Digital Commons Network^™

Integrative Machine Learning Of Genetic And Lifestyle Factors For Personalized Skin Health, Yassine Benachour, Lina Maloukh, Barbara Geusens

All Works

Objective: To develop an AI framework that combines genetic, phenotypic, and lifestyle data for profiling skin-health patterns and generating hypothesis-supporting summaries for potential decision support. Methods and procedures: A dataset of 5,254 individuals integrates six genes (FLG, AQP3, MMP-1, MMP-3, SOD2, GPX), six phenotype severities, and 20+ lifestyle factors. Mutation burden and interactions are tested by ANOVA. K-modes clustering identifies four interpretable dermatological profiles within the cohort and is embedded in leakage-free nested cross-validation (train-only selection; test labels from training centroids). Subtypes are predicted from genetics plus lifestyle using an XGBoost (XGB) classifier; explainability uses gain, permutation importance, and SHAP …

Machine Learning For Wearable Sensor-Based Human Movement Rehabilitation: A Five-Year Systematic Review, Yassine Benachour, Farid Flitti, Lina Maloukh, Aicha Beya Far, Elhocine Boutellaa, Mohamed Bentoumi, Marwa Chendeb El Rai, Nour Aburaed, Khaled Ali, Moez Rehman, Sultan Mosleh, Rania Dghaim, Sadok Bouamama

All Works

Wearable-sensor-based human movement analysis is an increasingly important component of digital health and rehabilitation, enabling objective monitoring and data-driven personalization of therapy. In parallel, machine learning (ML) methods have rapidly expanded for interpreting multimodal movement signals, yet the evidence base remains heterogeneous and difficult to benchmark. This PRISMA-guided systematic review synthesizes recent ML approaches for wearable human motion analysis in rehabilitation-oriented health applications. We searched IEEE Xplore, PubMed, and Scopus for English-language studies published from 2021 to 2025 and extracted information on sensor modalities, ML task formulations and model families, dataset characteristics, validation protocols, and reported performance metrics, together with …

Green Modification Of A Coupling Reaction Between N-Acetylated Amino Acids And P-Nitrothiophenol, Kaitlyn S. Marley, Hannah M. Werle, John P. Morgan

SURF Posters 2026

Native Chemical Ligation (NCL) is a process by which smaller peptides can be linked together to form larger peptides and proteins, but the preparation of necessary thioester starting materials often relies on hazardous reagents. We report a greener synthesis of S-(4- nitrophenyl) N-acetylphenylalaninethioate, building upon the methodology reported by Raines et al.1 This literature synthesis uses N,N-dimethylformamide (DMF) as solvent in the final thioester coupling step to accommodate the limited solubility of N-acetylphenylalanine in less polar solvents. However, DMF is a known reproductive toxin and environmental hazard. Our research demonstrates that acidic water serves as a highly effective, benign alternative …

Microfluidic Encapsulation Of Sorafenib-Loaded Zif-8 Nanoparticles In Ph-Responsive Alginate Microparticles For Oral Chemotherapy Of Hepatocellular Carcinoma, Mojdeh Mirshafiei, Zahra Mahmoudi, Mehdi Mehrpouya, Mahdi Mahmoudi, Masoud Rezaeian, Mona Navaei-Nigjeh, Zahra Katoli, Lobat Tayebi

Electrical & Computer Engineering Faculty Publications

Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related mortality. Sorafenib is the current first-line oral therapy; however, its therapeutic efficacy is limited by poor aqueous solubility, low bioavailability, and gastrointestinal instability. This study aimed to develop a pH-responsive nano-in-microparticle delivery system using a single-step droplet-based microfluidic process to protect sorafenib in the gastric environment and achieve controlled release for enhanced oral chemotherapy. Sorafenib-loaded ZIF-8 nanoparticles (SZ NPs) were synthesized and characterized by scanning electron microscopy (SEM), Fourier-transform infrared (FTIR) spectroscopy, Energy-dispersive X-ray (EDX) spectroscopy, and X-ray diffraction (XRD), exhibiting a mean diameter of about 72 nm and …

Healthcare Digital Twins: A Methodological Literature Review On Integrating Iot And Ai For Personalized Medicine And Predictive Care, Sara Shahnazinia, Mahsa Tavasoli, Abdolhossein Sarrafzadeh, Ali Karimoddini

Electrical & Computer Engineering Faculty Publications

Digital Twin (DT) technology has the potential to revolutionize healthcare delivery and enhance patient outcomes through personalized and precision medicine, simulation models for operations and interventions, and drug discovery. However, successful implementation of DTs in Internet of Things (IoT) and artificial intelligence (AI) healthcare is contingent upon addressing key challenges such as privacy, ethics, and robust data security. This paper presents a methodological literature review of DT applications in healthcare, systematically analyzing the current state of research, key enabling technologies, and implementation challenges. The review summarizes DT categorization approaches (application-based, technology-based, and real-time function-based); delineates core DT components such as …

From Screens To Stress: Public Health Implications Of Cancer Worry In A Digitally Connected World, Shreya Mathur, Ethan Burns, Michael Pokojovy, Tzu-Liang Bill Tseng, Sunil Mathur

Mathematics & Statistics Faculty Publications

Aims:

This study examines how digital information environments, genetic testing experiences, health behaviors, and psychological distress influence cancer-related worry among adults in the United States. It further considers the public health implications of elevated or reduced cancer worry for prevention and risk communication.

Methods:

Data were drawn from a nationally representative survey of 6,252 U.S. adults. Measures included reliance on social media for health decision-making, smoking status, history of genetic testing, and psychological distress. Multivariable analyses assessed associations between these factors and levels of cancer worry.

Results/Findings:

Reliance on social media for health decisions was associated with greater cancer worry, …

Technological Interventions For Reducing Climate Change Impacts On Health: An Overview Of Future Possibilities, Sujatha Alla, Vijay Kumar Chattu, Bawa Singh

Engineering Management & Systems Engineering Faculty Publications

Globally, over five million deaths annually are attributed to extreme weather events exacerbated by climate change, such as heatwaves, hurricanes, wildfires, droughts, and floods, which create health crises and economic losses. The 2015 Paris Declaration emphasized climate technology mechanisms, including research and development, to enhance resilience and reduce greenhouse gas emissions. Biotechnological advancements have mitigated about 20% of economic losses since the 1960s, highlighting technology’s potential to address climate-induced health risks.

A Bayesian Late-Fusion Supportability Framework For Rare-Disease Severity Prediction In Glut1 Deficiency Syndrome, Jordan M. Rodriguez

Mathematics Dissertations

Glucose transporter type 1 deficiency syndrome (GLUT1-DS) is a rare neurometabolic disorder with heterogeneous neurological and developmental severity. Because patient-level severity is not observed as a single validated outcome, this dissertation develops a Bayesian late-fusion supportability framework for constructing and predicting an ordered latent severity phenotype from clinical, genetic, and EEG-derived evidence. The primary target was constructed in a larger clinical cohort using age-5 symptom burden and learning cognition, then assigned to an aligned multimodal prediction cohort. Target-defining variables were excluded from supervised predictors, and models were evaluated using patient-exclusive cross-validation with training-fold preprocessing and fold-wise EEG PCA.

The primary …

Absolute Quantification And Identification Of Rna From Rna-Lipid Nanoparticles Using High Resolution Mass Spectrometry, Jason C. Funderburk

Theses and Dissertations

RNA therapeutics are a rising drug category with potential use for a range of conditions encompassing infectious diseases to therapies for cancer, diseases, and genetic disorders. RNA-lipid nanoparticles (RNA-LNPs) are the prominent delivery method for these therapeutics approved products include mRNA vaccines and polyneuropathy treatments. The emergency use authorizations and orphan drug status of current RNA-LNP drugs has allowed approval without finalization of the regulatory analytical procedures for quality monitoring. The objective of the study was to develop an LC-MS assay to simultaneously measure identity and concentration of two therapeutically relevant intact RNA constructs extracted from RNA-LNPs to enhance quality …

A Framework For Digital Energy Modulation And Translation In Projectional X-Ray Imaging, Richard R. Wargo

Theses and Dissertations

In projectional X-ray imaging, acquisition energy fundamentally influences subject contrast, image noise, and radiation dose. While dual-energy techniques exploit spectral differences to optimize contrast and enable material separation, acquiring multiple images of the same anatomy often requires additional exposures and increased system complexity.

This dissertation presents a framework for digital energy modulation (DEM), in which computational models are trained to translate projection images between energy domains without requiring additional X-ray acquisitions. DEM is conceived as a generalizable strategy for controlled contrast modulation in projection imaging, with potential applications in image optimization, radiation therapy alignment, and dual-energy applications.

The framework was …

The Role Of Resistance Training In Managing Menopause-Related Changes, Alyson K. Strauss

Honors Undergraduate Theses

Resistance training serves as a critical non-pharmacological strategy for mitigating the physiological and psychological declines associated with the menopausal transition. This natural biological phase, marked by declining estrogen levels, typically leads to accelerated bone mineral density loss, sarcopenia, metabolic regulation disturbance, and heightened mood disturbances. To evaluate the efficacy of targeted exercise interventions, a systematic review was conducted following PRISMA guidelines. From an initial pool of 60 relevant articles, 13 studies met the specific inclusion criteria, focusing on structured resistance training protocols for perimenopausal, menopausal, and postmenopausal cohorts.

The synthesis of these studies indicates that resistance training is a highly …

Research In Progress. Why Physiology Feels Hard: Supporting The Transition To Self-Directed Learning In Post-Secondary Physiology, Jaelyn Zoe Rohrbaugh, Mark Paternostro

2026 Scholarly Teaching Conference: Poster Session Papers

Physiology is widely recognized as one of the most challenging courses for STEM students. Contributing factors include the discipline’s inherent complexity, instructional methods, and student preparedness, with both faculty and students identifying the nature of the discipline as the primary challenge. Many undergraduates enter college having experienced predominantly teacher-directed, pedagogical instruction, whereas success in college-level physiology requires self-directed, andragogical learning strategies. We hypothesize that students’ difficulties arise more from a lack of self-directed learning skills than from content complexity alone. To investigate this, we administered an anonymous survey to 340 students spanning freshman to professional levels, achieving a 93% completion …

Excision Followed By Standing Cone Deformity Repair Is Tissue Conservative, Henry Lim, Christian Scheufele, Marshall Hall, Stephen Weis, Dustin Wilkes

North Texas GME Research Forum 2026

Introduction Elliptical excisions are the most common method to treat benign and malignant tumors in dermatologic surgery. This standardized design is traditionally 3 to 4.5 times longer than the excisional width to prevent unsightly protrusions of skin after primary closure, known as “standing cone,” “Burow’s triangles” or “dog ear” deformities. This results in a scar significantly longer than the original tumor that may be less easily concealed.

Objective To compare the final repair length of a tissue conservative method to remove skin tumors and close resultant defects against predicted repair length of traditional elliptical excision.

Methods Eighteen cases are presented …

Reduced Threat-Related Neural Efficiency: A Possible Biomarker For Pediatric Anxiety Disorders, Julia O. Linke, Reut Naim, Simona P. Haller, Parmis Khosravi, Beck Scheinberg, Meghan E. Byrne, Anita Harrewijn, Ellen Leibenluft, Melissa A. Brotman, Anderson M. Winkler, Daniel Samuel Pine

School of Medicine Publications

Objective: Pediatric anxiety disorders are common and predict adult psychopathology, yet current treatments, such as cognitive-behavioral therapy (CBT), produce lasting remission in less than 50% of affected youths. To support the search for improved, mechanistically grounded interventions, this study evaluated neural efficiency, defined as similarity in functional connectivity between a threat task and rest, as a potential biomarker. The study evaluated neural efficiency in relation to anxiety diagnosis and treatment response.

Methods: The authors compared 103 youths with an anxiety disorder diagnosis (mean age, 12.5 years [SD=2.91], 62% female) to 103 youths with no psychiatric diagnosis (mean age, 13.4 years …

Cognitive Load Classification Using Functional Near-Infrared Spectroscopy, Pratham Shah

Theses and Dissertations (Comprehensive)

This thesis investigates the classification of cognitive load using functional near-infrared spectroscopy (fNIRS) signals recorded during an N-back working memory task. The study introduces a novel short-channel correction layer designed to suppress superficial physiological noise adaptively, addressing limitations of traditional General Linear Model (GLM) based regression. A single participant dataset comprising 69 validated sessions was analyzed using both conventional machine learning and deep learning approaches. Traditional classifiers: Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Random Forests, and Gradient Boosting were first evaluated using statistical features (mean, variance, peak, and slope). Among these, Gradient Boosting achieved the highest accuracy (55.6%), …

Taylorkan-Vit: Parameter-Efficient Vision Transformers For Medical Image Classification, Kaniz Fatema, Dr. Emad Mohammed, Dr. Sukhjit Singh Sehra

Theses and Dissertations (Comprehensive)

Effective and interpretable classification of medical images remains a critical challenge in computer-aided diagnosis, particularly in data-scarce and resource-constrained clinical settings where traditional deep learning models prove impractical. This study addresses the fundamental barrier to Vision Transformer adoption in medical imaging—massive parameter counts and data requirements—through a systematic two-phase methodology. Phase 1 evaluates three spline-based Kolmogorov–Arnold Network (KAN) variants to identify the optimal nonlinear approximation function for parameter-efficient medical image classification: SBTAYLOR-KAN (B-splines with Taylor series), SBRBF-KAN (B-splines with Radial Basis Functions), and SBWAVELET-KAN (B-splines with Morlet wavelets). Comprehensive experiments across brain MRI, chest X-rays, and tuberculosis datasets—without any image …

Sustainable Community-Wide Model To Enhance Cancer Data Usage And Utility, Emily S Boja, Mousumi Ghosh, Ying Huang, Tung-Shing Mamie Lih, Rawan Shraim, Gregory Wheeler, Diana Thomas, Rakesh Khanna, Stephanie Sandor, Jinghui Zhang, Sapna Oberoi, Nicolas J Llosa, Aditya Suru, Fernanda Silva Michels, Eric Durbin, Anna Fernandez, Lucy Han, James Galbraith, Yin Lu, Brian Furner, James H Tanis, Michael Watkins, David Higgins, Gavriel Matt, Xin Zhou, Yang Li, Marcin Cieslik, Jamie Estill, Michael Sierk, Yanling Sun, Weiping Ma, Joseph Flores-Toro, Freddie Pruitt, Subhashini Jagu, Elmer A Fernández, Daniela Orschanski, Stephanie J Spielman, Jaclyn Taroni, Trinh Nguyen, Brian Capaldo, Erin Beck, Tanja Davidsen, Johanna Goderre Jones, Minghong Ward, Anne Sturcke, Jennifer M Torres Del Valle, Sean Hanlon, Danielle Daee, Brandon Wright, Nathaniel Boyd, Heather Basehore, Liang Liu, Ying Hu, Daoud Meerzaman, Huiqing Li, Ying Wu, Gregory H Reaman, Erin Rudzinski, Jack F Shern, Brigitte Widemann, Warren Kibbe, Jaime Guidry Auvil

Children’s Nutrition Research Center Staff Publications

Despite vast investments in data collection, generation, and sharing from childhood cancer studies, for example, Gabriella Miller Kids First Research Program, Childhood Cancer Data Initiative, and other efforts, secondary use of data remains a challenge especially for rare diseases. The National Cancer Institute (NCI) Office of Data Sharing (ODS) aimed to promote FAIR (Findable, Accessible, Interoperable, Reusable) data sharing practices to enhance data utility, accelerate discovery, and foster interdisciplinary collaboration. NCI ODS launched its inaugural Data Jamboree focusing on childhood cancer alongside its third Annual Symposium in September 2025. Over 120 participants with diverse backgrounds coalesced into 23 multidisciplinary teams …

Medical Image Feature Extraction And Selection Based On Inception V3 And Gini Index For Cervical Cancer Cells Identification, Rachida Assawab, Mounir Ouzir, Badreddine Benyacoub, Abderrahim Electrochemical Sensor For Tenofovir Allati, Ismail El Moudden

Research and Infrastructure Service Enterprise (RISE) Faculty Publications

Background: Cervical cancer remains the fourth most common cancer in women globally, with 604,000 new cases annually. Early detection through cytological screening is critical, but manual interpretation suffers from high false negative rates and requires expert pathologists often unavailable in resource-limited settings.

Methods: We developed a novel hybrid framework combining InceptionV3-based deep feature extraction with Gini Index feature selection for automated cervical cancer cell classification. Using the Herlev dataset (917 Pap smear images: 242 normal, 675 abnormal), we extracted 2048 deep features and applied systematic feature selection to identify optimal discriminative subsets. Comprehensive clustering analysis (K-means, K-medoid, Fuzzy clustering) validated …

The Financial Effects Of Clinically Significant Readmissions In Florida Hospitals, Brian Patrick Cotton

Doctor of Business Administration Dissertations

This study examines how the financial performance of hospitals in the state of Florida impacts the quality of care. This study used data from 2017-2019 to evaluate how the number of hospital readmissions – a proxy for care quality – are associated with and vary in relation to the stability of the financial resources held by the hospital. The Hospital Readmission Reduction Program (HRRP), a Medicare value-based purchasing program, uses a mandatory pay-for-performance process that was initiated to decrease the 30-day hospital readmission rate. Hospitals with high readmissions are held accountable and financially encouraged to increase their quality of care …

First-Job Contract Review Cheat Sheet, Johanna Jones-Morris, Ashlee Martellacci

Teaching and Learning Resources

This cheat sheet helps first-time employees understand what to review before signing an employment contract. It highlights job duties, compensation, scheduling, employment terms, benefits, restrictive clauses, worker classification, and common red flags so that individuals can ask informed questions and recognize potentially unfair or unclear terms.

Latent Classification Of Time-Dependent Transition Rates In Longitudinal Binary Outcome Data, Joonha Chang, Wenyaw Chan

Faculty, Staff and Student Publications

Continuous-time Markov chain (CTMC) models and latent classification methods are commonly used to analyze longitudinal categorical outcomes in medical research. While CTMC models are popular for their simplicity and effectiveness, their assumption of constant transition rates presents limitations in capturing dynamic behaviors. To address this, non-homogeneous continuous-time Markov chains (NH-CTMCs) have been developed, incorporating time-varying transition rates to enhance model flexibility. In this study, we leverage closed-form transition probabilities for a fully ergodic two-state NH-CTMC model and propose a latent class clustering approach to identify heterogeneous transition rate patterns within the population. We emphasize the potential advantages of these models …

Comparative Analysis Of Left Ventricular Sympathetic Axons In Shr And Wky Rats, Parker R. Tyson

Honors Undergraduate Theses

Hypertension is a leading cause of cardiovascular morbidity and mortality and is strongly associated with dysregulation of the sympathetic nervous system, which plays a central role in controlling heart rate, vascular tone, and blood pressure. Although increased sympathetic activity has been widely documented in hypertensive disease, the precise structural organization of sympathetic innervation within the hypertensive heart remains incompletely characterized. This study investigates whether chronic hypertension is associated with structural remodeling of sympathetic axons in the left ventricle by comparing autonomic innervation patterns between spontaneously hypertensive rats (SHR) and normotensive Wistar Kyoto rats (WKY). Hearts were collected following transcardial perfusion …

Synthesis And Characterization Of Succinate Dehydrogenase Inhibitors H2/Z14 And C6/Z96 In Order To Combat Small Cell Lung Cancer, Lauren M. Graves

Honors Undergraduate Theses

The use of ubiquinone inhibitors to combat cancer is a recent development in medicinal science and procedures to efficiently synthesize these drugs remain limited. C6/Z96 and H2/Z14, two succinate dehydrogenase inhibitors, have been previously tested in vitro against NSCLC with positive results. Their similarity to ubiquinone is what makes them a strong candidate for a succinate dehydrogenase inhibitor at the ubiquinone binding site. The heterocyclic scaffolds and substituents are proposed to bind strongly through a combination of electronics, hydrogen-bonding, and fit, allowing them to bind well and prove to be more favorable than ubiquinone when competing for the Q-site. By …

Comorbid Psychosis In The General Population, Mariam Sankoh

Theses and Dissertations

Psychosis is a complex psychiatric phenotype characterized by substantial heterogeneity in symptom presentation, psychosis diagnosis, comorbidity patterns, and genetic architecture across diverse populations in the general population. Despite advances in psychiatric epidemiology and genomics, important gaps remain in understanding how psychosis symptoms and psychosis-related diagnoses manifest across racial and ethnic groups and how evolutionary genomic factors may contribute to disease risk and cross-ancestry genetic prediction. This dissertation, Characterizing Comorbidity in Psychosis in the General Population, integrates epidemiological, psychometric, and evolutionary genetic approaches to examine psychosis across diverse populations.

Aim 1 examined racial and ethnic differences in psychosis comorbidity patterns …

24-H Circadian Rest-Activity Rhythms And Physical Frailty In Older Adults: A Biopsychosocial Perspective On Sex Differences And Social Capital, Jisu Kim

Theses and Dissertations

Physical frailty is a prevalent geriatric syndrome characterized by age-related decline across multiple physiological systems. Physical frailty is associated with an elevated risk of adverse health outcomes, including chronic disease progression and all-cause mortality, representing a significant and growing public health concern. Disrupted circadian rest-activity rhythms (RARs) are increasingly recognized as clinically meaningful markers of physical frailty in older adults. However, the extent to which biological sex and social determinants of health (SDoH), particularly social capital, contribute to or modify the associations between RARs and physical frailty remains underexplored.

Therefore, this dissertation summarizes the existing literature on age-related changes in …

Targeting Nuclear Receptors In Muscular Dystrophies And Regenerative Myogenesis, Eira Mann, Hoang Vu, Vihang A Narkar, Yu Liu

The Brown Foundation: Institute of Molecular Medicine

Skeletal muscle is a highly plastic tissue with a robust capacity for regeneration, largely driven by resident satellite cells. Muscular dystrophies comprise a heterogeneous group of inherited disorders characterized by progressive muscle degeneration, chronic inflammation, and impaired regenerative capacity. Despite well-defined genetic etiologies, effective disease-modifying therapies for these disorders, as well as many acquired myopathies, remain limited. Emerging evidence identifies nuclear receptors (NRs) as key regulators of skeletal muscle homeostasis, integrating hormonal, metabolic, and environmental signals to control transcriptional programs governing mitochondrial function, metabolism, inflammation, and myogenesis. In this review, we summarize the diverse roles and mechanisms of action of …

Surgical Interventions For Treating Vesicovaginal Fistula In Women, Yoshiyuki Okada, Tomomi Matsushita, Takeshi Hasegawa, Hisashi Noma, Erika Ota, Bob Achila, Yasukuni Yoshimura

Obstetrics and Gynaecology, East Africa

Objectives This is a protocol for a Cochrane Review (intervention). The objectives are as follows:

To assess the benefits and harms of surgical interventions for treating vesicovaginal fistula in women.

Ai Privacy And Security In Healthcare: A Systematic Literature Review, Diane Dolezel, Karima Lalani, Valerie Watzlaf, Kerryn Butler-Henderson, Elise V Z Lambert, Mary Morton, Jamie Sand, David Gibbs, Susan Fenton

Faculty, Staff and Student Publications

Background: Artificial intelligence is expanding into telemedicine and telerehabilitation, yet significant privacy and security concerns persist.

Scope: To synthesize empirical evidence on privacy and security approaches in health care, particularly those relevant to distributed home care.

Methodology: A systematic review identified 80 studies (2019 to 2025), and Latent Dirichlet Allocation (LDA) topic modeling characterized the privacy and security themes.

Results: Sixty-six studies addressed privacy, only seventeen addressed security, and three studies addressed both. LDA identified four themes: patient data privacy, federated learning for medical imaging, encrypted training and secure computation, and healthcare data governance. Most studies emphasized privacy-preserving approaches, like …

A Structured Review Of Emerging Prompt Instability Indices (Piis) To Evaluate The Reproducibility Of Prompt-Driven Image Segmentation Models., Adib Shafipour, Deevakar Rogith, Zulfiia Ditto, Hien V Tran, Katie Stinson, Toufeeq Ahmed Syed

Faculty, Staff and Student Publications

Prompt-aware image segmentation has introduced new flexibility by allowing models to incorporate user-provided cues, such as points, boxes, or text prompts, to guide object delineation across diverse imaging domains. This flexibility introduces a fundamental challenge: ensuring segmentation results remain reproducible when prompts are altered. Minor variations in prompts produce substantially different outputs, raising concerns about the reliability and scientific validity of research and clinical applications. This paper conducts a structured review of emerging Prompt Instability Indices (PIIs), quantitative measures developed to assess the reproducibility of prompt-driven segmentation models. A systematic search of PubMed and arXiv (2020-2025) identified relevant studies, analyzed …

Safety And Efficacy Of 6% Hydroxyethyl Starch In Patients Undergoing Major Surgery: The Randomised Controlled Phoneics Trial, Wolfgang Buhre, Óscar Díaz-Cambronero, Simon Schaefer, Martin Novacek, Marina Soro Domingo, Bjorn Stessel, Aurelio Rodríguez-Pérez, Torsten Richter, Georg Rohe, Bernard Cholley, Matthias Gruenewald, Gerhardus Kuiper, Samir Jaber, Dianne De Korte, Javier Belda, Marcelo Gama De Abreu, Robert Baronica, Thomas Scheeren, Carlos Ferrando-Ortolá, Wojciech Szczeklik, Dana Tomescu, Tomas Vyzamal, Zejka Gavranovic, María Pilar Argente-Navarro, Guido Mazzinari, Sarah Thaler, Nuria García-Gregorio, Jeroen Vandenbrande, Diane Zlotnik, Jakob Wittenstein, Sonja Schmier, Susanne Rohn, Christoph Glasmacher, Martin Holler, Cornelius Jungheinrich, Ulf Niess, Daniel I Sessler, Martin Westphal

Faculty, Staff and Student Publications

Abstract

Background: Hydroxyethyl starch (HES) is often used for maintaining vascular volume during major surgery. Long-term high-dose HES in septic patients promotes renal injury, whereas meta-analyses of current HES products in surgical patients do not show such effects.

Objective: We studied if the peri-operative use of HES is noninferior to crystalloids in terms of acute kidney injury. Secondary outcome was the noninferiority of HES on worsening of renal injury and/or the incidence of a composite endpoint of major complications and mortality until postoperative day 90.

Design: Randomised double-blind trial in patients undergoing elective abdominal surgery with expected blood loss at …