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Takkavatakarn K, Dai Y, Hsun Wen H, Kauffman J, Charney A, Coca SG, Nadkarni GN, Chan L. Comparison of predicting cardiovascular disease hospitalization using individual, ZIP code-derived, and machine learning model-predicted educational attainment in New York City. PLoS One 2024; 19:e0297919. [PMID: 38329973 PMCID: PMC10852236 DOI: 10.1371/journal.pone.0297919] [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: 09/20/2023] [Accepted: 01/15/2024] [Indexed: 02/10/2024] Open
Abstract
BACKGROUND Area-level social determinants of health (SDOH) based on patients' ZIP codes or census tracts have been commonly used in research instead of individual SDOHs. To our knowledge, whether machine learning (ML) could be used to derive individual SDOH measures, specifically individual educational attainment, is unknown. METHODS This is a retrospective study using data from the Mount Sinai BioMe Biobank. We included participants that completed a validated questionnaire on educational attainment and had home addresses in New York City. ZIP code-level education was derived from the American Community Survey matched for the participant's gender and race/ethnicity. We tested several algorithms to predict individual educational attainment from routinely collected clinical and demographic data. To evaluate how using different measures of educational attainment will impact model performance, we developed three distinct models for predicting cardiovascular (CVD) hospitalization. Educational attainment was imputed into models as either survey-derived, ZIP code-derived, or ML-predicted educational attainment. RESULTS A total of 20,805 participants met inclusion criteria. Concordance between survey and ZIP code-derived education was 47%, while the concordance between survey and ML model-predicted education was 67%. A total of 13,715 patients from the cohort were included into our CVD hospitalization prediction models, of which 1,538 (11.2%) had a history of CVD hospitalization. The AUROC of the model predicting CVD hospitalization using survey-derived education was significantly higher than the model using ZIP code-level education (0.77 versus 0.72; p < 0.001) and the model using ML model-predicted education (0.77 versus 0.75; p < 0.001). The AUROC for the model using ML model-predicted education was also significantly higher than that using ZIP code-level education (p = 0.003). CONCLUSION The concordance of survey and ZIP code-level educational attainment in NYC was low. As expected, the model utilizing survey-derived education achieved the highest performance. The model incorporating our ML model-predicted education outperformed the model relying on ZIP code-derived education. Implementing ML techniques can improve the accuracy of SDOH data and consequently increase the predictive performance of outcome models.
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Affiliation(s)
- Kullaya Takkavatakarn
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- Division of Nephrology, Department of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Bangkok, Thailand
| | - Yang Dai
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Huei Hsun Wen
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Justin Kauffman
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Alexander Charney
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Steven G. Coca
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Girish N. Nadkarni
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Lili Chan
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
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Ufongene C, Van Hyfte G, Agarwal P, Goldstein J, Mathew B, Navis A, McCarthy L, Kwon CS, Gururangan K, Balchandani P, Marcuse L, Naasan G, Singh A, Young J, Charney A, Nadkarni G, Jette N, Blank LJ. Older adults with epilepsy and COVID-19: Outcomes in a multi-hospital health system. Seizure 2024; 114:33-39. [PMID: 38039805 PMCID: PMC10841585 DOI: 10.1016/j.seizure.2023.11.018] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 10/26/2023] [Accepted: 11/24/2023] [Indexed: 12/03/2023] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) is associated with high rates of mortality and morbidity in older adults, especially those with pre-existing conditions. There is little work investigating how neurological conditions affect older adults with COVID-19. We aimed to compare in-hospital outcomes, including mortality, in older adults with and without epilepsy. METHODS This retrospective study in a large multicenter New York health system included consecutive older patients (age ≥65 years) either with or without epilepsy who were admitted with COVID-19 between 3/2020-5/2021. Epilepsy was identified using a validated International Classification of Disease (ICD) and antiseizure medicationbased case definition. Univariate comparisons were calculated using Chi-square, Fisher's exact, Mann-Whitney U, or Student's t-tests. Multivariable logistic regression models were generated to examine factors associated with mortality, discharge disposition and length of stay (LOS). RESULTS We identified 5384 older adults admitted with COVID-19 of whom 173 (3.21 %) had epilepsy. Mean age was significantly lower in those with (75.44, standard deviation (SD): 7.23) compared to those without epilepsy (77.98, SD: 8.68, p = 0.007). Older adults with epilepsy were more likely to be ventilated (35.84 % vs. 16.18 %, p < 0.001), less likely to be discharged home (21.39 % vs. 43.12 %, p < 0.001), had longer median LOS (13 days vs. 8 days, p < 0.001), and had higher in-hospital death (35.84 % vs. 28.29 %, p = 0.030) compared to those without epilepsy. Epilepsy in older adults was associated with increased odds of in-hospital death (adjusted odds ratio (aOR), 1.55; 95 % CI 1.12-2.14, p = 0.032), non-routine discharge disposition (aOR, 3.34; 95 % CI 2.21-5.03, p < 0.001), and longer LOS (46.46 % 95 % CI 34 %-59 %, p < 0.001). CONCLUSIONS In models that adjusted for multiple confounders including comorbidity and age, our study found that epilepsy was still associated with higher in-hospital mortality, longer LOS and worse discharge dispositions in older adults with COVID-19 higher in-hospital mortality, longer LOS and worse discharge dispositions in older adults with COVID-19. This work reinforces that epilepsy is a risk factor for worse outcomes in older adults admitted with COVID-19. Timely identification and treatment of COVID-19 in epilepsy may improve outcomes in older people with epilepsy.
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Affiliation(s)
- Claire Ufongene
- Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, United States
| | - Grace Van Hyfte
- Department of Neurology, ISMMS, New York, NY, United States; Institute for HealthCare Delivery Science, Department of Population Health Science and Policy, ISMMS, New York, NY, United States
| | - Parul Agarwal
- Department of Neurology, ISMMS, New York, NY, United States; Institute for HealthCare Delivery Science, Department of Population Health Science and Policy, ISMMS, New York, NY, United States
| | - Jonathan Goldstein
- Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, United States
| | - Brian Mathew
- Department of Neurology, ISMMS, New York, NY, United States
| | - Allison Navis
- Department of Neurology, ISMMS, New York, NY, United States
| | | | - Churl-Su Kwon
- Department of Neurology, Epidemiology, Neurosurgery and the Gertrude H. Sergievsky Center, Columbia University, New York, NY, United States
| | | | - Priti Balchandani
- BioMedical Engineering and Imaging Institute, ISMMS, New York, NY, United States
| | - Lara Marcuse
- Department of Neurology, ISMMS, New York, NY, United States
| | - Georges Naasan
- Department of Neurology, ISMMS, New York, NY, United States
| | - Anuradha Singh
- Department of Neurology, ISMMS, New York, NY, United States
| | - James Young
- Department of Neurology, ISMMS, New York, NY, United States
| | | | | | - Nathalie Jette
- Department of Neurology, ISMMS, New York, NY, United States; Institute for HealthCare Delivery Science, Department of Population Health Science and Policy, ISMMS, New York, NY, United States; Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
| | - Leah J Blank
- Department of Neurology, ISMMS, New York, NY, United States; Institute for HealthCare Delivery Science, Department of Population Health Science and Policy, ISMMS, New York, NY, United States.
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Ufongene C, Van Hyfte G, Agarwal P, Blank LJ, Goldstein J, Mathew B, Lin JY, Navis A, McCarthy L, Gururangan K, Peschansky V, Kwon CS, Cohen A, Chan AHW, Dhamoon M, Deng P, Gutzwiller EM, Hao Q, He C, Heredia Nunez WD, Klenofsky B, Lemus HN, Marcuse L, Roberts M, Schorr EM, Singh A, Tantillo G, Young J, Balchandani P, Festa J, Naasan G, Charney A, Nadkarni G, Jetté N. In-hospital outcomes in patients with and without epilepsy diagnosed with COVID-19-A cohort study. Epilepsia 2023; 64:2725-2737. [PMID: 37452760 DOI: 10.1111/epi.17715] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 07/07/2023] [Accepted: 07/07/2023] [Indexed: 07/18/2023]
Abstract
OBJECTIVES Coronavirus disease 2019 (COVID-19) is associated with mortality in persons with comorbidities. The aim of this study was to evaluate in-hospital outcomes in patients with COVID-19 with and without epilepsy. METHODS We conducted a retrospective study of patients with COVID-19 admitted to a multicenter health system between March 15, 2020, and May 17, 2021. Patients with epilepsy were identified using a validated International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)/ICD-10-CM case definition. Logistic regression models and Kaplan-Meier analyses were conducted for mortality and non-routine discharges (i.e., not discharged home). An ordinary least-squares regression model was fitted for length of stay (LOS). RESULTS We identified 9833 people with COVID-19 including 334 with epilepsy. On univariate analysis, people with epilepsy had significantly higher ventilator use (37.70% vs 14.30%, p < .001), intensive care unit (ICU) admissions (39.20% vs 17.70%, p < .001) mortality rate (29.60% vs 19.90%, p < .001), and longer LOS (12 days vs 7 days, p < .001). and fewer were discharged home (29.64% vs 57.37%, p < .001). On multivariate analysis, only non-routine discharge (adjusted odds ratio [aOR] 2.70, 95% confidence interval [CI] 2.00-3.70; p < .001) and LOS (32.50% longer, 95% CI 22.20%-43.60%; p < .001) were significantly different. Factors associated with higher odds of mortality in epilepsy were older age (aOR 1.05, 95% CI 1.03-1.08; p < .001), ventilator support (aOR 7.18, 95% CI 3.12-16.48; p < .001), and higher Charlson comorbidity index (CCI) (aOR 1.18, 95% CI 1.04-1.34; p = .010). In epilepsy, admissions between August and December 2020 or January and May 2021 were associated with a lower odds of non-routine discharge and decreased LOS compared to admissions between March and July 2020, but this difference was not statistically significant. SIGNIFICANCE People with COVID-19 who had epilepsy had a higher odds of non-routine discharge and longer LOS but not higher mortality. Older age (≥65), ventilator use, and higher CCI were associated with COVID-19 mortality in epilepsy. This suggests that older adults with epilepsy and multimorbidity are more vulnerable than those without and should be monitored closely in the setting of COVID-19.
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Affiliation(s)
- Claire Ufongene
- Icahn School of Medicine at Mount Sinai (ISMMS), New York, New York, USA
| | - Grace Van Hyfte
- Institute for HealthCare Delivery Science, Department of Population Health Science and Policy, ISMMS, New York, New York, USA
| | - Parul Agarwal
- Institute for HealthCare Delivery Science, Department of Population Health Science and Policy, ISMMS, New York, New York, USA
- Department of Neurology, ISMMS, New York, New York, USA
| | - Leah J Blank
- Institute for HealthCare Delivery Science, Department of Population Health Science and Policy, ISMMS, New York, New York, USA
- Department of Neurology, ISMMS, New York, New York, USA
| | - Jonathan Goldstein
- Icahn School of Medicine at Mount Sinai (ISMMS), New York, New York, USA
| | - Brian Mathew
- Department of Neurology, ISMMS, New York, New York, USA
| | - Jung-Yi Lin
- Institute for HealthCare Delivery Science, Department of Population Health Science and Policy, ISMMS, New York, New York, USA
| | - Allison Navis
- Department of Neurology, ISMMS, New York, New York, USA
| | | | | | - Veronica Peschansky
- Department of Neurology, ISMMS, New York, New York, USA
- Department of Neurology, Columbia University, New York, New York, USA
| | - Churl-Su Kwon
- Department of Neurology, Columbia University, New York, New York, USA
- Department of Neurosurgery, Epidemiology, and the Gertrude H. Sergievsky Center, Columbia University, New York, New York, USA
| | - Ariella Cohen
- Icahn School of Medicine at Mount Sinai (ISMMS), New York, New York, USA
| | | | | | - Pojen Deng
- Department of Neurology, ISMMS, New York, New York, USA
| | | | - Qing Hao
- Department of Neurology, ISMMS, New York, New York, USA
| | - Celestine He
- Icahn School of Medicine at Mount Sinai (ISMMS), New York, New York, USA
| | | | | | | | - Lara Marcuse
- Department of Neurology, ISMMS, New York, New York, USA
| | | | - Emily M Schorr
- Division of Neuroimmunology and Neuroinfectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - Gabriela Tantillo
- Department of Neurology, Baylor College of Medicine, Houston, Texas, USA
| | - James Young
- Department of Neurology, ISMMS, New York, New York, USA
| | - Priti Balchandani
- BioMedical Engineering and Imaging Institute, ISMMS, New York, New York, USA
| | - Joanne Festa
- Department of Neurology, ISMMS, New York, New York, USA
- The Barbara and Maurice Deane Center for Wellness and Cognitive Health, Mount Sinai, New York, New York, USA
| | - Georges Naasan
- Department of Neurology, ISMMS, New York, New York, USA
- The Barbara and Maurice Deane Center for Wellness and Cognitive Health, Mount Sinai, New York, New York, USA
| | - Alexander Charney
- Department of Psychiatry, ISMMS, New York, New York, USA
- Department of Genetics and Genomic Sciences, ISMMS, New York, New York, USA
| | | | - Nathalie Jetté
- Institute for HealthCare Delivery Science, Department of Population Health Science and Policy, ISMMS, New York, New York, USA
- Department of Neurology, ISMMS, New York, New York, USA
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
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Vaid A, Jiang J, Sawant A, Lerakis S, Argulian E, Ahuja Y, Lampert J, Charney A, Greenspan H, Narula J, Glicksberg B, Nadkarni GN. A foundational vision transformer improves diagnostic performance for electrocardiograms. NPJ Digit Med 2023; 6:108. [PMID: 37280346 PMCID: PMC10242218 DOI: 10.1038/s41746-023-00840-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [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: 01/13/2023] [Accepted: 05/05/2023] [Indexed: 06/08/2023] Open
Abstract
The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer learning approaches for biomedical problems may result in suboptimal performance when pre-training is done on natural images. We leveraged masked image modeling to create a vision-based transformer model, HeartBEiT, for electrocardiogram waveform analysis. We pre-trained this model on 8.5 million ECGs and then compared performance vs. standard CNN architectures for diagnosis of hypertrophic cardiomyopathy, low left ventricular ejection fraction and ST elevation myocardial infarction using differing training sample sizes and independent validation datasets. We find that HeartBEiT has significantly higher performance at lower sample sizes compared to other models. We also find that HeartBEiT improves explainability of diagnosis by highlighting biologically relevant regions of the EKG vs. standard CNNs. Domain specific pre-trained transformer models may exceed the classification performance of models trained on natural images especially in very low data regimes. The combination of the architecture and such pre-training allows for more accurate, granular explainability of model predictions.
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Affiliation(s)
- Akhil Vaid
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, USA.
| | - Joy Jiang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ashwin Sawant
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stamatios Lerakis
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edgar Argulian
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yuri Ahuja
- Department of Medicine, NYU Langone Health, New York, NY, USA
| | - Joshua Lampert
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexander Charney
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hayit Greenspan
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin Glicksberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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5
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Ford EE, Tieri D, Rodriguez OL, Francoeur NJ, Soto J, Kos JT, Peres A, Gibson WS, Silver CA, Deikus G, Hudson E, Woolley CR, Beckmann N, Charney A, Mitchell TC, Yaari G, Sebra RP, Watson CT, Smith ML. FLAIRR-Seq: A Method for Single-Molecule Resolution of Near Full-Length Antibody H Chain Repertoires. J Immunol 2023; 210:1607-1619. [PMID: 37027017 PMCID: PMC10152037 DOI: 10.4049/jimmunol.2200825] [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] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 03/14/2023] [Indexed: 04/08/2023]
Abstract
Current Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) using short-read sequencing strategies resolve expressed Ab transcripts with limited resolution of the C region. In this article, we present the near-full-length AIRR-seq (FLAIRR-seq) method that uses targeted amplification by 5' RACE, combined with single-molecule, real-time sequencing to generate highly accurate (99.99%) human Ab H chain transcripts. FLAIRR-seq was benchmarked by comparing H chain V (IGHV), D (IGHD), and J (IGHJ) gene usage, complementarity-determining region 3 length, and somatic hypermutation to matched datasets generated with standard 5' RACE AIRR-seq using short-read sequencing and full-length isoform sequencing. Together, these data demonstrate robust FLAIRR-seq performance using RNA samples derived from PBMCs, purified B cells, and whole blood, which recapitulated results generated by commonly used methods, while additionally resolving H chain gene features not documented in IMGT at the time of submission. FLAIRR-seq data provide, for the first time, to our knowledge, simultaneous single-molecule characterization of IGHV, IGHD, IGHJ, and IGHC region genes and alleles, allele-resolved subisotype definition, and high-resolution identification of class switch recombination within a clonal lineage. In conjunction with genomic sequencing and genotyping of IGHC genes, FLAIRR-seq of the IgM and IgG repertoires from 10 individuals resulted in the identification of 32 unique IGHC alleles, 28 (87%) of which were previously uncharacterized. Together, these data demonstrate the capabilities of FLAIRR-seq to characterize IGHV, IGHD, IGHJ, and IGHC gene diversity for the most comprehensive view of bulk-expressed Ab repertoires to date.
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Affiliation(s)
- Easton E. Ford
- Department of Microbiology and Immunology, University of Louisville School of Medicine, Louisville, KY
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - David Tieri
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Oscar L. Rodriguez
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Nancy J. Francoeur
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Juan Soto
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Justin T. Kos
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Ayelet Peres
- Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnology and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
| | - William S. Gibson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Catherine A. Silver
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Gintaras Deikus
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Elizabeth Hudson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Cassandra R. Woolley
- Department of Microbiology and Immunology, University of Louisville School of Medicine, Louisville, KY
| | - Noam Beckmann
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Alexander Charney
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Thomas C. Mitchell
- Department of Microbiology and Immunology, University of Louisville School of Medicine, Louisville, KY
| | - Gur Yaari
- Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnology and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
| | - Robert P. Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Corey T. Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Melissa L. Smith
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
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6
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Nadkarni G, Paranjpe I, Jayaraman P, Su CY, Zhou S, Chen S, Valle DD, Thompson R, Kenigsberg E, Zhao S, Jaladanki S, Chaudhary K, Ascolillo S, Vaid A, Gonzalez-Kozlova E, Kumar A, Paranjpe M, O'Hagan R, Kamat S, Gulamali F, Kauffman J, Xie H, Harris J, Patel M, Argueta K, Batchelor C, Nie K, Dellepiane S, Scott L, Levin M, He J, Suárez-Fariñas M, Coca S, Chan L, Azeloglu E, Schadt E, Beckmann N, Gnjatic S, Merad M, Kim-Schulze S, Richards JB, Glicksberg B, Charney A. Proteomic Characterization of Acute Kidney Injury in Patients Hospitalized with SARS-CoV2 Infection. Res Sq 2023:rs.3.rs-2379226. [PMID: 36993735 PMCID: PMC10055503 DOI: 10.21203/rs.3.rs-2379226/v1] [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] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Abstract
Background Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. Methods Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N= 437), we identified 413 higher plasma abundances of protein targets and 40 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p <0.05). Of these, 62 proteins were validated in an external cohort (p <0.05, N =261). Results We demonstrate that COVID-AKI is associated with increased markers of tubular injury ( NGAL ) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p <0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2 , trefoil factor 3 , transmembrane emp24 domain-containing protein 10 , and cystatin-C indicating tubular dysfunction and injury. Conclusions Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.
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7
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Adekkanattu P, Olfson M, Susser LC, Patra B, Vekaria V, Coombes BJ, Lepow L, Fennessy B, Charney A, Ryu E, Miller KD, Pan L, Yangchen T, Talati A, Wickramaratne P, Weissman M, Mann J, Biernacka JM, Pathak J. Comorbidity and healthcare utilization in patients with treatment resistant depression: A large-scale retrospective cohort analysis using electronic health records. J Affect Disord 2023; 324:102-113. [PMID: 36529406 PMCID: PMC10327872 DOI: 10.1016/j.jad.2022.12.044] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/09/2022] [Accepted: 12/11/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Medical comorbidity and healthcare utilization in patients with treatment resistant depression (TRD) is usually reported in convenience samples, making estimates unreliable. There is only limited large-scale clinical research on comorbidities and healthcare utilization in TRD patients. METHODS Electronic Health Record data from over 3.3 million patients from the INSIGHT Clinical Research Network in New York City was used to define TRD as initiation of a third antidepressant regimen in a 12-month period among patients diagnosed with major depressive disorder (MDD). Age and sex matched TRD and non-TRD MDD patients were compared for anxiety disorder, 27 comorbid medical conditions, and healthcare utilization. RESULTS Out of 30,218 individuals diagnosed with MDD, 15.2 % of patients met the criteria for TRD (n = 4605). Compared to MDD patients without TRD, the TRD patients had higher rates of anxiety disorder and physical comorbidities. They also had higher odds of ischemic heart disease (OR = 1.38), stroke/transient ischemic attack (OR = 1.57), chronic kidney diseases (OR = 1.53), arthritis (OR = 1.52), hip/pelvic fractures (OR = 2.14), and cancers (OR = 1.41). As compared to non-TRD MDD, TRD patients had higher rates of emergency room visits, and inpatient stays. In relation to patients without MDD, both TRD and non-TRD MDD patients had significantly higher levels of anxiety disorder and physical comorbidities. LIMITATIONS The INSIGHT-CRN data lack information on depression severity and medication adherence. CONCLUSIONS TRD patients compared to non-TRD MDD patients have a substantially higher prevalence of various psychiatric and medical comorbidities and higher health care utilization. These findings highlight the challenges of developing interventions and care coordination strategies to meet the complex clinical needs of TRD patients.
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Affiliation(s)
| | - Mark Olfson
- Columbia University and New York State Psychiatric Institute, New York, NY, USA
| | | | | | | | | | - Lauren Lepow
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brian Fennessy
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | | | | | - Lifang Pan
- Columbia University and New York State Psychiatric Institute, New York, NY, USA
| | - Tenzin Yangchen
- Columbia University and New York State Psychiatric Institute, New York, NY, USA
| | - Ardesheer Talati
- Columbia University and New York State Psychiatric Institute, New York, NY, USA
| | - Priya Wickramaratne
- Columbia University and New York State Psychiatric Institute, New York, NY, USA
| | - Myrna Weissman
- Columbia University and New York State Psychiatric Institute, New York, NY, USA
| | - John Mann
- Columbia University and New York State Psychiatric Institute, New York, NY, USA
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8
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Gulamali FF, Sawant AS, Kovatch P, Glicksberg B, Charney A, Nadkarni GN, Oermann E. Autoencoders for sample size estimation for fully connected neural network classifiers. NPJ Digit Med 2022; 5:180. [PMID: 36513729 PMCID: PMC9747810 DOI: 10.1038/s41746-022-00728-0] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 11/29/2022] [Indexed: 12/15/2022] Open
Abstract
Sample size estimation is a crucial step in experimental design but is understudied in the context of deep learning. Currently, estimating the quantity of labeled data needed to train a classifier to a desired performance, is largely based on prior experience with similar models and problems or on untested heuristics. In many supervised machine learning applications, data labeling can be expensive and time-consuming and would benefit from a more rigorous means of estimating labeling requirements. Here, we study the problem of estimating the minimum sample size of labeled training data necessary for training computer vision models as an exemplar for other deep learning problems. We consider the problem of identifying the minimal number of labeled data points to achieve a generalizable representation of the data, a minimum converging sample (MCS). We use autoencoder loss to estimate the MCS for fully connected neural network classifiers. At sample sizes smaller than the MCS estimate, fully connected networks fail to distinguish classes, and at sample sizes above the MCS estimate, generalizability strongly correlates with the loss function of the autoencoder. We provide an easily accessible, code-free, and dataset-agnostic tool to estimate sample sizes for fully connected networks. Taken together, our findings suggest that MCS and convergence estimation are promising methods to guide sample size estimates for data collection and labeling prior to training deep learning models in computer vision.
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Affiliation(s)
- Faris F. Gulamali
- grid.59734.3c0000 0001 0670 2351Icahn School of Medicine, New York, NY 10029 USA
| | - Ashwin S. Sawant
- grid.59734.3c0000 0001 0670 2351Icahn School of Medicine, New York, NY 10029 USA
| | - Patricia Kovatch
- grid.59734.3c0000 0001 0670 2351Icahn School of Medicine, New York, NY 10029 USA
| | - Benjamin Glicksberg
- grid.59734.3c0000 0001 0670 2351Icahn School of Medicine, New York, NY 10029 USA
| | - Alexander Charney
- grid.59734.3c0000 0001 0670 2351Icahn School of Medicine, New York, NY 10029 USA
| | - Girish N. Nadkarni
- grid.59734.3c0000 0001 0670 2351Icahn School of Medicine, New York, NY 10029 USA
| | - Eric Oermann
- grid.137628.90000 0004 1936 8753New York University, New York, NY 10016 USA
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9
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Wickramaratne PJ, Yangchen T, Lepow L, Patra BG, Glicksburg B, Talati A, Adekkanattu P, Ryu E, Biernacka JM, Charney A, Mann JJ, Pathak J, Olfson M, Weissman MM. Social connectedness as a determinant of mental health: A scoping review. PLoS One 2022; 17:e0275004. [PMID: 36228007 PMCID: PMC9560615 DOI: 10.1371/journal.pone.0275004] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 09/08/2022] [Indexed: 11/29/2022] Open
Abstract
Public health and epidemiologic research have established that social connectedness promotes overall health. Yet there have been no recent reviews of findings from research examining social connectedness as a determinant of mental health. The goal of this review was to evaluate recent longitudinal research probing the effects of social connectedness on depression and anxiety symptoms and diagnoses in the general population. A scoping review was performed of PubMed and PsychInfo databases from January 2015 to December 2021 following PRISMA-ScR guidelines using a defined search strategy. The search yielded 66 unique studies. In research with other than pregnant women, 83% (19 of 23) studies reported that social support benefited symptoms of depression with the remaining 17% (5 of 23) reporting minimal or no evidence that lower levels of social support predict depression at follow-up. In research with pregnant women, 83% (24 of 29 studies) found that low social support increased postpartum depressive symptoms. Among 8 of 9 studies that focused on loneliness, feeling lonely at baseline was related to adverse outcomes at follow-up including higher risks of major depressive disorder, depressive symptom severity, generalized anxiety disorder, and lower levels of physical activity. In 5 of 8 reports, smaller social network size predicted depressive symptoms or disorder at follow-up. In summary, most recent relevant longitudinal studies have demonstrated that social connectedness protects adults in the general population from depressive symptoms and disorders. The results, which were largely consistent across settings, exposure measures, and populations, support efforts to improve clinical detection of high-risk patients, including adults with low social support and elevated loneliness.
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Affiliation(s)
- Priya J. Wickramaratne
- Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, United States of America
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, NY, United States of America
| | - Tenzin Yangchen
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, NY, United States of America
| | - Lauren Lepow
- Departments of Psychiatry and Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Braja G. Patra
- Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States of America
| | - Benjamin Glicksburg
- Departments of Psychiatry and Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Ardesheer Talati
- Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, United States of America
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, NY, United States of America
| | - Prakash Adekkanattu
- Department of Information Technologies and Services, Weill Cornell Medicine, New York, NY, United States of America
| | - Euijung Ryu
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States of America
| | - Joanna M. Biernacka
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States of America
| | - Alexander Charney
- Departments of Psychiatry and Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - J. John Mann
- Division of Molecular Imaging and the Neuropathology, Departments of Psychiatry and Radiology, New York State Psychiatric Institute, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Jyotishman Pathak
- Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States of America
| | - Mark Olfson
- Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Myrna M. Weissman
- Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, United States of America
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, NY, United States of America
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10
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Bowles KR, Pugh DA, Liu Y, Patel T, Renton AE, Bandres-Ciga S, Gan-Or Z, Heutink P, Siitonen A, Bertelsen S, Cherry JD, Karch CM, Frucht SJ, Kopell BH, Peter I, Park YJ, Charney A, Raj T, Crary JF, Goate AM. 17q21.31 sub-haplotypes underlying H1-associated risk for Parkinson's disease are associated with LRRC37A/2 expression in astrocytes. Mol Neurodegener 2022; 17:48. [PMID: 35841044 PMCID: PMC9284779 DOI: 10.1186/s13024-022-00551-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [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: 09/23/2021] [Accepted: 06/21/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Parkinson's disease (PD) is genetically associated with the H1 haplotype of the MAPT 17q.21.31 locus, although the causal gene and variants underlying this association have not been identified. METHODS To better understand the genetic contribution of this region to PD and to identify novel mechanisms conferring risk for the disease, we fine-mapped the 17q21.31 locus by constructing discrete haplotype blocks from genetic data. We used digital PCR to assess copy number variation associated with PD-associated blocks, and used human brain postmortem RNA-seq data to identify candidate genes that were then further investigated using in vitro models and human brain tissue. RESULTS We identified three novel H1 sub-haplotype blocks across the 17q21.31 locus associated with PD risk. Protective sub-haplotypes were associated with increased LRRC37A/2 copy number and expression in human brain tissue. We found that LRRC37A/2 is a membrane-associated protein that plays a role in cellular migration, chemotaxis and astroglial inflammation. In human substantia nigra, LRRC37A/2 was primarily expressed in astrocytes, interacted directly with soluble α-synuclein, and co-localized with Lewy bodies in PD brain tissue. CONCLUSION These data indicate that a novel candidate gene, LRRC37A/2, contributes to the association between the 17q21.31 locus and PD via its interaction with α-synuclein and its effects on astrocytic function and inflammatory response. These data are the first to associate the genetic association at the 17q21.31 locus with PD pathology, and highlight the importance of variation at the 17q21.31 locus in the regulation of multiple genes other than MAPT and KANSL1, as well as its relevance to non-neuronal cell types.
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Affiliation(s)
- Kathryn R. Bowles
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Derian A. Pugh
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Yiyuan Liu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Tulsi Patel
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Alan E. Renton
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Sara Bandres-Ciga
- Laboratory of Neurogenetics, National Institute On Aging, National Institutes of Health, Bethesda, MD USA
| | - Ziv Gan-Or
- Department of Human Genetics, McGill University, Montréal, Québec Canada
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montréal, Québec Canada
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec Canada
| | - Peter Heutink
- Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Ari Siitonen
- Institute of Clinical Medicine, Department of Neurology, University of Oulu, Oulu, Finland
- Department of Neurology and Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Sarah Bertelsen
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Jonathan D. Cherry
- Alzheimer’s Disease and CTE Center, Boston University, Boston University School of Medicine, Boston, MA USA
- Department of Neurology, Boston University School of Medicine, Boston, MA USA
- VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA USA
- Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA USA
| | - Celeste M. Karch
- Department of Psychiatry, Washington University in St Louis, St. Louis, MO USA
| | - Steven J. Frucht
- Department of Neurology, Fresco Institute for Parkinson’s and Movement Disorders, New York University Langone, New York, NY USA
| | - Brian H. Kopell
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Center for Neuromodulation, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Inga Peter
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Y. J. Park
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | | | - Alexander Charney
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Towfique Raj
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - John F. Crary
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - A. M. Goate
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY USA
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11
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Geanon D, Lee B, Gonzalez‐Kozlova E, Kelly G, Handler D, Upadhyaya B, Leech J, De Real RM, Herbinet M, Magen A, Del Valle D, Charney A, Kim‐Schulze S, Gnjatic S, Merad M, Rahman AH. A streamlined whole blood CyTOF workflow defines a circulating immune cell signature of COVID-19. Cytometry A 2021; 99:446-461. [PMID: 33496367 PMCID: PMC8013522 DOI: 10.1002/cyto.a.24317] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [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: 10/21/2020] [Revised: 12/10/2020] [Accepted: 01/06/2021] [Indexed: 01/21/2023]
Abstract
Mass cytometry (CyTOF) represents one of the most powerful tools in immune phenotyping, allowing high throughput quantification of over 40 parameters at single-cell resolution. However, wide deployment of CyTOF-based immune phenotyping studies are limited by complex experimental workflows and the need for specialized CyTOF equipment and technical expertise. Furthermore, differences in cell isolation and enrichment protocols, antibody reagent preparation, sample staining, and data acquisition protocols can all introduce technical variation that can confound integrative analyses of large data-sets of samples processed across multiple labs. Here, we present a streamlined whole blood CyTOF workflow which addresses many of these sources of experimental variation and facilitates wider adoption of CyTOF immune monitoring across sites with limited technical expertise or sample-processing resources or equipment. Our workflow utilizes commercially available reagents including the Fluidigm MaxPar Direct Immune Profiling Assay (MDIPA), a dry tube 30-marker immunophenotyping panel, and SmartTube Proteomic Stabilizer, which allows for simple and reliable fixation and cryopreservation of whole blood samples. We validate a workflow that allows for streamlined staining of whole blood samples with minimal processing requirements or expertise at the site of sample collection, followed by shipment to a central CyTOF core facility for batched downstream processing and data acquisition. We apply this workflow to characterize 184 whole blood samples collected longitudinally from a cohort of 72 hospitalized COVID-19 patients and healthy controls, highlighting dynamic disease-associated changes in circulating immune cell frequency and phenotype.
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Affiliation(s)
- Daniel Geanon
- Human Immune Monitoring CenterIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Brian Lee
- Human Immune Monitoring CenterIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Edgar Gonzalez‐Kozlova
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Geoffrey Kelly
- Human Immune Monitoring CenterIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Diana Handler
- Human Immune Monitoring CenterIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Bhaskar Upadhyaya
- Human Immune Monitoring CenterIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - John Leech
- Human Immune Monitoring CenterIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Ronaldo M. De Real
- Human Immune Monitoring CenterIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Manon Herbinet
- Human Immune Monitoring CenterIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Assaf Magen
- Department of Oncological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Diane Del Valle
- Department of Oncological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Alexander Charney
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Seunghee Kim‐Schulze
- Human Immune Monitoring CenterIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Sacha Gnjatic
- Human Immune Monitoring CenterIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA,Department of Oncological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Miriam Merad
- Human Immune Monitoring CenterIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA,Department of Oncological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Adeeb H. Rahman
- Human Immune Monitoring CenterIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA,Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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12
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Hirten RP, Danieletto M, Tomalin L, Choi KH, Zweig M, Golden E, Kaur S, Helmus D, Biello A, Pyzik R, Charney A, Miotto R, Glicksberg BS, Levin M, Nabeel I, Aberg J, Reich D, Charney D, Bottinger EP, Keefer L, Suarez-Farinas M, Nadkarni GN, Fayad ZA. Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study. J Med Internet Res 2021; 23:e26107. [PMID: 33529156 PMCID: PMC7901594 DOI: 10.2196/26107] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 01/14/2021] [Accepted: 01/29/2021] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. OBJECTIVE We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. METHODS Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily. RESULTS Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19-related symptom compared to all other symptom-free days (P=.01). CONCLUSIONS Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19-related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.
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Affiliation(s)
- Robert P Hirten
- The Dr Henry D Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Matteo Danieletto
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Lewis Tomalin
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Katie Hyewon Choi
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Micol Zweig
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Eddye Golden
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Sparshdeep Kaur
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Drew Helmus
- The Dr Henry D Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Anthony Biello
- The Dr Henry D Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Renata Pyzik
- The BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Alexander Charney
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Riccardo Miotto
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Matthew Levin
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Ismail Nabeel
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Judith Aberg
- Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - David Reich
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Dennis Charney
- Office of the Dean, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Erwin P Bottinger
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Laurie Keefer
- The Dr Henry D Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Mayte Suarez-Farinas
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Girish N Nadkarni
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Zahi A Fayad
- The BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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13
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Sigel K, Swartz T, Golden E, Paranjpe I, Somani S, Richter F, De Freitas JK, Miotto R, Zhao S, Polak P, Mutetwa T, Factor S, Mehandru S, Mullen M, Cossarini F, Bottinger E, Fayad Z, Merad M, Gnjatic S, Aberg J, Charney A, Nadkarni G, Glicksberg BS. Coronavirus 2019 and People Living With Human Immunodeficiency Virus: Outcomes for Hospitalized Patients in New York City. Clin Infect Dis 2021; 71:2933-2938. [PMID: 32594164 PMCID: PMC7337691 DOI: 10.1093/cid/ciaa880] [Citation(s) in RCA: 138] [Impact Index Per Article: 46.0] [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: 05/15/2020] [Accepted: 06/22/2020] [Indexed: 12/15/2022] Open
Abstract
Background There are limited data regarding the clinical impact of coronavirus disease 2019 (COVID-19) on people living with human immunodeficiency virus (PLWH). In this study, we compared outcomes for PLWH with COVID-19 to a matched comparison group. Methods We identified 88 PLWH hospitalized with laboratory-confirmed COVID-19 in our hospital system in New York City between 12 March and 23 April 2020. We collected data on baseline clinical characteristics, laboratory values, HIV status, treatment, and outcomes from this group and matched comparators (1 PLWH to up to 5 patients by age, sex, race/ethnicity, and calendar week of infection). We compared clinical characteristics and outcomes (death, mechanical ventilation, hospital discharge) for these groups, as well as cumulative incidence of death by HIV status. Results Patients did not differ significantly by HIV status by age, sex, or race/ethnicity due to the matching algorithm. PLWH hospitalized with COVID-19 had high proportions of HIV virologic control on antiretroviral therapy. PLWH had greater proportions of smoking (P < .001) and comorbid illness than uninfected comparators. There was no difference in COVID-19 severity on admission by HIV status (P = .15). Poor outcomes for hospitalized PLWH were frequent but similar to proportions in comparators; 18% required mechanical ventilation and 21% died during follow-up (compared with 23% and 20%, respectively). There was similar cumulative incidence of death over time by HIV status (P = .94). Conclusions We found no differences in adverse outcomes associated with HIV infection for hospitalized COVID-19 patients compared with a demographically similar patient group.
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Affiliation(s)
- Keith Sigel
- Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Talia Swartz
- Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Eddye Golden
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ishan Paranjpe
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Sulaiman Somani
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Felix Richter
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jessica K De Freitas
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Riccardo Miotto
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Shan Zhao
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Anaesthesia, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Paz Polak
- Department of Oncologic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Tinaye Mutetwa
- Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Stephanie Factor
- Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Saurabh Mehandru
- Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Michael Mullen
- Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Francesca Cossarini
- Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Erwin Bottinger
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Digital Health Center, Hasso Plattner Institute, University of Potsdam, Professor-Dr.-Helmert-Strasse 2-3, Potsdam, Germany
| | - Zahi Fayad
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Miriam Merad
- Department of Oncologic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Sacha Gnjatic
- Department of Oncologic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Judith Aberg
- Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Alexander Charney
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish Nadkarni
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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14
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Wang Z, Zheutlin A, Kao YH, Ayers K, Gross S, Kovatch P, Nirenberg S, Charney A, Nadkarni G, De Freitas JK, O'Reilly P, Just A, Horowitz C, Martin G, Branch A, Glicksberg BS, Charney D, Reich D, Oh WK, Schadt E, Chen R, Li L. Hospitalised COVID-19 patients of the Mount Sinai Health System: a retrospective observational study using the electronic medical records. BMJ Open 2020; 10:e040441. [PMID: 33109676 PMCID: PMC7592304 DOI: 10.1136/bmjopen-2020-040441] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 09/28/2020] [Accepted: 10/07/2020] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVE To assess association of clinical features on COVID-19 patient outcomes. DESIGN Retrospective observational study using electronic medical record data. SETTING Five member hospitals from the Mount Sinai Health System in New York City (NYC). PARTICIPANTS 28 336 patients tested for SARS-CoV-2 from 24 February 2020 to 15 April 2020, including 6158 laboratory-confirmed COVID-19 cases. MAIN OUTCOMES AND MEASURES Positive test rates and in-hospital mortality were assessed for different racial groups. Among positive cases admitted to the hospital (N=3273), we estimated HR for both discharge and death across various explanatory variables, including patient demographics, hospital site and unit, smoking status, vital signs, lab results and comorbidities. RESULTS Hispanics (29%) and African Americans (25%) had disproportionately high positive case rates relative to their representation in the overall NYC population (p<0.05); however, no differences in mortality rates were observed in hospitalised patients based on race. Outcomes differed significantly between hospitals (Gray's T=248.9; p<0.05), reflecting differences in average baseline age and underlying comorbidities. Significant risk factors for mortality included age (HR 1.05, 95% CI 1.04 to 1.06; p=1.15e-32), oxygen saturation (HR 0.985, 95% CI 0.982 to 0.988; p=1.57e-17), care in intensive care unit areas (HR 1.58, 95% CI 1.29 to 1.92; p=7.81e-6) and elevated creatinine (HR 1.75, 95% CI 1.47 to 2.10; p=7.48e-10), white cell count (HR 1.02, 95% CI 1.01 to 1.04; p=8.4e-3) and body mass index (BMI) (HR 1.02, 95% CI 1.00 to 1.03; p=1.09e-2). Deceased patients were more likely to have elevated markers of inflammation. CONCLUSIONS While race was associated with higher risk of infection, we did not find racial disparities in inpatient mortality suggesting that outcomes in a single tertiary care health system are comparable across races. In addition, we identified key clinical features associated with reduced mortality and discharge. These findings could help to identify which COVID-19 patients are at greatest risk of a severe infection response and predict survival.
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Affiliation(s)
| | | | | | | | - Susan Gross
- Sema4, Stamford, Connecticut, USA
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Patricia Kovatch
- Mount Sinai Data Warehouse, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Sharon Nirenberg
- Mount Sinai Data Warehouse, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Alexander Charney
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish Nadkarni
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jessica K De Freitas
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Paul O'Reilly
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Allan Just
- Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Carol Horowitz
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Glenn Martin
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Andrea Branch
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Benjamin S Glicksberg
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Dennis Charney
- The Office of the Dean, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - David Reich
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - William K Oh
- Tisch Cancer Institute and Division of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Eric Schadt
- Sema4, Stamford, Connecticut, USA
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Rong Chen
- Sema4, Stamford, Connecticut, USA
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Li Li
- Sema4, Stamford, Connecticut, USA
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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15
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Lala A, Johnson KW, Januzzi JL, Russak AJ, Paranjpe I, Richter F, Zhao S, Somani S, Van Vleck T, Vaid A, Chaudhry F, De Freitas JK, Fayad ZA, Pinney SP, Levin M, Charney A, Bagiella E, Narula J, Glicksberg BS, Nadkarni G, Mancini DM, Fuster V. Prevalence and Impact of Myocardial Injury in Patients Hospitalized With COVID-19 Infection. J Am Coll Cardiol 2020; 76:533-546. [PMID: 32517963 PMCID: PMC7279721 DOI: 10.1016/j.jacc.2020.06.007] [Citation(s) in RCA: 509] [Impact Index Per Article: 127.3] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 06/02/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND The degree of myocardial injury, as reflected by troponin elevation, and associated outcomes among U.S. hospitalized patients with coronavirus disease-2019 (COVID-19) are unknown. OBJECTIVES The purpose of this study was to describe the degree of myocardial injury and associated outcomes in a large hospitalized cohort with laboratory-confirmed COVID-19. METHODS Patients with COVID-19 admitted to 1 of 5 Mount Sinai Health System hospitals in New York City between February 27, 2020, and April 12, 2020, with troponin-I (normal value <0.03 ng/ml) measured within 24 h of admission were included (n = 2,736). Demographics, medical histories, admission laboratory results, and outcomes were captured from the hospitals' electronic health records. RESULTS The median age was 66.4 years, with 59.6% men. Cardiovascular disease (CVD), including coronary artery disease, atrial fibrillation, and heart failure, was more prevalent in patients with higher troponin concentrations, as were hypertension and diabetes. A total of 506 (18.5%) patients died during hospitalization. In all, 985 (36%) patients had elevated troponin concentrations. After adjusting for disease severity and relevant clinical factors, even small amounts of myocardial injury (e.g., troponin I >0.03 to 0.09 ng/ml; n = 455; 16.6%) were significantly associated with death (adjusted hazard ratio: 1.75; 95% CI: 1.37 to 2.24; p < 0.001) while greater amounts (e.g., troponin I >0.09 ng/dl; n = 530; 19.4%) were significantly associated with higher risk (adjusted HR: 3.03; 95% CI: 2.42 to 3.80; p < 0.001). CONCLUSIONS Myocardial injury is prevalent among patients hospitalized with COVID-19; however, troponin concentrations were generally present at low levels. Patients with CVD are more likely to have myocardial injury than patients without CVD. Troponin elevation among patients hospitalized with COVID-19 is associated with higher risk of mortality.
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Affiliation(s)
- Anuradha Lala
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York.
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - James L Januzzi
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts; Baim Institute for Clinical Research, Cardiometabolic Trials, Boston, Massachusetts
| | - Adam J Russak
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ishan Paranjpe
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York
| | - Felix Richter
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Shan Zhao
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York; Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Sulaiman Somani
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York
| | - Tielman Van Vleck
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Akhil Vaid
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Fayzan Chaudhry
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York
| | - Jessica K De Freitas
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Zahi A Fayad
- The BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Sean P Pinney
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Matthew Levin
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Alexander Charney
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Emilia Bagiella
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Jagat Narula
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Girish Nadkarni
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York; Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Donna M Mancini
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Valentin Fuster
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
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16
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Gruber C, Patel R, Trachman R, Lepow L, Amanat F, Krammer F, Wilson KM, Onel K, Geanon D, Tuballes K, Patel M, Mouskas K, Simons N, Barcessat V, Valle DD, Udondem S, Kang G, Gangadharan S, Ofori-Amanfo G, Rahman A, Kim-Schulze S, Charney A, Gnjatic S, Gelb BD, Merad M, Bogunovic D. Mapping Systemic Inflammation and Antibody Responses in Multisystem Inflammatory Syndrome in Children (MIS-C). medRxiv 2020:2020.07.04.20142752. [PMID: 32676612 PMCID: PMC7359537 DOI: 10.1101/2020.07.04.20142752] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [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: 12/13/2022]
Abstract
Initially, the global outbreak of COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spared children from severe disease. However, after the initial wave of infections, clusters of a novel hyperinflammatory disease have been reported in regions with ongoing SARS-CoV-2 epidemics. While the characteristic clinical features are becoming clear, the pathophysiology remains unknown. Herein, we report on the immune profiles of eight Multisystem Inflammatory Syndrome in Children (MIS-C) cases. We document that all MIS-C patients had evidence of prior SARS-CoV-2 exposure, mounting an antibody response with normal isotype-switching and neutralization capability. We further profiled the secreted immune response by high-dimensional cytokine assays, which identified elevated signatures of inflammation (IL-18 and IL-6), lymphocytic and myeloid chemotaxis and activation (CCL3, CCL4, and CDCP1) and mucosal immune dysregulation (IL-17A, CCL20, CCL28). Mass cytometry immunophenotyping of peripheral blood revealed reductions of mDC1 and non-classical monocytes, as well as both NK- and T- lymphocytes, suggesting extravasation to affected tissues. Markers of activated myeloid function were also evident, including upregulation of ICAM1 and FcγR1 in neutrophil and non-classical monocytes, well-documented markers in autoinflammation and autoimmunity that indicate enhanced antigen presentation and Fc-mediated responses. Finally, to assess the role for autoimmunity secondary to infection, we profiled the auto-antigen reactivity of MIS-C plasma, which revealed both known disease-associated autoantibodies (anti-La) and novel candidates that recognize endothelial, gastrointestinal and immune-cell antigens. All patients were treated with anti-IL6R antibody or IVIG, which led to rapid disease resolution tracking with normalization of inflammatory markers.
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Affiliation(s)
- Conor Gruber
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, NY, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, NY, NY, USA
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Roosheel Patel
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, NY, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, NY, NY, USA
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Rebecca Trachman
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, NY, NY, USA
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Lauren Lepow
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Fatima Amanat
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Florian Krammer
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Karen M. Wilson
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, NY, NY, USA
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Kenan Onel
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Daniel Geanon
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Kevin Tuballes
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Manishkumar Patel
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Konstantinos Mouskas
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Nicole Simons
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Vanessa Barcessat
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Diane Del Valle
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Samantha Udondem
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Gurpawan Kang
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Sandeep Gangadharan
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - George Ofori-Amanfo
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Adeeb Rahman
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Seunghee Kim-Schulze
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Alexander Charney
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Sacha Gnjatic
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Bruce D. Gelb
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, NY, NY, USA
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Miriam Merad
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Dusan Bogunovic
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, NY, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, NY, NY, USA
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, NY, NY, USA
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Del Valle DM, Kim-Schulze S, Hsin-Hui H, Beckmann ND, Nirenberg S, Wang B, Lavin Y, Swartz T, Madduri D, Stock A, Marron T, Xie H, Patel MK, van Oekelen O, Rahman A, Kovatch P, Aberg J, Schadt E, Jagannath S, Mazumdar M, Charney A, Firpo-Betancourt A, Mendu DR, Jhang J, Reich D, Sigel K, Cordon-Cardo C, Feldmann M, Parekh S, Merad M, Gnjatic S. An inflammatory cytokine signature helps predict COVID-19 severity and death. medRxiv 2020. [PMID: 32511562 DOI: 10.1101/2020.05.28.20115758] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
The COVID-19 pandemic caused by infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led to more than 100,000 deaths in the United States. Several studies have revealed that the hyper-inflammatory response induced by SARS-CoV-2 is a major cause of disease severity and death in infected patients. However, predictive biomarkers of pathogenic inflammation to help guide targetable immune pathways are critically lacking. We implemented a rapid multiplex cytokine assay to measure serum IL-6, IL-8, TNF-α, and IL-1β in hospitalized COVID-19 patients upon admission to the Mount Sinai Health System in New York. Patients (n=1484) were followed up to 41 days (median 8 days) and clinical information, laboratory test results and patient outcomes were collected. In 244 patients, cytokine measurements were repeated over time, and effect of drugs could be assessed. Kaplan-Meier methods were used to compare survival by cytokine strata, followed by Cox regression models to evaluate the independent predictive value of baseline cytokines. We found that high serum IL-6, IL-8, and TNF-α levels at the time of hospitalization were strong and independent predictors of patient survival. Importantly, when adjusting for disease severity score, common laboratory inflammation markers, hypoxia and other vitals, demographics, and a range of comorbidities, IL-6 and TNF-α serum levels remained independent and significant predictors of disease severity and death. We propose that serum IL-6 and TNF-α levels should be considered in the management and treatment of COVID-19 patients to stratify prospective clinical trials, guide resource allocation and inform therapeutic options. We also propose that patients with high IL-6 and TNF-α levels should be assessed for combinatorial blockade of pathogenic inflammation in this disease.
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Somani S, Richter F, Fuster V, De Freitas J, Naik N, Sigel K, Boettinger EP, Levin MA, Fayad Z, Just AC, Charney A, Zhao S, Glicksberg BS, Lala A, Nadkarni G. Characterization of Patients Who Return to Hospital Following Discharge from Hospitalization For COVID-19. medRxiv 2020. [PMID: 32511547 DOI: 10.1101/2020.05.17.20104604] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Background: Data on patients with coronavirus disease 2019 (COVID-19) who return to hospital after discharge are scarce. Characterization of these patients may inform post-hospitalization care. Methods and Findings: Retrospective cohort study of patients with confirmed SARS-CoV-2 discharged alive from five hospitals in New York City with index hospitalization between February 27th-April 12th, 2020, with follow-up of ≥14 days. Significance was defined as P<0.05 after multiplying P by 125 study-wide comparisons. Of 2,864 discharged patients, 103 (3.6%) returned for emergency care after a median of 4.5 days, with 56 requiring inpatient readmission. The most common reason for return was respiratory distress (50%). Compared to patients who did not return, among those who returned there was a higher proportion of COPD (6.8% vs 2.9%) and hypertension (36% vs 22.1%). Patients who returned also had a shorter median length of stay (LOS) during index hospitalization (4.5 [2.9,9.1] vs. 6.7 [3.5, 11.5] days; P adjusted =0.006), and were less likely to have required intensive care on index hospitalization (5.8% vs 19%; P adjusted =0.001). A trend towards association between absence of in-hospital anticoagulation on index admission and return to hospital was also observed (20.9% vs 30.9%, P adjusted =0.064). On readmission, rates of intensive care and death were 5.8% and 3.6%, respectively. Conclusions: Return to hospital after admission for COVID-19 was infrequent within 14 days of discharge. The most common cause for return was respiratory distress. Patients who returned had higher proportion of COPD and hypertension with shorter LOS on index hospitalization, and a trend towards lower rates of in-hospital treatment-dose anticoagulation. Future studies should focus on whether these comorbid conditions, longer LOS and anticoagulation are associated with reduced readmissions.
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Lala A, Johnson KW, Russak AJ, Paranjpe I, Zhao S, Solani S, Vaid A, Chaudhry F, De Freitas JK, Fayad ZA, Pinney SP, Levin M, Charney A, Bagiella E, Narula J, Glicksberg BS, Nadkarni G, Januzzi J, Mancini DM, Fuster V. Prevalence and Impact of Myocardial Injury in Patients Hospitalized with COVID-19 Infection. medRxiv 2020. [PMID: 32511658 DOI: 10.1101/2020.04.20.20072702] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND The degree of myocardial injury, reflected by troponin elevation, and associated outcomes among hospitalized patients with Coronavirus Disease (COVID-19) in the US are unknown. OBJECTIVES To describe the degree of myocardial injury and associated outcomes in a large hospitalized cohort with laboratory-confirmed COVID-19. METHODS Patients with COVID-19 admitted to one of five Mount Sinai Health System hospitals in New York City between February 27th and April 12th, 2020 with troponin-I (normal value <0.03ng/mL) measured within 24 hours of admission were included (n=2,736). Demographics, medical history, admission labs, and outcomes were captured from the hospital EHR. RESULTS The median age was 66.4 years, with 59.6% men. Cardiovascular disease (CVD) including coronary artery disease, atrial fibrillation, and heart failure, was more prevalent in patients with higher troponin concentrations, as were hypertension and diabetes. A total of 506 (18.5%) patients died during hospitalization. Even small amounts of myocardial injury (e.g. troponin I 0.03-0.09ng/mL, n=455, 16.6%) were associated with death (adjusted HR: 1.77, 95% CI 1.39-2.26; P<0.001) while greater amounts (e.g. troponin I>0.09 ng/dL, n=530, 19.4%) were associated with more pronounced risk (adjusted HR 3.23, 95% CI 2.59-4.02). CONCLUSIONS Myocardial injury is prevalent among patients hospitalized with COVID-19, and is associated with higher risk of mortality. Patients with CVD are more likely to have myocardial injury than patients without CVD. Troponin elevation likely reflects non-ischemic or secondary myocardial injury.
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Zheutlin AB, Dennis J, Karlsson Linnér R, Moscati A, Restrepo N, Straub P, Ruderfer D, Castro VM, Chen CY, Ge T, Huckins LM, Charney A, Kirchner HL, Stahl EA, Chabris CF, Davis LK, Smoller JW. Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia in 106,160 Patients Across Four Health Care Systems. Am J Psychiatry 2019; 176:846-855. [PMID: 31416338 PMCID: PMC6961974 DOI: 10.1176/appi.ajp.2019.18091085] [Citation(s) in RCA: 126] [Impact Index Per Article: 25.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVE Individuals at high risk for schizophrenia may benefit from early intervention, but few validated risk predictors are available. Genetic profiling is one approach to risk stratification that has been extensively validated in research cohorts. The authors sought to test the utility of this approach in clinical settings and to evaluate the broader health consequences of high genetic risk for schizophrenia. METHODS The authors used electronic health records for 106,160 patients from four health care systems to evaluate the penetrance and pleiotropy of genetic risk for schizophrenia. Polygenic risk scores (PRSs) for schizophrenia were calculated from summary statistics and tested for association with 1,359 disease categories, including schizophrenia and psychosis, in phenome-wide association studies. Effects were combined through meta-analysis across sites. RESULTS PRSs were robustly associated with schizophrenia (odds ratio per standard deviation increase in PRS, 1.55; 95% CI=1.4, 1.7), and patients in the highest risk decile of the PRS distribution had up to 4.6-fold higher odds of schizophrenia compared with those in the bottom decile (95% CI=2.9, 7.3). PRSs were also positively associated with other phenotypes, including anxiety, mood, substance use, neurological, and personality disorders, as well as suicidal behavior, memory loss, and urinary syndromes; they were inversely related to obesity. CONCLUSIONS The study demonstrates that an available measure of genetic risk for schizophrenia is robustly associated with schizophrenia in health care settings and has pleiotropic effects on related psychiatric disorders as well as other medical syndromes. The results provide an initial indication of the opportunities and limitations that may arise with the future application of PRS testing in health care systems.
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Affiliation(s)
- Amanda B Zheutlin
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Jessica Dennis
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Richard Karlsson Linnér
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Arden Moscati
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Nicole Restrepo
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Peter Straub
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Douglas Ruderfer
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Victor M Castro
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Chia-Yen Chen
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Laura M Huckins
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Alexander Charney
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - H Lester Kirchner
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Eli A Stahl
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Christopher F Chabris
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Lea K Davis
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
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Song J, Bergen SE, Di Florio A, Karlsson R, Charney A, Ruderfer DM, Stahl EA, Chambert KD, Moran JL, Gordon-Smith K, Forty L, Green EK, Jones I, Jones L, Scolnick EM, Sklar P, Smoller JW, Lichtenstein P, Hultman C, Craddock N, Landén M. Genome-wide association study identifies SESTD1 as a novel risk gene for lithium-responsive bipolar disorder. Mol Psychiatry 2017; 22:1223. [PMID: 28194006 PMCID: PMC7608474 DOI: 10.1038/mp.2016.246] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
This corrects the article DOI: 10.1038/mp.2015.165.
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Bansilal S, Vedanthan R, Woodward M, Iyengar R, Hunn M, Lewis M, Francis L, Charney A, Graves C, Farkouh ME, Fuster V. Cardiovascular risk surveillance to develop a nationwide health promotion strategy: the grenada heart project. Glob Heart 2015; 7:87-94. [PMID: 25691303 DOI: 10.1016/j.gheart.2012.06.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2011] [Revised: 06/07/2012] [Accepted: 06/08/2012] [Indexed: 11/30/2022] Open
Abstract
OBJECTIVE The Grenada Heart Project aims to study the clinical, biological, and psychosocial determinants of the cardiovascular health in Grenada in order to develop and implement a nationwide cardiovascular health promotion program. METHODS We recruited 2,827 adults randomly selected from the national electronic voter list. The main outcome measures were self-reported cardiovascular disease and behavioral risk factors, anthropometric measures, blood pressure, point-of-care testing for glucose and lipids, and ankle-brachial index. Risk factors were also compared with the U.S. National Health and Nutritional Survey data. RESULTS Prevalence of cardiovascular disease risk factors were: overweight and obesity-57.7% of the population, physical inactivity-23.4%, diabetes-13.3%, hypertension-29.7%, hypercholesterolemia-8.6%, and smoking-7%. Subjects who were physically active had a significantly lower 10-year Framingham risk score (p<0.001). Compared with the U.S. National Health and Nutrition Survey data, Grenadian women had higher rates of adiposity, diabetes, hypertension, and elevated low-density lipoprotein cholesterol, whereas Grenadian men had a higher rate of diabetes, a similar rate of hypertension, and lower rates of the other risk factors. Prevalence of peripheral arterial disease was 7.6%; stroke and coronary heart disease were equally prevalent at ∼2%. CONCLUSIONS This randomly selected adult sample in Grenada reveals prevalence rates of obesity, hypertension, and diabetes significantly exceeding those seen in the United States. The contrasting, paradoxically low levels of prevalent cardiovascular disease support the concept that Grenada is experiencing an obesity-related "risk transition." These data form the basis for the implementation of a pilot intervention program based on the Institute of Medicine recommendations and may serve as a model for other low- and middle-income countries.
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Affiliation(s)
| | | | - Mark Woodward
- George Institute, University of Sydney, Sydney, Australia
| | - Rupa Iyengar
- Mount Sinai School of Medicine, New York, NY, USA
| | - Marilyn Hunn
- Mount Sinai School of Medicine, New York, NY, USA
| | | | | | | | | | - Michael E Farkouh
- Mount Sinai School of Medicine, New York, NY, USA; University of Toronto, Toronto, Canada
| | - Valentin Fuster
- Mount Sinai School of Medicine, New York, NY, USA; Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
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Roussos P, Mitchell AC, Voloudakis G, Fullard JF, Pothula VM, Tsang J, Stahl EA, Georgakopoulos A, Ruderfer DM, Charney A, Okada Y, Siminovitch KA, Worthington J, Padyukov L, Klareskog L, Gregersen PK, Plenge RM, Raychaudhuri S, Fromer M, Purcell SM, Brennand KJ, Robakis NK, Schadt EE, Akbarian S, Sklar P. A role for noncoding variation in schizophrenia. Cell Rep 2014; 9:1417-29. [PMID: 25453756 DOI: 10.1016/j.celrep.2014.10.015] [Citation(s) in RCA: 185] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2014] [Revised: 07/31/2014] [Accepted: 10/03/2014] [Indexed: 01/20/2023] Open
Abstract
A large portion of common variant loci associated with genetic risk for schizophrenia reside within noncoding sequence of unknown function. Here, we demonstrate promoter and enhancer enrichment in schizophrenia variants associated with expression quantitative trait loci (eQTL). The enrichment is greater when functional annotations derived from the human brain are used relative to peripheral tissues. Regulatory trait concordance analysis ranked genes within schizophrenia genome-wide significant loci for a potential functional role, based on colocalization of a risk SNP, eQTL, and regulatory element sequence. We identified potential physical interactions of noncontiguous proximal and distal regulatory elements. This was verified in prefrontal cortex and -induced pluripotent stem cell-derived neurons for the L-type calcium channel (CACNA1C) risk locus. Our findings point to a functional link between schizophrenia-associated noncoding SNPs and 3D genome architecture associated with chromosomal loopings and transcriptional regulation in the brain.
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Affiliation(s)
- Panos Roussos
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; James J. Peters VA Medical Center, Mental Illness Research Education and Clinical Center (MIRECC), 130 West Kingsbridge Road, Bronx, NY 10468, USA.
| | - Amanda C Mitchell
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Georgios Voloudakis
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - John F Fullard
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Venu M Pothula
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jonathan Tsang
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Eli A Stahl
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Douglas M Ruderfer
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Alexander Charney
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yukinori Okada
- Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 230-0045, Japan; Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
| | - Katherine A Siminovitch
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada; Toronto General Research Institute, Toronto, ON M5G 2M9, Canada; Department of Medicine, University of Toronto, Toronto, ON M5S 2J7, Canada
| | - Jane Worthington
- Arthritis Research UK Centre for Genetics and Genomics, Musculoskeletal Research Centre, Institute for Inflammation and Repair, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9NT, UK; National Institute for Health Research, Manchester Musculoskeletal Biomedical Research Unit, Central Manchester University Hospitals National Health Service Foundation Trust, Manchester Academic Health Sciences Centre, Manchester M13 9NT, UK
| | - Leonid Padyukov
- Rheumatology Unit, Department of Medicine (Solna), Karolinska Institutet, Stockholm 171 76, Sweden
| | - Lars Klareskog
- Rheumatology Unit, Department of Medicine (Solna), Karolinska Institutet, Stockholm 171 76, Sweden
| | - Peter K Gregersen
- The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, NY 11030, USA
| | - Robert M Plenge
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
| | - Soumya Raychaudhuri
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA; NIHR Manchester Musculoskeletal Biomedical Research Unit, Central Manchester NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester M13 9NT, UK
| | - Menachem Fromer
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Shaun M Purcell
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kristen J Brennand
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Nikolaos K Robakis
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Schahram Akbarian
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Pamela Sklar
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
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Ivanov I, Charney A. Treating pediatric patients with antipsychotic drugs: balancing benefits and safety. ACTA ACUST UNITED AC 2008; 75:276-86. [DOI: 10.1002/msj.20051] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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Abstract
Potassium adaptation involves the development of the ability of the kidneys to secrete large amounts of potassium into the urine. This is accompanied by an adaptive increase in the specific activity of sodium-potassium-ATPase in the kidney, predominantly in the medulla and the papilla, but also involving the cortex. It is likely that these changes are localized to the distal tubule and are especially marked in the collecting ducts although there is no direct evidence bearing on this. Net secretion of potassium in isolated kidneys taken from chronically potassium loaded animals is completely eliminated when ouabain, a specific inhibitor of sodium-potassium-ATPase, is added to the perfusion medium. The secretion of potassium appears also to depend critically on the availability of glucose as substrate.
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Strauch BS, Charney A, Doctorouff S, Kashgarian M. Goodpasture syndrome with recovery after renal failure. JAMA 1974; 229:444. [PMID: 4406940] [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: 01/10/2023]
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