic questionnaires administered via Qualtrics. The endoscopist survey, distributed through the American Society for Gastrointestinal Endoscopy, assessed practice patterns, acceptability of early (postoperative day [POD] +1), intermediate (POD +3), and late (POD +5) resumption strategies, and perceptions of clinical equipoise. The patient survey, distributed through two advocacy organizations, assessed perceived confidence in existing guidance and prioritization of bleeding versus thromboembolic risk. Results A total of 201 endoscopists and 477 patients (92.5% taking a DOAC) provided evaluable responses. Endoscopists demonstrated wide variability in preferred timing of DOAC resumption after a standardized high-risk mucosal resection vignette, ranging from same-day resumption to delays beyond five days. POD +2 was the most commonly selected strategy, and most respondents rated more than one proposed RESUME trial arm as acceptable. Nearly all endoscopists (98.9%) rated a randomized trial to determine optimal timing as important. Patient preferences regarding bleeding versus stroke risk were heterogeneous and symmetrically distributed around the neutral response on a five-point ordinal scale. Preferences did not differ by prior stroke or transient ischemic attack, prior major bleeding, age, sex, or geographic region. More than half of patients (54.6%) reported being very or somewhat confident that clear guidance exists regarding DOAC resumption, despite the absence of high-quality randomized evidence informing this question. Conclusions Endoscopists demonstrate substantial practice variability and clinical equipoise, and patients demonstrate heterogeneous and balanced outcome preferences, regarding the timing of DOAC resumption after high-risk endoscopy. These findings support the ethical justification and relevance of the planned RESUME trial.

Purpose. To evaluate whether a Geographic Atrophy Morphology-based Mapping Algorithm (GAMMA) grid, informed by geographic atrophy (GA) lesion morphology, can accelerate functional progression detection compared with a conventional 10-2 grid and a dense grid (129 locations). This work is motivated by emerging regulatory expectations requiring at least five locations to worsen by [&ge;]7 dB from baseline. Methods. Binary atrophy masks from six autofluorescence images were used to simulate GA expansion over 3 years at 3-month intervals using a stochastic perimeter-growth model with a fixed preferential expansion direction (Pdir). For each image, 32 independent growth histories and 32 microperimetric test realisations per history were generated. For each grid (10-2, Dense, and GAMMA), 5-point clusters were selected outside the baseline GA lesion along three directions (0{degrees}, 30{degrees}, 120{degrees}) away from Pdir, simulating full, partial, and no prior knowledge of Pdir. Ground-truth sensitivities were <0 dB inside the GA lesion and normal outside, calculated using a published normative equation. Response variability was simulated following Henson et al. with baseline averaging. Detection time was the first visit at which all five selected locations showed [&ge;]7 dB loss from baseline. Survival curves and median detection times (T50) were used to compare grid performance. Results. The GAMMA grid achieved the earliest progression detection across all scenarios. Under full knowledge of the expansion direction, T50 was 1.0 year for GAMMA versus 1.25 and 1.5 years for Dense and 10-2, respectively. With partial knowledge, GAMMA's T50 was 1.25 years versus 1.5 and 2.0 years for Dense and 10-2. Even under no knowledge, GAMMA detected progression earliest (T50 = 1.5 years), while Dense required 6 months longer and 10-2 nearly double the time (2.75 years). Conclusions. The automatic GAMMA grid accelerates detection of localised functional progression compared with conventional and dense grids. Structure-informed grid optimisation may better align testing with likely expansion paths, potentially reducing follow-up duration and sample sizes in perimetry-based interventional trials.

Light reaching the retina is a primary regulator of human circadian physiology, acting largely through melanopsin-expressing retinal ganglion cells with peak short-wavelength sensitivity. Delivering known, repeatable retinal doses outside the laboratory is difficult because conventional light sources leave viewing geometry, gaze, and ambient conditions uncontrolled. Consumer extended-reality (XR) glasses fix a bright binocular display in constant geometry relative to the eye, but their suitability as calibrated photic stimulators has not been established. Here we validate a commercial micro-OLED XR display (VITURE Luma Ultra) for controlled retinal photostimulation. A purpose-built host application renders exact 8-bit RGB stimuli while independently controlling hardware brightness and logging all intensity-determining state; spectral radiance was measured at the retinal position of a 3D-printed phantom head with an open-source miniature spectroradiometer, anchored to absolute units by a luminance transfer calibration. The blue primary peaks at 461 nm (FWHM 43 nm), is spectrally invariant across a >10-fold intensity range, and at maximum output delivers an estimated 299 lx melanopic equivalent daylight illuminance, above consensus daytime recommendations, while remaining roughly two orders of magnitude below photobiological safety limits. The red primary is visually effective with minimal melanopic drive (melanopic DER 0.10), enabling spectrally shifted evening stimulation. Unlike the immersive virtual-reality headsets previously used for calibrated light delivery, the see-through form factor preserves the wearer's view of the surroundings--relevant for clinical monitoring in supervised settings such as the intensive care unit. These results show that consumer XR glasses can serve as a dose-calibrated platform for wearable photostimulation using an open-source measurement chain, and provide groundwork for application-layer dose-response studies.