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Suver C, Harper J, Loomba J, Saltz M, Solway J, Anzalone AJ, Walters K, Pfaff E, Walden A, McMurry J, Chute CG, Haendel M. The N3C governance ecosystem: A model socio-technical partnership for the future of collaborative analytics at scale. J Clin Transl Sci 2023; 7:e252. [PMID: 38229902 PMCID: PMC10789985 DOI: 10.1017/cts.2023.681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/22/2023] [Accepted: 11/06/2023] [Indexed: 01/18/2024] Open
Abstract
The National COVID Cohort Collaborative (N3C) is a public-private-government partnership established during the Coronavirus pandemic to create a centralized data resource called the "N3C data enclave." This resource contains individual-level health data from participating healthcare sites nationwide to support rapid collaborative analytics. N3C has enabled analytics within a cloud-based enclave of data from electronic health records from over 17 million people (with and without COVID-19) in the USA. To achieve this goal of a shared data resource, N3C implemented a shared governance strategy involving stakeholders in decision-making. The approach leveraged best practices in data stewardship and team science to rapidly enable COVID-19-related research at scale while respecting the privacy of data subjects and participating institutions. N3C balanced equitable access to data, team-based scientific productivity, and individual professional recognition - a key incentive for academic researchers. This governance approach makes N3C research sustainable and effective beyond the initial days of the pandemic. N3C demonstrated that shared governance can overcome traditional barriers to data sharing without compromising data security and trust. The governance innovations described herein are a helpful framework for other privacy-preserving data infrastructure programs and provide a working model for effective team science beyond COVID-19.
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Affiliation(s)
- Christine Suver
- Research Governance & Ethics, Sage Bionetworks, Seattle, WA, USA
| | | | - Johanna Loomba
- Integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, Charlottesville, VA, USA
| | - Mary Saltz
- Department of Biomedical Informatics, Stony Brook University, New York, NY, USA
| | - Julian Solway
- Institute for Translational Medicine, University of Chicago, Chicago, IL, USA
| | - Alfred Jerrod Anzalone
- Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
| | | | - Emily Pfaff
- University of North Carolina, Chapel Hill, NC, USA
| | - Anita Walden
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Julie McMurry
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Christopher G. Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | - Melissa Haendel
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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Abell-Hart K, Hajagos J, Garcia V, Kaan J, Zhu W, Saltz M, Saltz J, Tassiopoulos A. Investigation of commonly used aortic aneurysm growth rate metrics: Comparing their suitability for clinical and research applications. PLoS One 2023; 18:e0289078. [PMID: 37566584 PMCID: PMC10420361 DOI: 10.1371/journal.pone.0289078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 07/11/2023] [Indexed: 08/13/2023] Open
Abstract
An aneurysm is a pathological widening of a blood vessel. Aneurysms of the aorta are often asymptomatic until they rupture, killing approximately 10,000 Americans per year. Fortunately, rupture can be prevented through early detection and surgical repair. However, surgical risk outweighs rupture risk for small aortic aneurysms, necessitating a policy of surveillance. Understanding the growth rate of aneurysms is essential for determining appropriate surveillance windows. Further, identifying risk factors for fast growth can help identify potential interventions. However, studies in the literature have applied many different methods for defining the growth rate of abdominal aortic aneurysms. It is unclear which of these methods is most accurate and clinically meaningful, and whether these heterogeneous methodologies may have contributed to the varied results reported in the literature. To help future researchers best plan their studies and to help clinicians interpret existing studies, we compared five different models of aneurysmal growth rate. We examined their noise tolerance, temporal bias, predictive accuracy, and statistical power to detect risk factors. We found that hierarchical mixed effects models were more noise tolerant than traditional, unpooled models. We also found that linear models were sensitive to temporal bias, assigning lower growth rates to aneurysms that were detected earlier in their course. Our exponential mixed model was noise-tolerant, resistant to temporal bias, and detected the greatest number of clinical risk factors. We conclude that exponential mixed models may be optimal for large studies. Because our results suggest that choice of method can materially impact a study's findings, we recommend that future studies clearly state the method used and demonstrate its appropriateness.
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Affiliation(s)
- Kayley Abell-Hart
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States of America
| | - Janos Hajagos
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States of America
| | - Victor Garcia
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States of America
| | - James Kaan
- Department of Vascular Surgery, Stony Brook University Hospital, Stony Brook, NY, United States of America
| | - Wei Zhu
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States of America
| | - Mary Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States of America
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States of America
| | - Apostolos Tassiopoulos
- Department of Vascular Surgery, Stony Brook University Hospital, Stony Brook, NY, United States of America
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Wong R, Vaddavalli R, Hall MA, Patel MV, Bramante CT, Casarighi E, Johnson SG, Lingam V, Miller JD, Reusch J, Saltz M, Stürmer T, Tronieri JS, Wilkins KJ, Buse JB, Saltz J, Huling JD, Moffitt R. Effect of SARS-CoV-2 Infection and Infection Severity on Longer-Term Glycemic Control and Weight in People With Type 2 Diabetes. Diabetes Care 2022; 45:2709-2717. [PMID: 36098660 PMCID: PMC9679257 DOI: 10.2337/dc22-0730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 07/16/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To evaluate the association of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and severity of infection with longer-term glycemic control and weight in people with type 2 diabetes (T2D) in the U.S. RESEARCH DESIGN AND METHODS We conducted a retrospective cohort study using longitudinal electronic health record data of patients with SARS-CoV-2 infection from the National COVID Cohort Collaborative (N3C). Patients were ≥18 years old with an ICD-10 diagnosis of T2D and at least one HbA1c and weight measurement prior to and after an index date of their first coronavirus disease 2019 (COVID-19) diagnosis or negative SARS-CoV-2 test. We used propensity scores to identify a matched cohort balanced on demographic characteristics, comorbidities, and medications used to treat diabetes. The primary outcome was the postindex average HbA1c and postindex average weight over a 1 year time period beginning 90 days after the index date among patients who did and did not have SARS-CoV-2 infection. Secondary outcomes were postindex average HbA1c and weight in patients who required hospitalization or mechanical ventilation. RESULTS There was no significant difference in the postindex average HbA1c or weight in patients who had SARS-CoV-2 infection compared with control subjects. Mechanical ventilation was associated with a decrease in average HbA1c after COVID-19. CONCLUSIONS In a multicenter cohort of patients in the U.S. with preexisting T2D, there was no significant change in longer-term average HbA1c or weight among patients who had COVID-19. Mechanical ventilation was associated with a decrease in HbA1c after COVID-19.
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Affiliation(s)
- Rachel Wong
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - Rohith Vaddavalli
- Department of Computer Science, Stony Brook University, Stony Brook, NY
| | - Margaret A. Hall
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - Monil V. Patel
- Department of Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY
| | - Carolyn T. Bramante
- Division of General Internal Medicine, University of Minnesota Medical School, Minneapolis, MN
| | - Elena Casarighi
- AnacletoLab, Department of Computer Science “Giovanni degli Antoni,” Università degli Studi di Milano, Milan, Italy
- CINI, Infolife National Laboratory, Roma, Italy
| | - Steven G. Johnson
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN
| | - Veena Lingam
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - Joshua D. Miller
- Division of Endocrinology and Metabolism, Department of Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY
| | - Jane Reusch
- Division of Endocrinology, Metabolism & Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Mary Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - Til Stürmer
- Department of Epidemiology, University of North Carolina at Chapel Hill Gillings School of Global Public Health, Chapel Hill, NC
| | - Jena S. Tronieri
- Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Kenneth J. Wilkins
- Office of the Director, Biostatistics Program/Office of Clinical Research Support, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD
| | - John B. Buse
- Division of Endocrinology and Metabolism, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC
- North Carolina Translational and Clinical Sciences Institute, University of North Carolina School of Medicine, Chapel Hill, NC
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - Jared D. Huling
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
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Wong R, Hall M, Vaddavalli R, Anand A, Arora N, Bramante CT, Garcia V, Johnson S, Saltz M, Tronieri JS, Yoo YJ, Buse JB, Saltz J, Miller J, Moffitt R. Glycemic Control and Clinical Outcomes in U.S. Patients With COVID-19: Data From the National COVID Cohort Collaborative (N3C) Database. Diabetes Care 2022; 45:dc212186. [PMID: 35202458 PMCID: PMC9174972 DOI: 10.2337/dc21-2186] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 01/28/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE The purpose of the study is to evaluate the relationship between HbA1c and severity of coronavirus disease 2019 (COVID-19) outcomes in patients with type 2 diabetes (T2D) with acute COVID-19 infection. RESEARCH DESIGN AND METHODS We conducted a retrospective study using observational data from the National COVID Cohort Collaborative (N3C), a longitudinal, multicenter U.S. cohort of patients with COVID-19 infection. Patients were ≥18 years old with T2D and confirmed COVID-19 infection by laboratory testing or diagnosis code. The primary outcome was 30-day mortality following the date of COVID-19 diagnosis. Secondary outcomes included need for invasive ventilation or extracorporeal membrane oxygenation (ECMO), hospitalization within 7 days before or 30 days after COVID-19 diagnosis, and length of stay (LOS) for patients who were hospitalized. RESULTS The study included 39,616 patients (50.9% female, 55.4% White, 26.4% Black or African American, and 16.1% Hispanic or Latino, with mean ± SD age 62.1 ± 13.9 years and mean ± SD HbA1c 7.6% ± 2.0). There was an increasing risk of hospitalization with incrementally higher HbA1c levels, but risk of death plateaued at HbA1c >8%, and risk of invasive ventilation or ECMO plateaued >9%. There was no significant difference in LOS across HbA1c levels. CONCLUSIONS In a large, multicenter cohort of patients in the U.S. with T2D and COVID-19 infection, risk of hospitalization increased with incrementally higher HbA1c levels. Risk of death and invasive ventilation also increased but plateaued at different levels of glycemic control.
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Affiliation(s)
- Rachel Wong
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - Margaret Hall
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - Rohith Vaddavalli
- Department of Computer Science, Stony Brook University, Stony Brook, NY
| | - Adit Anand
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - Neha Arora
- Division of Endocrinology and Metabolism, Department of Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY
| | - Carolyn T. Bramante
- Division of General Internal Medicine, University of Minnesota Medical School, Minneapolis, MN
| | - Victor Garcia
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - Steven Johnson
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN
| | - Mary Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - Jena S. Tronieri
- Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Yun Jae Yoo
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - John B. Buse
- Division of Endocrinology and Metabolism, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC
- North Carolina Translational and Clinical Sciences Institute, University of North Carolina School of Medicine, Chapel Hill, NC
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - Joshua Miller
- Division of Endocrinology and Metabolism, Department of Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
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Dong X, Deng J, Rashidian S, Abell-Hart K, Hou W, Rosenthal RN, Saltz M, Saltz JH, Wang F. Identifying risk of opioid use disorder for patients taking opioid medications with deep learning. J Am Med Inform Assoc 2021; 28:1683-1693. [PMID: 33930132 DOI: 10.1093/jamia/ocab043] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 02/02/2020] [Accepted: 03/01/2021] [Indexed: 01/16/2023] Open
Abstract
OBJECTIVE The United States is experiencing an opioid epidemic. In recent years, there were more than 10 million opioid misusers aged 12 years or older annually. Identifying patients at high risk of opioid use disorder (OUD) can help to make early clinical interventions to reduce the risk of OUD. Our goal is to develop and evaluate models to predict OUD for patients on opioid medications using electronic health records and deep learning methods. The resulting models help us to better understand OUD, providing new insights on the opioid epidemic. Further, these models provide a foundation for clinical tools to predict OUD before it occurs, permitting early interventions. METHODS Electronic health records of patients who have been prescribed with medications containing active opioid ingredients were extracted from Cerner's Health Facts database for encounters between January 1, 2008, and December 31, 2017. Long short-term memory models were applied to predict OUD risk based on five recent prior encounters before the target encounter and compared with logistic regression, random forest, decision tree, and dense neural network. Prediction performance was assessed using F1 score, precision, recall, and area under the receiver-operating characteristic curve. RESULTS The long short-term memory (LSTM) model provided promising prediction results which outperformed other methods, with an F1 score of 0.8023 (about 0.016 higher than dense neural network (DNN)) and an area under the receiver-operating characteristic curve (AUROC) of 0.9369 (about 0.145 higher than DNN). CONCLUSIONS LSTM-based sequential deep learning models can accurately predict OUD using a patient's history of electronic health records, with minimal prior domain knowledge. This tool has the potential to improve clinical decision support for early intervention and prevention to combat the opioid epidemic.
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Affiliation(s)
- Xinyu Dong
- Department of Computer Science, Stony Brook University, Stony Brook, New York, USA
| | - Jianyuan Deng
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA
| | - Sina Rashidian
- Department of Computer Science, Stony Brook University, Stony Brook, New York, USA
| | - Kayley Abell-Hart
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA
| | - Wei Hou
- Department of Family, Population and Preventive Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA
| | - Richard N Rosenthal
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA
| | - Mary Saltz
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA
| | - Joel H Saltz
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA
| | - Fusheng Wang
- Department of Computer Science, Stony Brook University, Stony Brook, New York, USA.,Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA
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Dong X, Deng J, Hou W, Rashidian S, Rosenthal RN, Saltz M, Saltz JH, Wang F. Predicting opioid overdose risk of patients with opioid prescriptions using electronic health records based on temporal deep learning. J Biomed Inform 2021; 116:103725. [PMID: 33711546 DOI: 10.1016/j.jbi.2021.103725] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 02/22/2021] [Indexed: 01/04/2023]
Abstract
The US is experiencing an opioid epidemic, and opioid overdose is causing more than 100 deaths per day. Early identification of patients at high risk of Opioid Overdose (OD) can help to make targeted preventative interventions. We aim to build a deep learning model that can predict the patients at high risk for opioid overdose and identify most relevant features. The study included the information of 5,231,614 patients from the Health Facts database with at least one opioid prescription between January 1, 2008 and December 31, 2017. Potential predictors (n = 1185) were extracted to build a feature matrix for prediction. Long Short-Term Memory (LSTM) based models were built to predict overdose risk in the next hospital visit. Prediction performance was compared with other machine learning methods assessed using machine learning metrics. Our sequential deep learning models built upon LSTM outperformed the other methods on opioid overdose prediction. LSTM with attention mechanism achieved the highest F-1 score (F-1 score: 0.7815, AUCROC: 0.8449). The model is also able to reveal top ranked predictive features by permutation important method, including medications and vital signs. This study demonstrates that a temporal deep learning based predictive model can achieve promising results on identifying risk of opioid overdose of patients using the history of electronic health records. It provides an alternative informatics-based approach to improving clinical decision support for possible early detection and intervention to reduce opioid overdose.
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Affiliation(s)
- Xinyu Dong
- Department of Computer Science, Stony Brook University, Stony Brook, NY, United States
| | - Jianyuan Deng
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
| | - Wei Hou
- Department of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY, United States
| | - Sina Rashidian
- Department of Computer Science, Stony Brook University, Stony Brook, NY, United States
| | - Richard N Rosenthal
- Department of Psychiatry, Renaissance Stony Brook Medicine, Stony Brook, NY, United States
| | - Mary Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
| | - Joel H Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
| | - Fusheng Wang
- Department of Computer Science, Stony Brook University, Stony Brook, NY, United States; Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States.
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Chen X, Hou W, Rashidian S, Wang Y, Zhao X, Leibowitz GS, Rosenthal RN, Saltz M, Saltz JH, Schoenfeld ER, Wang F. A large-scale retrospective study of opioid poisoning in New York State with implications for targeted interventions. Sci Rep 2021; 11:5152. [PMID: 33664282 PMCID: PMC7933431 DOI: 10.1038/s41598-021-84148-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 02/12/2021] [Indexed: 01/11/2023] Open
Abstract
Opioid overdose related deaths have increased dramatically in recent years. Combating the opioid epidemic requires better understanding of the epidemiology of opioid poisoning (OP). To discover trends and patterns of opioid poisoning and the demographic and regional disparities, we analyzed large scale patient visits data in New York State (NYS). Demographic, spatial, temporal and correlation analyses were performed for all OP patients extracted from the claims data in the New York Statewide Planning and Research Cooperative System (SPARCS) from 2010 to 2016, along with Decennial US Census and American Community Survey zip code level data. 58,481 patients with at least one OP diagnosis and a valid NYS zip code address were included. Main outcome and measures include OP patient counts and rates per 100,000 population, patient level factors (gender, age, race and ethnicity, residential zip code), and zip code level social demographic factors. The results showed that the OP rate increased by 364.6%, and by 741.5% for the age group > 65 years. There were wide disparities among groups by race and ethnicity on rates and age distributions of OP. Heroin and non-heroin based OP rates demonstrated distinct temporal trends as well as major geospatial variation. The findings highlighted strong demographic disparity of OP patients, evolving patterns and substantial geospatial variation.
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Affiliation(s)
- Xin Chen
- Department of Biomedical Informatics, Stony Brook University, 2313D Computer Science, Stony Brook, NY, 11794-8330, USA
| | - Wei Hou
- Department of Family, Population and Preventive Medicine, Renaissance School of Medicine, Stony Brook, NY, USA
| | - Sina Rashidian
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Yu Wang
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Xia Zhao
- School of Health Technology and Management, Stony Brook University, Stony Brook, NY, USA
| | | | - Richard N Rosenthal
- Department of Psychiatry, Renaissance School of Medicine, Stony Brook, NY, USA
| | - Mary Saltz
- Department of Biomedical Informatics, Stony Brook University, 2313D Computer Science, Stony Brook, NY, 11794-8330, USA
- Department of Radiology, Renaissance School of Medicine, Stony Brook, NY, USA
| | - Joel H Saltz
- Department of Biomedical Informatics, Stony Brook University, 2313D Computer Science, Stony Brook, NY, 11794-8330, USA
| | - Elinor Randi Schoenfeld
- Department of Family, Population and Preventive Medicine, Renaissance School of Medicine, Stony Brook, NY, USA
| | - Fusheng Wang
- Department of Biomedical Informatics, Stony Brook University, 2313D Computer Science, Stony Brook, NY, 11794-8330, USA.
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA.
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Rashidian S, Abell-Hart K, Hajagos J, Moffitt R, Lingam V, Garcia V, Tsai CW, Wang F, Dong X, Sun S, Deng J, Gupta R, Miller J, Saltz J, Saltz M. Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health Records for Quality Improvement: Temporal Deep Learning Approach. JMIR Med Inform 2020; 8:e22649. [PMID: 33331828 PMCID: PMC7775195 DOI: 10.2196/22649] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Revised: 09/24/2020] [Accepted: 09/27/2020] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Diabetes affects more than 30 million patients across the United States. With such a large disease burden, even a small error in classification can be significant. Currently billing codes, assigned at the time of a medical encounter, are the "gold standard" reflecting the actual diseases present in an individual, and thus in aggregate reflect disease prevalence in the population. These codes are generated by highly trained coders and by health care providers but are not always accurate. OBJECTIVE This work provides a scalable deep learning methodology to more accurately classify individuals with diabetes across multiple health care systems. METHODS We leveraged a long short-term memory-dense neural network (LSTM-DNN) model to identify patients with or without diabetes using data from 5 acute care facilities with 187,187 patients and 275,407 encounters, incorporating data elements including laboratory test results, diagnostic/procedure codes, medications, demographic data, and admission information. Furthermore, a blinded physician panel reviewed discordant cases, providing an estimate of the total impact on the population. RESULTS When predicting the documented diagnosis of diabetes, our model achieved an 84% F1 score, 96% area under the curve-receiver operating characteristic curve, and 91% average precision on a heterogeneous data set from 5 distinct health facilities. However, in 81% of cases where the model disagreed with the documented phenotype, a blinded physician panel agreed with the model. Taken together, this suggests that 4.3% of our studied population have either missing or improper diabetes diagnosis. CONCLUSIONS This study demonstrates that deep learning methods can improve clinical phenotyping even when patient data are noisy, sparse, and heterogeneous.
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Affiliation(s)
- Sina Rashidian
- Department of Computer Science, Stony Brook University, Stony Brook, NY, United States
| | - Kayley Abell-Hart
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Janos Hajagos
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Richard Moffitt
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Veena Lingam
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Victor Garcia
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Chao-Wei Tsai
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Fusheng Wang
- Department of Computer Science, Stony Brook University, Stony Brook, NY, United States
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Xinyu Dong
- Department of Computer Science, Stony Brook University, Stony Brook, NY, United States
| | - Siao Sun
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States
| | - Jianyuan Deng
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Joshua Miller
- Department of Medicine, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Joel Saltz
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
| | - Mary Saltz
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, NY, United States
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9
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Chaudhri I, Moffitt R, Taub E, Annadi RR, Hoai M, Bolotova O, Yoo J, Dhaliwal S, Sahib H, Daccueil F, Hajagos J, Saltz M, Saltz J, Mallipattu SK, Koraishy FM. Association of Proteinuria and Hematuria with Acute Kidney Injury and Mortality in Hospitalized Patients with COVID-19. Kidney Blood Press Res 2020; 45:1018-1032. [PMID: 33171466 DOI: 10.1159/000511946] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 09/24/2020] [Indexed: 11/19/2022] Open
Abstract
INTRODUCTION Acute kidney injury (AKI) is strongly associated with poor outcomes in hospitalized patients with coronavirus disease 2019 (COVID-19), but data on the association of proteinuria and hematuria are limited to non-US populations. In addition, admission and in-hospital measures for kidney abnormalities have not been studied separately. METHODS This retrospective cohort study aimed to analyze these associations in 321 patients sequentially admitted between March 7, 2020 and April 1, 2020 at Stony Brook University Medical Center, New York. We investigated the association of proteinuria, hematuria, and AKI with outcomes of inflammation, intensive care unit (ICU) admission, invasive mechanical ventilation (IMV), and in-hospital death. We used ANOVA, t test, χ2 test, and Fisher's exact test for bivariate analyses and logistic regression for multivariable analysis. RESULTS Three hundred patients met the inclusion criteria for the study cohort. Multivariable analysis demonstrated that admission proteinuria was significantly associated with risk of in-hospital AKI (OR 4.71, 95% CI 1.28-17.38), while admission hematuria was associated with ICU admission (OR 4.56, 95% CI 1.12-18.64), IMV (OR 8.79, 95% CI 2.08-37.00), and death (OR 18.03, 95% CI 2.84-114.57). During hospitalization, de novo proteinuria was significantly associated with increased risk of death (OR 8.94, 95% CI 1.19-114.4, p = 0.04). In-hospital AKI increased (OR 27.14, 95% CI 4.44-240.17) while recovery from in-hospital AKI decreased the risk of death (OR 0.001, 95% CI 0.001-0.06). CONCLUSION Proteinuria and hematuria both at the time of admission and during hospitalization are associated with adverse clinical outcomes in hospitalized patients with COVID-19.
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Affiliation(s)
- Imran Chaudhri
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook University, Stony Brook, New York, USA
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Erin Taub
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook University, Stony Brook, New York, USA
| | - Raji R Annadi
- Department of Computer Science, Stony Brook University, Stony Brook, New York, USA
| | - Minh Hoai
- Department of Computer Science, Stony Brook University, Stony Brook, New York, USA
| | - Olena Bolotova
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook University, Stony Brook, New York, USA
| | - Jeanwoo Yoo
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook University, Stony Brook, New York, USA
| | - Simrat Dhaliwal
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook University, Stony Brook, New York, USA
| | - Haseena Sahib
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook University, Stony Brook, New York, USA
| | - Farah Daccueil
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook University, Stony Brook, New York, USA
| | - Janos Hajagos
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Mary Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Sandeep K Mallipattu
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook University, Stony Brook, New York, USA.,Renal Section, Northport VA Medical Center, Northport, New York, USA
| | - Farrukh M Koraishy
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook University, Stony Brook, New York, USA, .,Renal Section, Northport VA Medical Center, Northport, New York, USA,
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10
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Mallipattu SK, Jawa R, Moffitt R, Hajagos J, Fries B, Nachman S, Gan TJ, Saltz M, Saltz J, Kaushansky K, Skopicki H, Abell-Hart K, Chaudhri I, Deng J, Garcia V, Gayen S, Kurc T, Bolotova O, Yoo J, Dhaliwal S, Nataraj N, Sun S, Tsai C, Wang Y, Abbasi S, Abdullah R, Ahmad S, Bai K, Bennett-Guerrero E, Chua A, Gomes C, Griffel M, Kalogeropoulos A, Kiamanesh D, Kim N, Koraishy F, Lingham V, Mansour M, Marcos L, Miller J, Poovathor S, Rubano J, Rutigliano D, Sands M, Santora C, Schwartz J, Shroyer K, Spitzer S, Stopeck A, Talamini M, Tharakan M, Vosswinkel J, Wertheim W, Mallipattu SK, Jawa R, Moffitt R, Hajagos J, Fries B, Nachman S, Gan TJ, Saltz M, Saltz J, Kaushansky K, Skopicki H, Abell-Hart K, Chaudhri I, Deng J, Garcia V, Gayen S, Kurc T, Bolotova O, Yoo J, Dhaliwal S, Nataraj N, Sun S, Tsai C, Wang Y, Abbasi S, Abdullah R, Ahmad S, Bai K, Bennett-Guerrero E, Chua A, Gomes C, Griffel M, Kalogeropoulos A, Kiamanesh D, Kim N, Koraishy F, Lingham V, Mansour M, Marcos L, Miller J, Poovathor S, Rubano J, Rutigliano D, Sands M, Santora C, Schwartz J, Shroyer K, Spitzer S, Stopeck A, Talamini M, Tharakan M, Vosswinkel J, Wertheim W. Geospatial Distribution and Predictors of Mortality in Hospitalized Patients With COVID-19: A Cohort Study. Open Forum Infect Dis 2020; 7:ofaa436. [PMID: 33117852 PMCID: PMC7543608 DOI: 10.1093/ofid/ofaa436] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 09/09/2020] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND The global coronavirus disease 2019 (COVID-19) pandemic offers the opportunity to assess how hospitals manage the care of hospitalized patients with varying demographics and clinical presentations. The goal of this study was to demonstrate the impact of densely populated residential areas on hospitalization and to identify predictors of length of stay and mortality in hospitalized patients with COVID-19 in one of the hardest hit counties internationally. METHODS This was a single-center cohort study of 1325 sequentially hospitalized patients with COVID-19 in New York between March 2, 2020, to May 11, 2020. Geospatial distribution of study patients' residences relative to population density in the region were mapped, and data analysis included hospital length of stay, need and duration of invasive mechanical ventilation (IMV), and mortality. Logistic regression models were constructed to predict discharge dispositions in the remaining active study patients. RESULTS The median age of the study cohort (interquartile range [IQR]) was 62 (49-75) years, and more than half were male (57%) with history of hypertension (60%), obesity (41%), and diabetes (42%). Geographic residence of the study patients was disproportionately associated with areas of higher population density (r s = 0.235; P = .004), with noted "hot spots" in the region. Study patients were predominantly hypertensive (MAP > 90 mmHg; 670, 51%) on presentation with lymphopenia (590, 55%), hyponatremia (411, 31%), and kidney dysfunction (estimated glomerular filtration rate < 60 mL/min/1.73 m2; 381, 29%). Of the patients with a disposition (1188/1325), 15% (182/1188) required IMV and 21% (250/1188) developed acute kidney injury. In patients on IMV, the median (IQR) hospital length of stay in survivors (22 [16.5-29.5] days) was significantly longer than that of nonsurvivors (15 [10-23.75] days), but this was not due to prolonged time on the ventilator. The overall mortality in all hospitalized patients was 15%, and in patients receiving IMV it was 48%, which is predicted to minimally rise from 48% to 49% based on logistic regression models constructed to project disposition in the remaining patients on ventilators. Acute kidney injury during hospitalization (odds ratioE, 3.23) was the strongest predictor of mortality in patients requiring IMV. CONCLUSIONS This is the first study to collectively utilize the demographics, clinical characteristics, and hospital course of COVID-19 patients to identify predictors of poor outcomes that can be used for resource allocation in future waves of the pandemic.
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Affiliation(s)
| | - S K Mallipattu
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - R Jawa
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - R Moffitt
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Hajagos
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - B Fries
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Nachman
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - T J Gan
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Saltz
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Saltz
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Kaushansky
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - H Skopicki
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Abell-Hart
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - I Chaudhri
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Deng
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - V Garcia
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Gayen
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - T Kurc
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - O Bolotova
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Yoo
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Dhaliwal
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - N Nataraj
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Sun
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - C Tsai
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - Y Wang
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Abbasi
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - R Abdullah
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Ahmad
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Bai
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - E Bennett-Guerrero
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - A Chua
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - C Gomes
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Griffel
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - A Kalogeropoulos
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - D Kiamanesh
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - N Kim
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - F Koraishy
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - V Lingham
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Mansour
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - L Marcos
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Miller
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Poovathor
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Rubano
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - D Rutigliano
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Sands
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - C Santora
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Schwartz
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Shroyer
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Spitzer
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - A Stopeck
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Talamini
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Tharakan
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Vosswinkel
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - W Wertheim
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S K Mallipattu
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - R Jawa
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - R Moffitt
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Hajagos
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - B Fries
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Nachman
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - T J Gan
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Saltz
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Saltz
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Kaushansky
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - H Skopicki
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Abell-Hart
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - I Chaudhri
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Deng
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - V Garcia
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Gayen
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - T Kurc
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - O Bolotova
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Yoo
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Dhaliwal
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - N Nataraj
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Sun
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - C Tsai
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - Y Wang
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Abbasi
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - R Abdullah
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Ahmad
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Bai
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - E Bennett-Guerrero
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - A Chua
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - C Gomes
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Griffel
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - A Kalogeropoulos
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - D Kiamanesh
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - N Kim
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - F Koraishy
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - V Lingham
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Mansour
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - L Marcos
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Miller
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Poovathor
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Rubano
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - D Rutigliano
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Sands
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - C Santora
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Schwartz
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - K Shroyer
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - S Spitzer
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - A Stopeck
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Talamini
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - M Tharakan
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - J Vosswinkel
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
| | - W Wertheim
- Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, New York, USA
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11
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Dong X, Rashidian S, Wang Y, Hajagos J, Zhao X, Rosenthal RN, Kong J, Saltz M, Saltz J, Wang F. Machine Learning Based Opioid Overdose Prediction Using Electronic Health Records. AMIA Annu Symp Proc 2020; 2019:389-398. [PMID: 32308832 PMCID: PMC7153049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Opioid addiction in the United States has come to national attention as opioid overdose (OD) related deaths have risen at alarming rates. Combating opioid epidemic becomes a high priority for not only governments but also healthcare providers. This depends on critical knowledge to understand the risk of opioid overdose of patients. In this paper, we present our work on building machine learning based prediction models to predict opioid overdose of patients based on the history of patients' electronic health records (EHR). We performed two studies using New York State claims data (SPARCS) with 440,000 patients and Cerner's Health Facts database with 110,000 patients. Our experiments demonstrated that EHR based prediction can achieve best recall with random forest method (precision: 95.3%, recall: 85.7%, F1 score: 90.3%), best precision with deep learning (precision: 99.2%, recall: 77.8%, F1 score: 87.2%). We also discovered that clinical events are among critical features for the predictions.
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Affiliation(s)
| | | | - Yu Wang
- Stony Brook University, Stony Brook, NY
| | | | - Xia Zhao
- Stony Brook University, Stony Brook, NY
| | | | - Jun Kong
- Stony Brook University, Stony Brook, NY
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12
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Rashidian S, Wang F, Moffitt R, Garcia V, Dutt A, Chang W, Pandya V, Hajagos J, Saltz M, Saltz J. SMOOTH-GAN: Towards Sharp and Smooth Synthetic EHR Data Generation. Artif Intell Med 2020. [DOI: 10.1007/978-3-030-59137-3_4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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13
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Almeida JS, Hajagos J, Saltz J, Saltz M. Serverless OpenHealth at data commons scale-traversing the 20 million patient records of New York's SPARCS dataset in real-time. PeerJ 2019; 7:e6230. [PMID: 30671301 PMCID: PMC6338105 DOI: 10.7717/peerj.6230] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 12/07/2018] [Indexed: 12/05/2022] Open
Abstract
In a previous report, we explored the serverless OpenHealth approach to the Web as a Global Compute space. That approach relies on the modern browser full stack, and, in particular, its configuration for application assembly by code injection. The opportunity, and need, to expand this approach has since increased markedly, reflecting a wider adoption of Open Data policies by Public Health Agencies. Here, we describe how the serverless scaling challenge can be achieved by the isomorphic mapping between the remote data layer API and a local (client-side, in-browser) operator. This solution is validated with an accompanying interactive web application (bit.ly/loadsparcs) capable of real-time traversal of New York’s 20 million patient records of the Statewide Planning and Research Cooperative System (SPARCS), and is compared with alternative approaches. The results obtained strengthen the argument that the FAIR reproducibility needed for Population Science applications in the age of P4 Medicine is particularly well served by the Web platform.
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Affiliation(s)
- Jonas S Almeida
- Biomedical Informatics, State University of New York at Stony Brook, Stony Brook, NY, United States of America
| | - Janos Hajagos
- Biomedical Informatics, State University of New York at Stony Brook, Stony Brook, NY, United States of America
| | - Joel Saltz
- Biomedical Informatics, State University of New York at Stony Brook, Stony Brook, NY, United States of America
| | - Mary Saltz
- Radiology, State University of New York at Stony Brook, Stony Brook, NY, United States of America
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Pandrekar S, Chen X, Gopalkrishna G, Srivastava A, Saltz M, Saltz J, Wang F. Social Media Based Analysis of Opioid Epidemic Using Reddit. AMIA Annu Symp Proc 2018; 2018:867-876. [PMID: 30815129 PMCID: PMC6371364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Opioid-abuse epidemic in the United States has escalated to national attention due to the dramatic increase of opioid overdose deaths. Analyzing opioid-related social media has the potential to reveal patterns of opioid abuse at a national scale, understand opinions of the public, and provide insights to support prevention and treatment. Reddit is a community based social media with more reliable content curated by the community through voting. In this study, we collected and analyzed all opioid related discussions from January 2014 to October 2017, which contains 51,537 posts by 16,162 unique users. We analyzed the data to understand the psychological categories of the posts, and performed topic modeling to reveal the major topics of interest. We also characterized the extent of social support received from comments and scores by each post. Last, we analyzed statistically significant difference in the posts between anonymous and non-anonymous users.
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Affiliation(s)
| | - Xin Chen
- Stony Brook University, Stony Brook, NY
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Chen X, Wang Y, Yu X, Schoenfeld E, Saltz M, Saltz J, Wang F. Large-scale Analysis of Opioid Poisoning Related Hospital Visits in New York State. AMIA Annu Symp Proc 2018; 2017:545-554. [PMID: 29854119 PMCID: PMC5977648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Opioid related deaths are increasing dramatically in recent years, and opioid epidemic is worsening in the United States. Combating opioid epidemic becomes a high priority for both the U.S. government and local governments such as New York State. Analyzing patient level opioid related hospital visits provides a data driven approach to discover both spatial and temporal patterns and identity potential causes of opioid related deaths, which provides essential knowledge for governments on decision making. In this paper, we analyzed opioid poisoning related hospital visits using New York State SPARCS data, which provides diagnoses of patients in hospital visits. We identified all patients with primary diagnosis as opioid poisoning from 2010-2014 for our main studies, and from 2003-2014 for temporal trend studies. We performed demographical based studies, and summarized the historical trends of opioid poisoning. We used frequent item mining to find co-occurrences of diagnoses for possible causes of poisoning or effects from poisoning. We provided zip code level spatial analysis to detect local spatial clusters, and studied potential correlations between opioid poisoning and demographic and social-economic factors.
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Affiliation(s)
- Xin Chen
- Stony Brook University, Stony Brook, NY
| | - Yu Wang
- Stony Brook University, Stony Brook, NY
| | - Xiaxia Yu
- Stony Brook University, Stony Brook, NY
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16
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Saltz J, Sharma A, Iyer G, Bremer E, Wang F, Jasniewski A, DiPrima T, Almeida JS, Gao Y, Zhao T, Saltz M, Kurc T. A Containerized Software System for Generation, Management, and Exploration of Features from Whole Slide Tissue Images. Cancer Res 2017; 77:e79-e82. [PMID: 29092946 DOI: 10.1158/0008-5472.can-17-0316] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Revised: 06/17/2017] [Accepted: 09/01/2017] [Indexed: 11/16/2022]
Abstract
Well-curated sets of pathology image features will be critical to clinical studies that aim to evaluate and predict treatment responses. Researchers require information synthesized across multiple biological scales, from the patient to the molecular scale, to more effectively study cancer. This article describes a suite of services and web applications that allow users to select regions of interest in whole slide tissue images, run a segmentation pipeline on the selected regions to extract nuclei and compute shape, size, intensity, and texture features, store and index images and analysis results, and visualize and explore images and computed features. All the services are deployed as containers and the user-facing interfaces as web-based applications. The set of containers and web applications presented in this article is used in cancer research studies of morphologic characteristics of tumor tissues. The software is free and open source. Cancer Res; 77(21); e79-82. ©2017 AACR.
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Affiliation(s)
- Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York.
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University, Atlanta, Georgia
| | - Ganesh Iyer
- Department of Biomedical Informatics, Emory University, Atlanta, Georgia
| | - Erich Bremer
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Feiqiao Wang
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Alina Jasniewski
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Tammy DiPrima
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Jonas S Almeida
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Yi Gao
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Tianhao Zhao
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York.,Department of Pathology, Stony Brook University, Stony Brook, New York
| | - Mary Saltz
- Department of Radiology, Stony Brook University, Stony Brook, New York
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York.,Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, Tennessee
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Saltz J, Almeida J, Gao Y, Sharma A, Bremer E, DiPrima T, Saltz M, Kalpathy-Cramer J, Kurc T. Towards Generation, Management, and Exploration of Combined Radiomics and Pathomics Datasets for Cancer Research. AMIA Jt Summits Transl Sci Proc 2017; 2017:85-94. [PMID: 28815113 PMCID: PMC5543366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Cancer is a complex multifactorial disease state and the ability to anticipate and steer treatment results will require information synthesis across multiple scales from the host to the molecular level. Radiomics and Pathomics, where image features are extracted from routine diagnostic Radiology and Pathology studies, are also evolving as valuable diagnostic and prognostic indicators in cancer. This information explosion provides new opportunities for integrated, multi-scale investigation of cancer, but also mandates a need to build systematic and integrated approaches to manage, query and mine combined Radiomics and Pathomics data. In this paper, we describe a suite of tools and web-based applications towards building a comprehensive framework to support the generation, management and interrogation of large volumes of Radiomics and Pathomics feature sets and the investigation of correlations between image features, molecular data, and clinical outcome.
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Affiliation(s)
- Joel Saltz
- Biomedical Informatics Department, Stony Brook University, Stony Brook, NY
| | - Jonas Almeida
- Biomedical Informatics Department, Stony Brook University, Stony Brook, NY
| | - Yi Gao
- Biomedical Informatics Department, Stony Brook University, Stony Brook, NY
| | - Ashish Sharma
- Biomedical Informatics Department, Emory University, Atlanta, GA
| | - Erich Bremer
- Biomedical Informatics Department, Stony Brook University, Stony Brook, NY
| | - Tammy DiPrima
- Biomedical Informatics Department, Stony Brook University, Stony Brook, NY
| | - Mary Saltz
- Department of Radiology, Stony Brook University, Stony Brook, NY
| | | | - Tahsin Kurc
- Biomedical Informatics Department, Stony Brook University, Stony Brook, NY
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN
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Chen X, Wang Y, Schoenfeld E, Saltz M, Saltz J, Wang F. Spatio-temporal Analysis for New York State SPARCS Data. AMIA Jt Summits Transl Sci Proc 2017; 2017:483-492. [PMID: 28815148 PMCID: PMC5543354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Increased accessibility of health data provides unique opportunities to discover spatio-temporal patterns of diseases. For example, New York State SPARCS (Statewide Planning and Research Cooperative System) data collects patient level detail on patient demographics, diagnoses, services, and charges for each hospital inpatient stay and outpatient visit. Such data also provides home addresses for each patient. This paper presents our preliminary work on spatial, temporal, and spatial-temporal analysis of disease patterns for New York State using SPARCS data. We analyzed spatial distribution patterns of typical diseases at ZIP code level. We performed temporal analysis of common diseases based on 12 years' historical data. We then compared the spatial variations for diseases with different levels of clustering tendency, and studied the evolution history of such spatial patterns. Case studies based on asthma demonstrated that the discovered spatial clusters are consistent with prior studies. We visualized our spatial-temporal patterns as animations through videos.
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Almeida JS, Hajagos J, Crnosija I, Kurc T, Saltz M, Saltz J. OpenHealth Platform for Interactive Contextualization of Population Health Open Data. AMIA Annu Symp Proc 2015; 2015:297-305. [PMID: 26958160 PMCID: PMC4765591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The financial incentives for data science applications leading to improved health outcomes, such as DSRIP (bit.ly/dsrip), are well-aligned with the broad adoption of Open Data by State and Federal agencies. This creates entirely novel opportunities for analytical applications that make exclusive use of the pervasive Web Computing platform. The framework described here explores this new avenue to contextualize Health data in a manner that relies exclusively on the native JavaScript interpreter and data processing resources of the ubiquitous Web Browser. The OpenHealth platform is made publicly available, and is publicly hosted with version control and open source, at https://github.com/mathbiol/openHealth. The different data/analytics workflow architectures explored are accompanied with live applications ranging from DSRIP, such as Hospital Inpatient Prevention Quality Indicators at http://bit.ly/pqiSuffolk, to The Cancer Genome Atlas (TCGA) as illustrated by http://bit.ly/tcgascopeGBM.
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Affiliation(s)
- Jonas S Almeida
- Dept Biomedical Informatics, Stony Brook University, State University of New York
| | - Janos Hajagos
- Dept Biomedical Informatics, Stony Brook University, State University of New York
| | - Ivan Crnosija
- Dept Biomedical Informatics, Stony Brook University, State University of New York
| | - Tahsin Kurc
- Dept Biomedical Informatics, Stony Brook University, State University of New York
| | - Mary Saltz
- Dept Radiology, Stony Brook University, State University of New York
| | - Joel Saltz
- Dept Biomedical Informatics, Stony Brook University, State University of New York
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Saltz M, Peterson R. Bringing technology closer to the patient. Healthc Inform 1995; 12:138, 140. [PMID: 10143294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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Kreis DJ, Montero N, Saltz M, Saltz R, Echenique M, Plasencia G, Santiesteban R, Gomez GA, Vopal JJ, Civetta JM. The role of splenorrhaphy in splenic trauma. Am Surg 1987; 53:307-9. [PMID: 3579042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Eighty-five cases of splenic trauma that were treated surgically from 1981 to 1983 were reviewed to define the exact role of splenorrhaphy. There were 73 male and 12 female patients with a mean age of 34 years. The mechanism of injury was blunt trauma in 51 and penetrating trauma in 34. The incidence of associated intraabdominal injury was 31 per cent and 79 per cent in blunt and penetrating trauma, respectively. Splenectomy was performed in 43 (51%) and splenorrhaphy in 42 (49%). Splenorrhaphy was performed in 19 (37%) who had blunt trauma and 23 (67%) who had penetrating trauma (P less than 0.01). Overall six patients died, three in the splenorrhaphy group (7.1%). Only one patient who had splenorrhaphy required reoperation for splenic hemorrhage. The authors conclude that about 50 per cent of all injured spleens in the patient population studied can be salvaged during laparotomy for splenic trauma, the splenic salvage rate is higher in penetrating trauma, and splenorrhaphy is a safe operation.
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Kreis DJ, Guerra JJ, Saltz M, Santiesteban R, Byers P. Gastrointestinal hemorrhage due to carcinoid tumors of the small intestine. JAMA 1986; 255:234-6. [PMID: 3484526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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