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1. 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
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2. Primary Care Opioid Taper Plans Are Associated with Sustained Opioid Dose Reduction

Author

David Cronkite, Denise M. Boudreau, Mark Sullivan, Gladys Salgado, Laura Ichikawa, Michael VonKorff, David Carrell, and Ladia Albertson-Junkans

Subjects
medicine.medical_specialty, business.industry, 010102 general mathematics, Case-control study, Psychological intervention, 01 natural sciences, 03 medical and health sciences, 0302 clinical medicine, Opioid, Internal medicine, Cohort, Internal Medicine, medicine, Morphine, Dose reduction, 030212 general & internal medicine, Dosing, 0101 mathematics, Medical prescription, business, medicine.drug
Abstract

Primary care providers prescribe most long-term opioid therapy and are increasingly asked to taper the opioid doses of these patients to safer levels. A recent systematic review suggests that multiple interventions may facilitate opioid taper, but many of these are not feasible within the usual primary care practice. To determine if opioid taper plans documented by primary care providers in the electronic health record are associated with significant and sustained opioid dose reductions among patients on long-term opioid therapy. A nested case-control design was used to compare cases (patients with a sustained opioid taper defined as average daily opioid dose of ≤ 30 mg morphine equivalent (MME) or a 50% reduction in MME) to controls (patients matched to cases on year and quarter of cohort entry, sex, and age group, who had not achieved a sustained taper). Each case was matched with four controls. Two thousand four hundred nine patients receiving a ≥ 60-day supply of opioids with an average daily dose of ≥ 50 MME during 2011–2015. Opioid taper plans documented in prescription instructions or clinical notes within the electronic health record identified through natural language processing; opioid dosing, patient characteristics, and taper plan components also abstracted from the electronic health record. Primary care taper plans were associated with an increased likelihood of sustained opioid taper after adjusting for all patient covariates and near peak dose (OR = 3.63 [95% CI 2.96–4.46], p < 0.0001). Both taper plans in prescription instructions (OR = 4.03 [95% CI 3.19–5.09], p < 0.0001) and in clinical notes (OR = 2.82 [95% CI 2.00–3.99], p < 0.0001) were associated with sustained taper. These results suggest that planning for opioid taper during primary care visits may facilitate significant and sustained opioid dose reduction.

Published
2020
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3. 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
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4. Validity of diagnosis and procedure codes for identifying neural tube defects in infants

Author

James P. Trinidad, Jennifer F. Bobb, Rulin C. Hechter, Marie R. Griffin, Denise M. Boudreau, Sascha Dublin, David H. Smith, Gaia Pocobelli, Timothy J Maarup, Gladys Salgado, Kecia N. Carroll, Carrie Ceresa, Lockwood G. Taylor, Cecilia Portugal, Sengwee Toh, T. Craig Cheetham, Lawrence Wong, Ladia Albertson-Junkans, Miriam Dinatale, Marsha A. Raebel, Mercedes A. Munis, Pamala A. Pawloski, Susan E. Andrade, Wei Hua, and De-Kun Li

Subjects
congenital, hereditary, and neonatal diseases and abnormalities, Pediatrics, medicine.medical_specialty, Databases, Factual, Epidemiology, Birth certificate, 030226 pharmacology & pharmacy, Medical Records, Cohort Studies, 03 medical and health sciences, 0302 clinical medicine, Chart, Predictive Value of Tests, Pregnancy, medicine, Humans, Pharmacology (medical), 030212 general & internal medicine, Neural Tube Defects, business.industry, Medical record, Infant, medicine.disease, Confidence interval, Cohort, Observational study, Female, Diagnosis code, business
Abstract

Purpose The use of validated criteria to identify birth defects in electronic healthcare databases can avoid the cost and time-intensive efforts required to conduct chart reviews to confirm outcomes. This study evaluated the validity of various case-finding methodologies to identify neural tube defects (NTDs) in infants using an electronic healthcare database. Methods This analysis used data generated from a study whose primary aim was to evaluate the association between first-trimester maternal prescription opioid use and NTDs. The study was conducted within the Medication Exposure in Pregnancy Risk Evaluation Program. A broad approach was used to identify potential NTDs including diagnosis and procedure codes from inpatient and outpatient settings, death certificates and birth defect flags in birth certificates. Potential NTD cases were chart abstracted and confirmed by clinical experts. Positive predictive values (PPVs) and 95% confidence intervals (95% CI) are reported. Results The cohort included 113 168 singleton live-born infants: 55 960 infants with opioid exposure in pregnancy and 57 208 infants unexposed in pregnancy. Seventy-three potential NTD cases were available for the validation analysis. The overall PPV was 41% using all diagnosis and procedure codes plus birth certificates. Restricting approaches to codes recorded in the infants' medical record or to birth certificate flags increased the PPVs (72% and 80%, respectively) but missed a substantial proportion of confirmed NTDs. Conclusions Codes in electronic healthcare data did not accurately identify confirmed NTDs. These results indicate that chart review with adjudication of outcomes is important when conducting observational studies of NTDs using electronic healthcare data.

Published
2020

5. Measuring problem prescription opioid use among patients receiving long-term opioid analgesic treatment: development and evaluation of an algorithm for use in EHR and claims data

Author

Susan M. Shortreed, Carlos G. Grijalva, David Carrell, Michael Von Korff, Brian Hazlehurst, Shannon L. Janoff, Jane M. Lange, Carla A. Green, Ladia Albertson-Junkans, Kris Hansen, David Cronkite, Eric O. Johnson, Grant Scull, Arvind Ramaprasan, Caihua Liang, Andrew Baer, Matt Mackwood, Angela DeVeaugh-Geiss, Paul Coplan, and Cheryl Enger

Subjects
business.industry, Treatment development, Opioid-Related Disorders, 030204 cardiovascular system & hematology, Pain Medicine, Term (time), InformationSystems_GENERAL, 03 medical and health sciences, electronic health records, 0302 clinical medicine, Opioid, Prescription opioid, Claims data, opioid-related disorders, Medicine, Medical prescription, business, Opioid analgesics, population surveillance, Algorithm, Algorithms, 030217 neurology & neurosurgery, medicine.drug
Abstract

Objective: Opioid surveillance in response to the opioid epidemic will benefit from scalable, automated algorithms for identifying patients with clinically documented signs of problem prescription opioid use. Existing algorithms lack accuracy. We sought to develop a high-sensitivity, high-specificity classification algorithm based on widely available structured health data to identify patients receiving chronic extended-release/long-acting (ER/LA) therapy with evidence of problem use to support subsequent epidemiologic investigations. Methods: Outpatient medical records of a probability sample of 2,000 Kaiser Permanente Washington patients receiving ≥60 days’ supply of ER/LA opioids in a 90-day period from 1 January 2006 to 30 June 2015 were manually reviewed to determine the presence of clinically documented signs of problem use and used as a reference standard for algorithm development. Using 1,400 patients as training data, we constructed candidate predictors from demographic, enrollment, encounter, diagnosis, procedure, and medication data extracted from medical claims records or the equivalent from electronic health record (EHR) systems, and we used adaptive least absolute shrinkage and selection operator (LASSO) regression to develop a model. We evaluated this model in a comparable 600-patient validation set. We compared this model to ICD-9 diagnostic codes for opioid abuse, dependence, and poisoning. This study was registered with ClinicalTrials.gov as study NCT02667262 on 28 January 2016. Results: We operationalized 1,126 potential predictors characterizing patient demographics, procedures, diagnoses, timing, dose, and location of medication dispensing. The final model incorporating 53 predictors had a sensitivity of 0.582 at positive predictive value (PPV) of 0.572. ICD-9 codes for opioid abuse, dependence, and poisoning had a sensitivity of 0.390 at PPV of 0.599 in the same cohort. Conclusions: Scalable methods using widely available structured EHR/claims data to accurately identify problem opioid use among patients receiving long-term ER/LA therapy were unsuccessful. This approach may be useful for identifying patients needing clinical evaluation.

Published
2020
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6. Interactive Visualization of a Patient’s Electronic Health Information to Assist Manual Chart Review Using Tableau® Software

Author

Ladia Albertson-Junkans, David Carrell, Andrew Baer, David Cronkite, and Arvind Ramaprasan

Subjects
business.industry, Addiction, media_common.quotation_subject, Information technology, General Medicine, medicine.disease, Substance abuse, World Wide Web, Software, Chart review, Medicine, Health information, business, Interactive visualization, media_common
Published
2017
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