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1,188 results on '"Nadkarni, Girish N"'

1. Extracting Social Support and Social Isolation Information from Clinical Psychiatry Notes: Comparing a Rule-based NLP System and a Large Language Model

Author

Patra, Braja Gopal, Lepow, Lauren A., Kumar, Praneet Kasi Reddy Jagadeesh, Vekaria, Veer, Sharma, Mohit Manoj, Adekkanattu, Prakash, Fennessy, Brian, Hynes, Gavin, Landi, Isotta, Sanchez-Ruiz, Jorge A., Ryu, Euijung, Biernacka, Joanna M., Nadkarni, Girish N., Talati, Ardesheer, Weissman, Myrna, Olfson, Mark, Mann, J. John, Charney, Alexander W., and Pathak, Jyotishman

Subjects
Computer Science - Computation and Language
Abstract

Background: Social support (SS) and social isolation (SI) are social determinants of health (SDOH) associated with psychiatric outcomes. In electronic health records (EHRs), individual-level SS/SI is typically documented as narrative clinical notes rather than structured coded data. Natural language processing (NLP) algorithms can automate the otherwise labor-intensive process of data extraction. Data and Methods: Psychiatric encounter notes from Mount Sinai Health System (MSHS, n=300) and Weill Cornell Medicine (WCM, n=225) were annotated and established a gold standard corpus. A rule-based system (RBS) involving lexicons and a large language model (LLM) using FLAN-T5-XL were developed to identify mentions of SS and SI and their subcategories (e.g., social network, instrumental support, and loneliness). Results: For extracting SS/SI, the RBS obtained higher macro-averaged f-scores than the LLM at both MSHS (0.89 vs. 0.65) and WCM (0.85 vs. 0.82). For extracting subcategories, the RBS also outperformed the LLM at both MSHS (0.90 vs. 0.62) and WCM (0.82 vs. 0.81). Discussion and Conclusion: Unexpectedly, the RBS outperformed the LLMs across all metrics. Intensive review demonstrates that this finding is due to the divergent approach taken by the RBS and LLM. The RBS were designed and refined to follow the same specific rules as the gold standard annotations. Conversely, the LLM were more inclusive with categorization and conformed to common English-language understanding. Both approaches offer advantages and are made available open-source for future testing., Comment: 2 figures, 3 tables

Published
2024
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2. Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study

Author

Hirten, Robert P, Danieletto, Matteo, Tomalin, Lewis, Choi, Katie Hyewon, Zweig, Micol, Golden, Eddye, Kaur, Sparshdeep, Helmus, Drew, Biello, Anthony, Pyzik, Renata, Charney, Alexander, Miotto, Riccardo, Glicksberg, Benjamin S, Levin, Matthew, Nabeel, Ismail, Aberg, Judith, Reich, David, Charney, Dennis, Bottinger, Erwin P, Keefer, Laurie, Suarez-Farinas, Mayte, Nadkarni, Girish N, and Fayad, Zahi A

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundChanges in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. ObjectiveWe performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. MethodsHealth 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. ResultsUsing 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). ConclusionsLongitudinally 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.

Published
2021
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3. Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach

Author

Vaid, Akhil, Jaladanki, Suraj K, Xu, Jie, Teng, Shelly, Kumar, Arvind, Lee, Samuel, Somani, Sulaiman, Paranjpe, Ishan, De Freitas, Jessica K, Wanyan, Tingyi, Johnson, Kipp W, Bicak, Mesude, Klang, Eyal, Kwon, Young Joon, Costa, Anthony, Zhao, Shan, Miotto, Riccardo, Charney, Alexander W, Böttinger, Erwin, Fayad, Zahi A, Nadkarni, Girish N, Wang, Fei, and Glicksberg, Benjamin S

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundMachine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. ObjectiveWe aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. MethodsPatient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. ResultsThe LASSOfederated model outperformed the LASSOlocal model at 3 hospitals, and the MLPfederated model performed better than the MLPlocal model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSOpooled model outperformed the LASSOfederated model at all hospitals, and the MLPfederated model outperformed the MLPpooled model at 2 hospitals. ConclusionsThe federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.

Published
2021
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5. Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation

Author

Vaid, Akhil, Somani, Sulaiman, Russak, Adam J, De Freitas, Jessica K, Chaudhry, Fayzan F, Paranjpe, Ishan, Johnson, Kipp W, Lee, Samuel J, Miotto, Riccardo, Richter, Felix, Zhao, Shan, Beckmann, Noam D, Naik, Nidhi, Kia, Arash, Timsina, Prem, Lala, Anuradha, Paranjpe, Manish, Golden, Eddye, Danieletto, Matteo, Singh, Manbir, Meyer, Dara, O'Reilly, Paul F, Huckins, Laura, Kovatch, Patricia, Finkelstein, Joseph, Freeman, Robert M., Argulian, Edgar, Kasarskis, Andrew, Percha, Bethany, Aberg, Judith A, Bagiella, Emilia, Horowitz, Carol R, Murphy, Barbara, Nestler, Eric J, Schadt, Eric E, Cho, Judy H, Cordon-Cardo, Carlos, Fuster, Valentin, Charney, Dennis S, Reich, David L, Bottinger, Erwin P, Levin, Matthew A, Narula, Jagat, Fayad, Zahi A, Just, Allan C, Charney, Alexander W, Nadkarni, Girish N, and Glicksberg, Benjamin S

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundCOVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. ObjectiveThe aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. MethodsWe used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19–positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. ResultsUpon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. ConclusionsWe externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

Published
2020
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7. Neuropsychiatric complications of coronavirus disease 2019: Mount Sinai Health System cohort study

Author

Gururangan, Kapil, Peschansky, Veronica J., Van Hyfte, Grace, Agarwal, Parul, Blank, Leah J., Mathew, Brian, Goldstein, Jonathan, Kwon, Churl-Su, McCarthy, Louise, Cohen, Ariella, Chan, Andy Ho Wing, Deng, Pojen, Dhamoon, Mandip, Gutzwiller, Eveline, Hao, Qing, He, Celestine, Klenofsky, Britany, Lemus, Hernan Nicolas, Marcuse, Lara, Navis, Allison, Heredia Nunez, Wilson D., Luckey, Mallory N., Schorr, Emily M., Singh, Anuradha, Tantillo, Gabriela B., Ufongene, Claire, Young, James J., Balchandani, Priti, Festa, Joanne R., Naasan, Georges, Charney, Alexander W., Nadkarni, Girish N., and Jetté, Nathalie

Published
2024
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8. An AI-Guided Data Centric Strategy to Detect and Mitigate Biases in Healthcare Datasets

Author

Gulamali, Faris F., Sawant, Ashwin S., Liharska, Lora, Horowitz, Carol R., Chan, Lili, Kovatch, Patricia H., Hofer, Ira, Singh, Karandeep, Richardson, Lynne D., Mensah, Emmanuel, Charney, Alexander W, Reich, David L., Hu, Jianying, and Nadkarni, Girish N.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

The adoption of diagnosis and prognostic algorithms in healthcare has led to concerns about the perpetuation of bias against disadvantaged groups of individuals. Deep learning methods to detect and mitigate bias have revolved around modifying models, optimization strategies, and threshold calibration with varying levels of success. Here, we generate a data-centric, model-agnostic, task-agnostic approach to evaluate dataset bias by investigating the relationship between how easily different groups are learned at small sample sizes (AEquity). We then apply a systematic analysis of AEq values across subpopulations to identify and mitigate manifestations of racial bias in two known cases in healthcare - Chest X-rays diagnosis with deep convolutional neural networks and healthcare utilization prediction with multivariate logistic regression. AEq is a novel and broadly applicable metric that can be applied to advance equity by diagnosing and remediating bias in healthcare datasets.

Published
2023

9. Derivation, external and clinical validation of a deep learning approach for detecting intracranial hypertension

Author

Gulamali, Faris, Jayaraman, Pushkala, Sawant, Ashwin S., Desman, Jacob, Fox, Benjamin, Chang, Annette, Soong, Brian Y., Arivazagan, Naveen, Reynolds, Alexandra S., Duong, Son Q., Vaid, Akhil, Kovatch, Patricia, Freeman, Robert, Hofer, Ira S., Sakhuja, Ankit, Dangayach, Neha S., Reich, David S., Charney, Alexander W., and Nadkarni, Girish N.

Published
2024
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10. Author Correction: Divergent landscapes of A-to-I editing in postmortem and living human brain

Author

Rodriguez de los Santos, Miguel, Kopell, Brian H., Buxbaum Grice, Ariela, Ganesh, Gauri, Yang, Andy, Amini, Pardis, Liharska, Lora E., Vornholt, Eric, Fullard, John F., Dong, Pengfei, Park, Eric, Zipkowitz, Sarah, Kaji, Deepak A., Thompson, Ryan C., Liu, Donjing, Park, You Jeong, Cheng, Esther, Ziafat, Kimia, Moya, Emily, Fennessy, Brian, Wilkins, Lillian, Silk, Hannah, Linares, Lisa M., Sullivan, Brendan, Cohen, Vanessa, Kota, Prashant, Feng, Claudia, Johnson, Jessica S., Rieder, Marysia-Kolbe, Scarpa, Joseph, Nadkarni, Girish N., Wang, Minghui, Zhang, Bin, Sklar, Pamela, Beckmann, Noam D., Schadt, Eric E., Roussos, Panos, Charney, Alexander W., and Breen, Michael S.

Published
2024
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11. Divergent landscapes of A-to-I editing in postmortem and living human brain

Author

Rodriguez de los Santos, Miguel, Kopell, Brian H., Buxbaum Grice, Ariela, Ganesh, Gauri, Yang, Andy, Amini, Pardis, Liharska, Lora E., Vornholt, Eric, Fullard, John F., Dong, Pengfei, Park, Eric, Zipkowitz, Sarah, Kaji, Deepak A., Thompson, Ryan C., Liu, Donjing, Park, You Jeong, Cheng, Esther, Ziafat, Kimia, Moya, Emily, Fennessy, Brian, Wilkins, Lillian, Silk, Hannah, Linares, Lisa M., Sullivan, Brendan, Cohen, Vanessa, Kota, Prashant, Feng, Claudia, Johnson, Jessica S., Rieder, Marysia-Kolbe, Scarpa, Joseph, Nadkarni, Girish N., Wang, Minghui, Zhang, Bin, Sklar, Pamela, Beckmann, Noam D., Schadt, Eric E., Roussos, Panos, Charney, Alexander W., and Breen, Michael S.

Published
2024
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16. Genome-wide association study of thoracic aortic aneurysm and dissection in the Million Veteran Program

Author

Klarin, Derek, Devineni, Poornima, Sendamarai, Anoop K, Angueira, Anthony R, Graham, Sarah E, Shen, Ying H, Levin, Michael G, Pirruccello, James P, Surakka, Ida, Karnam, Purushotham R, Roychowdhury, Tanmoy, Li, Yanming, Wang, Minxian, Aragam, Krishna G, Paruchuri, Kaavya, Zuber, Verena, Shakt, Gabrielle E, Tsao, Noah L, Judy, Renae L, Vy, Ha My T, Verma, Shefali S, Rader, Daniel J, Do, Ron, Bavaria, Joseph E, Nadkarni, Girish N, Ritchie, Marylyn D, Burgess, Stephen, Guo, Dong-chuan, Ellinor, Patrick T, LeMaire, Scott A, Milewicz, Dianna M, Willer, Cristen J, Natarajan, Pradeep, Tsao, Philip S, Pyarajan, Saiju, and Damrauer, Scott M

Subjects
Cardiovascular, Genetics, Human Genome, Prevention, Aetiology, 2.1 Biological and endogenous factors, Humans, Genome-Wide Association Study, Veterans, Pedigree, Aortic Aneurysm, Thoracic, Aortic Dissection, VA Million Veteran Program, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

The current understanding of the genetic determinants of thoracic aortic aneurysms and dissections (TAAD) has largely been informed through studies of rare, Mendelian forms of disease. Here, we conducted a genome-wide association study (GWAS) of TAAD, testing ~25 million DNA sequence variants in 8,626 participants with and 453,043 participants without TAAD in the Million Veteran Program, with replication in an independent sample of 4,459 individuals with and 512,463 without TAAD from six cohorts. We identified 21 TAAD risk loci, 17 of which have not been previously reported. We leverage multiple downstream analytic methods to identify causal TAAD risk genes and cell types and provide human genetic evidence that TAAD is a non-atherosclerotic aortic disorder distinct from other forms of vascular disease. Our results demonstrate that the genetic architecture of TAAD mirrors that of other complex traits and that it is not solely inherited through protein-altering variants of large effect size.

Published
2023

17. Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup

Author

Kashani, Kianoush B., Awdishu, Linda, Bagshaw, Sean M., Barreto, Erin F., Claure-Del Granado, Rolando, Evans, Barbara J., Forni, Lui G., Ghosh, Erina, Goldstein, Stuart L., Kane-Gill, Sandra L., Koola, Jejo, Koyner, Jay L., Liu, Mei, Murugan, Raghavan, Nadkarni, Girish N., Neyra, Javier A., Ninan, Jacob, Ostermann, Marlies, Pannu, Neesh, Rashidi, Parisa, Ronco, Claudio, Rosner, Mitchell H., Selby, Nicholas M., Shickel, Benjamin, Singh, Karandeep, Soranno, Danielle E., Sutherland, Scott M., Bihorac, Azra, and Mehta, Ravindra L.

Published
2023
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19. Advancing Genetic Testing in Kidney Diseases: Report From a National Kidney Foundation Working Group

Author

Ars, Elisabet, Reza Bekheirnia, Mir, Bier, Louise, Bleyer, Anthony J., Sr., Fuller, Lindsey J., Halbritter, Jan, Harris, Peter C., Kiryluk, Krzysztof, Knoers, Nine V.A.M., Kopp, Jeffrey B., Kramer, Holly, Lagas, Sharon S., Lieske, John C., Lu, Weining, Mannon, Roslyn B., Markowitz, Glen, Moe, Orson W., Nadkarni, Girish N., Nast, Cynthia C., Parekh, Rulan S., Pei, York, Reed, Katie, Rehm, Heidi L., Richards, Denay J., Roberts, Mary-Beth, Sabatello, Maya, Salant, David J., Sampson, Matthew G., Sanna-Cherchi, Simone, Santoriello, Dominick, Sedor, John R., Sneddon, Tam P., Watnick, Terry, Wilfond, Benjamin S., Williams, Winfred W., Wong, Craig S., Franceschini, Nora, Feldman, David L., Berg, Jonathan S., Besse, Whitney, Chang, Alexander R., Dahl, Neera K., Gbadegesin, Rasheed, Pollak, Martin R., Rasouly, Hila Milo, Smith, Richard J.H., Winkler, Cheryl A., and Gharavi, Ali G.

Published
2024
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20. Insights From a Large-Scale Whole-Genome Sequencing Study of Systolic Blood Pressure, Diastolic Blood Pressure, and Hypertension

Author

Kelly, Tanika N, Sun, Xiao, He, Karen Y, Brown, Michael R, Taliun, Sarah A Gagliano, Hellwege, Jacklyn N, Irvin, Marguerite R, Mi, Xuenan, Brody, Jennifer A, Franceschini, Nora, Guo, Xiuqing, Hwang, Shih-Jen, de Vries, Paul S, Gao, Yan, Moscati, Arden, Nadkarni, Girish N, Yanek, Lisa R, Elfassy, Tali, Smith, Jennifer A, Chung, Ren-Hua, Beitelshees, Amber L, Patki, Amit, Aslibekyan, Stella, Blobner, Brandon M, Peralta, Juan M, Assimes, Themistocles L, Palmas, Walter R, Liu, Chunyu, Bress, Adam P, Huang, Zhijie, Becker, Lewis C, Hwa, Chii-Min, O’Connell, Jeffrey R, Carlson, Jenna C, Warren, Helen R, Das, Sayantan, Giri, Ayush, Martin, Lisa W, Johnson, W Craig, Fox, Ervin R, Bottinger, Erwin P, Razavi, Alexander C, Vaidya, Dhananjay, Chuang, Lee-Ming, Chang, Yen-Pei C, Naseri, Take, Jain, Deepti, Kang, Hyun Min, Hung, Adriana M, Srinivasasainagendra, Vinodh, Snively, Beverly M, Gu, Dongfeng, Montasser, May E, Reupena, Muagututi A Sefuiva, Heavner, Benjamin D, LeFaive, Jonathon, Hixson, James E, Rice, Kenneth M, Wang, Fei Fei, Nielsen, Jonas B, Huang, Jianfeng, Khan, Alyna T, Zhou, Wei, Nierenberg, Jovia L, Laurie, Cathy C, Armstrong, Nicole D, Shi, Mengyao, Pan, Yang, Stilp, Adrienne M, Emery, Leslie, Wong, Quenna, Hawley, Nicola L, Minster, Ryan L, Curran, Joanne E, Munroe, Patricia B, Weeks, Daniel E, North, Kari E, Tracy, Russell P, Kenny, Eimear E, Shimbo, Daichi, Chakravarti, Aravinda, Rich, Stephen S, Reiner, Alex P, Blangero, John, Redline, Susan, Mitchell, Braxton D, Rao, Dabeeru C, Chen, Yii-Der Ida, Kardia, Sharon LR, Kaplan, Robert C, Mathias, Rasika A, He, Jiang, Psaty, Bruce M, Fornage, Myriam, Loos, Ruth JF, Correa, Adolfo, Boerwinkle, Eric, Rotter, Jerome I, Kooperberg, Charles, and Edwards, Todd L

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Human Genome, Genetics, 2.1 Biological and endogenous factors, Aetiology, Cardiovascular, Good Health and Well Being, Blood Pressure, Genome-Wide Association Study, Genomics, Humans, Hypertension, Polymorphism, Single Nucleotide, Precision Medicine, allele, blood pressure, genome, hypertension, whole genome sequencing, Samoan Obesity, Lifestyle, and Genetic Adaptations Study (OLaGA) Group, ‡ NHLBI Trans-Omics for Precision Medicine TOPMed) Consortium, Cardiorespiratory Medicine and Haematology, Public Health and Health Services, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundThe availability of whole-genome sequencing data in large studies has enabled the assessment of coding and noncoding variants across the allele frequency spectrum for their associations with blood pressure.MethodsWe conducted a multiancestry whole-genome sequencing analysis of blood pressure among 51 456 Trans-Omics for Precision Medicine and Centers for Common Disease Genomics program participants (stage-1). Stage-2 analyses leveraged array data from UK Biobank (N=383 145), Million Veteran Program (N=318 891), and Reasons for Geographic and Racial Differences in Stroke (N=10 643) participants, along with whole-exome sequencing data from UK Biobank (N=199 631) participants.ResultsTwo blood pressure signals achieved genome-wide significance in meta-analyses of stage-1 and stage-2 single variant findings (P

Published
2022

21. Longitudinal Plasma Metabolome Patterns and Relation to Kidney Function and Proteinuria in Pediatric CKD

Author

Lee, Arthur M., Xu, Yunwen, Hu, Jian, Xiao, Rui, Hooper, Stephen R., Hartung, Erum A., Coresh, Josef, Rhee, Eugene P., Vasan, Ramachandran S., Kimmel, Paul L., Warady, Bradley A., Furth, Susan L., Denburg, Michelle R., Abraham, Alison, Anderson, Amanda, Ballard, Shawn, Bonventre, Joseph, Clish, Clary, Collins, Heather, Coca, Steven, Coresh, Josef, Deo, Rajat, Denburg, Michelle, Dubin, Ruth, Feldman, Harold I., Ferket, Bart S., Foster, Meredith, Furth, Susan, Ganz, Peter, Gossett, Daniel, Grams, Morgan, Greenberg, Jason, Gutiérrez, Orlando M., Hostetter, Tom, Inker, Lesley A., Ix, Joachim, Kimmel, Paul L., Klein, Jon, Levey, Andrew S., Massaro, Joseph, McMahon, Gearoid, Mifflin, Theodore, Nadkarni, Girish N., Parikh, Chirag, Ramachandran, Vasan S., Rebholz, Casey, Rhee, Eugene, Rovin, Brad, Sarnak, Mark, Sabbisetti, Venkata, Schelling, Jeffrey, Seegmiller, Jesse, Shlipak, Michael G., Shou, Haochang, Tin, Adriene, Waikar, Sushrut, Warady, Bradley, Whitehead, Krista, and Xie, Dawei

Published
2024
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25. Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection

Author

Paranjpe, Ishan, Jayaraman, Pushkala, Su, Chen-Yang, Zhou, Sirui, Chen, Steven, Thompson, Ryan, Del Valle, Diane Marie, Kenigsberg, Ephraim, Zhao, Shan, Jaladanki, Suraj, Chaudhary, Kumardeep, Ascolillo, Steven, Vaid, Akhil, Gonzalez-Kozlova, Edgar, Kauffman, Justin, Kumar, Arvind, Paranjpe, Manish, Hagan, Ross O., Kamat, Samir, Gulamali, Faris F., Xie, Hui, Harris, Joceyln, Patel, Manishkumar, Argueta, Kimberly, Batchelor, Craig, Nie, Kai, Dellepiane, Sergio, Scott, Leisha, Levin, Matthew A., He, John Cijiang, Suarez-Farinas, Mayte, Coca, Steven G., Chan, Lili, Azeloglu, Evren U., Schadt, Eric, Beckmann, Noam, Gnjatic, Sacha, Merad, Miram, Kim-Schulze, Seunghee, Richards, Brent, Glicksberg, Benjamin S., Charney, Alexander W., and Nadkarni, Girish N.

Published
2023
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27. Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network

Author

Pacheco, Jennifer A., Rasmussen, Luke V., Wiley, Jr., Ken, Person, Thomas Nate, Cronkite, David J., Sohn, Sunghwan, Murphy, Shawn, Gundelach, Justin H., Gainer, Vivian, Castro, Victor M., Liu, Cong, Mentch, Frank, Lingren, Todd, Sundaresan, Agnes S., Eickelberg, Garrett, Willis, Valerie, Furmanchuk, Al’ona, Patel, Roshan, Carrell, David S., Deng, Yu, Walton, Nephi, Satterfield, Benjamin A., Kullo, Iftikhar J., Dikilitas, Ozan, Smith, Joshua C., Peterson, Josh F., Shang, Ning, Kiryluk, Krzysztof, Ni, Yizhao, Li, Yikuan, Nadkarni, Girish N., Rosenthal, Elisabeth A., Walunas, Theresa L., Williams, Marc S., Karlson, Elizabeth W., Linder, Jodell E., Luo, Yuan, Weng, Chunhua, and Wei, WeiQi

Published
2023
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29. Type 2 Diabetes Partitioned Polygenic Scores Associate With Disease Outcomes in 454,193 Individuals Across 13 Cohorts

Author

DiCorpo, Daniel, LeClair, Jessica, Cole, Joanne B, Sarnowski, Chloé, Ahmadizar, Fariba, Bielak, Lawrence F, Blokstra, Anneke, Bottinger, Erwin P, Chaker, Layal, Chen, Yii-Der I, Chen, Ye, de Vries, Paul S, Faquih, Tariq, Ghanbari, Mohsen, Gudmundsdottir, Valborg, Guo, Xiuqing, Hasbani, Natalie R, Ibi, Dorina, Ikram, M Arfan, Kavousi, Maryam, Leonard, Hampton L, Leong, Aaron, Mercader, Josep M, Morrison, Alanna C, Nadkarni, Girish N, Nalls, Mike A, Noordam, Raymond, Preuss, Michael, Smith, Jennifer A, Trompet, Stella, Vissink, Petra, Yao, Jie, Zhao, Wei, Boerwinkle, Eric, Goodarzi, Mark O, Gudnason, Vilmundur, Jukema, J Wouter, Kardia, Sharon LR, Loos, Ruth JF, Liu, Ching-Ti, Manning, Alisa K, Mook-Kanamori, Dennis, Pankow, James S, Picavet, H Susan J, Sattar, Naveed, Simonsick, Eleanor M, Verschuren, WM Monique, van Dijk, Ko Willems, Florez, Jose C, Rotter, Jerome I, Meigs, James B, Dupuis, Josée, and Udler, Miriam S

Subjects
Epidemiology, Biomedical and Clinical Sciences, Health Sciences, Genetics, Obesity, Heart Disease - Coronary Heart Disease, Liver Disease, Heart Disease, Cardiovascular, Digestive Diseases, Diabetes, Nutrition, Prevention, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Good Health and Well Being, Alleles, Cross-Sectional Studies, Diabetes Mellitus, Type 2, Genetic Loci, Humans, Pharmaceutical Preparations, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences, Health sciences
Abstract

ObjectiveType 2 diabetes (T2D) has heterogeneous patient clinical characteristics and outcomes. In previous work, we investigated the genetic basis of this heterogeneity by clustering 94 T2D genetic loci using their associations with 47 diabetes-related traits and identified five clusters, termed β-cell, proinsulin, obesity, lipodystrophy, and liver/lipid. The relationship between these clusters and individual-level metabolic disease outcomes has not been assessed.Research design and methodsHere we constructed individual-level partitioned polygenic scores (pPS) for these five clusters in 12 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (n = 454,193) and tested for cross-sectional association with T2D-related outcomes, including blood pressure, renal function, insulin use, age at T2D diagnosis, and coronary artery disease (CAD).ResultsDespite all clusters containing T2D risk-increasing alleles, they had differential associations with metabolic outcomes. Increased obesity and lipodystrophy cluster pPS, which had opposite directions of association with measures of adiposity, were both significantly associated with increased blood pressure and hypertension. The lipodystrophy and liver/lipid cluster pPS were each associated with CAD, with increasing and decreasing effects, respectively. An increased liver/lipid cluster pPS was also significantly associated with reduced renal function. The liver/lipid cluster includes known loci linked to liver lipid metabolism (e.g., GCKR, PNPLA3, and TM6SF2), and these findings suggest that cardiovascular disease risk and renal function may be impacted by these loci through their shared disease pathway.ConclusionsOur findings support that genetically driven pathways leading to T2D also predispose differentially to clinical outcomes.

Published
2022

30. Polygenic prediction of preeclampsia and gestational hypertension

Author

Honigberg, Michael C., Truong, Buu, Khan, Raiyan R., Xiao, Brenda, Bhatta, Laxmi, Vy, Ha My T., Guerrero, Rafael F., Schuermans, Art, Selvaraj, Margaret Sunitha, Patel, Aniruddh P., Koyama, Satoshi, Cho, So Mi Jemma, Vellarikkal, Shamsudheen Karuthedath, Trinder, Mark, Urbut, Sarah M., Gray, Kathryn J., Brumpton, Ben M., Patil, Snehal, Zöllner, Sebastian, Antopia, Mariah C., Saxena, Richa, Nadkarni, Girish N., Do, Ron, Yan, Qi, Pe’er, Itsik, Verma, Shefali Setia, Gupta, Rajat M., Haas, David M., Martin, Hilary C., van Heel, David A., Laisk, Triin, and Natarajan, Pradeep

Published
2023
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34. Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients

Author

Wanyan, Tingyi, Honarvar, Hossein, Jaladanki, Suraj K., Zang, Chengxi, Naik, Nidhi, Somani, Sulaiman, De Freitas, Jessica K., Paranjpe, Ishan, Vaid, Akhil, Miotto, Riccardo, Nadkarni, Girish N., Zitnik, Marinka, ArifulAzad, Wang, Fei, Ding, Ying, and Glicksberg, Benjamin S.

Subjects
Computer Science - Machine Learning
Abstract

Machine Learning (ML) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. This has been a major issue for developing ML models for the coronavirus-disease 2019 (COVID-19) pandemic where data is highly imbalanced, particularly within electronic health records (EHR) research. Conventional approaches in ML use cross-entropy loss (CEL) that often suffers from poor margin classification. For the first time, we show that contrastive loss (CL) improves the performance of CEL especially for imbalanced EHR data and the related COVID-19 analyses. This study has been approved by the Institutional Review Board at the Icahn School of Medicine at Mount Sinai. We use EHR data from five hospitals within the Mount Sinai Health System (MSHS) to predict mortality, intubation, and intensive care unit (ICU) transfer in hospitalized COVID-19 patients over 24 and 48 hour time windows. We train two sequential architectures (RNN and RETAIN) using two loss functions (CEL and CL). Models are tested on full sample data set which contain all available data and restricted data set to emulate higher class imbalance.CL models consistently outperform CEL models with the restricted data set on these tasks with differences ranging from 0.04 to 0.15 for AUPRC and 0.05 to 0.1 for AUROC. For the restricted sample, only the CL model maintains proper clustering and is able to identify important features, such as pulse oximetry. CL outperforms CEL in instances of severe class imbalance, on three EHR outcomes with respect to three performance metrics: predictive power, clustering, and feature importance. We believe that the developed CL framework can be expanded and used for EHR ML work in general.

Published
2021

36. Rare Coding Variants Associated With Electrocardiographic Intervals Identify Monogenic Arrhythmia Susceptibility Genes: A Multi-Ancestry Analysis.

Author

Choi, Seung Hoan, Jurgens, Sean J, Haggerty, Christopher M, Hall, Amelia W, Halford, Jennifer L, Morrill, Valerie N, Weng, Lu-Chen, Lagerman, Braxton, Mirshahi, Tooraj, Pettinger, Mary, Guo, Xiuqing, Lin, Henry J, Alonso, Alvaro, Soliman, Elsayed Z, Kornej, Jelena, Lin, Honghuang, Moscati, Arden, Nadkarni, Girish N, Brody, Jennifer A, Wiggins, Kerri L, Cade, Brian E, Lee, Jiwon, Austin-Tse, Christina, Blackwell, Tom, Chaffin, Mark D, Lee, Christina J-Y, Rehm, Heidi L, Roselli, Carolina, Regeneron Genetics Center, Redline, Susan, Mitchell, Braxton D, Sotoodehnia, Nona, Psaty, Bruce M, Heckbert, Susan R, Loos, Ruth JF, Vasan, Ramachandran S, Benjamin, Emelia J, Correa, Adolfo, Boerwinkle, Eric, Arking, Dan E, Rotter, Jerome I, Rich, Stephen S, Whitsel, Eric A, Perez, Marco, Kooperberg, Charles, Fornwalt, Brandon K, Lunetta, Kathryn L, Ellinor, Patrick T, Lubitz, Steven A, and NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium

Subjects
Regeneron Genetics Center, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Humans, Long QT Syndrome, Death, Sudden, Cardiac, Genetic Predisposition to Disease, Electrocardiography, Heterozygote, Female, Male, Genetic Variation, Exome Sequencing, death, sudden, cardiac, epidemiology, genetics, genome, population, Clinical Research, Cardiovascular, Genetics, Heart Disease, Human Genome, Biotechnology, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, death, sudden, cardiac
Abstract

BackgroundAlterations in electrocardiographic (ECG) intervals are well-known markers for arrhythmia and sudden cardiac death (SCD) risk. While the genetics of arrhythmia syndromes have been studied, relations between electrocardiographic intervals and rare genetic variation at a population level are poorly understood.MethodsUsing a discovery sample of 29 000 individuals with whole-genome sequencing from Trans-Omics in Precision Medicine and replication in nearly 100 000 with whole-exome sequencing from the UK Biobank and MyCode, we examined associations between low-frequency and rare coding variants with 5 routinely measured electrocardiographic traits (RR, P-wave, PR, and QRS intervals and corrected QT interval).ResultsWe found that rare variants associated with population-based electrocardiographic intervals identify established monogenic SCD genes (KCNQ1, KCNH2, and SCN5A), a controversial monogenic SCD gene (KCNE1), and novel genes (PAM and MFGE8) involved in cardiac conduction. Loss-of-function and pathogenic SCN5A variants, carried by 0.1% of individuals, were associated with a nearly 6-fold increased odds of the first-degree atrioventricular block (P=8.4×10-5). Similar variants in KCNQ1 and KCNH2 (0.2% of individuals) were associated with a 23-fold increased odds of marked corrected QT interval prolongation (P=4×10-25), a marker of SCD risk. Incomplete penetrance of such deleterious variation was common as over 70% of carriers had normal electrocardiographic intervals.ConclusionsOur findings indicate that large-scale high-depth sequence data and electrocardiographic analysis identifies monogenic arrhythmia susceptibility genes and rare variants with large effects. Known pathogenic variation in conventional arrhythmia and SCD genes exhibited incomplete penetrance and accounted for only a small fraction of marked electrocardiographic interval prolongation.

Published
2021

37. Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline

Author

Gorski, Mathias, Jung, Bettina, Li, Yong, Matias-Garcia, Pamela R, Wuttke, Matthias, Coassin, Stefan, Thio, Chris HL, Kleber, Marcus E, Winkler, Thomas W, Wanner, Veronika, Chai, Jin-Fang, Chu, Audrey Y, Cocca, Massimiliano, Feitosa, Mary F, Ghasemi, Sahar, Hoppmann, Anselm, Horn, Katrin, Li, Man, Nutile, Teresa, Scholz, Markus, Sieber, Karsten B, Teumer, Alexander, Tin, Adrienne, Wang, Judy, Tayo, Bamidele O, Ahluwalia, Tarunveer S, Almgren, Peter, Bakker, Stephan JL, Banas, Bernhard, Bansal, Nisha, Biggs, Mary L, Boerwinkle, Eric, Bottinger, Erwin P, Brenner, Hermann, Carroll, Robert J, Chalmers, John, Chee, Miao-Li, Chee, Miao-Ling, Cheng, Ching-Yu, Coresh, Josef, de Borst, Martin H, Degenhardt, Frauke, Eckardt, Kai-Uwe, Endlich, Karlhans, Franke, Andre, Freitag-Wolf, Sandra, Gampawar, Piyush, Gansevoort, Ron T, Ghanbari, Mohsen, Gieger, Christian, Hamet, Pavel, Ho, Kevin, Hofer, Edith, Holleczek, Bernd, Foo, Valencia Hui Xian, Hutri-Kähönen, Nina, Hwang, Shih-Jen, Ikram, M Arfan, Josyula, Navya Shilpa, Kähönen, Mika, Khor, Chiea-Chuen, Koenig, Wolfgang, Kramer, Holly, Krämer, Bernhard K, Kühnel, Brigitte, Lange, Leslie A, Lehtimäki, Terho, Lieb, Wolfgang, Loos, Ruth JF, Lukas, Mary Ann, Lyytikäinen, Leo-Pekka, Meisinger, Christa, Meitinger, Thomas, Melander, Olle, Milaneschi, Yuri, Mishra, Pashupati P, Mononen, Nina, Mychaleckyj, Josyf C, Nadkarni, Girish N, Nauck, Matthias, Nikus, Kjell, Ning, Boting, Nolte, Ilja M, O’Donoghue, Michelle L, Orho-Melander, Marju, Pendergrass, Sarah A, Penninx, Brenda WJH, Preuss, Michael H, Psaty, Bruce M, Raffield, Laura M, Raitakari, Olli T, Rettig, Rainer, Rheinberger, Myriam, Rice, Kenneth M, Rosenkranz, Alexander R, Rossing, Peter, Rotter, Jerome I, Sabanayagam, Charumathi, Schmidt, Helena, and Schmidt, Reinhold

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Prevention, Human Genome, Kidney Disease, Rare Diseases, Genetics, Aetiology, 2.1 Biological and endogenous factors, Renal and urogenital, AMP-Activated Protein Kinases, Creatinine, Genome-Wide Association Study, Glomerular Filtration Rate, Humans, Kidney, Protein Disulfide-Isomerases, United Kingdom, acute kidney injury, end-stage kidney disease, genome-wide association study, rapid eGFRcrea decline, Lifelines Cohort Study, Regeneron Genetics Center, Urology & Nephrology, Clinical sciences
Abstract

Rapid decline of glomerular filtration rate estimated from creatinine (eGFRcrea) is associated with severe clinical endpoints. In contrast to cross-sectionally assessed eGFRcrea, the genetic basis for rapid eGFRcrea decline is largely unknown. To help define this, we meta-analyzed 42 genome-wide association studies from the Chronic Kidney Diseases Genetics Consortium and United Kingdom Biobank to identify genetic loci for rapid eGFRcrea decline. Two definitions of eGFRcrea decline were used: 3 mL/min/1.73m2/year or more ("Rapid3"; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25% or more and eGFRcrea under 60 mL/min/1.73m2 at follow-up among those with eGFRcrea 60 mL/min/1.73m2 or more at baseline ("CKDi25"; encompassing 19,901 cases, 175,244 controls). Seven independent variants were identified across six loci for Rapid3 and/or CKDi25: consisting of five variants at four loci with genome-wide significance (near UMOD-PDILT (2), PRKAG2, WDR72, OR2S2) and two variants among 265 known eGFRcrea variants (near GATM, LARP4B). All these loci were novel for Rapid3 and/or CKDi25 and our bioinformatic follow-up prioritized variants and genes underneath these loci. The OR2S2 locus is novel for any eGFRcrea trait including interesting candidates. For the five genome-wide significant lead variants, we found supporting effects for annual change in blood urea nitrogen or cystatin-based eGFR, but not for GATM or LARP4B. Individuals at high compared to those at low genetic risk (8-14 vs. 0-5 adverse alleles) had a 1.20-fold increased risk of acute kidney injury (95% confidence interval 1.08-1.33). Thus, our identified loci for rapid kidney function decline may help prioritize therapeutic targets and identify mechanisms and individuals at risk for sustained deterioration of kidney function.

Published
2021

38. Circulating Metabolomic Associations with Neurocognitive Outcomes in Pediatric CKD

Author

Lee, Arthur M., Xu, Yunwen, Hooper, Stephen R., Abraham, Alison G., Hu, Jian, Xiao, Rui, Matheson, Matthew B., Brunson, Celina, Rhee, Eugene P., Coresh, Josef, Vasan, Ramachandran S., Schrauben, Sarah, Kimmel, Paul L., Warady, Bradley A., Furth, Susan L., Hartung, Erum A., Denburg, Michelle R., Abraham, Alison, Anderson, Amanda, Ballard, Shawn, Bonventre, Joseph, Clish, Clary, Collins, Heather, Coca, Steven, Coresh, Josef, Deo, Rajat, Denburg, Michelle, Dubin, Ruth, Feldman, Harold I., Ferket, Bart S., Foster, Meredith, Furth, Susan, Ganz, Peter, Gossett, Daniel, Grams, Morgan, Greenberg, Jason, Gutiérrez, Orlando M., Hostetter, Tom, Inker, Lesley A., Ix, Joachim, Kimmel, Paul L., Klein, Jon, Levey, Andrew S., Massaro, Joseph, McMahon, Gearoid, Mifflin, Theodore, Nadkarni, Girish N., Parikh, Chirag, Ramachandran, Vasan S., Rebholz, Casey, Rhee, Eugene, Rovin, Brad, Sarnak, M., Sabbisetti, Venkata, Schelling, Jeffrey, Seegmiller, Jesse, Shlipak, Michael G., Shou, Haochang, Tin, Adriene, Waikar, Sushrut, Warady, Bradley, Whitehead, Krista, and Xie, Dawei

Published
2024
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42. Characteristics and Outcomes of Individuals With Pre-existing Kidney Disease and COVID-19 Admitted to Intensive Care Units in the United States

Author

Flythe, Jennifer E, Assimon, Magdalene M, Tugman, Matthew J, Chang, Emily H, Gupta, Shruti, Shah, Jatan, Sosa, Marie Anne, Renaghan, Amanda DeMauro, Melamed, Michal L, Wilson, F Perry, Neyra, Javier A, Rashidi, Arash, Boyle, Suzanne M, Anand, Shuchi, Christov, Marta, Thomas, Leslie F, Edmonston, Daniel, Leaf, David E, Walther, Carl P, Anumudu, Samaya J, Arunthamakun, Justin, Kopecky, Kathleen F, Milligan, Gregory P, McCullough, Peter A, Nguyen, Thuy-Duyen, Shaefi, Shahzad, Krajewski, Megan L, Shankar, Sidharth, Pannu, Ameeka, Valencia, Juan D, Waikar, Sushrut S, Kibbelaar, Zoe A, Athavale, Ambarish M, Hart, Peter, Upadhyay, Shristi, Vohra, Ishaan, Green, Adam, Rachoin, Jean-Sebastien, Schorr, Christa A, Shea, Lisa, Edmonston, Daniel L, Mosher, Christopher L, Shehata, Alexandre M, Cohen, Zaza, Allusson, Valerie, Bambrick-Santoyo, Gabriela, Bhatti, Noor ul aain, Mehta, Bijal, Williams, Aquino, Brenner, Samantha K, Walters, Patricia, Go, Ronaldo C, Rose, Keith M, Chan, Lili, Mathews, Kusum S, Coca, Steven G, Altman, Deena R, Saha, Aparna, Soh, Howard, Wen, Huei Hsun, Bose, Sonali, Leven, Emily A, Wang, Jing G, Mosoyan, Gohar, Nadkarni, Girish N, Pattharanitima, Pattharawin, Gallagher, Emily J, Friedman, Allon N, Guirguis, John, Kapoor, Rajat, Meshberger, Christopher, Kelly, Katherine J, Parikh, Chirag R, Garibaldi, Brian T, Corona-Villalobos, Celia P, Wen, Yumeng, Menez, Steven, Malik, Rubab F, Cervantes, Carmen Elena, Gautam, Samir C, Mallappallil, Mary C, Ouyang, Jie, John, Sabu, Yap, Ernie, Melaku, Yohannes, Mohamed, Ibrahim, Bajracharya, Siddhartha, Puri, Isha, Thaxton, Mariah, Bhattacharya, Jyotsna, Wagner, John, Boudourakis, Leon, Nguyen, H Bryant, Ahoubim, Afshin, Kashani, Kianoush, Tehranian, Shahrzad, Sirganagari, Dheeraj Reddy, Guru, Pramod K, and Zhou, Yan

Subjects
Clinical Research, Cardiovascular, Clinical Trials and Supportive Activities, Kidney Disease, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, Renal and urogenital, Good Health and Well Being, Aged, COVID-19, Comorbidity, Critical Illness, Female, Hospital Mortality, Humans, Intensive Care Units, Kidney Function Tests, Male, Renal Dialysis, Renal Insufficiency, Chronic, Retrospective Studies, Risk Factors, SARS-CoV-2, Treatment Outcome, United States, STOP-COVID Investigators, COVID-19 outcome, Coronavirus disease 2019, altered mental status, chronic kidney disease, clinical course, clinical trajectory, critical illness, dialysis, end-stage kidney disease, end-stage renal disease, glomerular filtration rate, in-hospital mortality, intensive care unit, prognosis, renal function, severe COVID-19, severe acute respiratory syndrome coronavirus 2, Clinical Sciences, Public Health and Health Services, Urology & Nephrology
Abstract

Rationale & objectiveUnderlying kidney disease is an emerging risk factor for more severe coronavirus disease 2019 (COVID-19) illness. We examined the clinical courses of critically ill COVID-19 patients with and without pre-existing chronic kidney disease (CKD) and investigated the association between the degree of underlying kidney disease and in-hospital outcomes.Study designRetrospective cohort study.Settings & participants4,264 critically ill patients with COVID-19 (143 patients with pre-existing kidney failure receiving maintenance dialysis; 521 patients with pre-existing non-dialysis-dependent CKD; and 3,600 patients without pre-existing CKD) admitted to intensive care units (ICUs) at 68 hospitals across the United States.Predictor(s)Presence (vs absence) of pre-existing kidney disease.Outcome(s)In-hospital mortality (primary); respiratory failure, shock, ventricular arrhythmia/cardiac arrest, thromboembolic events, major bleeds, and acute liver injury (secondary).Analytical approachWe used standardized differences to compare patient characteristics (values>0.10 indicate a meaningful difference between groups) and multivariable-adjusted Fine and Gray survival models to examine outcome associations.ResultsDialysis patients had a shorter time from symptom onset to ICU admission compared to other groups (median of 4 [IQR, 2-9] days for maintenance dialysis patients; 7 [IQR, 3-10] days for non-dialysis-dependent CKD patients; and 7 [IQR, 4-10] days for patients without pre-existing CKD). More dialysis patients (25%) reported altered mental status than those with non-dialysis-dependent CKD (20%; standardized difference=0.12) and those without pre-existing CKD (12%; standardized difference=0.36). Half of dialysis and non-dialysis-dependent CKD patients died within 28 days of ICU admission versus 35% of patients without pre-existing CKD. Compared to patients without pre-existing CKD, dialysis patients had higher risk for 28-day in-hospital death (adjusted HR, 1.41 [95% CI, 1.09-1.81]), while patients with non-dialysis-dependent CKD had an intermediate risk (adjusted HR, 1.25 [95% CI, 1.08-1.44]).LimitationsPotential residual confounding.ConclusionsFindings highlight the high mortality of individuals with underlying kidney disease and severe COVID-19, underscoring the importance of identifying safe and effective COVID-19 therapies in this vulnerable population.

Published
2021

45. Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations

Author

Chen, Ming-Huei, Raffield, Laura M, Mousas, Abdou, Sakaue, Saori, Huffman, Jennifer E, Moscati, Arden, Trivedi, Bhavi, Jiang, Tao, Akbari, Parsa, Vuckovic, Dragana, Bao, Erik L, Zhong, Xue, Manansala, Regina, Laplante, Véronique, Chen, Minhui, Lo, Ken Sin, Qian, Huijun, Lareau, Caleb A, Beaudoin, Mélissa, Hunt, Karen A, Akiyama, Masato, Bartz, Traci M, Ben-Shlomo, Yoav, Beswick, Andrew, Bork-Jensen, Jette, Bottinger, Erwin P, Brody, Jennifer A, van Rooij, Frank JA, Chitrala, Kumaraswamynaidu, Cho, Kelly, Choquet, Hélène, Correa, Adolfo, Danesh, John, Di Angelantonio, Emanuele, Dimou, Niki, Ding, Jingzhong, Elliott, Paul, Esko, Tõnu, Evans, Michele K, Floyd, James S, Broer, Linda, Grarup, Niels, Guo, Michael H, Greinacher, Andreas, Haessler, Jeff, Hansen, Torben, Howson, Joanna MM, Huang, Qin Qin, Huang, Wei, Jorgenson, Eric, Kacprowski, Tim, Kähönen, Mika, Kamatani, Yoichiro, Kanai, Masahiro, Karthikeyan, Savita, Koskeridis, Fotis, Lange, Leslie A, Lehtimäki, Terho, Lerch, Markus M, Linneberg, Allan, Liu, Yongmei, Lyytikäinen, Leo-Pekka, Manichaikul, Ani, Martin, Hilary C, Matsuda, Koichi, Mohlke, Karen L, Mononen, Nina, Murakami, Yoshinori, Nadkarni, Girish N, Nauck, Matthias, Nikus, Kjell, Ouwehand, Willem H, Pankratz, Nathan, Pedersen, Oluf, Preuss, Michael, Psaty, Bruce M, Raitakari, Olli T, Roberts, David J, Rich, Stephen S, Rodriguez, Benjamin AT, Rosen, Jonathan D, Rotter, Jerome I, Schubert, Petra, Spracklen, Cassandra N, Surendran, Praveen, Tang, Hua, Tardif, Jean-Claude, Trembath, Richard C, Ghanbari, Mohsen, Völker, Uwe, Völzke, Henry, Watkins, Nicholas A, Zonderman, Alan B, Program, VA Million Veteran, Wilson, Peter WF, Li, Yun, Butterworth, Adam S, Gauchat, Jean-François, Chiang, Charleston WK, and Li, Bingshan

Subjects
Genetics, Good Health and Well Being, Asian People, Genome-Wide Association Study, HEK293 Cells, Humans, Interleukin-7, Mutation, Missense, Phenotype, Polymorphism, Single Nucleotide, White People, VA Million Veteran Program, interleukin-7, genetic architecture, fine-mapping, selective sweeps, polygenic trait score, phenome-wide association study, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Most loci identified by GWASs have been found in populations of European ancestry (EUR). In trans-ethnic meta-analyses for 15 hematological traits in 746,667 participants, including 184,535 non-EUR individuals, we identified 5,552 trait-variant associations at p < 5 × 10-9, including 71 novel associations not found in EUR populations. We also identified 28 additional novel variants in ancestry-specific, non-EUR meta-analyses, including an IL7 missense variant in South Asians associated with lymphocyte count in vivo and IL-7 secretion levels in vitro. Fine-mapping prioritized variants annotated as functional and generated 95% credible sets that were 30% smaller when using the trans-ethnic as opposed to the EUR-only results. We explored the clinical significance and predictive value of trans-ethnic variants in multiple populations and compared genetic architecture and the effect of natural selection on these blood phenotypes between populations. Altogether, our results for hematological traits highlight the value of a more global representation of populations in genetic studies.

Published
2020

46. Multi-ancestry GWAS of the electrocardiographic PR interval identifies 202 loci underlying cardiac conduction.

Author

Ntalla, Ioanna, Weng, Lu-Chen, Cartwright, James H, Hall, Amelia Weber, Sveinbjornsson, Gardar, Tucker, Nathan R, Choi, Seung Hoan, Chaffin, Mark D, Roselli, Carolina, Barnes, Michael R, Mifsud, Borbala, Warren, Helen R, Hayward, Caroline, Marten, Jonathan, Cranley, James J, Concas, Maria Pina, Gasparini, Paolo, Boutin, Thibaud, Kolcic, Ivana, Polasek, Ozren, Rudan, Igor, Araujo, Nathalia M, Lima-Costa, Maria Fernanda, Ribeiro, Antonio Luiz P, Souza, Renan P, Tarazona-Santos, Eduardo, Giedraitis, Vilmantas, Ingelsson, Erik, Mahajan, Anubha, Morris, Andrew P, Del Greco M, Fabiola, Foco, Luisa, Gögele, Martin, Hicks, Andrew A, Cook, James P, Lind, Lars, Lindgren, Cecilia M, Sundström, Johan, Nelson, Christopher P, Riaz, Muhammad B, Samani, Nilesh J, Sinagra, Gianfranco, Ulivi, Sheila, Kähönen, Mika, Mishra, Pashupati P, Mononen, Nina, Nikus, Kjell, Caulfield, Mark J, Dominiczak, Anna, Padmanabhan, Sandosh, Montasser, May E, O'Connell, Jeff R, Ryan, Kathleen, Shuldiner, Alan R, Aeschbacher, Stefanie, Conen, David, Risch, Lorenz, Thériault, Sébastien, Hutri-Kähönen, Nina, Lehtimäki, Terho, Lyytikäinen, Leo-Pekka, Raitakari, Olli T, Barnes, Catriona LK, Campbell, Harry, Joshi, Peter K, Wilson, James F, Isaacs, Aaron, Kors, Jan A, van Duijn, Cornelia M, Huang, Paul L, Gudnason, Vilmundur, Harris, Tamara B, Launer, Lenore J, Smith, Albert V, Bottinger, Erwin P, Loos, Ruth JF, Nadkarni, Girish N, Preuss, Michael H, Correa, Adolfo, Mei, Hao, Wilson, James, Meitinger, Thomas, Müller-Nurasyid, Martina, Peters, Annette, Waldenberger, Melanie, Mangino, Massimo, Spector, Timothy D, Rienstra, Michiel, van de Vegte, Yordi J, van der Harst, Pim, Verweij, Niek, Kääb, Stefan, Schramm, Katharina, Sinner, Moritz F, Strauch, Konstantin, Cutler, Michael J, Fatkin, Diane, London, Barry, Olesen, Morten, and Roden, Dan M

Subjects
Humans, Cardiovascular Diseases, Genetic Predisposition to Disease, Electrocardiography, Gene Expression, Multifactorial Inheritance, Quantitative Trait Loci, Female, Male, Arrhythmias, Cardiac, Genetic Variation, Genome-Wide Association Study, Genetic Loci, Endophenotypes, Arrhythmias, Cardiac
Abstract

The electrocardiographic PR interval reflects atrioventricular conduction, and is associated with conduction abnormalities, pacemaker implantation, atrial fibrillation (AF), and cardiovascular mortality. Here we report a multi-ancestry (N = 293,051) genome-wide association meta-analysis for the PR interval, discovering 202 loci of which 141 have not previously been reported. Variants at identified loci increase the percentage of heritability explained, from 33.5% to 62.6%. We observe enrichment for cardiac muscle developmental/contractile and cytoskeletal genes, highlighting key regulation processes for atrioventricular conduction. Additionally, 8 loci not previously reported harbor genes underlying inherited arrhythmic syndromes and/or cardiomyopathies suggesting a role for these genes in cardiovascular pathology in the general population. We show that polygenic predisposition to PR interval duration is an endophenotype for cardiovascular disease, including distal conduction disease, AF, and atrioventricular pre-excitation. These findings advance our understanding of the polygenic basis of cardiac conduction, and the genetic relationship between PR interval duration and cardiovascular disease.

Published
2020

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