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2. Restoration of T Cell function in multi-drug resistant bacterial sepsis after interleukin-7, anti-PD-L1, and OX-40 administration.

Author

Lukose K Thampy, Kenneth E Remy, Andrew H Walton, Zachery Hong, Kelilah Liu, Rebecca Liu, Victoria Yi, Carey-Ann D Burnham, and Richard S Hotchkiss

Subjects
Medicine, Science
Abstract

BACKGROUND:Multidrug resistant (MDR) bacterial pathogens are a serious problem of increasing importance facing the medical community. MDR bacteria typically infect the most immunologically vulnerable: patients in intensive care units, patients with extensive comorbidities, oncology patients, hemodialysis patients, and other immune suppressed individuals are likely to fall victim to these pathogens. One promising novel approach to treatment of MDR bacteria is immuno-adjuvant therapy to boost patient immunity. Success with this strategy would have the major benefit of providing protection against a number of MDR pathogens. OBJECTIVES:This study had two main objectives. First, immunophenotyping of peripheral blood mononuclear cells from patients with sepsis associated with MDR bacteria was performed to examine for findings indicative of immunosuppression. Second, the ability of three immuno-adjuvants with distinct mechanisms of action to reverse CD4 and CD8 T cell dysfunction, a pathophysiological hallmark of sepsis, was evaluated. RESULTS:Septic patients with MDR bacteria had increased expression of the inhibitory receptor PD-1 and its ligand PD-L1 and decreased monocyte HLA-DR expression compared to non-septic patients. All three immuno-adjuvants, IL-7, anti-PD-L1, and OX-40L, increased T cell production of IFN-γ in a subset of septic patients with MDR bacteria: IL-7 was most efficacious. There was a strong trend toward increased mortality in patients whose T cells failed to increase IFN-γ production in response to the three treatments. CONCLUSION:Immuno-adjuvant therapy reversed T cell dysfunction, a key pathophysiological mechanism in septic patients with MDR bacteria.

Published
2018
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3. Alternative discharge destination following lobectomy: Analysis of a national quality improvement databaseCentral MessagePerspective

Author

Victoria Yin, MD, MPH, Sean C. Wightman, MD, Takashi Harano, MD, Scott M. Atay, MD, and Anthony W. Kim, MD

Subjects
discharge outcomes, lobectomy, Frailty Index, Diseases of the circulatory (Cardiovascular) system, RC666-701, Surgery, RD1-811
Abstract

Objective: To determine factors significantly associated with alternative discharge destination (ADCD) following lobectomy, including the modified 5-item Frailty Index (mFI-5). Methods: Patients in the 2017-2020 NSQIP who underwent elective lobectomy and were admitted from home were included, with ADCD defined as a patient who was discharged to any nonhome location. Four multivariable logistic regression models for ADCD were evaluated for predictive power. Model A was created from backward selection of variables significantly associated with ADCD in bivariate analyses, model B was the mFI-5, model C was mFI-5 and a minimally invasive approach, and model D was mFI-5 and age group. Results: Among the 15,868 patients, 687 (4.3%) experienced ADCD. Model A identified older age, hypertension, dyspnea, history of chronic obstructive pulmonary disease, and increased length of stay as significantly associated with ADCD. A minimally invasive approach was significantly protective of ADCD. Model A had the best predictive power of the models tested (C-statistic = 0.785). Model B, which assessed mFI-5 alone, had fair predictive power (C-statistic = 0.637). Adding surgical approach (C-statistic = 0.673; model C) or age group (C-statistic = 0.682; model D) as independent variables with mFI-5 improved model fit. Conclusions: Patients who were frail or age >75 years were more likely to have postlobectomy ADCD. Although the variables identified in model A better predict ADCD, consideration of surgical approach or age with mFI-5 can help surgeons anticipate discharge destination following lobectomy.

Published
2024
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4. Neptune: an environment for the delivery of genomic medicine

Author

Venner Eric, Victoria Yi, David Murdock, Sara E. Kalla, Tsung-Jung Wu, Aniko Sabo, Shoudong Li, Qingchang Meng, Xia Tian, Mullai Murugan, Michelle Cohen, Christie Kovar, Wei-Qi Wei, Wendy K. Chung, Chunhua Weng, Georgia L. Wiesner, Gail P. Jarvik, Donna Muzny, Richard A. Gibbs, Debra Abrams, Samuel E. Adunyah, Ladia Albertson-Junkans, Berta Almoguera, Darren C. Ames, Paul Appelbaum, Samuel Aronson, Sharon Aufox, Lawrence J. Babb, Adithya Balasubramanian, Hana Bangash, Melissa Basford, Lisa Bastarache, Samantha Baxter, Meckenzie Behr, Barbara Benoit, Elizabeth Bhoj, Suzette J. Bielinski, Sarah T. Bland, Carrie Blout, Kenneth Borthwick, Erwin P. Bottinger, Mark Bowser, Harrison Brand, Murray Brilliant, Wendy Brodeur, Pedro Caraballo, David Carrell, Andrew Carroll, Lisa Castillo, Victor Castro, Gauthami Chandanavelli, Theodore Chiang, Rex L. Chisholm, Kurt D. Christensen, Wendy Chung, Christopher G. Chute, Brittany City, Beth L. Cobb, John J. Connolly, Paul Crane, Katherine Crew, David R. Crosslin, Jyoti Dayal, Mariza De Andrade, Jessica De la Cruz, Josh C. Denny, Shawn Denson, Tim DeSmet, Ozan Dikilitas, Michael J. Dinsmore, Sheila Dodge, Phil Dunlea, Todd L. Edwards, Christine M. Eng, David Fasel, Alex Fedotov, Qiping Feng, Mark Fleharty, Andrea Foster, Robert Freimuth, Christopher Friedrich, Stephanie M. Fullerton, Birgit Funke, Stacey Gabriel, Vivian Gainer, Ali Gharavi, Andrew M. Glazer, Joseph T. Glessner, Jessica Goehringer, Adam S. Gordon, Chet Graham, Robert C. Green, Justin H. Gundelach, Heather S. Hain, Hakon Hakonarson, Maegan V. Harden, John Harley, Margaret Harr, Andrea Hartzler, M. Geoffrey Hayes, Scott Hebbring, Nora Henrikson, Andrew Hershey, Christin Hoell, Ingrid Holm, Kayla M. Howell, George Hripcsak, Jianhong Hu, Elizabeth Duffy Hynes, Joy C. Jayaseelan, Yunyun Jiang, Yoonjung Yoonie Joo, Sheethal Jose, Navya Shilpa Josyula, Anne E. Justice, Divya Kalra, Elizabeth W. Karlson, Brendan J. Keating, Melissa A. Kelly, Eimear E. Kenny, Dustin Key, Krzysztof Kiryluk, Terrie Kitchner, Barbara Klanderman, Eric Klee, David C. Kochan, Viktoriya Korchina, Leah Kottyan, Emily Kudalkar, Alanna Kulchak Rahm, Iftikhar J. Kullo, Philip Lammers, Eric B. Larson, Matthew S. Lebo, Magalie Leduc, Ming Ta (Michael) Lee, Niall J. Lennon, Kathleen A. Leppig, Nancy D. Leslie, Rongling Li, Wayne H. Liang, Chiao-Feng Lin, Jodell E. Linder, Noralane M. Lindor, Todd Lingren, James G. Linneman, Cong Liu, Wen Liu, Xiuping Liu, John Lynch, Hayley Lyon, Alyssa Macbeth, Harshad Mahadeshwar, Lisa Mahanta, Bradley Malin, Teri Manolio, Maddalena Marasa, Keith Marsolo, Michelle L. McGowan, Elizabeth McNally, Jim Meldrim, Frank Mentch, Hila Milo Rasouly, Jonathan Mosley, Shubhabrata Mukherjee, Thomas E. Mullen, Jesse Muniz, David R. Murdock, Shawn Murphy, Melanie F. Myers, Bahram Namjou, Yizhao Ni, Robert C. Onofrio, Aniwaa Owusu Obeng, Thomas N. Person, Josh F. Peterson, Lynn Petukhova, Cassandra J. Pisieczko, Siddharth Pratap, Cynthia A. Prows, Megan J. Puckelwartz, Ritika Raj, James D. Ralston, Arvind Ramaprasan, Andrea Ramirez, Luke Rasmussen, Laura Rasmussen-Torvik, Soumya Raychaudhuri, Heidi L. Rehm, Marylyn D. Ritchie, Catherine Rives, Beenish Riza, Dan M. Roden, Elisabeth A. Rosenthal, Avni Santani, Schaid Dan, Steven Scherer, Stuart Scott, Aaron Scrol, Soumitra Sengupta, Ning Shang, Himanshu Sharma, Richard R. Sharp, Rajbir Singh, Patrick M.A. Sleiman, Kara Slowik, Joshua C. Smith, Maureen E. Smith, Duane T. Smoot, Jordan W. Smoller, Sunghwan Sohn, Ian B. Stanaway, Justin Starren, Mary Stroud, Jessica Su, Casey Overby Taylor, Kasia Tolwinski, Sara L. Van Driest, Sean M. Vargas, Matthew Varugheese, David Veenstra, Eric Venner, Miguel Verbitsky, Gina Vicente, Michael Wagner, Kimberly Walker, Theresa Walunas, Liwen Wang, Qiaoyan Wang, Scott T. Weiss, Quinn S. Wells, Peter S. White, Ken L. Wiley, Janet L. Williams, Marc S. Williams, Michael W. Wilson, Leora Witkowski, Laura Allison Woods, Betty Woolf, Julia Wynn, Yaping Yang, Ge Zhang, Lan Zhang, and Hana Zouk

Subjects
Computer science, business.industry, Process (engineering), MEDLINE, High-Throughput Nucleotide Sequencing, Genomics, Data science, Article, Personalization, Variety (cybernetics), Workflow, Neptune, Pharmacogenomics, Health care, Electronic Health Records, Humans, business, Software, Genetics (clinical)
Abstract

Genomic medicine holds great promise for improving health care, but integrating searchable and actionable genetic data into electronic health records (EHRs) remains a challenge. Here we describe Neptune, a system for managing the interaction between a clinical laboratory and an EHR system during the clinical reporting process. We developed Neptune and applied it to two clinical sequencing projects that required report customization, variant reanalysis, and EHR integration. Neptune has been applied for the generation and delivery of over 15,000 clinical genomic reports. This work spans two clinical tests based on targeted gene panels that contain 68 and 153 genes respectively. These projects demanded customizable clinical reports that contained a variety of genetic data types including single-nucleotide variants (SNVs), copy-number variants (CNVs), pharmacogenomics, and polygenic risk scores. Two variant reanalysis activities were also supported, highlighting this important workflow. Methods are needed for delivering structured genetic data to EHRs. This need extends beyond developing data formats to providing infrastructure that manages the reporting process itself. Neptune was successfully applied on two high-throughput clinical sequencing projects to build and deliver clinical reports to EHR systems. The software is open source and available at https://gitlab.com/bcm-hgsc/neptune .

Published
2021

5. Harmonizing Clinical Sequencing and Interpretation for the eMERGE III Network

Author

Ian B. Stanaway, Dan M. Roden, Divya Kalra, Dustin Key, Debra J. Abrams, David Fasel, Victor Castro, Brad Malin, Berta Almoguera, Beenish Riza, Meckenzie A. Behr, Eric Venner, Christine M. Eng, Joy Jayaseelan, Scott J. Hebbring, Michelle L. McGowan, Steven E. Scherer, Theresa L. Walunas, Mark Bowser, James D. Ralston, Wei-Qi Wei, Liwen Wang, David R. Murdock, Wayne H. Liang, Julia Wynn, Nancy D. Leslie, Laura J. Rasmussen-Torvik, Ming Ta (Michael) Lee, Frank D. Mentch, Lan Zhang, Alanna Kulchak Rahm, Josh F. Peterson, Jodell E. Linder, Joshua C. Smith, Soumitra Sengupta, Brendan J. Keating, Gina Vicente, Andrew Carroll, Nora B. Henrikson, Anne E. Justice, Heather S. Hain, Wen Liu, Andrea H. Ramirez, Matthew S. Lebo, Hana Zouk, Georgia L. Wiesner, Andrea L. Hartzler, Cassandra J. Pisieczko, Catherine M. Rives, Jessica Goehringer, Maegan V. Harden, John Lynch, Chiao-Feng Lin, Peter White, Phil Dunlea, Shawn N. Murphy, Mullai Murugan, Harshad Mahadeshwar, Mark Fleharty, Andrea Foster, Arvind Ramaprasan, Christopher A. Friedrich, Justin H. Gundelach, Hayley Lyon, Niall J. Lennon, Eric W. Klee, David R. Crosslin, Ge Zhang, Rongling Li, Ozan Dikilitas, Xiuping Liu, Christin Hoell, Aniwaa Owusu Obeng, Katherine D. Crew, Lisa M. Castillo, Justin Starren, Jonathan D. Mosley, Carrie L. Blout, Himanshu Sharma, Elizabeth M. McNally, Sarah T. Bland, Megan J. Puckelwartz, Matthew Varugheese, Keith Marsolo, Betty Woolf, Sharon Aufox, Janet L. Williams, Kimberly Walker, Murray H. Brilliant, Birgit Funke, Laura Allison Woods, Marylyn D. Ritchie, Brittany City, Todd Lingren, Hila Milo Rasouly, Lawrence J. Babb, Alex Fedotov, Robert C. Onofrio, Margaret Harr, Suzette J. Bielinski, Michael W. Wilson, Shubhabrata Mukherjee, Robert R. Freimuth, Chet Graham, Todd L. Edwards, Quinn S. Wells, Marc S. Williams, Jordan W. Smoller, Wendy K. Chung, Avni Santani, Paul K. Crane, George Hripcsak, QiPing Feng, Ali G. Gharavi, Yizhao Ni, Iftikhar J. Kullo, Michael Wagner, Philip E. Lammers, Michael J. Dinsmore, Thomas N. Person, Victoria Yi, Samuel E. Adunyah, Tim DeSmet, Eric B. Larson, Elizabeth Hynes, David C. Kochan, Eimear E. Kenny, Magalie S. Leduc, Lisa Mahanta, David Carrell, Paul S. Appelbaum, Viktoriya Korchina, Beth L. Cobb, Lynn Petukhova, Jessica De la Cruz, Patrick M. A. Sleiman, Stuart A. Scott, Tsung-Jung Wu, Gail P. Jarvik, Erwin P. Bottinger, Ken Wiley, Josh C. Denny, Melissa A. Basford, Samuel J. Aronson, David L. Veenstra, Yaping Yang, Kayla Marie Howell, John J. Connolly, Jessica Su, Yoonjung Yoonie Joo, Miguel Verbitsky, Sean M. Vargas, Cong Liu, Barbara Benoit, Andrew Hershey, Richard A. Gibbs, Cynthia A. Prows, Hana Bangash, Wendy Brodeur, Gauthami Chandanavelli, Sara L. Van Driest, Kurt D. Christensen, Elizabeth J. Bhoj, Vivian S. Gainer, Adam S. Gordon, Robert C. Green, Hakon Hakonarson, Krzysztof Kiryluk, Elisabeth A. Rosenthal, Rajbir Singh, James G. Linneman, Harrison Brand, Theodore Chiang, Sheila Dodge, Ingrid A. Holm, M. Geoffrey Hayes, Yunyun Jiang, Ning Shang, Samantha Baxter, Noralane M. Lindor, Kathleen A. Leppig, Teri A. Manolio, Sara E. Kalla, Pedro J. Caraballo, Ritika Raj, Aaron Scrol, Jyoti G. Dayal, Richard R. Sharp, Christie Kovar, Soumya Raychaudhuri, Sunghwan Sohn, Emily Kudalkar, Maddalena Marasa, Stacey Gabriel, Dan Schaid, Ladia Albertson-Junkans, Rex L. Chisholm, Maureen E. Smith, Donna M. Muzny, Casey Overby Taylor, Jianhong Hu, Elizabeth W. Karlson, Lisa Bastarache, Darren C. Ames, Joseph T. Glessner, Leora Witkowski, Siddharth Pratap, Qiaoyan Wang, Melissa A. Kelly, Adithya Balasubramanian, Kara Slowik, Terrie Kitchner, Barbara J. Klanderman, Shawn Denson, Mary Stroud, Alyssa Macbeth, Melanie F. Myers, Jesse Muniz, Kasia Tolwinski, Scott T. Weiss, Chunhua Weng, Stephanie M. Fullerton, John B. Harley, Christopher G. Chute, Heidi L. Rehm, Sheethal Jose, Andrew M. Glazer, Navya Shilpa Josyula, Kenneth M. Borthwick, Thomas E. Mullen, Mariza de Andrade, Leah C. Kottyan, Luke V. Rasmussen, James Meldrim, and Bahram Namjou

Subjects
0301 basic medicine, Standardization, Test data generation, business.industry, Computer science, Sequence Analysis, DNA, 030105 genetics & heredity, Precision medicine, Data science, Clinical decision support system, Biobank, Article, 3. Good health, Data sharing, 03 medical and health sciences, 030104 developmental biology, Genetics, Humans, Genetic Testing, Prospective Studies, Sample collection, Personalized medicine, Precision Medicine, business, Genetics (clinical)
Abstract

The advancement of precision medicine requires new methods to coordinate and deliver genetic data from heterogeneous sources to physicians and patients. The eMERGE III Network enrolled >25,000 participants from biobank and prospective cohorts of predominantly healthy individuals for clinical genetic testing to determine clinically actionable findings. The network developed protocols linking together the 11 participant collection sites and 2 clinical genetic testing laboratories. DNA capture panels targeting 109 genes were used for testing of DNA and sample collection, data generation, interpretation, reporting, delivery, and storage were each harmonized. A compliant and secure network enabled ongoing review and reconciliation of clinical interpretations, while maintaining communication and data sharing between clinicians and investigators. A total of 202 individuals had positive diagnostic findings relevant to the indication for testing and 1,294 had additional/secondary findings of medical significance deemed to be returnable, establishing data return rates for other testing endeavors. This study accomplished integration of structured genomic results into multiple electronic health record (EHR) systems, setting the stage for clinical decision support to enable genomic medicine. Further, the established processes enable different sequencing sites to harmonize technical and interpretive aspects of sequencing tests, a critical achievement toward global standardization of genomic testing. The eMERGE protocols and tools are available for widespread dissemination.

Published
2019

6. Genetic testing in ambulatory cardiology clinics reveals high rate of findings with clinical management implications

Author

Hadley Stevens Smith, Ashok Balasubramanyam, Zohra A. Huda, Christopher I. Amos, Lee Leiber, Xander H.T. Wehrens, Mullai Murugan, Alexandria Buentello, Trevor D. Hadley, Aniko Sabo, Daniel L. Riconda, Paul S. de Vries, Aliza Hussain, Jianhong Hu, Varuna Chander, Marie-Claude Gingras, Shoudong Li, Stacey Pereira, Xiaoming Jia, Michelle Cohen, Eric Venner, Chad Howard, Donna M. Muzny, Ali M. Agha, Viktoriya Korchina, Christie Kovar, Richard A. Gibbs, Amy L. McGuire, Qingchang Meng, Victoria Yi, Mihail G. Chelu, Ronald Maag, Xia Tian, Christie M. Ballantyne, Eric Boerwinkle, David R. Murdock, Ginger A. Metcalf, Andrew B. Civitello, and Patricia R. Marino

Subjects
Adult, medicine.medical_specialty, MEDLINE, Cardiology, Disease, Article, Coronary artery disease, Internal medicine, Medicine, Humans, Genetic Testing, Genetics (clinical), Genetic testing, Cause of death, medicine.diagnostic_test, business.industry, Warfarin, medicine.disease, United States, Pharmacogenomic Testing, Cardiovascular Diseases, Pharmacogenetics, Cohort, business, medicine.drug
Abstract

Purpose Cardiovascular disease (CVD) is the leading cause of death in adults in the United States, yet the benefits of genetic testing are not universally accepted. Methods We developed the "HeartCare" panel of genes associated with CVD, evaluating high-penetrance Mendelian conditions, coronary artery disease (CAD) polygenic risk, LPA gene polymorphisms, and specific pharmacogenetic (PGx) variants. We enrolled 709 individuals from cardiology clinics at Baylor College of Medicine, and samples were analyzed in a CAP/CLIA-certified laboratory. Results were returned to the ordering physician and uploaded to the electronic medical record. Results Notably, 32% of patients had a genetic finding with clinical management implications, even after excluding PGx results, including 9% who were molecularly diagnosed with a Mendelian condition. Among surveyed physicians, 84% reported medical management changes based on these results, including specialist referrals, cardiac tests, and medication changes. LPA polymorphisms and high polygenic risk of CAD were found in 20% and 9% of patients, respectively, leading to diet, lifestyle, and other changes. Warfarin and simvastatin pharmacogenetic variants were present in roughly half of the cohort. Conclusion Our results support the use of genetic information in routine cardiovascular health management and provide a roadmap for accompanying research.

Published
2021

7. Genomic Considerations for FHIR; eMERGE Implementation Lessons

Author

Kevin Power, Victoria Yi, David Fasel, Lawrence J. Babb, Casey Overby Taylor, Samuel J. Aronson, Stephen J. Granite, Wei-Qi Wei, David R. Crosslin, Hana Bangash, Heidi L. Rehm, Mullai Murugan, Eric Venner, John J. Connolly, Richard A. Gibbs, Robert R. Freimuth, Gail P. Jarvik, Alexander Fedotov, Hakon Hakonarson, Ken Wiley, Iftikhar J. Kullo, Luke V. Rasmussen, Hana Zouk, Pedro J. Caraballo, Fei Yan, and Jordan G. Nestor

Subjects
Clinical genomics, business.industry, computer.internet_protocol, Health information technology, Computer science, Medical record, Interoperability, Precision medicine, Data science, Electronic health record, Health care, business, computer, XML
Abstract

Structured representation of clinical genetic results is necessary for advancing precision medicine. The Electronic Medical Records and Genomics (eMERGE) Network’s Phase III program initially used a commercially developed XML message format for standardized and structured representation of genetic results for electronic health record (EHR) integration. In a desire to move towards a standard representation, the network created a new standardized format based upon Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR), to represent clinical genomics results. These new standards improve the utility of HL7 FHIR as an international healthcare interoperability standard for management of genetic data from patients. This work advances the establishment of standards that are being designed for broad adoption in the current health information technology landscape.

Published
2021

8. Genomic considerations for FHIR®; eMERGE implementation lessons

Author

John J. Connolly, Lawrence J. Babb, David R. Crosslin, Victoria Yi, Richard A. Gibbs, Samuel J. Aronson, Mullai Murugan, Wei-Qi Wei, Robert R. Freimuth, Alexander Fedotov, Eric Venner, David Fasel, Heidi L. Rehm, Stephen J. Granite, Kevin Power, Casey Overby Taylor, Hakon Hakonarson, Hana Bangash, Iftikhar J. Kullo, Ken Wiley, Luke V. Rasmussen, Gail P. Jarvik, Hana Zouk, Pedro J. Caraballo, Fei Yan, and Jordan G. Nestor

Subjects
Computer science, Health information technology, computer.internet_protocol, Message format, Interoperability, Health Informatics, Clinical decision support system, Article, 03 medical and health sciences, 0302 clinical medicine, Health care, Electronic Health Records, Humans, 030212 general & internal medicine, Precision Medicine, 030304 developmental biology, Health Level Seven, 0303 health sciences, business.industry, Medical record, Genomics, Precision medicine, Data science, Computer Science Applications, business, computer, XML, Medical Informatics
Abstract

Structured representation of clinical genetic results is necessary for advancing precision medicine. The Electronic Medical Records and Genomics (eMERGE) Network's Phase III program initially used a commercially developed XML message format for standardized and structured representation of genetic results for electronic health record (EHR) integration. In a desire to move towards a standard representation, the network created a new standardized format based upon Health Level Seven Fast Healthcare Interoperability Resources (HL7® FHIR®), to represent clinical genomics results. These new standards improve the utility of HL7® FHIR® as an international healthcare interoperability standard for management of genetic data from patients. This work advances the establishment of standards that are being designed for broad adoption in the current health information technology landscape.

Published
2021

9. Association between underweight status and chylothorax after esophagectomy for esophageal cancer: A propensity score–matched analysisCentral MessagePerspective

Author

Victoria Yin, BA, Alexander T. Kim, BA, Sean C. Wightman, MD, Takashi Harano, MD, Scott M. Atay, MD, and Anthony W. Kim, MD

Subjects
esophagectomy, chylothorax, risk factors, underweight, body mass index, Diseases of the circulatory (Cardiovascular) system, RC666-701, Surgery, RD1-811
Abstract

Objective: To use a nationwide database of hospitalizations to investigate underweight status as a risk factor for postesophagectomy complications. Methods: We identified all patients who underwent esophagectomy with a diagnosis of esophageal cancer and known body mass index in the 2018-2020 Nationwide Readmissions Database. All hospital visits for esophagectomy and within 30 days of initial discharge were analyzed for postoperative complications, including chylothorax. Patients who were underweight were propensity score matched with patients who were not. Multivariable logistic regression was performed to identify complications that were significantly associated with underweight status. Results: There were 1877 patients with esophageal cancer meeting inclusion criteria. Following propensity score matching, 433 patients who were underweight were matched to 433 patients who were not. In the multivariable model of the matched sample, which adjusted for age, sex, Charlson Comorbidity Index, history of chemotherapy or radiation therapy, and preoperative surgical feeding access, patients who were underweight were estimated to have 2.06 times the odds for chylothorax (95% confidence interval [CI], 1.07-4.25, P = .035). Underweight status was also significantly associated with acute bleed (odds ratio [OR], 1.52; 95% CI, 1.12-2.05, P = .007), pneumothorax (OR, 2.33; 95% CI, 1.19-4.85; P = .017), pneumonia (OR, 2.30; 95% CI, 1.53-3.50, P

Published
2024
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10. ARBoR: an identity and security solution for clinical reporting

Author

Shan Lu, Victoria Yi, Richard A. Gibbs, Mullai Murugan, Walker Hale, Eric Venner, and Jordan M Jones

Subjects
0301 basic medicine, Computer science, Hash function, Health Informatics, Certification, Case Reports, Encryption, Medical Records, World Wide Web, 03 medical and health sciences, 0302 clinical medicine, Digital signature, Block (programming), Humans, 030212 general & internal medicine, Genetic Testing, Computer Security, 030304 developmental biology, 0303 health sciences, Information retrieval, Security solution, business.industry, Clinical Laboratory Techniques, Genome, Human, fungi, Electronic medical record, Geneticist, Genomics, Ordering Physician, 030104 developmental biology, Ledger, Organizational Case Studies, Identity (object-oriented programming), business, Laboratories
Abstract

Motivation Clinical genome sequencing laboratories return reports containing clinical testing results, signed by a board-certified clinical geneticist, to the ordering physician. This report is often a PDF, but can also be a paper copy or a structured data file. The reports are frequently modified and reissued due to changes in variant interpretation or clinical attributes. Materials and Methods To precisely track report authenticity, we developed ARBoR (Authenticated Resources in a Hashed Block Registry), an application for tracking the authenticity and lineage of versioned clinical reports even when they are distributed as PDF or paper copies. ARBoR tracks clinical reports as cryptographically signed hash blocks in an electronic ledger file, which is then exactly replicated to many clients. Results ARBoR was implemented for clinical reporting in the Human Genome Sequencing Center Clinical Laboratory, initially as part of the National Institute of Health's Electronic Medical Record and Genomics (eMERGE) project. Conclusions To date, we have issued 15 205 versioned clinical reports tracked by ARBoR. This system has provided us with a simple and tamper-proof mechanism for tracking clinical reports with a complicated update history.

Published
2019

11. Automatic verbal analysis of interviews with schizophrenic patients

Author

Jimmy Lee Chee Keong, Nadia Magnenat Thalmann, Victoria Yi Han Chua, Justin Dauwels, Zixu Yang, Daniel Thalmann, Shihao Xu, Tomasz Maszczyk, Yasir Tahir, Debsubhra Chakraborty, Bhing-Leet Tan, School of Electrical and Electronic Engineering, Lee Kong Chian School of Medicine (LKCMedicine), 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), and Institute for Media Innovation (IMI)

Subjects
0301 basic medicine, medicine.medical_specialty, education.field_of_study, Schizophrenia (object-oriented programming), Population, Mental disease, Audiology, Affect (psychology), Ensemble learning, NLP, Correlation, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Clinical diagnosis, medicine, Schizophrenia, Computer science and engineering [Engineering], Psychology, education, 030217 neurology & neurosurgery, Diagnosis of schizophrenia
Abstract

Schizophrenia is a long-term mental disease associated with language impairments that affect about one percent of the population. Traditional assessment of schizophrenic patients is conducted by trained professionals, which requires tremendous resources of time and effort. This study is part of a larger research objective committed to creating automated platforms to aid clinical diagnosis and understanding of schizophrenia. We have analyzed non-verbal cues and movement signals in our previous work. In this study, we explore the feasibility of using automatic transcriptions of interviews to classify patients and predict the observability of negative symptoms in schizophrenic patients. Interview recordings of 50 schizophrenia patients and 25 age-matched healthy controls were automatically transcribed by a speech recognition toolkit. After which, Natural Language Processing techniques were applied to automatically extract the lexical features and document vectors of transcriptions. Using these features, we applied ensemble machine learning algorithm (by leave-one-out cross-validation) to predict the Negative Symptom Assessment subject ratings of schizophrenic patients, and to classify patients from controls, achieving a maximum accuracy of 78.7%. These results indicate that schizophrenic patients exhibit significant differences in lexical usage compared with healthy controls, and the possibility of using these lexical features in the understanding and diagnosis of schizophrenia. NRF (Natl Research Foundation, S’pore) NMRC (Natl Medical Research Council, S’pore) Accepted version

Published
2019

12. Automatic Verbal Analysis of Interviews with Schizophrenic Patients

Author

Xu, Shihao, primary, Yang, Zixu, additional, Chakraborty, Debsubhra, additional, Tahir, Yasir, additional, Maszczyk, Tomasz, additional, Chua, Victoria Yi Han, additional, Dauwels, Justin, additional, Thalmann, Daniel, additional, Thalmann, Nadia Magnenat, additional, Tan, Bhing-Leet, additional, and Keong, Jimmy Lee Chee, additional

Published
2018
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13. The Potential of Lakes for Extracting Renewable Energy—A Case Study of Brates Lake in the South-East of Europe

Author

Eugen Rusu, Puiu Lucian Georgescu, Florin Onea, Victoria Yildirir, and Silvia Dragan

Subjects
Romania, Brates Lake, floating solar, wind turbine, ERA5, evaporation, Engineering machinery, tools, and implements, TA213-215, Technological innovations. Automation, HD45-45.2
Abstract

The aim of this work is to provide some details regarding the energy potential of the local wind and solar resources near the Galati area (south-east of Romania) by considering the performances of a few recent technologies. Based on 22 years of ERA5 data (2001–2022), a picture concerning the renewable energy resources in the Brates Lake area is provided. Comparing the wind and solar resources with in situ and satellite data, a relatively good agreement was found, especially in regards to the average values. In terms of wind speed conditions at a hub height of 100 m, we can expect a maximum value of 19.28 m/s during the winter time, while for the solar irradiance the energy level can reach up to 932 W/m2 during the summer season. Several generators of 2 MW were considered for evaluation, for which a state-of-the-art system of 6.2 MW was also added. The expected capacity factor of the turbines is in the range of (11.71–21.23)%, with better performances being expected from the Gamesa G90 generator. As a next step, several floating solar units were considered in order to simulate large-scale solar projects that may cover between 10 and 40% of the Brates Lake surface. The amount of the evaporated water saved by these solar panels was also considered, being estimated that the water demand of at least 3.42 km2 of the agricultural areas can be covered on an annual scale.

Published
2023
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14. Wind Variation near the Black Sea Coastal Areas Reflected by the ERA5 Dataset

Author

Victoria Yildirir, Eugen Rusu, and Florin Onea

Subjects
Black Sea, coastal environment, wind power, wind turbines, onshore, offshore, Engineering machinery, tools, and implements, TA213-215, Technological innovations. Automation, HD45-45.2
Abstract

In the context of the European Green Deal implementation, it is expected that there will be an increase in number of the wind farms located near the coastal areas in order to support this initiative. The Black Sea represents an important source of wind energy, and as a consequence, in the present work the regional wind resources (onshore and offshore) are evaluated by considering a total of 20 years of ERA5 wind data covering the 20-year time interval from January 2002 to December 2021. From a general perspective, it is clear that the offshore areas (100 km from the shoreline) are defined by much higher wind speed values than in the onshore, reaching an average of 8.75 m/s for the points located on the western sector. During the winter, these values can go up to 8.75 m/s, with the mention that the northern sectors from Ukraine and Russia may easily exceed 8 m/s. In terms of the wind turbines’ selection, for the offshore areas defined by consistent wind resources, generators will be considered that are defined by a rated wind speed of 11 m/s. Finally, we can mention that a theoretical offshore wind turbine of 20 MW can reach a capacity factor located between 20.9 and 48.3%, while a maximum annual electricity production of 84.6 GWh may be obtained from the sites located near the Romanian and Ukrainian sectors, respectively.

Published
2022
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