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White matter hyperintensities and relapse risk in late-life depression

Journal of Affective Disorders 2025年 383卷 298-305页

作者： Pearcy, Leigh B. Karim, Helmet T. Butters, Meryl A. Krafty, Robert Boyd, Brian D. Banihashemi, Layla Szymkowicz, Sarah M. Landman, Bennett A. Ajilore, Olusola Taylor, Warren D. Andreescu, Carmen Department of Psychiatry University of Pittsburgh Pittsburgh PA United States Department of Bioengineering University of Pittsburgh Pittsburgh PA United States Center for Cognitive Medicine Department of Psychiatry and Behavioral Science Vanderbilt University Medical Center Nashville TN United States Geriatric Research Education and Clinical Center Veterans Affairs Tennessee Valley Health System Nashville TN United States Department of Psychiatry University of Illinois-Chicago Chicago IL United States Department of Biostatistics and Bioinformatics Emory University Atlanta GA United States Departments of Computer Science Electrical Engineering and Biomedical Engineering Vanderbilt University United States Department of Radiology and Radiological Sciences Vanderbilt University Medical Center United States

Background: White matter hyperintensities (WMHs) are associated with late-life depression (LLD) and are considered a hallmark of MRI-defined vascular depression. However, their impact on depression recurrence in LLD is less well known. Methods: We investigated this relationship using data from a 2-year multi-site, longitudinal study, where baseline WMH volumes were obtained using 3 T FLAIR magnetic resonance imaging (MRI) from 145 participants, of which 102 had remitted LLD and 43 were control participants. We analyzed the effect of baseline WMH volume on LLD relapse over 2 years using regression and adjusting for total intracranial volume, age, sex, race, education, and study site. We performed survival analyses using a Cox proportional hazard model to determine whether baseline WMH volume was associated with time to relapse in LLD. Results: We found that participants with LLD had greater WMH volume at baseline than control participants, but not if accounting for peripheral cardiovascular disease. Participants with LLD who relapsed within 8 months of baseline had larger WMH volume than control participants but did not statistically differ from those that remained remitted;this effect was lost when expanding to participants that relapsed at any point in the 2-year study. WMH burden was not associated with time to relapse, suggesting greater WMH volumes are not indicative of faster relapse rates in LLD. Conclusion: Our results show that WMH burden may play a role in the early - but not the delayed - relapse of LLD, and they underscore the intricate dynamic of the biological markers underlying LLD treatment response and relapse. © 2025 The Authors

关键词： Depression Late-life LLD Recurrence Relapse Small vessel disease WMH

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Long-term effects and effect heterogeneity of lifestyle and metformin interventions on type 2 diabetes incidence over 21 years in the US Diabetes Prevention Program randomised clinical trial

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The Lancet Diabetes and Endocrinology 2025年第6期13卷 469.0-481页

作者： Knowler, William C Doherty, Lindsay Edelstein, Sharon L Bennett, Peter H Dabelea, Dana Hoskin, Mary Kahn, Steven E Kalyani, Rita R Kim, Catherine Pi-Sunyer, F Xavier Raghavan, Sridharan Shah, Vallabh O Temprosa, Marinella Venditti, Elizabeth M Nathan, David M Biostatistics Center Milken Institute School of Public Health George Washington University Rockville MD United States National Institute of Diabetes and Digestive and Kidney Diseases Phoenix AZ United States Lifecourse Epidemiology of Adiposity and Diabetes Center University of Colorado Anschutz Medical Campus Aurora CO United States Department of Medicine VA Puget Sound Health Care System and University of Washington Seattle WA United States Division of Endocrinology Diabetes and Metabolism Johns Hopkins University School of Medicine Baltimore MD United States Department of Internal Medicine University of Michigan Medical School Ann Arbor MI United States Departments of Medicine and Epidemiology Columbia University Irving Medical Center New York NY United States Department of Medicine VA Eastern Colorado Health Care System and University of Colorado School of Medicine Aurora CO United States School of Medicine University of New Mexico Health Sciences Center Albuquerque NM United States Department of Biostatistics and Bioinformatics and Biostatistics Center Milken Institute School of Public Health George Washington University Rockville MD United States Department of Psychiatry University of Pittsburgh School of Medicine Pittsburgh PA United States Diabetes Research Center Massachusetts General Hospital Harvard Medical School Boston MA United States

Background: In the US Diabetes Prevention Program (DPP), a 3-year randomised clinical trial in 3234 adults with prediabetes, type 2 diabetes incidence was reduced by 58% with intensive lifestyle intervention (ILS) and by 31% with metformin, compared with placebo. We sought to assess the long-term effects and potential heterogeneity of treatment effects over approximately 21 years of follow-up. Methods: The DPP trial was continued with protocol modifications as the DPP Outcomes Study (DPPOS). In the DPPOS, placebo was discontinued, metformin (850 mg twice a day as tolerated) was continued after unmasking, and group-based booster intervention classes were offered to the ILS group twice a year;additionally, all participants were offered group-based lifestyle intervention four times a year. The prespecified primary outcome during DPP and DPPOS was diabetes incidence defined by American Diabetes Association criteria. The DPPOS protocol specified continued diabetes incidence as an outcome;Feb 23, 2020, was chosen as the closing date for the present analysis, as a date prior to the COVID-19 pandemic, which caused major disruptions in clinic visits and complicated longitudinal data analyses. We assessed long-term persistence of intervention effects on diabetes incidence, and heterogeneity of effects in subgroups defined by baseline diabetes risk factors. Follow-up is reported for the combined study from July 31, 1996, to Feb 23, 2020, and analysis was by intention to treat. The trial is registered with ***, NCT00004992 (DPP) and NCT00038727 (DPPOS);follow-up is ongoing but the trial is closed to enrolment except for previous DPP participants. Findings: 3195 participants originally enrolled in the DPP were included in the present analyses. This population comprised 2171 (67·9%) female participants and 1024 (32·1%) male participants, with a mean baseline age of 50·6 years (SD 10·7). Individual follow-up times ranged from 0·2 to 23·2 years (median 8·0 years [IQR

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Sociodemographic and Provider Based Disparities in the Management of Stage I Testicular Cancer

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Urology Practice 2017年第1期4卷 36-42页

作者： Agarwal, Gautum Sharma, Pranav Valderrama, Oscar Lin, Hui-Yi Yue, Binglin Nguyen, Sabine Fishman, Mayer Luchey, Adam Pow-Sang, Julio M. Spiess, Philippe E. Poch, Michael A. Sexton, Wade J. Departments of Genitourinary Oncology Biostatistics and Bioinformatics (HYL BY) Moffitt Cancer Center Tampa Florida United States

Introduction The treatment paradigm for stage I testicular cancer has changed in the setting of accurate staging, reliable followup and a greater understanding of treatment related side effects. We assessed the influences on management decisions in patients with stage I testicular cancer. Methods We retrospectively identified 121 patients with stage I testicular cancer who were evaluated at our institution from 1999 to 2013. Sociodemographic characteristics, pathological features and provider specific factors were compared in patients who underwent surveillance vs treatment. Differences in medians and proportions were determined using the Kruskal-Wallis and chi-square tests. Multivariate logistic regression analysis was performed to identify independent predictors of treatment. Results A total of 87 patients had stage I nonseminomatous germ cell tumor and 34 had pure seminoma. Patients with nonseminomatous germ cell tumor who were evaluated before 2011 and those seen by urological oncologists were more likely to undergo primary retroperitoneal lymph node dissection (p <0.01). Patients with nonseminomatous germ cell tumor who were evaluated by medical oncologists more often received chemotherapy (p <0.01). For nonseminomatous germ cell tumors treatment was independently associated with advanced tumor stage and lymph node invasion (OR 15.3, 95% CI 3.26–71.95, p = 0.001). In patients with pure seminoma the use of radiation therapy decreased from 40% to 5% after 2010 while surveillance increased from 47% to 74% (p = 0.056) and no recorded variable was predictive of treatment. Conclusions Advanced stage and lymph node invasion in patients with stage I nonseminomatous germ cell tumor are drivers of treatment. Management also depends on the specialty of the treating provider, suggesting the possibility of bias during patient counseling. In turn, this suggests the need for patient assessment through a multidisciplinary approach. © 2017 American Urological Association Educat

关键词： choice behaviors elective surgical procedures radiotherapy testicular neoplasms watchful waiting

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Artificial Intelligence in Radiology: Some Ethical Considerations for Radiologists and Algorithm Developers

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ACADEMIC RADIOLOGY 2020年第1期27卷 127-129页

作者： Mazurowski, Maciej A. Departments of Radiology Electrical and Computer Engineering and Biostatistics and Bioinformatics Duke University 2424 Erwin Rd Durham NC 27707

As artificial intelligence (Al) is finding its place in radiology, it is important to consider how to guide the research and clinical implementation in a way that will be most beneficial to patients. Although there are multiple aspects of this issue, I consider a specific one: a potential misalignment of the self-interests of radiologists and Al developers with the best interests of the patients. Radiologists know that supporting research into Al and advocating for its adoption in clinical settings could diminish their employment opportunities and reduce respect for their profession. This provides an incentive to oppose Al in various ways. Al developers have an incentive to hype their discoveries to gain attention. This could provide short-term personal gains, however, it could also create a distrust toward the field if it became apparent that the state of the art was far from where it was promised to be. The future research and clinical implementation of Al in radiology will be partially determined by radiologist and Al researchers. Therefore, it is very important that we recognize our own personal motivations and biases and act responsibly to ensure the highest benefit of the Al transformation to the patients.

关键词： Artificial intelligence Machine learning Ethics Algorithm development

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MorPhiC Consortium: towards functional characterization of all human genes

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Nature 2025年第8050期638卷 351-359页

作者： Adli, Mazhar Przybyla, Laralynne Burdett, Tony Burridge, Paul W. Cacheiro, Pilar Chang, Howard Y. Engreitz, Jesse M. Gilbert, Luke A. Greenleaf, William J. Hsu, Li Huangfu, Danwei Hung, Ling-Hong Kundaje, Anshul Li, Shuzhao Parkinson, Helen Qiu, Xiaojie Robson, Paul Schürer, Stephan C. Shojaie, Ali Skarnes, William C. Smedley, Damian Studer, Lorenz Sun, Wei Vidović, Dušica Vierbuchen, Thomas White, Brian S. Yeung, Ka Yee Yue, Feng Zhou, Ting Žurauskienė, Justina Zhong, Aaron Zheng, Shujian Zhao, Hengqiang Zhang, Nan Zhang, Zhehao Zhang, Stephen Zeng, Tony Yu, Bo Yordanova, Galabina N. Yang, Dapeng Xing, De Wang, Xianming Wang, Ping Torre, Denis Tang, Wenzhuo Shivalikanjli, Anu Sengupta, Kaustav Seker, Fidan Ventura, Enrique Sapena Ross, Nicolette C. Rivera-Diaz, Jamilex Rana, Arushi Pongpamorn, Praeploy Poirion, Olivier B. Pillai, Ajay Pilarczyk, Marcin Parker, Devon Ning, Gang Neyisci, Ozlem Nasam, Niharika Mittal, Varun Mitchell, Joshua M. Metzl-Raz, Eyal Meng, Chen Melo-Carrillo, Adrian McDonough, Justin A. Marengo, Gabriel Mao, Yuzhen Leite, Davi Lyra Luo, Renhe Liu, Nianping Liu, Dingyu Liu, Zukai Liu, Si Li, Zhaoheng Kraft, Katerina Koci, Orges A. Kirsammer, Gina Kendra, Angelina Hutter, Carolyn Huebner, Aaron J. Huang, Yung-Hsin Hu, Nan Gupta, Dipayan Gupta, Revant Guo, Tingfeng Griffiths, Alexandra M. Goyal, Juhee Glenn, Rachel A. Garofalo, Jessica L. Fukuda, Bryce Nobutoshi Fu, Yihao Flynn, William F. Flores, Jesse Fletcher, Colin F. Fleming, Patrick J. Felsenfeld, Adam L. Eraslan, Basak Doty, Anthony Dahlstrom, Thomas J. Chung, Caty E. Choudhary, Anshul Cho, Hyein S. Chen, Xintong Chen, Sijie Chan, Chia Baroch, Jacob J. Baker, Dylan S. Bai, Tao Avireddy, Rohan N. V. S. R. K. Amiri, Anahita Diniz, Juliana Alcoforado Abbaszadeh, Neda Robert H. Lurie Comprehensive Cancer Center Department of Obstetrics and Gynecology Northwestern University Feinberg School of Medicine Chicago IL United States Department of Biochemistry and Biophysics University of California San Francisco CA United States Omics Section European Bioinformatics Institute (EMBL-EBI) European Molecular Biology Laboratory Hinxton United Kingdom Department of Pharmacology Center for Pharmacogenomics Northwestern University Feinberg School of Medicine Evanston IL United States William Harvey Research Institute Clinical Pharmacology and Precision Medicine Queen Mary University of London London United Kingdom Department of Dermatology Stanford University Stanford CA United States Department of Genetics Stanford University Stanford CA United States Basic Science and Engineering (BASE) Initiative Stanford University Stanford CA United States Department of Urology University of California San Francisco CA United States Department of Biostatistics Public Health Sciences Fred Hutchinson Cancer Center Seattle WA United States Developmental Biology Program Sloan Kettering Institute New York NY USA School of Engineering and Technology University of Washington Tacoma Tacoma WA United States Departments of Genetics and Computer Science Stanford University Stanford CA United States Jackson Laboratory for Genomic Medicine Farmington CT United States Knowledge Management Section European Bioinformatics Institute (EMBL-EBI) European Molecular Biology Laboratory Hinxton United Kingdom Molecular and Cellular Pharmacology Sylvester Comprehensive Cancer Center University of Miami Coral Gables FL USA Department of Biostatistics University of Washington Seattle WA United States Department of Biochemistry and Molecular Genetics Robert H. Lurie Comprehensive Cancer Center Northwestern University Feinberg School of Medicine Chicago IL United States

Recent advances in functional genomics and human cellular models have substantially enhanced our understanding of the structure and regulation of the human genome. However, our grasp of the molecular functions of human genes remains incomplete and biased towards specific gene classes. The Molecular Phenotypes of Null Alleles in Cells (MorPhiC) Consortium aims to address this gap by creating a comprehensive catalogue of the molecular and cellular phenotypes associated with null alleles of all human genes using in vitro multicellular systems. In this Perspective, we present the strategic vision of the MorPhiC Consortium and discuss various strategies for generating null alleles, as well as the challenges involved. We describe the cellular models and scalable phenotypic readouts that will be used in the consortium's initial phase, focusing on 1,000 protein-coding genes. The resulting molecular and cellular data will be compiled into a catalogue of null-allele phenotypes. The methodologies developed in this phase will establish best practices for extending these approaches to all human protein-coding genes. The resources generated-including engineered cell lines, plasmids, phenotypic data, genomic information and computational tools-will be made available to the broader research community to facilitate deeper insights into human gene functions. © 2025. Springer Nature Limited.

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Decoding the Functional Interactome of Non-model Organisms with PHILHARMONIC 29th

Decoding the Functional Interactome of Non-model Organisms...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Sledzieski, Sam Versavel, Charlotte Singh, Rohit Ocitti, Faith Devkota, Kapil Kumar, Lokender Shpilker, Polina Roger, Liza Yang, Jinkyu Lewinski, Nastassja Putnam, Hollie Berger, Bonnie Klein-Seetharaman, Judith Cowen, Lenore Center for Computational Biology Flatiron Institute New YorkNY United States Department of Computer Science Tufts University MedfordMA United States Departments of Biostatistics and Bioinformatics Duke University DurhamNC United States School of Biotechnology Faculty of Applied Sciences and Biotechnology Shoolini University Himachal Pradesh Solan173229 India School of Molecular Sciences and School of Ocean Futures Arizona State University TempeAZ United States Department of Mechanical Engineering Seoul National University Seoul Korea Republic of Department of Chemical and Life Science Engineering Virginia Commonwealth University RichmondVA United States Department of Biological Sciences University of Rhode Island KingstonRI United States Computer Science and Artificial Intelligence Laboratory and Department of Mathematics MIT CambridgeMA United States School of Molecular Sciences and College of Health Solutions Arizona State University PhoenixAZ United States

ISBN: (纸本)9783031902512

We introduce PHILHARMONIC, a computational framework that couples deep learning de novo network inference with robust unsupervised spectral clustering algorithms to uncover functional relationships and high-level organization in non-model organisms. Our novel clustering approach produces highly informative functional modules by de-noising the predicted network. We also develop a novel algorithm called ReCIPE, which aims to reconnect disconnected clusters, increasing functional enrichment and biological interpretability. We initially perform remote homology-based functional annotation by leveraging hmmscan and GODomainMiner to assign initial functions to proteins at large evolutionary distances;our clusters then enable us to newly assign functions to uncharacterized proteins through "function by association." We validate the ability of PHILHARMONIC to recover gold-standard functional enrichments in the well-annotated fruit fly D. melanogaster, and apply it to investigate stress response in the reef-building coral P. damicornis and its algal symbiont C. goreaui. Easy to run end-to-end and requiring only a sequenced proteome, PHILHARMONIC is an engine for biological hypothesis generation and discovery in non-model organisms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Inconsistency between overall and subgroup analyses

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General Psychiatry 2022年第3期35卷 201-204页

作者： Hongyue Wang Bokai Wang Xin M Tu Changyong Feng Departments of Biostatistics&Computational Biology University of RochesterRochesterNew YorkUSA Division of Biostatistics and Bioinformatics Herbert Wertheim School of Public Health and Human Longevity ScienceUC San DiegoLa JollaCaliforniaUSA

Suppose we have a sample of subjects in two treatment *** study the difference of the treatment effects,we can analyse the data using all subjects(overall analysis).We may also divide the subjects into several subgroups based on some covariates of interest(eg,gender),and study the treatment effects within each *** results of these two analyses may be different or even in opposite *** this paper,we give a general sufficient condition of consistency between the overall and subgroup analyses.

关键词： subgroup consistency groups.

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Small sample sizes in high-throughput miRNA screens: A common pitfall for the identification of miRNA biomarkers

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Biomolecular Detection and Quantification 2018年 15卷 1-5页

作者： Kok, M.G.M. de Ronde, M.W.J. Moerland, P.D. Ruijter, J.M. Creemers, E.E. Pinto-Sietsma, S.J. Departments of Vascular Medicine University of Amsterdam Amsterdam Netherlands Departments of Clinical Epidemiology Biostatistics and Bioinformatics University of Amsterdam Amsterdam Netherlands Departments of Anatomy Embryology and Physiology University of Amsterdam Amsterdam Netherlands Departments of Experimental Cardiology Academic Medical Center University of Amsterdam Amsterdam Netherlands

Since the discovery of microRNAs (miRNAs), circulating miRNAs have been proposed as biomarkers for disease. Consequently, many groups have tried to identify circulating miRNA biomarkers for various types of diseases including cardiovascular disease and cancer. However, the replicability of these experiments has been disappointingly low. In order to identify circulating miRNA candidate biomarkers, in general, first an unbiased high-throughput screen is performed in which a large number of miRNAs is detected and quantified in the circulation. Because these are costly experiments, many of such studies have been performed using a low number of study subjects (small sample size). Due to lack of power in small sample size experiments, true effects are often missed and many of the detected effects are wrong. Therefore, it is important to have a good estimate of the appropriate sample size for a miRNA high-throughput screen. In this review, we discuss the effects of small sample sizes in high-throughput screens for circulating miRNAs. Using data from a miRNA high-throughput experiment on isolated monocytes, we illustrate that the implementation of power calculations in a high-throughput miRNA discovery experiment will avoid unnecessarily large and expensive experiments, while still having enough power to be able to detect clinically important differences. © 2017 The Authors

关键词： Array Biomarkers High-throughput screens Methodology MicroRNA Small sample size error

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STATISTICAL DESIGN AND RATIONALE OF THE BIOMARKERS FOR EVALUATING SPINE TREATMENTS (BEST) TRIAL

arXiv

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arXiv 2025年

作者： Sperger, John Kidwell, Kelley M. Mauck, Matthew C. Zhao, Beibo Anstrom, Kevin J. Batorsky, Anna Carey, Timothy S. Clauw, Daniel J. Freeman, Nikki L.B. Greco, Carol M. Ivanova, Anastasia Berkeley, Sara Jones McLean, Samuel A. Psioda, Matthew A. Rowland, Bryce Sowa, Gwendolyn A. Wasan, Ajay D. Zitovsky, Joshua P. Kosorok, Michael R. Department of Biostatistics Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Biostatistics School of Public Health University of Michigan United States Department of Anesthesiology University of North Carolina Chapel Hill United States Collaborative Studies Coordinating Center Department of Biostatistics Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Medicine University of North Carolina Chapel Hill United States Department of Anesthesiology Chronic Pain & Fatigue Research Center University of Michigan Ann Arbor United States Department of Biostatistics and Bioinformatics Duke University United States Department of Psychiatry University of Pittsburgh School of Medicine United States Department of Epidemiology Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Psychiatry University of North Carolina Chapel Hill United States Statistics and Data Science Innovation Hub Biostatistics GSK Department of Physical Medicine and Rehabilitation University of Pittsburgh School of Medicine United States Departments of Anesthesiology and Perioperative Medicine and Psychiatry University of Pittsburgh School of Medicine United States Amazon Postdoctoral Scientist United States Ferguson Laboratory for Orthopaedic and Spine Research Bethel Musculoskeletal Research Center Department of Orthopaedic Surgery University of Pittsburgh School of Medicine United States Department of Bioengineering University of Pittsburgh Swanson School of Engineering United States Clinical and Translational Science Institute University of Pittsburgh United States Carolina Pragmatic Trials Laboratory North Carolina Translational and Clinical Sciences Institute UNC School of Medicine United States Duke Clinical Research Institute United States

Background/Aims: Chronic low back pain (cLBP) is a widely prevalent condition with potentially severe impacts on day-to-day functioning and quality of life. A variety of evidence-based treatment modalities exist, but all approaches have modest average treatment effects—potentially due to individual variation in treatment response and the diverse etiologies of cLBP. The Biomarkers for Evaluating Spine Treatments (BEST) trial was designed to investigate the potential for using patient features including biomarkers and phenotypic measurements to inform cLBP treatment. Methods: We provide an overview of the BEST trial and detail the statistical rationale for key study design decisions. We discuss the salient features of the scientific and clinical context of cLBP treatment that motivated the study design and planned analysis, describe modifications to existing methods that were needed because of the setting, and present simulation studies that were used to inform the study design. Results: The BEST trial was designed as a multi-site sequential, multiple-assignment randomized trial (SMART) of four cLBP treatment modalities. The primary objective was to inform a precision medicine approach to the treatment of cLBP by estimating the optimal treatment or combination of treatments based on patient features and individual patient’s response to the initial treatment. The primary analysis includes estimating an interpretable dynamic treatment regime for recommending treatments based on patient features and investigating the subgroups that respond best to each one of the treatment modalities. The trial aimed to enroll 630 protocol completers. Broad eligibility criteria were used to improve the generalizability of study results. Notably, participants could be excluded from one of the four interventions and still be eligible for the study. We modified the minimization method for reducing covariate imbalance to allow for these exclusions. Conclusion: The BEST trial may pave the way

关键词： Patient treatment

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Efficacy of Prophylactic High-Dose Gabapentin and Venlafaxine on Reducing Oral Mucositis Pain Among Patients Treated With Chemoradiation for Head and Neck Cancer: A Single-Institution, Phase 2, Randomized Clinical Trial

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International Journal of Radiation Oncology Biology Physics 2023年第4期116卷 797-806页

作者： Ma, Sung Jun Iovoli, Austin J. Wang, Katy Neimanis, Debbie Smith, Kelsey A. Attwood, Kristopher Farrugia, Mark Hermann, Gregory Singh, Anurag K. Departments of Radiation Medicine Roswell Park Comprehensive Cancer Center BuffaloNew York United States Departments of Biostatistics and Bioinformatics Roswell Park Comprehensive Cancer Center BuffaloNew York United States Department of Radiation Oncology OSF Healthcare Saint Francis Medical Center University of Illinois College of Medicine at Peoria PeoriaIL United States

Purpose: Given the paucity of level 1 evidence, the optimal regimen to control oral mucositis pain remains unclear. Although national guidelines allow consideration of prophylactic gabapentin, prior trials showed improved pain control with venlafaxine among patients with diabetic neuropathy. We sought to investigate the role of prophylactic high-dose gabapentin with venlafaxine to reduce oral mucositis pain among patients with head and neck cancer. Methods and Materials: We performed a single-institution, phase 2 randomized trial on nonmetastatic squamous cell carcinoma of the head and neck treated with chemoradiation. Patients were randomized to either prophylactic gabapentin (3600 mg daily) with or without venlafaxine (150 mg daily). Primary endpoint was differences in pain levels at the end of chemoradiation. Secondary endpoint was toxicity profiles, quality of life changes, opioid use, and feeding tube placement. Differences between the 2 arms at multiple time points were evaluated using a generalized linear mixed regression model with Sidak correction. Results: Between May 2018 and March 2021, a total of 62 patients were enrolled and evaluable for analysis (n = 32 for the gabapentin alone arm, n = 30 for the gabapentin + venlafaxine arm). Over 90% of patients tolerated gabapentin well. Head and neck pain level showed a mean value of 45 (standard deviation, 23) and 43 (standard deviation, 21) for the gabapentin alone and the gabapentin + venlafaxine arms, respectively (P = .65). No statistically significant differences were observed in adverse events, opioid use, feeding tube placement, or quality of life. Conclusions: The addition of venlafaxine to prophylactic gabapentin did not result in improvements in pain control and quality of life among patients with head and neck cancer. © 2023 Elsevier Inc.

关键词： Regression analysis

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