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1. How Many Patients Do You Need? Investigating Trial Designs for Anti-Seizure Treatment in Acute Brain Injury Patients

   https://doi.org/10.1101/2023.08.21.23294339  

   Parikh, Harsh; Sun, Haoqi; Amerineni, Rajesh; Rosenthal, Eric S; Volfovsky, Alexander; Rudin, Cynthia; Westover, M Brandon; Zafar, Sahar F (August 2023, medRxiv)

   Abstract ObjectivesEpileptiform activity (EA) worsens outcomes in patients with acute brain injuries (e.g., aneurysmal subarachnoid hemorrhage [aSAH]). Randomized trials (RCTs) assessing anti-seizure interventions are needed. Due to scant drug efficacy data and ethical reservations with placebo utilization, RCTs are lacking or hindered by design constraints. We used a pharmacological model-guided simulator to design and determine feasibility of RCTs evaluating EA treatment. MethodsIn a single-center cohort of adults (age >18) with aSAH and EA, we employed a mechanistic pharmacokinetic-pharmacodynamic framework to model treatment response using observational data. We subsequently simulated RCTs for levetiracetam and propofol, each with three treatment arms mirroring clinical practice and an additional placebo arm. Using our framework we simulated EA trajectories across treatment arms. We predicted discharge modified Rankin Scale as a function of baseline covariates, EA burden, and drug doses using a double machine learning model learned from observational data. Differences in outcomes across arms were used to estimate the required sample size. ResultsSample sizes ranged from 500 for levetiracetam 7 mg/kg vs placebo, to >4000 for levetiracetam 15 vs. 7 mg/kg to achieve 80% power (5% type I error). For propofol 1mg/kg/hr vs. placebo 1200 participants were needed. Simulations comparing propofol at varying doses did not reach 80% power even at samples >1200. InterpretationOur simulations using drug efficacy show sample sizes are infeasible, even for potentially unethical placebo-control trials. We highlight the strength of simulations with observational data to inform the null hypotheses and assess feasibility of future trials of EA treatment. 

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2. Trialstreamer: A living, automatically updated database of clinical trial reports

   https://doi.org/10.1093/jamia/ocaa163  

   Marshall, Iain J; Nye, Benjamin; Kuiper, Joël; Noel-Storr, Anna; Marshall, Rachel; Maclean, Rory; Soboczenski, Frank; Nenkova, Ani; Thomas, James; Wallace, Byron C (September 2020, Journal of the American Medical Informatics Association)

   null (Ed.)

   Abstract Objective Randomized controlled trials (RCTs) are the gold standard method for evaluating whether a treatment works in health care but can be difficult to find and make use of. We describe the development and evaluation of a system to automatically find and categorize all new RCT reports. Materials and Methods Trialstreamer continuously monitors PubMed and the World Health Organization International Clinical Trials Registry Platform, looking for new RCTs in humans using a validated classifier. We combine machine learning and rule-based methods to extract information from the RCT abstracts, including free-text descriptions of trial PICO (populations, interventions/comparators, and outcomes) elements and map these snippets to normalized MeSH (Medical Subject Headings) vocabulary terms. We additionally identify sample sizes, predict the risk of bias, and extract text conveying key findings. We store all extracted data in a database, which we make freely available for download, and via a search portal, which allows users to enter structured clinical queries. Results are ranked automatically to prioritize larger and higher-quality studies. Results As of early June 2020, we have indexed 673 191 publications of RCTs, of which 22 363 were published in the first 5 months of 2020 (142 per day). We additionally include 304 111 trial registrations from the International Clinical Trials Registry Platform. The median trial sample size was 66. Conclusions We present an automated system for finding and categorizing RCTs. This yields a novel resource: a database of structured information automatically extracted for all published RCTs in humans. We make daily updates of this database available on our website (https://trialstreamer.robotreviewer.net). 

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3. Heterogeneity in the Effect of Early Goal-Directed Therapy for Septic Shock: A Secondary Analysis of Two Multicenter International Trials

   https://doi.org/10.1097/CCM.0000000000006463  

   Shah, Faraaz Ali; Talisa, Victor B; Chang, Chung-Chou H; Triantafyllou, Sofia; Tang, Lu; Mayr, Florian B; Higgins, Alisa M; Peake, Sandra L; Mouncey, Paul; Harrison, David A; et al (January 2025, Critical Care Medicine)

   OBJECTIVES:The optimal approach for resuscitation in septic shock remains unclear despite multiple randomized controlled trials (RCTs). Our objective was to investigate whether previously uncharacterized variation across individuals in their response to resuscitation strategies may contribute to conflicting average treatment effects in prior RCTs. DESIGN:We randomly split study sites from the Australian Resuscitation of Sepsis Evaluation (ARISE) and Protocolized Care for Early Septic Shock (ProCESS) trials into derivation and validation cohorts. We trained machine learning models to predict individual absolute risk differences (iARDs) in 90-day mortality in derivation cohorts and tested for heterogeneity of treatment effect (HTE) in validation cohorts and swapped these cohorts in sensitivity analyses. We fit the best-performing model in a combined dataset to explore roles of patient characteristics and individual components of early goal-directed therapy (EGDT) to determine treatment responses. SETTING:Eighty-one sites in Australia, New Zealand, Hong Kong, Finland, Republic of Ireland, and the United States. PATIENTS:Adult patients presenting to the emergency department with severe sepsis or septic shock. INTERVENTIONS:EGDT vs. usual care. MEASUREMENTS AND MAIN RESULTS:A local-linear random forest model performed best in predicting iARDs. In the validation cohort, HTE was confirmed, evidenced by an interaction between iARD prediction and treatment (p< 0.001). When patients were grouped based on predicted iARDs, treatment response increased from the lowest to the highest quintiles (absolute risk difference [95% CI], –8% [–19% to 4%] and relative risk reduction, 1.34 [0.89–2.01] in quintile 1 suggesting harm from EGDT, and 12% [1–23%] and 0.64 [0.42–0.96] in quintile 5 suggesting benefit). Sensitivity analyses showed similar findings. Pre-intervention albumin contributed the most to HTE. Analyses of individual EGDT components were inconclusive. CONCLUSIONS:Treatment response to EGDT varied across patients in two multicenter RCTs with large benefits for some patients while others were harmed. Patient characteristics, including albumin, were most important in identifying HTE. 

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4. Unified Scheduling for Predictable Communication Reliability in Industrial Cellular Networks

   https://doi.org/10.1109/ICII.2018.00022  

   Xie, Yuwei; Zhang, Hongwei; Ren, Pengfei (October 2018, IEEE International Conference on Industrial Internet (ICII))

   Cellular networks with D2D links are increasingly being explored for mission-critical applications (e.g., real-time control and AR/VR) which require predictable communication reliability. Thus it is critical to control interference among concurrent transmissions in a predictable manner to ensure the required communication reliability. To this end, we propose a Unified Cellular Scheduling (UCS) framework that, based on the Physical-Ratio-K (PRK) interference model, schedules uplink, downlink, and D2D transmissions in a unified manner to ensure predictable communication reliability while maximizing channel spatial reuse. UCS also provides a simple, effective approach to mode selection that maximizes the communication capacity for each involved communication pair. UCS effectively uses multiple channels for high throughput as well as resilience to channel fading and external interference. Leveraging the availability of base stations (BSes) as well as high-speed, out-of-band connectivity between BSes, UCS effectively orchestrates the functionalities of BSes and user equipment (UE) for light-weight control signaling and ease of incremental deployment and integration with existing cellular standards. We have implemented UCS using the open-source, standards-compliant cellular networking platform OpenAirInterface, and we have validated the UCS design and implementation using the USRP B210 software-defined radios in the ORBIT wireless testbed. We have also evaluated UCS through high-fidelity, at-scale simulation studies; we observe that UCS ensures predictable communication reliability while achieving a higher channel spatial reuse rate than existing mechanisms, and that the distributed UCS framework enables a channel spatial reuse rate statistically equal to that in the state-of-the-art centralized scheduling algorithm iOrder. 

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5. EXPLAINABLE MACHINE LEARNING FOR SOILCRETE UCS PREDICTIONS

   Cheng, Katherine; Gingery, J R; Yossifov, M D; Ziotopoulou, K (November 2024, Deep Foundations Institute)

   Soil mixing is a ground improvement method that consists of mixing cementitious binders with soil in-situ to create soilcrete. A key parameter in the design and construction of this method is the Unconfined Compressive Strength (UCS) of the soilcrete after a given curing time. This paper explores the intersection of Machine Learning (ML) with geotechnical engineering and soilcrete applications. A database of soilcrete UCS and site/soil/means/methods metadata is compiled from recent projects in the western United States and leveraged to explore UCS prediction with the eXtreme Gradient Boosting (XGBoost) ML algorithm which resulted in a ML model with a R2 value of 88%. To achieve insights from the ML model, the Explainable ML model SHapley Additive exPlanations (SHAP) was then applied to the XGBoost model to explain variable importances and influences for the final UCS prediction value. From this ML application, a blueprint of how to scaffold, feature engineer, and prepare soilcrete data for ML is showcased. Furthermore, the insights obtained from the SHAP model can be further pursued in traditional geotechnical research approaches to expand soil mixing knowledge. 

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