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5. Plasma proteomics of acute tubular injury

Author

Schmidt, Insa M., Surapaneni, Aditya L., Zhao, Runqi, Upadhyay, Dhairya, Yeo, Wan-Jin, Schlosser, Pascal, Huynh, Courtney, Srivastava, Anand, Palsson, Ragnar, Kim, Taesoo, Stillman, Isaac E., Barwinska, Daria, Barasch, Jonathan, Eadon, Michael T., El-Achkar, Tarek M., Henderson, Joel, Moledina, Dennis G., Rosas, Sylvia E., Claudel, Sophie E., Verma, Ashish, Wen, Yumeng, Lindenmayer, Maja, Huber, Tobias B., Parikh, Samir V., Shapiro, John P., Rovin, Brad H., Stanaway, Ian B., Sathe, Neha A., Bhatraju, Pavan K., Coresh, Josef, Rhee, Eugene P., Grams, Morgan E., and Waikar, Sushrut S.

Published
2024
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7. Optical IRS Aided B5G V2V Solution for Road Safety Applications

Author

Pal, Tathagat, Singh, Gurinder, Bohara, Vivek Ashok, and Srivastava, Anand

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

In this work, we showcase the potential benefit of employing optical intelligent reflecting surfaces (O-IRS) for improving safety message dissemination for vehicular visible light communication (V-VLC) systems particularly at the road intersections. Buildings, roadside structures, signboards, and other impediments commonly hinder line-of-sight (LoS) communication between vehicles at urban crossroads scenarios. We propose using O-IRS at road intersection to improve the communication link's reliability. We compare the performance of proposed scheme with baseline scenarios such as optical relay and non line-of-sight (NLOS) road reflection (NRR) aided vehicle-to-vehicle (V2V) communication. From obtained results, it has been shown that O-IRS offers considerable performance enhancement as compared to the baseline scenarios. In particular, O-IRS can achieve longer communication range as compared to the optical relay aided V-VLC systems while ensuring desired quality-of-service (QoS)., Comment: This work has been accepted for presentation at IEEE ANTS 2022

Published
2022

9. Towards 6G-V2X: Aggregated RF-VLC for Ultra-Reliable and Low-Latency Autonomous Driving

Author

Singh, Gurinder, Srivastava, Anand, Bohara, Vivek Ashok, Noor-A-Rahim, Md, Liu, Zilong, and Pesch, Dirk

Subjects
Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control
Abstract

We are witnessing a transition to a new era where driverless cars will be pervasively connected to deliver significantly improved safety, traffic efficiency, and travel experiences. A diverse set of advanced vehicular use cases including connected autonomous vehicles will be made possible by building upon the emerging sixth-generation (6G) wireless networks. Among many 6G wireless technologies, the principal objective of this paper is to introduce the potential benefits of the hybrid integration of Vehicular Visible Light Communication (V VLC) and Vehicular Radio Frequency (V RF) communication systems by studying the impact of interference as well as various meteorological phenomenon viz. rain, fog and dry snow. In particular, we show that regardless of any meteorological impact, a properly configured link-aggregated hybrid V-VLC/V-RF system is capable of meeting stringent ultra high reliability (>99.999%) and ultra-low latency (<3 ms) requirements, making it a promising candidate for 6G Vehicle-to-Everything (V2X) Communications. To stimulate future research in the hybrid RF-VLC V2X space, we also highlight the potential challenges and research directions.

Published
2022

10. Cache Enabled UAV HetNets Access xHaul Coverage Analysis and Optimal Resource Partitioning

Author

R, Neetu R, Ghatak, Gourab, Srivastava, Anand, and Bohara, Vivek Ashok

Subjects
Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing
Abstract

We study an urban wireless network in which cache-enabled UAV-Access points (UAV-APs) and UAV-Base stations (UAV-BSs) are deployed to provide higher throughput and ad-hoc coverage to users on the ground. The cache-enabled UAV-APs route the user data to the core network via either terrestrial base stations (TBSs) or backhaul-enabled UAV-BSs through an xHaul link. First, we derive the association probabilities in the access and xHaul links. Interestingly, we show that to maximize the line-of-sight (LoS) unmanned aerial vehicle (UAV) association, densifying the UAV deployment may not be beneficial after a threshold. Then, we obtain the signal to interference noise ratio (SINR) coverage probability of the typical user in the access link and the tagged UAV-AP in the xHaul link, respectively. The SINR coverage analysis is employed to characterize the successful content delivery probability by jointly considering the probability of successful access and xHaul transmissions and successful cache-hit probability. We numerically optimize the distribution of frequency resources between the access and the xHaul links to maximize the successful content delivery to the users. For a given storage capacity at the UAVs, our study prescribes the network operator optimal bandwidth partitioning factors and dimensioning rules concerning the deployment of the UAV-APs.

Published
2022

11. A Tutorial on Decoding Techniques of Sparse Code Multiple Access

Author

Chaturvedi, Saumya, Liu, Zilong, Bohara, Vivek Ashok, Srivastava, Anand, and Xiao, Pei

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Sparse Code Multiple Access (SCMA) is a disruptive code-domain non-orthogonal multiple access (NOMA) scheme to enable \color{black}future massive machine-type communication networks. As an evolved variant of code division multiple access (CDMA), multiple users in SCMA are separated by assigning distinctive sparse codebooks (CBs). Efficient multiuser detection is carried out at the receiver by employing the message passing algorithm (MPA) that exploits the sparsity of CBs to achieve error performance approaching to that of the maximum likelihood receiver. In spite of numerous research efforts in recent years, a comprehensive one-stop tutorial of SCMA covering the background, the basic principles, and new advances, is still missing, to the best of our knowledge. To fill this gap and to stimulate more forthcoming research, we provide a holistic introduction to the principles of SCMA encoding, CB design, and MPA based decoding in a self-contained manner. As an ambitious paper aiming to push the limits of SCMA, we present a survey of advanced decoding techniques with brief algorithmic descriptions as well as several promising directions., Comment: arXiv admin note: text overlap with arXiv:2105.06860

Published
2022

13. Black and White Adults With CKD Hospitalized With Acute Kidney Injury: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study.

Author

Feldman, Harold, Srivastava, Anand, Bhat, Zeenat, Saraf, Santosh, Chen, Teresa, He, Jiang, Estrella, Michelle, Go, Alan, Hsu, Chi-Yuan, Yang, Jingrong, Derebail, Vimal, Liu, Kathleen, and Muiru, Anthony

Subjects
Acute kidney injury (AKI), Black race, CRIC, White race, chronic kidney disease (CKD), clinical risk factors, health care inequity, hospitalization, racial disparities, serum creatinine (Scr), Adult, Humans, Acute Kidney Injury, Angiotensin-Converting Enzyme Inhibitors, Angiotensins, Apolipoprotein L1, Cohort Studies, Creatinine, Glomerular Filtration Rate, Hospitalization, Prospective Studies, Renal Insufficiency, Chronic, Risk Factors, Sickle Cell Trait, Black People, White People
Abstract

RATIONALE & OBJECTIVE: Few studies have investigated racial disparities in acute kidney injury (AKI), in contrast to the extensive literature on racial differences in the risk of kidney failure. We sought to study potential differences in risk in the setting of chronic kidney disease (CKD). STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: We studied 2,720 self-identified Black or White participants with CKD enrolled in the Chronic Renal Insufficiency Cohort (CRIC) Study from July 1, 2013, to December 31, 2017. EXPOSURE: Self-reported race (Black vs White). OUTCOME: Hospitalized AKI (≥50% increase from nadir to peak serum creatinine). ANALYTICAL APPROACH: Cox regression models adjusting for demographics (age and sex), prehospitalization clinical risk factors (diabetes, blood pressure, cardiovascular disease, estimated glomerular filtration rate, proteinuria, receipt of angiotensin-converting enzyme inhibitors or angiotensin-receptor blockers), and socioeconomic status (insurance status and education level). In a subset of participants with genotype data, we adjusted for apolipoprotein L1 gene (APOL1) high-risk status and sickle cell trait. RESULTS: Black participants (n = 1,266) were younger but had a higher burden of prehospitalization clinical risk factors. The incidence rate of first AKI hospitalization among Black participants was 6.3 (95% CI, 5.5-7.2) per 100 person-years versus 5.3 (95% CI, 4.6-6.1) per 100 person-years among White participants. In an unadjusted Cox regression model, Black participants were at a modestly increased risk of incident AKI (HR, 1.22 [95% CI, 1.01-1.48]) compared with White participants. However, this risk was attenuated and no longer significant after adjusting for prehospitalization clinical risk factors (adjusted HR, 1.02 [95% CI, 0.83-1.25]). There were only 11 AKI hospitalizations among individuals with high-risk APOL1 risk status and 14 AKI hospitalizations among individuals with sickle cell trait. LIMITATIONS: Participants were limited to research volunteers and potentially not fully representative of all CKD patients. CONCLUSIONS: In this multicenter prospective cohort of CKD patients, racial disparities in AKI incidence were modest and were explained by differences in prehospitalization clinical risk factors.

Published
2022

14. Low-Complexity Codebook Design for SCMA based Visible Light Communication

Author

Chaturvedi, Saumya, Anwar, Dil Nashin, Bohara, Vivek Ashok, Srivastava, Anand, and Liu, Zilong

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Sparse code multiple access (SCMA), as a code-domain non-orthogonal multiple access (NOMA) scheme, has received considerable research attention for enabling massive connectivity in future wireless communication systems. In this paper, we present a novel codebook (CB) design for SCMA based visible light communication (VLC) system, which suffers from shot noise. In particular, we introduce an iterative algorithm for designing and optimizing CB by considering the impact of shot noise at the VLC receiver. Based on the proposed CB, we derive and analyze the theoretical bit error rate (BER) expression for the resultant SCMA-VLC system. The simulation results show that our proposed CBs outperform CBs in the existing literature for different loading factors with much less complexity. Further, the derived analytical BER expression well aligns with simulated results, especially in high signal power regions.

Published
2022

16. Matrix Metalloproteinase-2 and CKD Progression: The Chronic Renal Insufficiency Cohort (CRIC) Study

Author

Appel, Lawrence J., Cohen, Debbie, Dember, Laura, Go, Alan S., Lash, James P., Nelson, Robert G., Rahman, Mahboob, Rao, Panduranga S., Shah, Vallabh O., Unruh, Mark L., Baudier, Robin L., Orlandi, Paula F., Yang, Wei, Chen, Hsiang-Yu, Bansal, Nisha, Blackston, J. Walker, Chen, Jing, Deo, Rajat, Dobre, Mirela, He, Hua, He, Jiang, Ricardo, Ana C., Shafi, Tariq, Srivastava, Anand, Xie, Dawei, Susztak, Katalin, Feldman, Harold I., and Anderson, Amanda H.

Published
2024
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17. Metabolites Associated With Uremic Symptoms in Patients With CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study

Author

Dember, Laura M., Landis, J. Richard, Townsend, Raymond R., Appel, Lawrence, Fink, Jeffrey, Rahman, Mahboob, Horwitz, Edward J., Taliercio, Jonathan J., Rao, Panduranga, Sondheimer, James H., Lash, James P., Chen, Jing, Go, Alan S., Parsa, Afshin, Rankin, Tracy, Wulczyn, Kendra E., Shafi, Tariq, Anderson, Amanda, Rincon-Choles, Hernan, Clish, Clary B., Denburg, Michelle, Feldman, Harold I., He, Jiang, Hsu, Chi-yuan, Kelly, Tanika, Kimmel, Paul L., Mehta, Rupal, Nelson, Robert G., Ramachandran, Vasan, Ricardo, Ana, Shah, Vallabh O., Srivastava, Anand, Xie, Dawei, Rhee, Eugene P., and Kalim, Sahir

Published
2024
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19. Plasma Kidney Injury Molecule 1 in CKD: Findings From the Boston Kidney Biopsy Cohort and CRIC Studies

Author

Schmidt, Insa M, Srivastava, Anand, Sabbisetti, Venkata, McMahon, Gearoid M, He, Jiang, Chen, Jing, Kusek, John W, Taliercio, Jonathan, Ricardo, Ana C, Hsu, Chi-yuan, Kimmel, Paul L, Liu, Kathleen D, Mifflin, Theodore E, Nelson, Robert G, Vasan, Ramachandran S, Xie, Dawei, Zhang, Xiaoming, Palsson, Ragnar, Stillman, Isaac E, Rennke, Helmut G, Feldman, Harold I, Bonventre, Joseph V, Waikar, Sushrut S, and Investigators, Chronic Kidney Disease Biomarkers Consortium and the CRIC Study

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Kidney Disease, Clinical Research, Renal and urogenital, Good Health and Well Being, Biomarkers, Biopsy, Boston, Cohort Studies, Cross-Sectional Studies, Disease Progression, Humans, Kidney, Prospective Studies, Renal Insufficiency, Chronic, Chronic Kidney Disease Biomarkers Consortium and the CRIC Study Investigators, Public Health and Health Services, Urology & Nephrology, Clinical sciences
Abstract

Rationale & objectivePlasma kidney injury molecule 1 (KIM-1) is a sensitive marker of proximal tubule injury, but its association with risks of adverse clinical outcomes across a spectrum of kidney diseases is unknown.Study designProspective, observational cohort study.Setting & participants524 individuals enrolled into the Boston Kidney Biopsy Cohort (BKBC) Study undergoing clinically indicated native kidney biopsy with biopsy specimens adjudicated for semiquantitative scores of histopathology by 2 kidney pathologists and 3,800 individuals with common forms of chronic kidney disease (CKD) enrolled into the Chronic Renal Insufficiency Cohort (CRIC) Study.ExposureHistopathologic lesions and clinicopathologic diagnosis in cross-sectional analyses, baseline plasma KIM-1 levels in prospective analyses.OutcomesBaseline plasma KIM-1 levels in cross-sectional analyses, kidney failure (defined as initiation of kidney replacement therapy) and death in prospective analyses.Analytical approachMultivariable-adjusted linear regression models tested associations of plasma KIM-1 levels with histopathologic lesions and clinicopathologic diagnoses. Cox proportional hazards models tested associations of plasma KIM-1 levels with future kidney failure and death.ResultsIn the BKBC Study, higher plasma KIM-1 levels were associated with more severe acute tubular injury, tubulointerstitial inflammation, and more severe mesangial expansion after multivariable adjustment. Participants with diabetic nephropathy, glomerulopathies, and tubulointerstitial disease had significantly higher plasma KIM-1 levels after multivariable adjustment. In the BKBC Study, CKD in 124 participants progressed to kidney failure and 85 participants died during a median follow-up time of 5 years. In the CRIC Study, CKD in 1,153 participants progressed to kidney failure and 1,356 participants died during a median follow-up time of 11.5 years. In both cohorts, each doubling of plasma KIM-1 level was associated with an increased risk of kidney failure after multivariable adjustment (hazard ratios of 1.19 [95% CI, 1.03-1.38] and 1.10 [95% CI, 1.06-1.15] for BKBC and CRIC, respectively). There was no statistically significant association of plasma KIM-1 levels with death in either cohort.LimitationsGeneralizability and unmeasured confounding.ConclusionsPlasma KIM-1 is associated with underlying tubulointerstitial and mesangial lesions and progression to kidney failure in 2 cohort studies of individuals with kidney diseases.

Published
2022

20. Absence of long-term changes in urine biomarkers after AKI: findings from the CRIC study

Author

McCoy, Ian E, Hsu, Jesse Y, Bonventre, Joseph V, Parikh, Chirag R, Go, Alan S, Liu, Kathleen D, Ricardo, Ana C, Srivastava, Anand, Cohen, Debbie L, He, Jiang, Chen, Jing, Rao, Panduranga S, Muiru, Anthony N, and Hsu, Chi-yuan

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Kidney Disease, Clinical Research, Renal and urogenital, Good Health and Well Being, Acute Kidney Injury, Adult, Biomarkers, Creatinine, Humans, Kidney Function Tests, Renal Insufficiency, Chronic, Acute kidney injury, Chronic kidney disease, Urology & Nephrology, Clinical sciences, Health services and systems, Nursing
Abstract

BackgroundMechanisms by which AKI leads to CKD progression remain unclear. Several urine biomarkers have been identified as independent predictors of progressive CKD. It is unknown whether AKI may result in long-term changes in these urine biomarkers, which may mediate the effect of AKI on CKD progression.MethodsWe selected 198 episodes of hospitalized AKI (defined as peak/nadir inpatient serum creatinine values ≥ 1.5) among adult participants in the Chronic Renal Insufficiency Cohort (CRIC) Study. We matched the best non-AKI hospitalization (unique patients) for each AKI hospitalization using pre-hospitalization characteristics including eGFR and urine protein/creatinine ratio. Biomarkers were measured in banked urine samples collected at annual CRIC study visits.ResultsUrine biomarker measurements occurred a median of 7 months before and 5 months after hospitalization. There were no significant differences in the change in urine biomarker-to-creatinine ratio between the AKI and non-AKI groups: KIM-1/Cr + 9% vs + 7%, MCP-1/Cr + 4% vs + 1%, YKL-40/Cr + 7% vs -20%, EGF/Cr -11% vs -8%, UMOD/Cr -2% vs -7% and albumin/Cr + 17% vs + 13% (all p > 0.05).ConclusionIn this cohort of adults with CKD, AKI did not associate with long-term changes in urine biomarkers.

Published
2022

21. A Tutorial to Sparse Code Multiple Access

Author

Chaturvedi, Saumya, Liu, Zilong, Bohara, Vivek Ashok, Srivastava, Anand, and Xiao, Pei

Subjects
Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing
Abstract

Sparse Code Multiple Access (SCMA) is an enabling code-domain non-orthogonal multiple access (NOMA)scheme for massive connectivity and ultra low-latency in future machine-type communication networks. As an evolved variant of code division multiple access (CDMA), multiple users in SCMA are separated by assigning distinctive codebooks which display certain sparsity. At an SCMA receiver, efficient multiuser detection is carried out by employing the message passing algorithm (MPA) which exploits the sparsity of codebooks to achieve error rate performance approaching to that of the maximum likelihood receiver. Despite numerous research efforts on SCMA in recent years, a comprehensive and in-depth tutorial to SCMA is missing, to the best of our knowledge. To fill this gap and to stimulate more forthcoming research, we introduce the principles of SCMA encoding, codebook design, and MPA based decoding in a self-contained manner for layman researchers and engineers.

Published
2021

23. Precision Medicine in Nephrology: An Integrative Framework of Multidimensional Data in the Kidney Precision Medicine Project

Author

Lake, Blue, Zhang, Kun, Lecker, Stewart, Morales, Alexander, Bogen, Steve, Amodu, Afolarin A., Beck, Laurence, Henderson, Joel, Ilori, Titlayo, Maikhor, Shana, Onul, Ingrid, Schmidt, Insa, Verma, Ashish, Waikar, Sushrut, Yadati, Pranav, Yu, Guanghao, Colona, Mia R., McMahon, Gearoid, Hacohen, Nir, Greka, Anna, Hoover, Paul J., Marshall, Jamie L., Aulisio, Mark, Bush, William, Chen, Yijiang, Crawford, Dana, Madabhushi, Anant, Viswanathan, Vidya S., Bush, Lakeshia, Cooperman, Leslie, Gadegbeku, Crystal, Herlitz, Leal, Jolly, Stacey, Nguyen, Jane, O’Malley, Charles, O’Toole, John, Palmer, Ellen, Poggio, Emilio, Spates-Harden, Kassandra, Sedor, John, Sendrey, Dianna, Taliercio, Jonathan, Appelbaum, Paul, Balderes, Olivia, Barasch, Jonathan, Berroue, Cecilia, Bomback, Andrew, Canetta, Pietro A., D’Agati, Vivette, Kiryluk, Krzysztof, Kudose, Satoru, Mehl, Karla, Sabatello, Maya, Shang, Ning, de Pinho Gonçalves, Joana, Lardenoije, Roy, Migas, Lukasz, Van de Plas, Raf, Rennke, Helmut, Azeloglu, Evren, Campbell, Kirk, Coca, Steven, He, Cijang, He, John, Iyengar, Srinivas Ravi, Lefferts, Seanee, Nadkarni, Girish, Patel, Marissa, Tokita, Joji, Ward, Stephen, Xiong, Yuguang, Verdoes, Abraham, Sabo, Angela, Barwinska, Daria, Gisch, Debora Lidia, Williams, James, Kelly, Katherine, Dunn, Kenneth, Asghari, Mahla, Eadon, Michael, Ferkowicz, Michael, Dagher, Pierre, Ferreira, Ricardo Melo, Winfree, Seth, Bledsoe, Sharon, Wofford, Stephanie, El-Achkar, Tarek, Sutton, Timothy, Bowen, William, Cheng, Ying-Hua, Slade, Austen, Record, Elizabeth, Cheng, Yinghua, Borner, Katy, Herr, Bruce, Jain, Yashvardhan, Quardokus, Ellen, Atta, Mohamed, Bernard, Lauren, Menez, Steven, Parikh, Chirag, Corona Villalobos, Celia Pamela, Wang, Ashley, Wen, Yumeng, Xu, Alan, Chen, Sarah, Donohoe, Isabel, Johansen, Camille, Rosas, Sylvia, Sun, Jennifer, Ardayfio, Joseph, Bebiak, Jack, Campbell, Taneisha, Fox, Monica, Knight, Richard, Koewler, Robert, Pinkeney, Roy, Saul, John, Shpigel, Anna, Prasad, Pottumarthi, Madhavan, Sethu M., Parikh, Samir, Rovin, Brad, Shapiro, John P., Anderton, Christopher, Lukowski, Jessica, Pasa-Tolic, Ljiljana, Velickovic, Dusan, Oliver, George, Mao, Weiguang, Sealfon, Rachel, Troyanskaya, Olga, Pollack, Ari, Goltsev, Yury, Ginley, Brandon, Anjani, Kavya, Laszik, Zoltan G., Mukatash, Tariq, Nolan, Garry, Beyda, David, Bracamonte, Erika, Brosius, Frank, Campos, Baltazar, Marquez, Nicole, Mendoza, Katherine, Scott, Raymond, Thajudeen, Bijin, Tsosie, Rebecca, Woodhead, Gregory, Saunders, Milda, Alloway, Rita R., Lee, Paul J., Rike, Adele, Shi, Tiffany, Woodle, E. Steve, Bjornstad, Petter, Hsieh, Elena, Kendrick, Jessica, Pyle, Laura, Thurman, Joshua, Vinovskis, Carissa, Wrobel, Julia, Lucarelli, Nicholas, Sarder, Pinaki, Bui, James, Carmona-Powell; Ron Gaba, Eunice, Kelly, Tanika, Lash, James, Meza, Natalie, Redmond, Devona, Renteria, Amada, Ricardo, Ana, Setty, Suman, Srivastava, Anand, Alakwaa, Fadhl, Ascani, Heather, Balis, Ul, Bitzer, Markus, Blanc, Victoria, Bonevich, Nikki, Conser, Ninive, Demeke, Dawit, Dull, Rachel, Eddy, Sean, Frey, Renee, Hartman, John, He, Yongqun Oliver, Hodgin, Jeffrey, Kretzler, Matthias, Lienczewski, Chrysta, Luo, Jinghui, Mariani, Laura, McCown, Phillip, Menon, Rajasree, Nair, Viji, Otto, Edgar, Reamy, Rebecca, Rose, Michael, Schaub, Jennifer, Steck, Becky, Wright, Zachary, Coleman, Alyson, Henderson-Brown; Jerica Berge, Dorisann, Caramori, Maria Luiza, Adeyi, Oyedele, Nachman, Patrick, Safadi, Sami, Flanagan, Siobhan, Ma, Sisi, Klett, Susan, Wolf, Susan, Harindhanavudhi, Tasma, Rao, Via, Bream, Peter, Froment, Anne, Kelley, Sara, Mottl, Amy, Chaudhury; Evan Zeitler, Prabir Roy, Bender, Filitsa, Elder, Michele, Gilliam, Matthew, Hall, Daniel E., Kellum, John A., Murugan, Raghavan, Palevsky, Paul, Rosengart, Matthew, Tan, Roderick, Tublin, Mitchell, Winters, James, Bansal, Shweta, Montellano, Richard, Pamreddy, Annapurna, Sharma, Kumar, Venkatachalam, Manjeri, Ye, Hongping, Zhang, Guanshi, Basit, Mujeeb, Cai, Qi, Hendricks, Allen, Hedayati, Susan, Kermani, Asra, Lee, Simon C., Ma, Shihong, Miller, Richard Tyler, Moe, Orson W., Park, Harold, Patel, Jiten, Pillai, Anil, Sambandam, Kamalanathan, Torrealba, Jose, Toto, Robert D., Vazquez, Miguel, Wang, Nancy, Wen, Natasha, Zhang, Dianbo, Alpers, Charles, Berglund, Ashley, Berry, Brooke, Blank, Kristina, Brown, Keith, Carson, Jonas, Daniel, Stephen, de Boer, Ian H., Dighe, Ashveena L., Dowd, Frederick, Grewenow, Stephanie M., Himmelfarb, Jonathan, Hoofnagle, Andrew, Jefferson, Nichole, Larson, Brandon, Limonte, Christine, McClelland, Robyn, Mooney, Sean, Nam, Yunbi, Park, Christopher, Phuong, Jimmy, Rezaei, Kasra, Roberts, Glenda, Sarkisova, Natalya, Shankland, Stuart, Snyder, Jaime, Stutzke, Christy, Tuttle, Katherine, Wangperawong, Artit, Wilcox, Adam, Williams, Kayleen, Young, Bessie, Allen, Jamie, Caprioli, Richard M., de Caestecker, Mark, Djambazova, Katerina, Dufresne, Martin, Farrow, Melissa, Fogo, Agnes, Sharman, Kavya, Spraggins, Jeffrey, Basta, Jeannine, Conlon, Kristine, Diettman, Sabine M., Gaut, Joseph, Kaushal, Madhurima, Jain, Sanjay, Knoten, Amanda, Minor, Brittany, Nwanne, Gerald, Vijayan, Anitha, Zhang, Bo, Arora, Tanima, Cantley, Lloyd, Victoria Castro, Angela M., Kakade, Vijayakumar, Moeckel, Gilbert, Moledina, Dennis, Shaw, Melissa, Wilson, Francis P., El-Achkar, Tarek M., and Eadon, Michael T.

Published
2024
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27. Molecular Signatures of Glomerular Neovascularization in a Patient with Diabetic Kidney Disease

Author

Ferkowicz, Michael J., Verma, Ashish, Barwinska, Daria, Melo Ferreira, Ricardo, Henderson, Joel M., Kirkpatrick, Mary, Silva, Paolo S., Steenkamp, Devin W., Phillips, Carrie L., Waikar, Sushrut S., Sutton, Timothy A., Lake, Blue, Zhang, Kun, Lecker, Stewart, Morales, Alexander, Stillman, Isaac, Bogen, Steve, Amodu, Afolarin A., Beck, Laurence, Henderson, Joel, Ilori, Titlayo, Maikhor, Shana, Onul, Ingrid, Schmidt, Insa, Verma, Ashish, Waikar, Sushrut, Yadati, Pranav, Yu, Guanghao, Colona, Mia R., McMahon, Gearoid, Weins, Astrid, Hacohen, Nir, Greka, Anna, Hoover, Paul J., Marshall, Jamie L., Aulisio, Mark, Bush, William, Chen, Yijiang, Crawford, Dana, Madabhushi, Anant, Viswanathan, Vidya S., Bush, Lakeshia, Cooperman, Leslie, Gadegbeku, Crystal, Herlitz, Leal, Jolly, Stacey, Nguyen, Jane, O’Malley, Charles, O’Toole, John, Palmer, Ellen, Poggio, Emilio, Spates-Harden, Kassandra, Sedor, John, Sendrey, Dianna, Taliercio, Jonathan, Appelbaum, Paul, Balderes, Olivia, Barasch, Jonathan, Berroue, Cecilia, Bomback, Andrew, Canetta, Pietro A., D’Agati, Vivette, Kiryluk, Krzysztof, Kudose, Satoru, Mehl, Karla, Sabatello, Maya, Shang, Ning, Varela, German, de Pinho Gonçalves, Joana, Lardenoije, Roy, Migas, Lukasz, Van de Plas, Raf, Barisoni, Laura, Rennke, Helmut, Azeloglu, Evren, Campbell, Kirk, Coca, Steven, He, Cijang, He, John, Iyengar, Srinivas Ravi, Lefferts, Seanee, Nadkarni, Girish, Patel, Marissa, Tokita, Joji, Ward, Stephen, Xiong, Yuguang, Verdoes, Abraham, Sabo, Angela, Barwinska, Daria, Gisch, Debora Lidia, Williams, James, Kelly, Katherine, Dunn, Kenneth, Asghari, Mahla, Eadon, Michael, Ferkowicz, Michael, Dagher, Pierre, Ferreira, Ricardo Melo, Winfree, Seth, Bledsoe, Sharon, Wofford, Stephanie, El-Achkar, Tarek, Sutton, Timothy, Bowen, William, Cheng, Ying-Hua, Slade, Austen, Record, Elizabeth, Cheng, Yinghua, Borner, Katy, Herr, Bruce, Jain, Yashvardhan, Quardokus, Ellen, Atta, Mohamed, Bernard, Lauren, Menez, Steven, Parikh, Chirag, Corona Villalobos, Celia Pamela, Wang, Ashley, Wen, Yumeng, Xu, Alan, Chen, Sarah, Donohoe, Isabel, Johansen, Camille, Rosas, Sylvia, Sun, Jennifer, Ardayfio, Joseph, Bebiak, Jack, Brown, Keith, Campbell, Taneisha, Fox, Monica, Hayashi, Lynda, Jefferson, Nichole, Richard Knight, Jennifer Jones, Koewler, Robert, Pinkeney, Roy, Saul, John, Shpigel, Anna, Stutzke, Christy, Prasad, Pottumarthi, Madhavan, Sethu M., Parikh, Samir, Rovin, Brad, Shapiro, John P., Anderton, Christopher, Lukowski, Jessica, Pasa-Tolic, Ljiljana, Velickovic, Dusan, Oliver, George, Mao, Weiguang, Sealfon, Rachel, Troyanskaya, Olga, Wong, Aaron, Pollack, Ari, Goltsev, Yury, Ginley, Brandon, Lutnick, Brendon, Anjani, Kavya, Laszik, Zoltan G., Mukatash, Tariq, Nolan, Garry, Beyda, David, Bracamonte, Erika, Brosius, Frank, Campos, Baltazar, Marquez, Nicole, Mendoza, Katherine, Scott, Raymond, Thajudeen, Bijin, Tsosie, Rebecca, Woodhead, Gregory, Saunders, Milda, Alloway, Rita R., Lee, Paul J., Rike, Adele, Shi, Tiffany, Woodle, E. Steve, Bjornstad, Petter, Hsieh, Elena, Kendrick, Jessica, Pyle, Laura, Thurman, Joshua, Vinovskis, Carissa, Wrobel, Julia, Lucarelli, Nicholas, Sarder, Pinaki, Bui, James, Carmona-Powell, Eunice, Gaba, Ron, Kelly, Tanika, Lash, James, Meza, Natalie, Redmond, Devona, Renteria, Amada, Ricardo, Ana, Setty, Suman, Srivastava, Anand, Alakwaa, Fadhl, Ascani, Heather, Balis, Ul, Bitzer, Markus, Blanc, Victoria, Bonevich, Nikki, Conser, Ninive, Demeke, Dawit, Dull, Rachel, Eddy, Sean, Frey, Renee, Hartman, John, He, Yongqun Oliver, Hodgin, Jeffrey, Kretzler, Matthias, Lienczewski, Chrysta, Luo, Jinghui, Mariani, Laura, McCown, Phillip, Menon, Rajasree, Nair, Viji, Otto, Edgar, Reamy, Rebecca, Rose, Michael, Schaub, Jennifer, Steck, Becky, Wright, Zachary, Coleman, Alyson, Henderson-Brown, Dorisann, Berge, Jerica, Caramori, Maria Luiza, Adeyi, Oyedele, Nachman, Patrick, Safadi, Sami, Flanagan, Siobhan, Ma, Sisi, Klett, Susan, Wolf, Susan, Harindhanavudhi, Tasma, Rao, Via, Bream, Peter, Froment, Anne, Kelley, Sara, Mottl, Amy, Roy-Chaudhury, Prabir, Zeitler, Evan, Bender, Filitsa, Elder, Michele, Gilliam, Matthew, Hall, Daniel E., Kellum, John A., Murugan, Raghavan, Palevsky, Paul, Rosengart, Matthew, Tan, Roderick, Tublin, Mitchell, Winters, James, Bansal, Shweta, Montellano, Richard, Pamreddy, Annapurna, Sharma, Kumar, Venkatachalam, Manjeri, Ye, Hongping, Zhang, Guanshi, Basit, Mujeeb, Cai, Qi, Hendricks, Allen, Hedayati, Susan, and Asra

Published
2024
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28. Participant Experience with Protocol Research Kidney Biopsies in the Kidney Precision Medicine Project

Author

Victoria-Castro, Angela M., Corona-Villalobos, Celia P., Xu, Alan Y., Onul, Ingrid, Huynh, Courtney, Chen, Sarah W., Ugwuowo, Ugochukwu, Sarkisova, Natalya, Dighe, Ashveena L., Blank, Kristina N., Blanc, Victoria M., Rose, Michael P., Himmelfarb, Jonathan, de Boer, Ian H., Tuttle, Katherine R., Roberts, Glenda V., Alexandrov, Theodore, Alloway, Rita R., Alpers, Charles E., Amodu, Afolarin A., Anderton, Christopher R., Anjani, Kavya, Appelbaum, Paul, Ardayfio, Joseph, Arora, Tanima, Ascani, Heather, El-Achkar, Tarek M., Aulisio, Mark, Azeloglu, Evren U., Balderes, Olivia, Balis, Ulysses G.J., Bansal, Shweta, Barasch, Jonathan M., Bansal, Shweta, Barkell, Alex, Barwinska, Daria, Basit, Mujeeb, Basta, Jeanine, Bebiak, Jack, Beck, Laurence H., Bender, Filitsa, Berglund, Ashley, Bernard, Lauren, Berrouet, Cecilia, Berry, Brooke, Bjornstad, Petter M., Blanc, Victoria M., Blank, Kristina N., Bledsoe, Sharon, Boada, Patrick, Bogen, Steve, Bomback, Andrew S., Bonevich, Nikole, Borner, Katy, Brown, Keith, Bueckle, Andreas, Burg, Ashley R., Burgess, Adam, Bush, Lakeshia, Bush, William S., Campbell, Catherine E., Campbell, Taneisha, Canetta, Pietro A., Cantley, Lloyd G., Caprioli, Richard M., Carson, Jonas, Chen, Sarah, Chen, Yijiang M., Cheng, Yinghua, Cimino, Jim, Colona, Mia R., Conser, Ninive C., Cooperman, Leslie, Crawford, Dana C., DʼAgati, Vivette D., Dagher, Pierre C., Daniel, Stephen, Daratha, Kenn, de Boer, Ian H., Diettman, Sabine M., Dighe, Ashveena L., Donohoe, Isabel, Dowd, Frederick, Dunn, Kenneth W., Eadon, Michael T., Eddy, Sean, Elder, Michele M., Ferkowicz, Michael J., Frey, Renee, Gadegbeku, Crystal A., Gaut, Joseph P., Gilliam, Matthew, Ginley, Brandon, Gisch, Debora, Goltsev, Yury, Gonzalez-Vicente, Agustin, Greka, Anna, Grewenow, Stephanie M., Hacohen, Nir, Hall, Daniel E., Hansen, Jens, Hayashi, Lynda, He, Cijang, He, Yougqun, Hedayati, S. Susan, Henderson, Joel M., Hendricks, Allen H., Herlitz, Leal, Herr, Bruce W., Himmelfarb, Jonathan, Hodgin, Jeffrey B., Hoofnagle, Andrew N., Hoover, Paul J., Ilori, Titlayo, Iyengar, Ravi, Jain, Sanjay, Jain, Yashvardhan, Janowczyk, Andrew, Jefferson, Nichole, Johansen, Camille, Jolly, Stacey, Kakade, Vijaykumar R., Kellum, John A., Kelly, Katherine J., Kermani, Asra, Kiryluk, Krzysztof, Knight, Richard, Koewler, Robert, Kretzler, Matthias, Kudose, Satoru, Lake, Blue B., Larson, Brandon, Laszik, Zoltan G., Lecker, Stewart H., Lee, Paul J., Lee, Simon C., Lienczewski, Chrysta, Limonte, Christine, Lu, Christopher Y., Lucarelli, Nicholas, Lukowski, Jessica, Luo, Jinghui, Lutnick, Brendon, Ma, Shihong, Madabhushi, Anant, Madhavan, Sethu M., Maikhor, Shana, Mariani, Laura H., Marshall, Jamie L., McClelland, Robyn L., McMahon, Gearoid M., Mehl, Karla, Ferreira, Ricardo Melo, Menez, Steven, Menon, Rajasree, Miller, R. Tyler, Moe, Orson W., Moledina, Dennis, Montellano, Richard, Mooney, Sean D., Morales, Martha Catalina, Mukatash, Tariq, Murugan, Raghavan, Nam, Yunbi, Nguyen, Jane, Nolan, Garry, Oʼtoole, John, Oliver, George (Holt), Onul, Ingrid, Otto, Edgar, Palevsky, Paul M., Palmer, Ellen, Pamreddy, Annapurna, Parikh, Chirag R., Parikh, Samir, Park, Christopher, Park, Harold, Pasa-Tolic, Ljiljana, Patel, Jiten, Patterson, Nathan, Phuong, Jim, Pillai, Anil, Pinkeney, Roy, Poggio, Emilio, Pollack, Ari, Prasad, Pottumarthi, Pyle, Laura, Quardokus, Ellen M., Randhawa, Parmjeet, Rauchman, Michael I., Record, Elizabeth, Rennke, Helmut, Rezaei, Kasra, Rike, Adele, Rivera, Marcelino, Roberts, Glenda V., Rosas, Sylvia E., Rosenberg, Avi, Rosengart, Matthew, Rovin, Brad, Roy, Neil, Sabatello, Maya, Sambandam, Kamalanathan, Sarder, Pinaki, Sarkisova, Natalya, Sarwal, Minnie, Saul, John, Schaub, Jennifer, Schmidt, Insa, Sealfon, Rachel, Sedor, John, Sendrey, Dianna, Shang, Ning, Shankland, Stuart, Shapiro, John P., Sharma, Kumar, Sharman, Kavya, Shaw, Melissa M., Shi, Tiffany, Shpigel, Anna, Sigdel, Tara, Slade, Austen, Snyder, Jamie, Spates-Harden, Kassandra, Spraggins, Jeffrey M., Srivastava, Anand, Steck, Becky, Stillman, Isaac, Stutzke, Christy, Su, Jing, Sun, Jennifer, Sutton, Timothy A., Taliercio, Jonathan, Tan, Roderick, Torrealba, Jose, Toto, Robert D., Troyanskaya, Olga, Tublin, Mitchell, Tuttle, Katherine R., Ugwuowo, Ugochukwu, Valerius, M. Todd, Van de Plas, Raf, Varela, German, Vazquez, Miguel, Velickovic, Dusan, Venkatachalam, Manjeri, Verma, Ashish, Victoria-Castro, Angela M., Vijayan, Anitha, Corona-Villalobos, Celia P., Vinovskis, Carissa, Viswanathan, Vidya S., Vita, Tina, Waikar, Sushrut, Wang, Ashley, Wang, Ruikang, Wang, Nancy, Weins, Astrid, Wen, Natasha, Wen, Yumeng, Wilcox, Adam, Williams, James C., Jr., Kayleen Williams, Williams, Mark, Wilson, Francis P., Winfree, Seth, Winters, James, Wofford, Stephanie, Wong, Aaron, Woodle, E. Steve, Xiong, Yuguang, Xu, Alan, Yadati, Pranav, Ye, Hongping, Yu, Guanghao, Zhang, Dianbo, Zhang, Guanshi, and Zhang, Kun

Published
2024
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29. Machine Learning Prediction of Death in Critically Ill Patients With Coronavirus Disease 2019.

Author

Churpek, Matthew, Gupta, Shruti, Spicer, Alexandra, Hayek, Salim, Srivastava, Anand, Chan, Lili, Melamed, Michal, Brenner, Samantha, Radbel, Jared, Madhani-Lovely, Farah, Bhatraju, Pavan, Bansal, Anip, Green, Adam, Goyal, Nitender, Shaefi, Shahzad, Parikh, Chirag, Semler, Matthew, and Leaf, David

Subjects
artificial intelligence, coronavirus disease 2019, intensive care unit, machine learning
Abstract

OBJECTIVES: Critically ill patients with coronavirus disease 2019 have variable mortality. Risk scores could improve care and be used for prognostic enrichment in trials. We aimed to compare machine learning algorithms and develop a simple tool for predicting 28-day mortality in ICU patients with coronavirus disease 2019. DESIGN: This was an observational study of adult patients with coronavirus disease 2019. The primary outcome was 28-day inhospital mortality. Machine learning models and a simple tool were derived using variables from the first 48 hours of ICU admission and validated externally in independent sites and temporally with more recent admissions. Models were compared with a modified Sequential Organ Failure Assessment score, National Early Warning Score, and CURB-65 using the area under the receiver operating characteristic curve and calibration. SETTING: Sixty-eight U.S. ICUs. PATIENTS: Adults with coronavirus disease 2019 admitted to 68 ICUs in the United States between March 4, 2020, and June 29, 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The study included 5,075 patients, 1,846 (36.4%) of whom died by day 28. eXtreme Gradient Boosting had the highest area under the receiver operating characteristic curve in external validation (0.81) and was well-calibrated, while k-nearest neighbors were the lowest performing machine learning algorithm (area under the receiver operating characteristic curve 0.69). Findings were similar with temporal validation. The simple tool, which was created using the most important features from the eXtreme Gradient Boosting model, had a significantly higher area under the receiver operating characteristic curve in external validation (0.78) than the Sequential Organ Failure Assessment score (0.69), National Early Warning Score (0.60), and CURB-65 (0.65; p < 0.05 for all comparisons). Age, number of ICU beds, creatinine, lactate, arterial pH, and Pao2/Fio2 ratio were the most important predictors in the eXtreme Gradient Boosting model. CONCLUSIONS: eXtreme Gradient Boosting had the highest discrimination overall, and our simple tool had higher discrimination than a modified Sequential Organ Failure Assessment score, National Early Warning Score, and CURB-65 on external validation. These models could be used to improve triage decisions and clinical trial enrichment.

Published
2021

30. Association Between Kidney Clearance of Secretory Solutes and Cardiovascular Events: The Chronic Renal Insufficiency Cohort (CRIC) Study

Author

Chen, Yan, Zelnick, Leila R, Huber, Matthew P, Wang, Ke, Bansal, Nisha, Hoofnagle, Andrew N, Paranji, Rajan K, Heckbert, Susan R, Weiss, Noel S, Go, Alan S, Hsu, Chi-yuan, Feldman, Harold I, Waikar, Sushrut S, Mehta, Rupal C, Srivastava, Anand, Seliger, Stephen L, Lash, James P, Porter, Anna C, Raj, Dominic S, Kestenbaum, Bryan R, Investigators, CRIC Study, Appel, Lawrence J, He, Jiang, Rao, Panduranga S, Rahman, Mahboob, and Townsend, Raymond R

Subjects
Kidney Disease, Clinical Research, Cardiovascular, Prevention, Heart Disease, Renal and urogenital, Aged, Albuminuria, Chromatography, Liquid, Cohort Studies, Cresols, Female, Glomerular Filtration Rate, Glycine, Heart Failure, Humans, Incidence, Indican, Kidney Tubules, Kynurenic Acid, Male, Middle Aged, Myocardial Infarction, Organic Anion Transporters, Proportional Hazards Models, Prospective Studies, Pyridoxic Acid, Renal Insufficiency, Chronic, Ribonucleosides, Stroke, Sulfuric Acid Esters, Tandem Mass Spectrometry, Xanthines, CRIC Study Investigators, cardiovascular disease, chronic kidney disease, cinnamoylglycine, glomerular filtration rate, heart failure, indoxyl sulfate, isovalerylglycine, kynurenic acid, myocardial infarction, p-cresol sulfate, protein-bound, proximal tubule, pyridoxic acid, renal function, secretory solute clearance, stroke, tiglylglycine, tubular secretion, tubular secretory clearance, uremic toxins, xanthosine, Clinical Sciences, Public Health and Health Services, Urology & Nephrology
Abstract

Rationale & objectiveThe clearance of protein-bound solutes by the proximal tubules is an innate kidney mechanism for removing putative uremic toxins that could exert cardiovascular toxicity in humans. However, potential associations between impaired kidney clearances of secretory solutes and cardiovascular events among patients with chronic kidney disease (CKD) remains uncertain.Study designA multicenter, prospective, cohort study.Setting & participantsWe evaluated 3,407 participants from the Chronic Renal Insufficiency Cohort (CRIC) study.ExposuresBaseline kidney clearances of 8 secretory solutes. We measured concentrations of secretory solutes in plasma and paired 24-hour urine specimens using liquid chromatography-tandem mass spectrometry (LC-MS/MS).OutcomesIncident heart failure, myocardial infarction, and stroke events.Analytical approachWe used Cox regression to evaluate associations of baseline secretory solute clearances with incident study outcomes adjusting for estimated GFR (eGFR) and other confounders.ResultsParticipants had a mean age of 56 years; 45% were women; 41% were Black; and the median estimated glomerular filtration rate (eGFR) was 43 mL/min/1.73 m2. Lower 24-hour kidney clearance of secretory solutes were associated with incident heart failure and myocardial infarction but not incident stroke over long-term follow-up after controlling for demographics and traditional risk factors. However, these associations were attenuated and not statistically significant after adjustment for eGFR.LimitationsExclusion of patients with severely reduced eGFR at baseline; measurement variability in secretory solutes clearances.ConclusionsIn a national cohort study of CKD, no clinically or statistically relevant associations were observed between the kidney clearances of endogenous secretory solutes and incident heart failure, myocardial infarction, or stroke after adjustment for eGFR. These findings suggest that tubular secretory clearance provides little additional information about the development of cardiovascular disease events beyond glomerular measures of GFR and albuminuria among patients with mild-to-moderate CKD.

Published
2021

31. Hospitalization Trajectories and Risks of ESKD and Death in Individuals With CKD

Author

Srivastava, Anand, Cai, Xuan, Mehta, Rupal, Lee, Jungwha, Chu, David I, Mills, Katherine T, Shafi, Tariq, Taliercio, Jonathan J, Hsu, Jesse Y, Schrauben, Sarah J, Saunders, Milda R, Diamantidis, Clarissa J, Hsu, Chi-yuan, Waikar, Sushrut S, Lash, James P, Isakova, Tamara, Investigators, CRIC Study, Appel, Lawrence J, Feldman, Harold I, Go, Alan S, He, Jiang, Nelson, Robert G, Rahman, Mahboob, Rao, Panduranga S, Shah, Vallabh O, Townsend, Raymond R, and Unruh, Mark L

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Health Sciences, Kidney Disease, Prevention, Renal and urogenital, Good Health and Well Being, chronic kidney disease, end-stage kidney disease, hospital utilization, hospitalization, trajectory, CRIC Study Investigators, Biomedical and clinical sciences, Health sciences
Abstract

IntroductionManagement of chronic kidney disease (CKD) entails high medical complexity and often results in high hospitalization burden. There are limited data on the associations of longitudinal hospital utilization patterns with adverse clinical outcomes in individuals with CKD.MethodsWe derived cumulative all-cause hospitalization trajectory groups using latent class trajectory analysis in 3012 participants of the Chronic Renal Insufficiency Cohort (CRIC) Study who were alive and did not reach end-stage kidney disease (ESKD) within 4 years of study entry. Cox proportional hazards models tested the associations between hospitalization trajectory groups and risks of ESKD and death prior to the onset of ESKD (ESKD-censored death).ResultsWithin 4 years of study entry, there were 5658 hospitalizations among 3012 participants. We identified 3 distinct subgroups of individuals with CKD based on cumulative all-cause hospitalization trajectories over 4 years: low-utilizer (n = 1066), intermediate-utilizer (n = 1802), and high-utilizer (n = 144). High-utilizers represented a patient population of lower socioeconomic status who had a greater prevalence of comorbid conditions and lower kidney function compared with intermediate- and low-utilizers. After the 4-year ascertainment period to form the trajectory subgroups, there were 544 ESKD events and 437 ESKD-censored deaths during a median follow-up time of 5.1 years. Compared with low-utilizers, intermediate-utilizers and high-utilizers were at 1.49-fold (95% confidence interval [CI] 1.22-1.84) and 1.75-fold (95% CI 1.20-2.56) higher risk of ESKD in adjusted analyses, respectively. Compared with low-utilizers, intermediate-utilizers and high-utilizers were at 1.48-fold (95% CI 1.17-1.87) and 2.58-fold (95% CI 1.74-3.83) higher risk of ESKD-censored death in adjusted analyses, respectively.ConclusionsTrajectories of cumulative all-cause hospitalization identify subgroups of individuals with CKD who are at high risk of ESKD and death.

Published
2021

34. Extracorporeal membrane oxygenation in patients with severe respiratory failure from COVID-19.

Author

Shaefi, Shahzad, Brenner, Samantha, Gupta, Shruti, OGara, Brian, Krajewski, Megan, Charytan, David, Chaudhry, Sobaata, Mirza, Sara, Peev, Vasil, Anderson, Mark, Bansal, Anip, Hayek, Salim, Srivastava, Anand, Mathews, Kusum, Johns, Tanya, Leonberg-Yoo, Amanda, Green, Adam, Arunthamakun, Justin, Wille, Keith, Shaukat, Tanveer, Singh, Harkarandeep, Admon, Andrew, Semler, Matthew, Hernán, Miguel, Mueller, Ariel, Wang, Wei, and Leaf, David

Subjects
ARDS, COVID-19, Extracorporeal membrane oxygenation, Mortality, Severe respiratory failure, VV-ECMO, Adult, COVID-19, Cohort Studies, Extracorporeal Membrane Oxygenation, Female, Humans, Male, Middle Aged, Respiratory Distress Syndrome, Treatment Outcome
Abstract

PURPOSE: Limited data are available on venovenous extracorporeal membrane oxygenation (ECMO) in patients with severe hypoxemic respiratory failure from coronavirus disease 2019 (COVID-19). METHODS: We examined the clinical features and outcomes of 190 patients treated with ECMO within 14 days of ICU admission, using data from a multicenter cohort study of 5122 critically ill adults with COVID-19 admitted to 68 hospitals across the United States. To estimate the effect of ECMO on mortality, we emulated a target trial of ECMO receipt versus no ECMO receipt within 7 days of ICU admission among mechanically ventilated patients with severe hypoxemia (PaO2/FiO2

Published
2021

35. Efficacy and adverse events profile of videolaryngoscopy in critically ill patients: subanalysis of the INTUBE study

Author

Anstey, Matthew, Colica, Sandra, Brewster, David, Simpson, Shannon, Regli, Adrian, O'Grady, Ross, Litton, Edward, Ferrier, Janet, Bartholdy, Roland, Tabah, Alexis, Bowen, David, Rowley, Rebecca, Gatward, Jonathan, Alonso, Julio, Varkey, Sneha, Palaniswamy, Vijayanand, Chimunda, Timothy, Reza, Syed T., Hossain, Mozaffer, Islam, Motiul, Hamid, Tarikul, Parotto, Matteo, Ajami, Samareh, Steel, Andrew, Del Sorbo, Lorenzo, Goffi, Alberto, Randall, Ian, Adhikari, Neill K.J., Mehesry, Tasneem H., Vera, Maria M., Bugedo, Guillermo, Labarca, Gonzalo, Silva, Monica, Ma, Wuhua, Li, Yongxing, Wu, Jiayan, Wu, Lun, Radivojević, Renata Curić, Matas, Marijana, Ivančan, Višnja, Pavlek, Mario, Mihaljević, Slobodan, Jumić, Aleksandra, Moguš, Mate, Tucić, Iva, Michalek, Pavel, Flaksa, Marek, Aguirre-Bermeo, Hernan, Tirape-Castro, Hugo, García Aguilera, Maria F., Montenegro, Diana Alvarez, Tutillo, Diego Morocho, Tutillo León, Jose A., Winiszewski, Hadrien, Piton, Gael, Aissaoui, Nadia, Augy, Jean-Loup, Champigneulle, Benoit, Zlotnik, Diane, Muller, Grégoire, Jacquier, Sophie, Hraiech, Sami, Guervilly, Christophe, Plantefeve, Gaetan, Contou, Damien, Ricard, Jean Damien, Besset, Sebastien, Colin, Gwenhael, Pouplet, Caroline, Mirouse, Adrien, Azoulay, Elie, Boissier, Florence, Frat, Jean-Pierre, Mercier, Emmanuelle, Salmon-Gandonnière, Charlotte, Lascarrou, Jean-Baptiste, Martin, Maelle, Ferre, Alexis, Legriel, Stephane, Bruel, Cedric, Philippard, Francois, Zarka, Jonathan, Chemouni, Frank, Hamzaoui, Olfa, Sztrymf, Benjamin, Brunin, Yannick, Pili-Floury, Sébastien, Constantin, Jean-Michel, Godet, Thomas, Maraffi, Tommaso, Dessap, Armand Mekontso, Jozwiak, Mathieu, Marin, Nathalie, Guitton, Christophe, Chudeau, Nicolas, Gros, Alexandre, Boyer, Alexandre, Papandreou, Eleni, Petsiou, Athanasia, Papanikolaou, Metaxia, Kyparissi, Aikaterini, Tileli, Maria, Makris, Alexandros, Tsiftsis, Dimitrios, El-Fellah, Nadia, Karametos, Ilias, Nakou, Evi, Chalkias, Athanasios, Arnaoutoglou, Eleni, Katsoulis, Panagiotis, Pouriki, Sofia, Vagdatli, Kyriaki, Dimitropoulou, Aikaterini, Kothekar, Amol, Baliga, Nishanth, Korula, Sara V., Philip, Sam, Singh, Lalit, Agrawal, Nipun, Jeswani, Deepak, Jeswani, Deepti, Jha, Simant, Singh, Nitesh, Bhattacharyya, Mahuya, Das, Amit, Kuragayala, Swarna D., Kesavarapu, Subba R., Shah, Bhagyesh, Kaushik, Shuchi, Sunil, Nilu, Gnanadurai, Kingsly, Singh, Atul K., Singh, Dinesh K., Khunteta, Sudhir, Gupta, Kulbhusahn, Sanyal, Rhik, Midya, Abhirup, Tyagi, Vijay N., Bendre, Prashant, Prashant, Kumar, Chaurasia, Satish, Mishra, Prasanna, Dash, Sampat, Sundrani, Omprakash, Lalwani, Jaya, Jain, Nikhilesh, Agrawal, Kehari, Ray, Banambar, Meher, Ranjan, Saravanabavan, Lakshmikanthcharan, Munusamy, Satheesh, Gupta, Manish, Ahmad, Meraj, Gopalakrishna, Kadarapura N., Suparna, Bharadwaj, Surath, Manimala R., Munta, Kartik, Jagiasi, Bharat, Srivastava, Anand, Sahu, Samir, Mrinal, Sircar, Kumar, Singh Sujeet, Shah, Mehul, Patel, Mayur, Bamane, Shrirang, Narkhede, Amit, Chawla, Rajesh, Chawla, Aakanksha, Maheshwarappa, Harish Mallapura, Manjunath, Ramya Ballekatte, Rahmani, Lua, Laffey, John G., Rona, Roberto, Benini, Annalisa, Russotto, Vincenzo, Rundo, Annalisa, Luzi, Annalisa, Esposito, Clelia, Nespoli, Moana R., Pradella, Andrea, Lungu, Ramona, Baccari, Laura, Chiumiento, Fernando, Mariano, Karim, Cotoia, Antonella, De Rosa, Silvia, Boni, Elisa, Palmese, Salvatore, Gammaldi, Renato, Spadaro, Savino, Santoro, Lida, Cracchiolo, Andrea N., Palma, Daniela M., Pinciroli, Riccardo, Giovannini, Ilaria, Calamai, Italo, Spina, Rosario, Cappellini, Iacopo, Tutino, Lorenzo, Bellissima, Agrippino, Maugeri, Jessica G., Riva, Ivano, Fabretti, Fabrizio, Brazzi, Luca, Sales, Gabriele, Montrucchio, Giorgia, Orsello, Alberto, Costamagna, Andrea, Canavosio, Federico G., Pelagalli, Lorella, Marcelli, Maria E., Cortegiani, Andrea, Tramarin, Jacopo, Musso, Stefania, Tarantino, Stefano, Di Giacinto, Ida, Licciardi, Anna L., Montini, Luca, De Pascale, Gennaro, Giacomucci, Angelo, Russo, Pierpaolo, Longhini, Federico, Garofalo, Eugenio, Ferluga, Massimo, Moro, Valeria, Cascella, Marco, Di Caprio, Barbara, Di Fenza, Raffaele, Nespoli, Francesca, Bassini, Ospedale E., Muttini, Stefano, Pezzi, Angelo, Elhadi, Muhammed, Ghula, Mohamed, Ahmed, Hazem Abdelkarem, Khaled, Ala, Elhadi, Ahmed, Alhadi, Abdulmueti, Mazlan, Mohd Z., Wan Hassan, Wan Mohd N., Hasan, Shahnaz, Jamaluddin, Muhamad F.H., Samat, Noryani Mohd, Ismail, Muhamad A., Alias, Anita, Hwa, Ngu Pei, Irtiza, Ismail Nahla, Khalidah, Hapiz, Kiok, Lee Chew, Nordin, Norbaniza Mohd, Wan Ismail, Wan N., Ali, Mohd N., Sánchez-Hurtado, Luis, Toledo-Salinas, Otoniel, Landaverde, Antonio, Sosa, Miguel A., Gonzalez, Mayra Martinez, Lopez Nava, Claudia L., San Juan Roman, Nandyelly, Gonzalez, Maria, Espinoza, Missael, González, Daira, Flores, Fernando, Pantoja Leal, Jesus N., Loza Gallardo, Luis R., Young, Paul, Mistry, Ravi, Browne, Alexander, Crone, Petra, Chandwani, Juhi, Hossein, Sazzad, Koul, Salman S., Aman, Rubina, Ali, Syed M., Akhtar, Shazia N., Jankowski, Milosz, Bielanski, Piotr, Mudyna, Wojciech, Franczyk, Pawel, Galkin, Piotr, Skowronski, Lukasz, Gaszynski, Tomasz, Piegat, Mariusz, Catorze, Nuno, Pinto, Marcia, Leonor, Tiago, Fernandes, Marco, Campos, Patricia, Aragão, Irene, Costa, Paulo F., Franco, Daniela G., Basto, Marta, Nogueira, Carla, Cunha, Rui P., Costa, Vasco, Lomivorotov, Vladimir, Nikitenko, Artem, Belsky, Vladislav, Furman, Mikhail, Magomedov, Marat, Baturova, Vera, Karelov, Alexey, Marova, Nadezhda, Almekhlafi, Ghaleb, Alghamdi, Adnan, Maseda, Emilio, Suarez de la Rica, Alejandro, Gonzalez, Jesus Flores, Ruiz, Miryam Pérez, Roca, Oriol, Santafe, Manel, Fernandez, Gemma Goma, Escudero-Acha, Patricia, González-Castro, Alejandro, Agvald-Öhman, Christina, Broman, Lina, Spangfors, Martin, Hannesdottir, Katrin, Persson, Elin, Rosell, Jon, Sperber, Jesper, Ohlsson, Annika, Von Seth, Magnus, Pedrotti, Niccolò, Wahlstrom, Carl, Meirik, Maria, Bandert, Anna, Krog, Ditte, Kuo, Lu-Cheng, Shin, Ming-Hann, Chien, Jung-Yien, Ku, Shih-Chi, Ruan, Sheng-Yuan, Huang, Chun-Kai, Yeh, Yu-Chang, Chao, Anne, Wang, Kuo-Chuan, Chiu, Ching-Tang, Lee, Chien-Chang, Chou, Nai-Kuan, Szakmany, Tamas, Jones, Benjamin, Jones, Laura, Della Torre, Valentina, Sinah, Ayush, Quayle, Alice, Cheetham, Olivia, Syed, Yadullah, Mensah, Kwabena, Edmunds, Christopher, Kaye, Callum T., Bauer, Philippe R., Odeyemi, Yewande E., Nates, Joseph, Laserna, Andres, Mosier, Jarrod, Hypes, Cameron, Gottesman, Eric, Mastroianni, Fiore, Fein, Daniel G., Zhao, Dawn, Fonseca Fuentes, Xavier E., Gallo de Moraes, Alice, Sandefur, Benjamin J., Khan, Akram, Matos, Dubier, Kaufman, David A., Lehr, Andrew, Bigatello, Luca, Bonney, Iwona, Lascarrou, Jean Baptiste, Tassistro, Elena, Antolini, Laura, Bauer, Philippe, Szułdrzyński, Konstanty, Camporota, Luigi, Putensen, Christian, Pelosi, Paolo, Sorbello, Massimiliano, Higgs, Andy, Greif, Robert, Grasselli, Giacomo, Valsecchi, Maria G., Fumagalli, Roberto, Foti, Giuseppe, Caironi, Pietro, Bellani, Giacomo, and Myatra, Sheila N.

Published
2023
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36. Cardiac Structure and Function and Subsequent Kidney Disease Progression in Adults With CKD: The Chronic Renal Insufficiency Cohort (CRIC) Study

Author

Cohen, Debbie L., Feldman, Harold I., Lash, James P., Nelson, Robert G., Rao, Panduranga S., Shah, Vallabh O., Unruh, Mark L., Ishigami, Junichi, Kansal, Mayank, Mehta, Rupal, Srivastava, Anand, Rahman, Mahboob, Dobre, Mirela, Al-Kindi, Sadeer G., Go, Alan S., Navaneethan, Sankar D., Chen, Jing, He, Jiang, Bhat, Zeenat Yousuf, Jaar, Bernard G., Appel, Lawrence J., and Matsushita, Kunihiro

Published
2023
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40. Immunoglobulin, glucocorticoid, or combination therapy for multisystem inflammatory syndrome in children: a propensity-weighted cohort study

Author

Chouli, Mohamed, Hamadouche, Nacera, Ladj, Mohamed Samir, Agrimbau Vázquez, Jorge, Carmona, Rodrigo, Collia, Adrian Gustavo, Ellis, Alejandro, Natta, Diego, Pérez, Laura, Rubiños, Mayra, Veliz, Natalia, Yori, Silvana, Britton, Philip N., Burgner, David P., Carey, Emma, Crawford, Nigel W., Giuliano, Hayley, McMinn, Alissa, Wong, Shirley, Wood, Nicholas, Holter, Wolfgang, Krainz, Matthias, Ulreich, Raphael, Zurl, Christoph, Dehoorne, Joke, Haerynck, Filomeen, Hoste, Levi, Schelstraete, Petra, Vandekerckhove, Kristof, Willems, Jef, Almeida Farias, Camila Giuliana, Almeida, Flávia Jacqueline, Alves Leal, Izabel, Araujo da Silva, André Ricardo, Araujo e Silva, Anna Esther, Barreiro, Sabrina T.A., Bomfim Prado da Silva, Daniella Gregória, Cervi, Maria Celia, dos Santos Naja Cardoso, Mirian Viviane, Henriques Teixeira, Cristiane, Jarovsky, Daniel, Martins Araujo, Julienne, Naaman Berezin, Eitan, Palazzi Sáfadi, Marco Aurélio, Paternina-de la Ossa, Rolando Andres, Souza Vieira, Cristina, Dimitrova, Anna, Ganeva, Margarita, Stefanov, Stefan, Telcharova-Mihaylovska, Albena, Biggs, Catherine M., Lopez, Alison, Scuccimarri, Rosie, Tan, Ryan, Wasserman, Sam, Withington, Davinia, Ampuero, Camila, Aravena, Javiera, Bustos B, Raul, Casanova, Daniel, Cruces, Pablo, Diaz, Franco, García-Salum, Tamara, Godoy, Loreto, Medina, Rafael A., Valenzuela Galaz, Gonzalo, Camacho-Moreno, Germán, Avila-Aguero, María L., Brenes-Chacón, Helena, Camacho-Badilla, Kattia, Ivankovich-Escoto, Gabriela, Naranjo-Zuniga, Gabriela, Soriano-Fallas, Alejandra, Ulloa-Gutierrez, Rolando, Yock-Corrales, Adriana, Amer, Maysa Abbas, Abdelmeguid, Yasmine, Ahmed, Yomna H.H.Z., Badib, Adham, Badreldin, Karim, Elkhashab, Yara, Heshmat, Hassan, Hussein, Amna, Mohamed Hussein, Amna Hussein, Ibrahim, Sandra, Shoman, Walaa, Yakout, Radwa M, Heinonen, Santtu, Angoulvant, François, Belot, Alexandre, Ouldali, Naïm, Beske, Florian, Heep, Axel, Masjosthusmann, Katja, Reiter, Karl, van den Heuvel, Ingeborg, von Both, Ulrich, Agrafiotou, Aikaterini, Antachopoulos, Charalampos, Charisi, Konstantina, Eleftheriou, Irini, Farmaki, Evangelia, Fotis, Lampros, Kafetzis, Dimitrios, Koletsi, Patra, Kourtesi, Katerina, Lampidi, Stavroula, Liakopoulou, Theodota, Maritsi, Despoina, Michailidou, Elisa, Milioudi, Maria, Mparmpounaki, Ioanna, Papadimitriou, Eleni, Papaevangelou, Vassiliki, Roilides, Emmanuel, Tsiatsiou, Olga, Tsolas, Georgios, Tsolia, Maria, Vantsi, Petrina, Banegas Pineda, Linda Yajeira, Borjas Aguilar, Karla Leversia, Cantillano Quintero, Edwin Mauricio, Ip, Patrick, Kwan, Mike Yat Wah, Kwok, Janette, Lau, Yu Lung, To, Kelvin, Wong, Joshua Sung Chih, David, Mate, Farkas, David, Kalcakosz, Szofia, Szekeres, Klaudia, Zsigmond, Borbala, Aslam, Nadeem, Luder, Anthony, Andreozzi, Laura, Bianco, Francesco, Bucciarelli, Valentina, Buonsenso, Danilo, Cimaz, Rolando, De Luca, Maia, Dellepiane, Rosa Maria, Fabi, Marianna, Filice, Emanuele, Lanari, Marcello, Lo Vecchio, Andrea, Mastrolia, Maria Vincenza, Mauro, Angela, Mazza, Angelo, Papa, Mario Virgilio, Romani, Lorenza, Scarano, Sara Maria, Simonini, Gabriele, Tipo, Vincenzo, Verdoni, Lucio, Macharia, Anne-Marie, Musiime, Grace, Reel, Bhupi, Wangai, Frederick, Pace, David, Torpiano, Paul, Anaya-Enriquez, Nancy, Carreon-Guerrero, Juan Manuel, Chacon-Cruz, Enrique, Cheung López, Mariana, Faugier Fuentes, Enrique, Fonseca Flores, Marisol, García-Domínguez, Miguel, Giron Vargas, Ana Luisa, Lopez-Delgado, Ivan, Lopez Hernández, Liliana, Menchaca Aguayo, Hector F., Montaño-Duron, Jesus Gilberto, Pérez-Gaxiola, Giordano, Ramos Tiñini, Pamela, Tostado-Morales, Edgardo, Valadez, Julio, Inchley, Christopher, Klevberg, Sjur, Knudsen, Per Kristian, Måseide, Per Helge, Carrera, Jose Manuel, Castaño, Elizabeth, Daza Timana, Carlos Alberto, De Leon, Tirza, Estripeaut, Dora, Levy, Jacqueline, Norero, Ximena, Record, Javier, Rojas-Bonilla, Magda, Wong, Mayra, Iramain, Ricardo, Hernandez, Roger, Huamán, Gian, Munaico, Manuel, Peralta, Carlos, Seminario, Diego, Zapata Yarlequé, Elmer Hans, Gadzinska, Justyna, Ludwikowska, Kamila, Mandziuk, Joanna, Okarska-Napierała, Magdalena, Alacheva, Zalina A., Alexeeva, Ekaterina, Ananin, Petr V., Antsupova, Margarita, Bakradze, Maya D., Berbenyuk, Anna, Bobkova, Polina, Borzakova, Svetlana, Chashchina, Irina L., El-Taravi, Yasmin, Fisenko, Andrey P., Gautier, Marina S., Glazyrina, Anastasia, Gorlenko, Cyrill, Grosheva, Mariia, Kiselev, Herman, Kondrikova, Elena, Korobyants, Evgeniya, Korsunskiy, Anatoliy A., Kovygina, Karina, Krasnaya, Ekaterina, Kurbanova, Seda, Kurdup, Maria K., Mamutova, Anna V., Mazankova, Lyudmila, Mitushin, Ilya L., Munblit, Daniel, Nargizyan, Anzhelika, Orlova, Yanina O., Osmanov, Ismail M., Polyakova, Anastasia S., Pushkareva, Anna, Romanova, Olga, Samitova, Elmira, Shvedova, Anastasia, Sologub, Anna, Iakovleva, Ekaterina, Tepaev, Rustem F., Tkacheva, Anna A., Yegiyan, Margarita, Yusupova, Valeriya, Zholobova, Elena, Grasa, Carlos Daniel, Epalza, Cristina, Lopez Segura, Nuria, Martinon-Torres, Federico, Melendo, Susana, Mendez-Echevarria, Ana, Mesa Guzmán, Juan Miguel, Palacios Argueta, Jorge Roberto, Rivero-Calle, Irene, Rivière, Jacques, Rodríguez-González, Moisés, Rojo, Pablo, Sanchez Manubens, Judith, Soler-Palacin, Pere, Soriano-Arandes, Antoni, Tagarro, Alfredo, Villaverde, Serena, Altman, Maria, Brodin, Petter, Horne, AnnaCarin, Palmblad, Karin, Brotschi, Barbara, Meyer Sauteur, Patrick, Pachlopnik Schmid, Jana, Prader, Seraina, Relly, Christa, Schlapbach, Luregn J., Seiler, Michelle, Strasser, Sophie, Trück, Johannes, Weber, Kathrin, Wütz, Daniela, Hamdan, Alaa, Melhem, Ibrahim, Moussa, Ahmed, Dunk, Joke, Ketharanathan, Naomi, Vermont, Clementien, Akyüz Özkan, Esra, Cetin, Benhur Sirvan, Erdeniz, Emine Hafize, Şahin, Irfan Oğuz, Borisova, Galina, Boyarchuk, Oksana, Boychenko, Lidiya, Boyko, Yaryna, Diudenko, Nadiia, Dyvonyak, Olha, Kasiyan, Olexandr, Katerynych, Kostiantyn, Kostyuchenko, Larysa, Mamenko, Marina, Melnyk, Kateryna, Miagka, Nelia, Nazarenko, Liliya, Nezgoda, Iryna, Rykova, Stanislava, Svyst, Olga, Teslenko, Maria, Trykosh, Mykola, Vasilenko, Nataliya, Volokha, Alla, Adams, Charlotte, Akomolafe, Toju, Al-Abadi, Eslam, Alders, Nele, Alifieraki, Styliani, Ansumanu, Hareef, Aston, Emily, Avram, Paula, Bamford, Alasdair, Banks, Millie, Basu Roy, Robin, Beattie, Thomas, Boleti, Olga, Bracken, Abbey, Broad, Jonathan, Cai, James, Carrol, Enitan D., Carter, Michael, Chandran, Anchit, Charlesworth, James, Chawla, Jaya, Cooper, Hannah, Cooray, Samantha, Davies, Patrick, Davis, Francesca, Drysdale, Simon B., Dzora, Ella, Emonts, Marieke, Evans, Ceri, Fidler, Katy, Foster, Caroline, Gong, Chen, Gongrun, Berin, Gonzalez, Carmen, Gorgun, Berin, Grandjean, Louis, Grant, Karlie, Guo, Jonathan, Hacohen, Yael, Hall, Jack, Hamid, Hytham K.S., Hassell, Jane, Hesketh, Christine, Hewlett, Jessica, Hnieno, Ahmad, Holt-Davis, Hannah, Hossain, Aleena, Hu, Shiying, Hudson, Lee D., Jheeta, Sharon, Johnson, Mae, Johnson, Sarah, Jyothish, Deepthi, Kampmann, Beate, Kavirayani, Akhila, Kelly, Deborah, Kirubakaran, Arangan, Kucera, Filip, Langer, Daniel, Lawson, George, Lees, Emily A, Lenihan, Rebecca, Lillie, Jon, Longbottom, Katherine, Lyall, Hermione, Mackdermott, Niamh, Maltby, Sarah, Mclelland, Thomas, McMahon, Anne-Marie, Miller, Danielle, Miranda, Mariana, Mirza, Luwaiza, Morrison, Zoe, Moshal, Karyn, Muller, Jennifer, Musuka, Phoebe, Myttaraki, Evangelia, Nadel, Simon, Nair, Sreedevi, Nuttall, Luke, Oremakinde, Oyinkansola, Osaghae, Daniella, Osman, Fatima, Ostrzewska, Anna, Paccagnella, Davide, Panthula, Mrinalini, Papachatzi, Eleni, Papadopoulou, Charalampia, Patel, Fahim, Patel, Harsita, Payne, Helen, Penner, Justin, Polandi, Shervin, Prendergast, Andrew J., Ramnarayan, Padmanabhan, Ranasinghe, Lasith, Ravichandran, Muthukumaran, Rhys-Evans, Sophie, Riordan, Andrew, Rodrigues, Charlene M.C., Roe, Lauren, Romaine, Sam, Schobi, Nina, Seddon, James, Shingadia, Delane, Sikdar, Oishi, Srivastava, Anand, Struik, Siske, Sun, Thomas, Tan, Rachel Wei, Taylor, Alice, Taylor, Amanda, Taylor, Andrew, Tran, Steven, Tsagkaris, Stavros, Tudor-Williams, Gareth, van den Berg, Sarah, van der Velden, Fabian, Ventilacion, Lyn, Wellman, Paul A., Withers Green, Joseph, Yanney, Michael P., Yeung, Shunmay, Badheka, Aditya, Badran, Sarah, Bailey, Dwight M., Burch, Anna Kathryn, Burns, Jane C., Cichon, Catherine, Cirks, Blake, Dallman, Michael D., Delany, Dennis R., Fairchok, Mary, Friedman, Samantha, Geracht, Jennifer, Langs-Barlow, Allison, Mann, Kelly, Padhye, Amruta, Quade, Alexis, Ramirez, Kacy Alyne, Rockett, John, Sayed, Imran Ali, Santos, Roberto P., Shahin, Amr A., Tremoulet, Adriana, Umaru, Samuel, Widener, Rebecca, Mujuru, Hilda Angela, Kandawasvika, Gwendoline, Channon-Wells, Samuel, Vito, Ortensia, McArdle, Andrew J, Seaby, Eleanor G, Shah, Priyen, Pazukhina, Ekaterina, Wilson, Clare, Broderick, Claire, D'Souza, Giselle, Keren, Ilana, Nijman, Ruud G, Carter, Michael J, De, Tisham, Hoggart, Clive, Whittaker, Elizabeth, Herberg, Jethro A, Kaforou, Myrsini, Cunnington, Aubrey J, Blyuss, Oleg, and Levin, Michael

Published
2023
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41. Early Prediction of Acute Kidney Injury in Critical Care Setting Using Clinical Notes

Author

Li, Yikuan, Yao, Liang, Mao, Chengsheng, Srivastava, Anand, Jiang, Xiaoqian, and Luo, Yuan

Subjects
Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods, Statistics - Machine Learning
Abstract

Acute kidney injury (AKI) in critically ill patients is associated with significant morbidity and mortality. Development of novel methods to identify patients with AKI earlier will allow for testing of novel strategies to prevent or reduce the complications of AKI. We developed data-driven prediction models to estimate the risk of new AKI onset. We generated models from clinical notes within the first 24 hours following intensive care unit (ICU) admission extracted from Medical Information Mart for Intensive Care III (MIMIC-III). From the clinical notes, we generated clinically meaningful word and concept representations and embeddings, respectively. Five supervised learning classifiers and knowledge-guided deep learning architecture were used to construct prediction models. The best configuration yielded a competitive AUC of 0.779. Our work suggests that natural language processing of clinical notes can be applied to assist clinicians in identifying the risk of incident AKI onset in critically ill patients upon admission to the ICU., Comment: 4 pages, 3 figures, accepted by BIBM 2018

Published
2018

42. Effect of Meissner screening and trapped magnetic flux on magnetization dynamics in thick Nb/Ni80Fe20/Nb trilayers

Author

Jeon, Kun-Rok, Ciccarelli, Chiara, Kurebayashi, Hidekazu, Cohen, Lesley F., Montiel, Xavier, Eschrig, Matthias, Wagner, Thomas, Komori, Sachio, Srivastava, Anand, Robinson, Jason W. A., and Blamire, Mark G.

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

We investigate the influence of Meissner screening and trapped magnetic flux on magnetization dynamics for a Ni80Fe20 film sandwiched between two thick Nb layers (100 nm) using broadband (5-20 GHz) ferromagnetic resonance (FMR) spectroscopy. Below the superconducting transition Tc of Nb, significant zero-frequency line broadening (5-6 mT) and DC resonance field shift (50 mT) to a low field are both observed if the Nb thickness is comparable to the London penetration depth of Nb films (>= 100 nm). We attribute the observed peculiar behaviors to the increased incoherent precession near the Ni80Fe20/Nb interface and the effectively focused magnetic flux in the middle Ni80Fe20 caused by strong Meissner screening and (defect-)trapped flux of the thick adjacent Nb layers. This explanation is supported by static magnetic properties of the samples and comparison with FMR data on thick Nb/Ni80Fe20 bilayers. Great care should therefore be taken in the analysis of FMR response in ferromagnetic Josephson structures with thick superconductors, a fundamental property for high-frequency device applications of spin-polarized supercurrents., Comment: 23 pages, 5 figures

Published
2018
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43. Probing the Association between Acute Kidney Injury and Cardiovascular Outcomes

Author

McCoy, Ian E., Hsu, Jesse Y., Zhang, Xiaoming, Diamantidis, Clarissa J., Taliercio, Jonathan, Go, Alan S., Liu, Kathleen D., Drawz, Paul, Srivastava, Anand, Horwitz, Edward J., He, Jiang, Chen, Jing, Lash, James P., Weir, Matthew R., Hsu, Chi-yuan, Appel, Lawrence J., Cohen, Debbie L., Feldman, Harold I., Nelson, Robert G., Rahman, Mahboob, Rao, Panduranga S., Shah, Vallabh O., and Unruh, Mark L.

Published
2023
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44. Matrix Metalloproteinase-2 and CKD Progression: The Chronic Renal Insufficiency Cohort (CRIC) Study

Author

Baudier, Robin L., primary, Orlandi, Paula F., additional, Yang, Wei, additional, Chen, Hsiang-Yu, additional, Bansal, Nisha, additional, Blackston, J. Walker, additional, Chen, Jing, additional, Deo, Rajat, additional, Dobre, Mirela, additional, He, Hua, additional, He, Jiang, additional, Ricardo, Ana C., additional, Shafi, Tariq, additional, Srivastava, Anand, additional, Xie, Dawei, additional, Susztak, Katalin, additional, Feldman, Harold I., additional, Anderson, Amanda H., additional, Appel, Lawrence J., additional, Cohen, Debbie, additional, Dember, Laura, additional, Go, Alan S., additional, Lash, James P., additional, Nelson, Robert G., additional, Rahman, Mahboob, additional, Rao, Panduranga S., additional, Shah, Vallabh O., additional, and Unruh, Mark L., additional

Published
2024
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48. WCN24-220 Associations of biomarkers of podocyte injury with histopathologic lesions and foot process effacement in individuals with glomerular disease

Author

Schmidt, Insa Marie, primary, Hassanein, Mohamed, additional, Verme, Ashish, additional, Claudel, Sophie, additional, Rosan, Sophia, additional, Huynh, Courtney, additional, Palsson, Ragnar, additional, Srivastava, Anand, additional, Avillach, Claire, additional, Huber, Tobias, additional, Betanzos, Carlos Morales, additional, Orcana, Mireia Fernandez, additional, Fader, Kelly Alana, additional, Ponda, Manish, additional, Berasi, Stephen, additional, and Waikar, Sushrut, additional

Published
2024
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