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Yan C, Ong HH, Grabowska ME, Krantz MS, Su WC, Dickson AL, Peterson JF, Feng Q, Roden DM, Stein CM, Kerchberger VE, Malin BA, Wei WQ. Large language models facilitate the generation of electronic health record phenotyping algorithms. J Am Med Inform Assoc 2024:ocae072. [PMID: 38613820 DOI: 10.1093/jamia/ocae072] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/21/2024] [Accepted: 03/22/2024] [Indexed: 04/15/2024] Open
Abstract
OBJECTIVES Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts. MATERIALS AND METHODS We prompted four LLMs-GPT-4 and GPT-3.5 of ChatGPT, Claude 2, and Bard-in October 2023, asking them to generate executable phenotyping algorithms in the form of SQL queries adhering to a common data model (CDM) for three phenotypes (ie, type 2 diabetes mellitus, dementia, and hypothyroidism). Three phenotyping experts evaluated the returned algorithms across several critical metrics. We further implemented the top-rated algorithms and compared them against clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network. RESULTS GPT-4 and GPT-3.5 exhibited significantly higher overall expert evaluation scores in instruction following, algorithmic logic, and SQL executability, when compared to Claude 2 and Bard. Although GPT-4 and GPT-3.5 effectively identified relevant clinical concepts, they exhibited immature capability in organizing phenotyping criteria with the proper logic, leading to phenotyping algorithms that were either excessively restrictive (with low recall) or overly broad (with low positive predictive values). CONCLUSION GPT versions 3.5 and 4 are capable of drafting phenotyping algorithms by identifying relevant clinical criteria aligned with a CDM. However, expertise in informatics and clinical experience is still required to assess and further refine generated algorithms.
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Affiliation(s)
- Chao Yan
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Henry H Ong
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Monika E Grabowska
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Matthew S Krantz
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Wu-Chen Su
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Alyson L Dickson
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Josh F Peterson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - QiPing Feng
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Dan M Roden
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - C Michael Stein
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - V Eric Kerchberger
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Bradley A Malin
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Department of Computer Science, Vanderbilt University, Nashville, TN 37203, United States
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Department of Computer Science, Vanderbilt University, Nashville, TN 37203, United States
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Yan C, Ong HH, Grabowska ME, Krantz MS, Su WC, Dickson AL, Peterson JF, Feng Q, Roden DM, Stein CM, Kerchberger VE, Malin BA, Wei WQ. Large Language Models Facilitate the Generation of Electronic Health Record Phenotyping Algorithms. medRxiv 2024:2023.12.19.23300230. [PMID: 38196578 PMCID: PMC10775330 DOI: 10.1101/2023.12.19.23300230] [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] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Objectives Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts. Materials and Methods We prompted four LLMs-GPT-4 and GPT-3.5 of ChatGPT, Claude 2, and Bard-in October 2023, asking them to generate executable phenotyping algorithms in the form of SQL queries adhering to a common data model (CDM) for three phenotypes (i.e., type 2 diabetes mellitus, dementia, and hypothyroidism). Three phenotyping experts evaluated the returned algorithms across several critical metrics. We further implemented the top-rated algorithms and compared them against clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network. Results GPT-4 and GPT-3.5 exhibited significantly higher overall expert evaluation scores in instruction following, algorithmic logic, and SQL executability, when compared to Claude 2 and Bard. Although GPT-4 and GPT-3.5 effectively identified relevant clinical concepts, they exhibited immature capability in organizing phenotyping criteria with the proper logic, leading to phenotyping algorithms that were either excessively restrictive (with low recall) or overly broad (with low positive predictive values). Conclusion GPT versions 3.5 and 4 are capable of drafting phenotyping algorithms by identifying relevant clinical criteria aligned with a CDM. However, expertise in informatics and clinical experience is still required to assess and further refine generated algorithms.
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Affiliation(s)
- Chao Yan
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Henry H. Ong
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Monika E. Grabowska
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Matthew S. Krantz
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Wu-Chen Su
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Alyson L. Dickson
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Josh F. Peterson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - QiPing Feng
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Dan M. Roden
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - C. Michael Stein
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - V. Eric Kerchberger
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Bradley A. Malin
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
- Department of Computer Science, Vanderbilt University, Nashville, TN
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
- Department of Computer Science, Vanderbilt University, Nashville, TN
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Yan C, Grabowska ME, Dickson AL, Li B, Wen Z, Roden DM, Michael Stein C, Embí PJ, Peterson JF, Feng Q, Malin BA, Wei WQ. Leveraging generative AI to prioritize drug repurposing candidates for Alzheimer's disease with real-world clinical validation. NPJ Digit Med 2024; 7:46. [PMID: 38409350 PMCID: PMC10897392 DOI: 10.1038/s41746-024-01038-3] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 02/14/2024] [Indexed: 02/28/2024] Open
Abstract
Drug repurposing represents an attractive alternative to the costly and time-consuming process of new drug development, particularly for serious, widespread conditions with limited effective treatments, such as Alzheimer's disease (AD). Emerging generative artificial intelligence (GAI) technologies like ChatGPT offer the promise of expediting the review and summary of scientific knowledge. To examine the feasibility of using GAI for identifying drug repurposing candidates, we iteratively tasked ChatGPT with proposing the twenty most promising drugs for repurposing in AD, and tested the top ten for risk of incident AD in exposed and unexposed individuals over age 65 in two large clinical datasets: (1) Vanderbilt University Medical Center and (2) the All of Us Research Program. Among the candidates suggested by ChatGPT, metformin, simvastatin, and losartan were associated with lower AD risk in meta-analysis. These findings suggest GAI technologies can assimilate scientific insights from an extensive Internet-based search space, helping to prioritize drug repurposing candidates and facilitate the treatment of diseases.
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Affiliation(s)
- Chao Yan
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Monika E Grabowska
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Alyson L Dickson
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bingshan Li
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zhexing Wen
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
- Department of Cell Biology, Emory University School of Medicine, Atlanta, GA, USA
| | - Dan M Roden
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - C Michael Stein
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Peter J Embí
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Josh F Peterson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - QiPing Feng
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bradley A Malin
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
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Grabowska ME, Van Driest SL, Robinson JR, Patrick AE, Guardo C, Gangireddy S, Ong HH, Feng Q, Carroll R, Kannankeril PJ, Wei WQ. Developing and evaluating pediatric phecodes (Peds-Phecodes) for high-throughput phenotyping using electronic health records. J Am Med Inform Assoc 2024; 31:386-395. [PMID: 38041473 PMCID: PMC10797257 DOI: 10.1093/jamia/ocad233] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/04/2023] [Accepted: 11/20/2023] [Indexed: 12/03/2023] Open
Abstract
OBJECTIVE Pediatric patients have different diseases and outcomes than adults; however, existing phecodes do not capture the distinctive pediatric spectrum of disease. We aim to develop specialized pediatric phecodes (Peds-Phecodes) to enable efficient, large-scale phenotypic analyses of pediatric patients. MATERIALS AND METHODS We adopted a hybrid data- and knowledge-driven approach leveraging electronic health records (EHRs) and genetic data from Vanderbilt University Medical Center to modify the most recent version of phecodes to better capture pediatric phenotypes. First, we compared the prevalence of patient diagnoses in pediatric and adult populations to identify disease phenotypes differentially affecting children and adults. We then used clinical domain knowledge to remove phecodes representing phenotypes unlikely to affect pediatric patients and create new phecodes for phenotypes relevant to the pediatric population. We further compared phenome-wide association study (PheWAS) outcomes replicating known pediatric genotype-phenotype associations between Peds-Phecodes and phecodes. RESULTS The Peds-Phecodes aggregate 15 533 ICD-9-CM codes and 82 949 ICD-10-CM codes into 2051 distinct phecodes. Peds-Phecodes replicated more known pediatric genotype-phenotype associations than phecodes (248 vs 192 out of 687 SNPs, P < .001). DISCUSSION We introduce Peds-Phecodes, a high-throughput EHR phenotyping tool tailored for use in pediatric populations. We successfully validated the Peds-Phecodes using genetic replication studies. Our findings also reveal the potential use of Peds-Phecodes in detecting novel genotype-phenotype associations for pediatric conditions. We expect that Peds-Phecodes will facilitate large-scale phenomic and genomic analyses in pediatric populations. CONCLUSION Peds-Phecodes capture higher-quality pediatric phenotypes and deliver superior PheWAS outcomes compared to phecodes.
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Affiliation(s)
- Monika E Grabowska
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Sara L Van Driest
- Department of Pediatrics and the Center for Pediatric Precision Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Jamie R Robinson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Department of Pediatric Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Anna E Patrick
- Department of Pediatrics and the Center for Pediatric Precision Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Chris Guardo
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Srushti Gangireddy
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Henry H Ong
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - QiPing Feng
- Department of Medicine, Division of Clinical Pharmacology, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Robert Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Prince J Kannankeril
- Department of Pediatrics and the Center for Pediatric Precision Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
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Grabowska ME, Huang A, Wen Z, Li B, Wei WQ. Drug repurposing for Alzheimer's disease from 2012-2022-a 10-year literature review. Front Pharmacol 2023; 14:1257700. [PMID: 37745051 PMCID: PMC10512468 DOI: 10.3389/fphar.2023.1257700] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [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: 07/13/2023] [Accepted: 08/28/2023] [Indexed: 09/26/2023] Open
Abstract
Background: Alzheimer's disease (AD) is a debilitating neurodegenerative condition with few treatment options available. Drug repurposing studies have sought to identify existing drugs that could be repositioned to treat AD; however, the effectiveness of drug repurposing for AD remains unclear. This review systematically analyzes the progress made in drug repurposing for AD throughout the last decade, summarizing the suggested drug candidates and analyzing changes in the repurposing strategies used over time. We also examine the different types of data that have been leveraged to validate suggested drug repurposing candidates for AD, which to our knowledge has not been previous investigated, although this information may be especially useful in appraising the potential of suggested drug repurposing candidates. We ultimately hope to gain insight into the suggested drugs representing the most promising repurposing candidates for AD. Methods: We queried the PubMed database for AD drug repurposing studies published between 2012 and 2022. 124 articles were reviewed. We used RxNorm to standardize drug names across the reviewed studies, map drugs to their constituent ingredients, and identify prescribable drugs. We used the Anatomical Therapeutic Chemical (ATC) Classification System to group drugs. Results: 573 unique drugs were proposed for repurposing in AD over the last 10 years. These suggested repurposing candidates included drugs acting on the nervous system (17%), antineoplastic and immunomodulating agents (16%), and drugs acting on the cardiovascular system (12%). Clozapine, a second-generation antipsychotic medication, was the most frequently suggested repurposing candidate (N = 6). 61% (76/124) of the reviewed studies performed a validation, yet only 4% (5/124) used real-world data for validation. Conclusion: A large number of potential drug repurposing candidates for AD has accumulated over the last decade. However, among these drugs, no single drug has emerged as the top candidate, making it difficult to establish research priorities. Validation of drug repurposing hypotheses is inconsistently performed, and real-world data has been critically underutilized for validation. Given the urgent need for new AD therapies, the utility of real-world data in accelerating identification of high-priority candidates for AD repurposing warrants further investigation.
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Affiliation(s)
- Monika E. Grabowska
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Annabelle Huang
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Zhexing Wen
- Departments of Psychiatry and Behavioral Sciences, Cell Biology, and Neurology, Emory University School of Medicine, Atlanta, GA, United States
| | - Bingshan Li
- Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
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Grabowska ME, Van Driest SL, Robinson JR, Patrick AE, Guardo C, Gangireddy S, Ong H, Feng Q, Carroll R, Kannankeril PJ, Wei WQ. Developing and Evaluating Pediatric Phecodes (Peds-Phecodes) for High-Throughput Phenotyping Using Electronic Health Records. medRxiv 2023:2023.08.22.23294435. [PMID: 37662278 PMCID: PMC10473796 DOI: 10.1101/2023.08.22.23294435] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Objective Pediatric patients have different diseases and outcomes than adults; however, existing phecodes do not capture the distinctive pediatric spectrum of disease. We aim to develop specialized pediatric phecodes (Peds-Phecodes) to enable efficient, large-scale phenotypic analyses of pediatric patients. Materials and Methods We adopted a hybrid data- and knowledge-driven approach leveraging electronic health records (EHRs) and genetic data from Vanderbilt University Medical Center to modify the most recent version of phecodes to better capture pediatric phenotypes. First, we compared the prevalence of patient diagnoses in pediatric and adult populations to identify disease phenotypes differentially affecting children and adults. We then used clinical domain knowledge to remove phecodes representing phenotypes unlikely to affect pediatric patients and create new phecodes for phenotypes relevant to the pediatric population. We further compared phenome-wide association study (PheWAS) outcomes replicating known pediatric genotype-phenotype associations between Peds-Phecodes and phecodes. Results The Peds-Phecodes aggregate 15,533 ICD-9-CM codes and 82,949 ICD-10-CM codes into 2,051 distinct phecodes. Peds-Phecodes replicated more known pediatric genotype-phenotype associations than phecodes (248 versus 192 out of 687 SNPs, p<0.001). Discussion We introduce Peds-Phecodes, a high-throughput EHR phenotyping tool tailored for use in pediatric populations. We successfully validated the Peds-Phecodes using genetic replication studies. Our findings also reveal the potential use of Peds-Phecodes in detecting novel genotype-phenotype associations for pediatric conditions. We expect that Peds-Phecodes will facilitate large-scale phenomic and genomic analyses in pediatric populations. Conclusion Peds-Phecodes capture higher-quality pediatric phenotypes and deliver superior PheWAS outcomes compared to phecodes.
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Yan C, Grabowska ME, Dickson AL, Li B, Wen Z, Roden DM, Stein CM, Embí PJ, Peterson JF, Feng Q, Malin BA, Wei WQ. Leveraging Generative AI to Prioritize Drug Repurposing Candidates: Validating Identified Candidates for Alzheimer's Disease in Real-World Clinical Datasets. medRxiv 2023:2023.07.07.23292388. [PMID: 37461512 PMCID: PMC10350158 DOI: 10.1101/2023.07.07.23292388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Drug repurposing represents an attractive alternative to the costly and time-consuming process of new drug development, particularly for serious, widespread conditions with limited effective treatments, such as Alzheimer's disease (AD). Emerging generative artificial intelligence (GAI) technologies like ChatGPT offer the promise of expediting the review and summary of scientific knowledge. To examine the feasibility of using GAI for identifying drug repurposing candidates, we iteratively tasked ChatGPT with proposing the twenty most promising drugs for repurposing in AD, and tested the top ten for risk of incident AD in exposed and unexposed individuals over age 65 in two large clinical datasets: 1) Vanderbilt University Medical Center and 2) the All of Us Research Program. Among the candidates suggested by ChatGPT, metformin, simvastatin, and losartan were associated with lower AD risk in meta-analysis. These findings suggest GAI technologies can assimilate scientific insights from an extensive Internet-based search space, helping to prioritize drug repurposing candidates and facilitate the treatment of diseases.
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Bruno A, Paletta N, Verma U, Grabowska ME, Haughey HM, Batchala PP, Abay S, Donahue J, Vender J, Sethuraman S, Nichols FT. Predicting Functional Outcome After Decompressive Craniectomy for Malignant Hemispheric Infarction: Clinical and Novel Imaging Factors. World Neurosurg 2021; 158:e1017-e1021. [PMID: 34906752 DOI: 10.1016/j.wneu.2021.12.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 09/21/2021] [Revised: 12/06/2021] [Accepted: 12/07/2021] [Indexed: 01/15/2023]
Abstract
OBJECTIVE Decompressive craniectomy (DC) is an established optional treatment for malignant hemispheric infarction (MHI). We analyzed relevant clinical factors and computed tomography (CT) measurements in patients with DC for MHI to identify predictors of functional outcome 3-6 months after stroke. METHODS This study was performed at 2 comprehensive stroke centers. The inclusion criteria required DC for MHI, no additional intraoperative procedures (strokectomy or cerebral ventricular drain placement), and documented functional status 3-6 months after the stroke. We classified functional outcome as acceptable if the modified Rankin Scale score was <5, or as unacceptable if it was 5 or 6 (bedbound and totally dependent on others or death). Multiple logistic regression analyzed relevant clinical factors and multiple perioperative CT measurements to identify predictors of acceptable functional outcome. RESULTS Of 87 identified consecutive patients, 66 met the inclusion criteria. Acceptable functional outcome occurred in 35 of 66 (53%) patients. Likelihood of acceptable functional outcome decreased significantly with increasing age (OR 0.92, 95% CI 0.82-0.97, P = 0.004) and with increasing post-DC midline brain shift (OR 0.78, 95% CI 0.64-0.96, P = 0.016), and decreased non-significantly with left-sided stroke (OR 0.30, 95% CI 0.08-1.10, P = 0.069) and with increasing craniectomy barrier thickness (OR 0.92, 95% CI 0.85-1.01, P = 0.076). CONCLUSIONS Patient age and the post-DC midline shift may be useful in prognosticating functional outcome after DC for MHI. Stroke side and craniectomy barrier thickness merit further ideally prospective outcome prediction testing.
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Affiliation(s)
- Askiel Bruno
- Department of Neurology, Medical College of Georgia, Augusta University, Augusta, Georgia, USA.
| | - Nina Paletta
- Medical College of Georgia, Augusta University, Augusta, Georgia, USA
| | - Uttam Verma
- Department of Neurology, Medical College of Georgia, Augusta University, Augusta, Georgia, USA
| | - Monika E Grabowska
- Department of Neurology, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Heather M Haughey
- Department of Neurology, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Prem P Batchala
- Department of Radiology, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Solomon Abay
- Department of Radiology, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Joseph Donahue
- Department of Radiology, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - John Vender
- Department of Neurosurgery, Medical College of Georgia, Augusta University, Augusta, Georgia, USA
| | | | - Fenwick T Nichols
- Department of Neurology, Medical College of Georgia, Augusta University, Augusta, Georgia, USA
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Bruno A, Paletta N, Verma U, Grabowska ME, Batchala PP, Abay S, Haughey HM, Donahue J, Vender J, Sethuraman S, Nichols FT. Limiting Brain Shift in Malignant Hemispheric Infarction by Decompressive Craniectomy. J Stroke Cerebrovasc Dis 2021; 30:105830. [PMID: 33945955 DOI: 10.1016/j.jstrokecerebrovasdis.2021.105830] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 03/15/2021] [Accepted: 04/11/2021] [Indexed: 10/21/2022] Open
Abstract
OBJECTIVE Decompressive craniectomy (DC) improves functional outcomes in selected patients with malignant hemispheric infarction (MHI), but variability in the surgical technique and occasional complications may be limiting the effectiveness of this procedure. Our aim was to evaluate predefined perioperative CT measurements for association with post-DC midline brain shift in patients with MHI. METHODS At two medical centers we identified 87 consecutive patients with MHI and DC between January 2007 and December 2019. We used our previously tested methods to measure the craniectomy surface area, extent of transcalvarial brain herniation, thickness of tissues overlying the craniectomy, diameter of the cerebral ventricle atrium contralateral to the stroke, extension of infarction beyond the craniectomy edges, and the pre and post-DC midline brain shifts. To avoid potential confounding from medical treatments and additional surgical procedures, we excluded patients with the first CT delayed >30 hours post-DC, resection of infarcted brain, or insertion of an external ventricular drain during DC. The primary outcome in multiple linear regression analysis was the postoperative midline brain shift. RESULTS We analyzed 72 qualified patients. The average midline brain shift decreased from 8.7 mm pre-DC to 5.4 post-DC. The only factors significantly associated with post-DC midline brain shift at the p<0.01 level were preoperative midline shift (coefficient 0.32, standard error 0.10, p=0.002) and extent of transcalvarial brain herniation (coefficient -0.20, standard error 0.05, p <0.001). CONCLUSIONS In patients with MHI and DC, smaller post-DC midline shift is associated with smaller pre-DC midline brain shift and greater transcalvarial brain herniation. This knowledge may prove helpful in assessing DC candidacy and surgical success. Additional studies to enhance the surgical success of DC are warranted.
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Affiliation(s)
- Askiel Bruno
- Department of Neurology, Medical College of Georgia, Augusta University, Augusta, 1120 15th Street BI3076, GA, United States.
| | - Nina Paletta
- Medical College of Georgia, Augusta University, Augusta, GA, United States
| | - Uttam Verma
- Department of Neurology, Medical College of Georgia, Augusta University, Augusta, 1120 15th Street BI3076, GA, United States
| | - Monika E Grabowska
- Department of Neurology, University of Virginia School of Medicine, Charlottesville, VA, United States
| | - Prem P Batchala
- Department of Radiology, University of Virginia School of Medicine, Charlottesville, VA, United States
| | - Solomon Abay
- Department of Radiology, University of Virginia School of Medicine, Charlottesville, VA, United States
| | - Heather M Haughey
- Department of Neurology, University of Virginia School of Medicine, Charlottesville, VA, United States
| | - Joseph Donahue
- Department of Radiology, University of Virginia School of Medicine, Charlottesville, VA, United States
| | - John Vender
- Department of Neurosurgery, Medical College of Georgia, Augusta University, Augusta, GA, United States
| | - Sankara Sethuraman
- Department of Mathematics, Augusta University, Augusta, GA, United States
| | - Fenwick T Nichols
- Department of Neurology, Medical College of Georgia, Augusta University, Augusta, 1120 15th Street BI3076, GA, United States
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Zhao J, Grabowska ME, Kerchberger VE, Smith JC, Eken HN, Feng Q, Peterson JF, Trent Rosenbloom S, Johnson KB, Wei WQ. ConceptWAS: A high-throughput method for early identification of COVID-19 presenting symptoms and characteristics from clinical notes. J Biomed Inform 2021; 117:103748. [PMID: 33774203 PMCID: PMC7992296 DOI: 10.1016/j.jbi.2021.103748] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 01/28/2021] [Accepted: 03/07/2021] [Indexed: 01/08/2023]
Abstract
OBJECTIVE Identifying symptoms and characteristics highly specific to coronavirus disease 2019 (COVID-19) would improve the clinical and public health response to this pandemic challenge. Here, we describe a high-throughput approach - Concept-Wide Association Study (ConceptWAS) - that systematically scans a disease's clinical manifestations from clinical notes. We used this method to identify symptoms specific to COVID-19 early in the course of the pandemic. METHODS We created a natural language processing pipeline to extract concepts from clinical notes in a local ER corresponding to the PCR testing date for patients who had a COVID-19 test and evaluated these concepts as predictors for developing COVID-19. We identified predictors from Firth's logistic regression adjusted by age, gender, and race. We also performed ConceptWAS using cumulative data every two weeks to identify the timeline for recognition of early COVID-19-specific symptoms. RESULTS We processed 87,753 notes from 19,692 patients subjected to COVID-19 PCR testing between March 8, 2020, and May 27, 2020 (1,483 COVID-19-positive). We found 68 concepts significantly associated with a positive COVID-19 test. We identified symptoms associated with increasing risk of COVID-19, including "anosmia" (odds ratio [OR] = 4.97, 95% confidence interval [CI] = 3.21-7.50), "fever" (OR = 1.43, 95% CI = 1.28-1.59), "cough with fever" (OR = 2.29, 95% CI = 1.75-2.96), and "ageusia" (OR = 5.18, 95% CI = 3.02-8.58). Using ConceptWAS, we were able to detect loss of smell and loss of taste three weeks prior to their inclusion as symptoms of the disease by the Centers for Disease Control and Prevention (CDC). CONCLUSION ConceptWAS, a high-throughput approach for exploring specific symptoms and characteristics of a disease like COVID-19, offers a promise for enabling EHR-powered early disease manifestations identification.
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Affiliation(s)
- Juan Zhao
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Monika E Grabowska
- Medical Scientist Training Program, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Vern Eric Kerchberger
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Medicine, Division of Allergy, Pulmonary & Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Joshua C Smith
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - H Nur Eken
- Vanderbilt University School of Medicine, Nashville, TN, USA
| | - QiPing Feng
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Josh F Peterson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - S Trent Rosenbloom
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kevin B Johnson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
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Grabowska ME, Chun B, Moya R, Saucerman JJ. Computational model of cardiomyocyte apoptosis identifies mechanisms of tyrosine kinase inhibitor-induced cardiotoxicity. J Mol Cell Cardiol 2021; 155:66-77. [PMID: 33667419 DOI: 10.1016/j.yjmcc.2021.02.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 01/21/2021] [Accepted: 02/25/2021] [Indexed: 12/16/2022]
Abstract
Despite clinical observations of cardiotoxicity among cancer patients treated with tyrosine kinase inhibitors (TKIs), the molecular mechanisms by which these drugs affect the heart remain largely unknown. Mechanistic understanding of TKI-induced cardiotoxicity has been limited in part due to the complexity of tyrosine kinase signaling pathways and the multi-targeted nature of many of these drugs. TKI treatment has been associated with reactive oxygen species generation, mitochondrial dysfunction, and apoptosis in cardiomyocytes. To gain insight into the mechanisms mediating TKI-induced cardiotoxicity, this study constructs and validates a computational model of cardiomyocyte apoptosis, integrating intrinsic apoptotic and tyrosine kinase signaling pathways. The model predicts high levels of apoptosis in response to sorafenib, sunitinib, ponatinib, trastuzumab, and gefitinib, and lower levels of apoptosis in response to nilotinib and erlotinib, with the highest level of apoptosis induced by sorafenib. Knockdown simulations identified AP1, ASK1, JNK, MEK47, p53, and ROS as positive functional regulators of sorafenib-induced apoptosis of cardiomyocytes. Overexpression simulations identified Akt, IGF1, PDK1, and PI3K among the negative functional regulators of sorafenib-induced cardiomyocyte apoptosis. A combinatorial screen of the positive and negative regulators of sorafenib-induced apoptosis revealed ROS knockdown coupled with overexpression of FLT3, FGFR, PDGFR, VEGFR, or KIT as a particularly potent combination in reducing sorafenib-induced apoptosis. Network simulations of combinatorial treatment with sorafenib and the antioxidant N-acetyl cysteine (NAC) suggest that NAC may protect cardiomyocytes from sorafenib-induced apoptosis.
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Affiliation(s)
- Monika E Grabowska
- Department of Biomedical Engineering, University of Virginia; Charlottesville, Virginia 22908, USA
| | - Bryan Chun
- Department of Biomedical Engineering, University of Virginia; Charlottesville, Virginia 22908, USA
| | - Raquel Moya
- Department of Biomedical Engineering, University of Virginia; Charlottesville, Virginia 22908, USA
| | - Jeffrey J Saucerman
- Department of Biomedical Engineering, University of Virginia; Charlottesville, Virginia 22908, USA.
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Zhao J, Grabowska ME, Kerchberger VE, Smith JC, Eken HN, Feng Q, Peterson JF, Rosenbloom ST, Johnson KB, Wei WQ. ConceptWAS: a high-throughput method for early identification of COVID-19 presenting symptoms. medRxiv 2020:2020.11.06.20227165. [PMID: 33200151 PMCID: PMC7668764 DOI: 10.1101/2020.11.06.20227165] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Objective Identifying symptoms highly specific to COVID-19 would improve the clinical and public health response to infectious outbreaks. Here, we describe a high-throughput approach - Concept-Wide Association Study (ConceptWAS) that systematically scans a disease's clinical manifestations from clinical notes. We used this method to identify symptoms specific to COVID-19 early in the course of the pandemic. Methods Using the Vanderbilt University Medical Center (VUMC) EHR, we parsed clinical notes through a natural language processing pipeline to extract clinical concepts. We examined the difference in concepts derived from the notes of COVID-19-positive and COVID-19-negative patients on the PCR testing date. We performed ConceptWAS using the cumulative data every two weeks for early identifying specific COVID-19 symptoms. Results We processed 87,753 notes 19,692 patients (1,483 COVID-19-positive) subjected to COVID-19 PCR testing between March 8, 2020, and May 27, 2020. We found 68 clinical concepts significantly associated with COVID-19. We identified symptoms associated with increasing risk of COVID-19, including "absent sense of smell" (odds ratio [OR] = 4.97, 95% confidence interval [CI] = 3.21-7.50), "fever" (OR = 1.43, 95% CI = 1.28-1.59), "with cough fever" (OR = 2.29, 95% CI = 1.75-2.96), and "ageusia" (OR = 5.18, 95% CI = 3.02-8.58). Using ConceptWAS, we were able to detect loss sense of smell or taste three weeks prior to their inclusion as symptoms of the disease by the Centers for Disease Control and Prevention (CDC). Conclusion ConceptWAS is a high-throughput approach for exploring specific symptoms of a disease like COVID-19, with a promise for enabling EHR-powered early disease manifestations identification.
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Affiliation(s)
- Juan Zhao
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Monika E Grabowska
- Medical Scientist Training Program, Vanderbilt University School of Medicine, Nashville, TN
| | - Vern Eric Kerchberger
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
- Department of Medicine, Division of Allergy, Pulmonary & Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Joshua C. Smith
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - H. Nur Eken
- Vanderbilt University School of Medicine, Nashville, TN
| | - QiPing Feng
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Josh F. Peterson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - S. Trent Rosenbloom
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Kevin B. Johnson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
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