Unsupervised clustering reveals phenotypes of AKI in ICU COVID-19 patients
Acute Kidney Injury (AKI) is a very frequent condition, occurring in about one in three patients admitted to an intensive care unit (ICU). AKI is a syndrome defined as a sudden decrease in glomerular filtration rate. However, this unified definition does not reflect the various mechanisms involved i...
Saved in:
Published in | Frontiers in medicine Vol. 9; p. 980160 |
---|---|
Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Media S.A
05.10.2022
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Acute Kidney Injury (AKI) is a very frequent condition, occurring in about one in three patients admitted to an intensive care unit (ICU). AKI is a syndrome defined as a sudden decrease in glomerular filtration rate. However, this unified definition does not reflect the various mechanisms involved in AKI pathophysiology, each with its own characteristics and sensitivity to therapy. In this study, we aimed at developing an innovative machine learning based method able to subphenotype AKI according to its pattern of risk factors.
We adopted a three-step pipeline of analyses. First, we looked for factors associated with AKI using a generalized additive model. Second, we calculated the importance of each identified AKI related factor in the estimated AKI risk to find the main risk factor for AKI, at the single patient level. Lastly, we clusterized AKI patients according to their profile of risk factors and compared the clinical characteristics and outcome of every cluster. We applied this method to a cohort of severe COVID-19 patients hospitalized in the ICU of the Geneva University Hospitals.
Among the 248 patients analyzed, we found 7 factors associated with AKI development. Using the individual expression of these factors, we identified three groups of AKI patients, based on the use of Lopinavir/Ritonavir, baseline eGFR, use of dexamethasone and AKI severity. The three clusters expressed distinct characteristics in terms of AKI severity and recovery, metabolic patterns and hospital mortality.
We propose here a new method to phenotype AKI patients according to their most important individual risk factors for AKI development. When applied to an ICU cohort of COVID-19 patients, we were able to differentiate three groups of patients. Each expressed specific AKI characteristics and outcomes, which probably reflect a distinct pathophysiology. |
---|---|
AbstractList | Acute Kidney Injury (AKI) is a very frequent condition, occurring in about one in three patients admitted to an intensive care unit (ICU). AKI is a syndrome defined as a sudden decrease in glomerular filtration rate. However, this unified definition does not reflect the various mechanisms involved in AKI pathophysiology, each with its own characteristics and sensitivity to therapy. In this study, we aimed at developing an innovative machine learning based method able to subphenotype AKI according to its pattern of risk factors.
We adopted a three-step pipeline of analyses. First, we looked for factors associated with AKI using a generalized additive model. Second, we calculated the importance of each identified AKI related factor in the estimated AKI risk to find the main risk factor for AKI, at the single patient level. Lastly, we clusterized AKI patients according to their profile of risk factors and compared the clinical characteristics and outcome of every cluster. We applied this method to a cohort of severe COVID-19 patients hospitalized in the ICU of the Geneva University Hospitals.
Among the 248 patients analyzed, we found 7 factors associated with AKI development. Using the individual expression of these factors, we identified three groups of AKI patients, based on the use of Lopinavir/Ritonavir, baseline eGFR, use of dexamethasone and AKI severity. The three clusters expressed distinct characteristics in terms of AKI severity and recovery, metabolic patterns and hospital mortality.
We propose here a new method to phenotype AKI patients according to their most important individual risk factors for AKI development. When applied to an ICU cohort of COVID-19 patients, we were able to differentiate three groups of patients. Each expressed specific AKI characteristics and outcomes, which probably reflect a distinct pathophysiology. BackgroundAcute Kidney Injury (AKI) is a very frequent condition, occurring in about one in three patients admitted to an intensive care unit (ICU). AKI is a syndrome defined as a sudden decrease in glomerular filtration rate. However, this unified definition does not reflect the various mechanisms involved in AKI pathophysiology, each with its own characteristics and sensitivity to therapy. In this study, we aimed at developing an innovative machine learning based method able to subphenotype AKI according to its pattern of risk factors.MethodsWe adopted a three-step pipeline of analyses. First, we looked for factors associated with AKI using a generalized additive model. Second, we calculated the importance of each identified AKI related factor in the estimated AKI risk to find the main risk factor for AKI, at the single patient level. Lastly, we clusterized AKI patients according to their profile of risk factors and compared the clinical characteristics and outcome of every cluster. We applied this method to a cohort of severe COVID-19 patients hospitalized in the ICU of the Geneva University Hospitals.ResultsAmong the 248 patients analyzed, we found 7 factors associated with AKI development. Using the individual expression of these factors, we identified three groups of AKI patients, based on the use of Lopinavir/Ritonavir, baseline eGFR, use of dexamethasone and AKI severity. The three clusters expressed distinct characteristics in terms of AKI severity and recovery, metabolic patterns and hospital mortality.ConclusionWe propose here a new method to phenotype AKI patients according to their most important individual risk factors for AKI development. When applied to an ICU cohort of COVID-19 patients, we were able to differentiate three groups of patients. Each expressed specific AKI characteristics and outcomes, which probably reflect a distinct pathophysiology. Background Acute Kidney Injury (AKI) is a very frequent condition, occurring in about one in three patients admitted to an intensive care unit (ICU). AKI is a syndrome defined as a sudden decrease in glomerular filtration rate. However, this unified definition does not reflect the various mechanisms involved in AKI pathophysiology, each with its own characteristics and sensitivity to therapy. In this study, we aimed at developing an innovative machine learning based method able to subphenotype AKI according to its pattern of risk factors. Methods We adopted a three-step pipeline of analyses. First, we looked for factors associated with AKI using a generalized additive model. Second, we calculated the importance of each identified AKI related factor in the estimated AKI risk to find the main risk factor for AKI, at the single patient level. Lastly, we clusterized AKI patients according to their profile of risk factors and compared the clinical characteristics and outcome of every cluster. We applied this method to a cohort of severe COVID-19 patients hospitalized in the ICU of the Geneva University Hospitals. Results Among the 248 patients analyzed, we found 7 factors associated with AKI development. Using the individual expression of these factors, we identified three groups of AKI patients, based on the use of Lopinavir/Ritonavir, baseline eGFR, use of dexamethasone and AKI severity. The three clusters expressed distinct characteristics in terms of AKI severity and recovery, metabolic patterns and hospital mortality. Conclusion We propose here a new method to phenotype AKI patients according to their most important individual risk factors for AKI development. When applied to an ICU cohort of COVID-19 patients, we were able to differentiate three groups of patients. Each expressed specific AKI characteristics and outcomes, which probably reflect a distinct pathophysiology. |
Author | Criton, Gilles Sgardello, Sebastian Le Terrier, Christophe Assouline, Benjamin Legouis, David Marchi, Elisa Sangla, Frédéric Pugin, Jérôme |
AuthorAffiliation | 2 Laboratory of Nephrology, Department of Medicine and Cell Physiology, University Hospital of Geneva , Geneva , Switzerland 4 Department of Surgery, Center Hospitalier du Valais Romand , Sion , Switzerland 3 Geneva School of Economics and Management, University of Geneva , Geneva , Switzerland 1 Division of Intensive Care, Department of Acute Medicine, University Hospital of Geneva , Geneva , Switzerland |
AuthorAffiliation_xml | – name: 3 Geneva School of Economics and Management, University of Geneva , Geneva , Switzerland – name: 4 Department of Surgery, Center Hospitalier du Valais Romand , Sion , Switzerland – name: 1 Division of Intensive Care, Department of Acute Medicine, University Hospital of Geneva , Geneva , Switzerland – name: 2 Laboratory of Nephrology, Department of Medicine and Cell Physiology, University Hospital of Geneva , Geneva , Switzerland |
Author_xml | – sequence: 1 givenname: David surname: Legouis fullname: Legouis, David organization: Laboratory of Nephrology, Department of Medicine and Cell Physiology, University Hospital of Geneva, Geneva, Switzerland – sequence: 2 givenname: Gilles surname: Criton fullname: Criton, Gilles organization: Geneva School of Economics and Management, University of Geneva, Geneva, Switzerland – sequence: 3 givenname: Benjamin surname: Assouline fullname: Assouline, Benjamin organization: Division of Intensive Care, Department of Acute Medicine, University Hospital of Geneva, Geneva, Switzerland – sequence: 4 givenname: Christophe surname: Le Terrier fullname: Le Terrier, Christophe organization: Division of Intensive Care, Department of Acute Medicine, University Hospital of Geneva, Geneva, Switzerland – sequence: 5 givenname: Sebastian surname: Sgardello fullname: Sgardello, Sebastian organization: Department of Surgery, Center Hospitalier du Valais Romand, Sion, Switzerland – sequence: 6 givenname: Jérôme surname: Pugin fullname: Pugin, Jérôme organization: Division of Intensive Care, Department of Acute Medicine, University Hospital of Geneva, Geneva, Switzerland – sequence: 7 givenname: Elisa surname: Marchi fullname: Marchi, Elisa organization: Division of Intensive Care, Department of Acute Medicine, University Hospital of Geneva, Geneva, Switzerland – sequence: 8 givenname: Frédéric surname: Sangla fullname: Sangla, Frédéric organization: Division of Intensive Care, Department of Acute Medicine, University Hospital of Geneva, Geneva, Switzerland |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36275817$$D View this record in MEDLINE/PubMed |
BookMark | eNpVkUtv3CAURlGVqknT7LuqvOzGE96GTaVo-nIbKZtO1R0CfJkQeYwL9kj59_V0kihZgS73Oxc4b9HJkAZA6D3BK8aUvgw76FYUU7rSChOJX6EzSrWslVB_Tp7tT9FFKXcYY8Ko4IS9QadM0kYo0pyhH5uhzCPkfSzQVb6fywQ5Dtsqwx5sX6rxFoY03Y9QqhSqq59tFYeqXW-q9c3v9nNNdDXaKcIwlXfodVgScPGwnqPN1y-_1t_r65tv7frquvaciKkW3jutSYOxVS4Q7TB2wQfJG8UYCYc6Z7wB7J3vLAkU-wBCSGCicx2R7By1R26X7J0Zc9zZfG-SjeZ_IeWtsXmKvgcjifAN9VJ70nHlmHU-UCYUbWTHPdiF9enIGme3fKdf3pFt_wL68mSIt2ab9kaLRnNGFsDHB0BOf2cok9nF4qHv7QBpLoY2VBEupGBLKz62-pxKyRCexhBsDkbNwag5GDVHo0vkw_PrPQUe_bF_M4ie1Q |
Cites_doi | 10.3389/fgene.2020.620143 10.1097/MCC.0000000000000772 10.2215/CJN.13381019 10.1038/s42003-022-03628-x 10.1681/ASN.2021050680 10.1186/1751-0473-3-17 10.1007/978-3-030-51935-3_34 10.1016/j.bpa.2003.08.002 10.1038/s41598-020-69405-0 10.1093/brain/awx118 10.1038/s42255-020-0238-1 10.1056/NEJMoa2000741 10.1097/00000542-198302000-00006 10.1097/00000542-197411000-00009 10.2215/CJN.10981016 10.1164/rccm.201604-0799OC 10.1007/s0054080020119 10.3390/jcm10050900 10.3389/fmed.2021.779627 10.2215/CJN.09330819 10.1371/journal.pone.0249760 10.1016/S0140-6736(21)00350-0 10.3389/fdata.2021.693674 10.1161/CIRCOUTCOMES.120.007071 10.1681/ASN.2010111124 10.1186/s13054-016-1546-4 10.1620/tjem.237.287 10.1001/jamanetworkopen.2019.16021 10.1371/journal.pone.0251048 10.1007/s00134-019-05631-z 10.1097/00003246-200011000-00027 10.1002/jmv.26992 10.1186/s12882-020-01871-0 10.1007/s00134-015-3934-7 10.1056/NEJMoa1803213 10.3181/00379727-161-40599 10.1186/s13054-021-03666-7 10.1378/chest.107.4.1095 10.1186/s13054-020-02866-x 10.1056/NEJMoa1603017 10.1101/2020.06.28.20141911 10.1016/j.ekir.2020.07.035 10.1016/j.bja.2018.07.033 10.32614/RJ-2021-041 10.1038/s41598-018-37545-z 10.1097/00000542-198812000-00010 10.1007/s00134-012-2796-5 10.1016/j.kint.2020.05.006 10.1164/rccm.201807-1346OC 10.3389/fmed.2021.719472 10.1186/s12882-017-0465-1 10.1371/journal.pone.0214904 |
ContentType | Journal Article |
Copyright | Copyright © 2022 Legouis, Criton, Assouline, Le Terrier, Sgardello, Pugin, Marchi and Sangla. Copyright © 2022 Legouis, Criton, Assouline, Le Terrier, Sgardello, Pugin, Marchi and Sangla. 2022 Legouis, Criton, Assouline, Le Terrier, Sgardello, Pugin, Marchi and Sangla |
Copyright_xml | – notice: Copyright © 2022 Legouis, Criton, Assouline, Le Terrier, Sgardello, Pugin, Marchi and Sangla. – notice: Copyright © 2022 Legouis, Criton, Assouline, Le Terrier, Sgardello, Pugin, Marchi and Sangla. 2022 Legouis, Criton, Assouline, Le Terrier, Sgardello, Pugin, Marchi and Sangla |
DBID | NPM AAYXX CITATION 7X8 5PM DOA |
DOI | 10.3389/fmed.2022.980160 |
DatabaseName | PubMed CrossRef MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
DatabaseTitle | PubMed CrossRef MEDLINE - Academic |
DatabaseTitleList | PubMed CrossRef MEDLINE - Academic |
Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Medicine |
EISSN | 2296-858X |
EndPage | 980160 |
ExternalDocumentID | oai_doaj_org_article_615c72c69c1d48b3abcf2358276d4cea 10_3389_fmed_2022_980160 36275817 |
Genre | Journal Article |
GrantInformation_xml | – fundername: ; |
GroupedDBID | 53G 5VS 9T4 AAFWJ ACGFS ACXDI ADBBV ADRAZ AFPKN ALMA_UNASSIGNED_HOLDINGS AOIJS BAWUL BCNDV DIK GROUPED_DOAJ HYE IAO IEA IHR IHW IPNFZ ISR KQ8 M48 M~E NPM OK1 PGMZT RIG RPM AAYXX CITATION 7X8 ITC 5PM |
ID | FETCH-LOGICAL-c415t-5ccb991700a8bf19b00bfcf6478331f700a4347e0cbcda1f20cfe556e35dbd163 |
IEDL.DBID | RPM |
ISSN | 2296-858X |
IngestDate | Thu Jul 04 21:10:11 EDT 2024 Tue Sep 17 21:31:43 EDT 2024 Fri Aug 16 06:19:03 EDT 2024 Thu Sep 26 19:19:59 EDT 2024 Sat Sep 28 08:15:11 EDT 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Keywords | COVID-19 critical care clustering machine learning AKI |
Language | English |
License | Copyright © 2022 Legouis, Criton, Assouline, Le Terrier, Sgardello, Pugin, Marchi and Sangla. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c415t-5ccb991700a8bf19b00bfcf6478331f700a4347e0cbcda1f20cfe556e35dbd163 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Chan Kam Wa, The University of Hong Kong, Hong Kong SAR, China Reviewed by: Jianfeng Wu, The First Affiliated Hospital of Sun Yat-sen University, China; Bassam G. Abu Jawdeh, Mayo Clinic Arizona, United States; Changli Wei, Rush University, United States These authors have contributed equally to this work This article was submitted to Nephrology, a section of the journal Frontiers in Medicine |
OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9579431/ |
PMID | 36275817 |
PQID | 2728145653 |
PQPubID | 23479 |
PageCount | 1 |
ParticipantIDs | doaj_primary_oai_doaj_org_article_615c72c69c1d48b3abcf2358276d4cea pubmedcentral_primary_oai_pubmedcentral_nih_gov_9579431 proquest_miscellaneous_2728145653 crossref_primary_10_3389_fmed_2022_980160 pubmed_primary_36275817 |
PublicationCentury | 2000 |
PublicationDate | 2022-10-05 |
PublicationDateYYYYMMDD | 2022-10-05 |
PublicationDate_xml | – month: 10 year: 2022 text: 2022-10-05 day: 05 |
PublicationDecade | 2020 |
PublicationPlace | Switzerland |
PublicationPlace_xml | – name: Switzerland |
PublicationTitle | Frontiers in medicine |
PublicationTitleAlternate | Front Med (Lausanne) |
PublicationYear | 2022 |
Publisher | Frontiers Media S.A |
Publisher_xml | – name: Frontiers Media S.A |
References | 36960332 - Front Med (Lausanne). 2023 Mar 07;10:1172589 B21 Sata (B48) 1988; 2 Allaoui (B33) 2020 Kellum (B14) 2017; 195 Barbar (B6) 2018; 379 Valenza (B47) 2000; 28 Smith (B43) 2017; 18 Gaudry (B3) 2016; 375 Legouis (B18) 2020; 2 Bhatraju (B16) 2019; 199 Cai (B44) 2021; 8 Wiersema (B15) 2020; 24 Adhikari (B26) 2019; 14 Legouis (B32) 2018; 121 Verissimo (B19) 2022; 33 Xu (B35) 2020; 11 Mousavi Movahed (B37) 2021; 93 Sanchez-Russo (B42) 2021; 10 Jannot (B12) 2017; 12 Pannu (B49) 2004; 18 Zhang (B11) 2019; 9 Peterson (B22) 2021; 13 Grimaldi (B40) 2020; 10 Binois (B38) 2020; 5 Farge (B52) 1995; 107 Zhou (B28) 2020; 21 de Almeida (B54) 2021; 16 Huang (B27) 2019; 2 Cheng (B30) 2021; 8 Thongprayoon (B29) 2020; 10 Bhatraju (B13) 2016; 20 Fereshtehnejad (B10) 2017; 140 Chaudhary (B17) 2020; 15 Han (B25) 2015; 237 Fewell (B53) 1979; 161 Hoste (B1) 2015; 41 (B5) 2020; 383 Jaeger (B24) 2021; 14 Waikar (B36) 2012; 23 Orieux (B41) 2021; 25 Duff (B20) 2020; 15 Huang (B34) 2022; 5 Bursac (B31) 2008; 3 Annat (B50) 1983; 58 Hirsch (B45) 2020; 98 Kellum (B7) 2012; 2 Gaudry (B4) 2021; 397 Endre (B9) 2020; 26 Castela Forte (B8) 2019; 45 Hall (B46) 1974; 41 Schneider (B39) 2021; 16 Jäger (B23) 2021; 4 Kharasch (B51) 1988; 69 Nisula (B2) 2013; 39 |
References_xml | – volume: 11 start-page: 620143 year: 2020 ident: B35 article-title: A t-SNE based classification approach to compositional microbiome data publication-title: Front Genet. doi: 10.3389/fgene.2020.620143 contributor: fullname: Xu – volume: 26 start-page: 519 year: 2020 ident: B9 article-title: Identification of acute kidney injury subphenotypes publication-title: Curr Opin Crit Care. doi: 10.1097/MCC.0000000000000772 contributor: fullname: Endre – volume: 15 start-page: 1358 year: 2020 ident: B20 article-title: Defining early recovery of acute kidney injury publication-title: CJASN Am Soc Nephrol. doi: 10.2215/CJN.13381019 contributor: fullname: Duff – volume: 5 start-page: 1 year: 2022 ident: B34 article-title: Towards a comprehensive evaluation of dimension reduction methods for transcriptomic data visualization publication-title: Commun Biol Nature Publishing Group. doi: 10.1038/s42003-022-03628-x contributor: fullname: Huang – volume: 33 start-page: 810 year: 2022 ident: B19 article-title: Decreased renal gluconeogenesis is a hallmark of chronic kidney disease publication-title: J Am Soc Nephrol. doi: 10.1681/ASN.2021050680 contributor: fullname: Verissimo – volume: 3 start-page: 17 year: 2008 ident: B31 article-title: Purposeful selection of variables in logistic regression publication-title: Source Code Biol Med. doi: 10.1186/1751-0473-3-17 contributor: fullname: Bursac – start-page: 317 volume-title: Image and Signal Processing. year: 2020 ident: B33 article-title: Considerably improving clustering algorithms using UMAP dimensionality reduction technique: a comparative study doi: 10.1007/978-3-030-51935-3_34 contributor: fullname: Allaoui – volume: 18 start-page: 189 year: 2004 ident: B49 article-title: Effect of mechanical ventilation on the kidney publication-title: Best Practice Res Clin Anaesthesiol. doi: 10.1016/j.bpa.2003.08.002 contributor: fullname: Pannu – volume: 10 start-page: 12316 year: 2020 ident: B29 article-title: Impact of admission serum ionized calcium levels on risk of acute kidney injury in hospitalized patients publication-title: Sci Rep. doi: 10.1038/s41598-020-69405-0 contributor: fullname: Thongprayoon – volume: 140 start-page: 1959 year: 2017 ident: B10 article-title: Clinical criteria for subtyping Parkinson's disease: biomarkers and longitudinal progression publication-title: Brain Oxford Univ Press. doi: 10.1093/brain/awx118 contributor: fullname: Fereshtehnejad – volume: 2 start-page: 732 year: 2020 ident: B18 article-title: Altered proximal tubular cell glucose metabolism during acute kidney injury is associated with mortality publication-title: Nat Metab. doi: 10.1038/s42255-020-0238-1 contributor: fullname: Legouis – volume: 383 start-page: 240 year: 2020 ident: B5 article-title: Investigators. Timing of initiation of renal-replacement therapy in acute kidney injury New England publication-title: J Med. doi: 10.1056/NEJMoa2000741 – volume: 58 start-page: 136 year: 1983 ident: B50 article-title: Effect of PEEP ventilation on renal function, plasma renin, aldosterone, neurophysins and urinary ADH, and prostaglandins publication-title: Anesthesiology. doi: 10.1097/00000542-198302000-00006 contributor: fullname: Annat – volume: 41 start-page: 452 year: 1974 ident: B46 article-title: Renal hemodynamics and function with continuous positive-pressure ventilation in dogs publication-title: Anesthesiology. doi: 10.1097/00000542-197411000-00009 contributor: fullname: Hall – volume: 12 start-page: 874 year: 2017 ident: B12 article-title: The diagnosis-wide landscape of hospital-acquired AKI publication-title: Clin J Am Soc Nephrol. doi: 10.2215/CJN.10981016 contributor: fullname: Jannot – volume: 195 start-page: 784 year: 2017 ident: B14 article-title: Recovery after acute kidney injury publication-title: Am J Respir Crit Care Med. doi: 10.1164/rccm.201604-0799OC contributor: fullname: Kellum – ident: B21 – volume: 2 start-page: 119 year: 1988 ident: B48 article-title: Increased release of alpha-atrial natriuretic peptide during controlled mechanical ventilation with positive end-expiratory pressure in humans publication-title: J Anesth. doi: 10.1007/s0054080020119 contributor: fullname: Sata – volume: 10 start-page: 900 year: 2021 ident: B42 article-title: COVID-19 and the kidney: a worrisome scenario of acute and chronic consequences publication-title: J Clin Med. doi: 10.3390/jcm10050900 contributor: fullname: Sanchez-Russo – volume: 8 start-page: 2436 year: 2021 ident: B30 article-title: Association between base excess and mortality among patients in ICU with acute kidney injury publication-title: Front Med. doi: 10.3389/fmed.2021.779627 contributor: fullname: Cheng – volume: 15 start-page: 1557 year: 2020 ident: B17 article-title: Utilization of deep learning for subphenotype identification in sepsis-associated acute kidney injury publication-title: Clin J Am Soc Nephrol. doi: 10.2215/CJN.09330819 contributor: fullname: Chaudhary – volume: 16 start-page: e0249760 year: 2021 ident: B39 article-title: Therapy with lopinavir/ritonavir and hydroxychloroquine is associated with acute kidney injury in COVID-19 patients publication-title: PLoS ONE. doi: 10.1371/journal.pone.0249760 contributor: fullname: Schneider – volume: 397 start-page: 1293 year: 2021 ident: B4 article-title: Comparison of two delayed strategies for renal replacement therapy initiation for severe acute kidney injury (AKIKI 2): a multicentre, open-label, randomised, controlled trial publication-title: Lancet Elsevier. doi: 10.1016/S0140-6736(21)00350-0 contributor: fullname: Gaudry – volume: 2 start-page: 1 year: 2012 ident: B7 article-title: KDIGO clinical practice guideline for acute kidney injury 2012 publication-title: Kidney Int Suppl. contributor: fullname: Kellum – volume: 4 start-page: 48 year: 2021 ident: B23 article-title: A benchmark for data imputation methods publication-title: Front Big Data. doi: 10.3389/fdata.2021.693674 contributor: fullname: Jäger – volume: 14 start-page: e007071 year: 2021 ident: B24 article-title: Improving outcome predictions for patients receiving mechanical circulatory support by optimizing imputation of missing values publication-title: Circ Cardiovasc Qual Outcomes. doi: 10.1161/CIRCOUTCOMES.120.007071 contributor: fullname: Jaeger – volume: 23 start-page: 13 year: 2012 ident: B36 article-title: Imperfect gold standards for kidney injury biomarker evaluation publication-title: J Am Soc Nephrol. doi: 10.1681/ASN.2010111124 contributor: fullname: Waikar – volume: 20 start-page: 372 year: 2016 ident: B13 article-title: Acute kidney injury subphenotypes based on creatinine trajectory identifies patients at increased risk of death publication-title: Crit Care. doi: 10.1186/s13054-016-1546-4 contributor: fullname: Bhatraju – volume: 237 start-page: 287 year: 2015 ident: B25 article-title: Anemia is a risk factor for acute kidney injury and long-term mortality in critically Ill patients publication-title: Tohoku J Exp Med. doi: 10.1620/tjem.237.287 contributor: fullname: Han – volume: 2 start-page: e1916021 year: 2019 ident: B27 article-title: Development and validation of a model for predicting the risk of acute kidney injury associated with contrast volume levels during percutaneous coronary intervention publication-title: JAMA Network Open. doi: 10.1001/jamanetworkopen.2019.16021 contributor: fullname: Huang – volume: 16 start-page: e0251048 year: 2021 ident: B54 article-title: Acute kidney injury: incidence, risk factors, and outcomes in severe COVID-19 patients publication-title: PLoS ONE. doi: 10.1371/journal.pone.0251048 contributor: fullname: de Almeida – volume: 45 start-page: 1025 year: 2019 ident: B8 article-title: The use of clustering algorithms in critical care research to unravel patient heterogeneity publication-title: Intensive Care Med. doi: 10.1007/s00134-019-05631-z contributor: fullname: Castela Forte – volume: 28 start-page: 3697 year: 2000 ident: B47 article-title: An improved in vivo rat model for the study of mechanical ventilatory support effects on organs distal to the lung publication-title: Crit Care Med. doi: 10.1097/00003246-200011000-00027 contributor: fullname: Valenza – volume: 93 start-page: 4411 year: 2021 ident: B37 article-title: Different incidences of acute kidney injury (AKI) and outcomes in COVID-19 patients with and without non-azithromycin antibiotics: a retrospective study publication-title: J Med Virol. doi: 10.1002/jmv.26992 contributor: fullname: Mousavi Movahed – volume: 21 start-page: 223 year: 2020 ident: B28 article-title: Association of overweight with postoperative acute kidney injury among patients receiving orthotopic liver transplantation: an observational cohort study publication-title: BMC Nephrol. doi: 10.1186/s12882-020-01871-0 contributor: fullname: Zhou – volume: 41 start-page: 1411 year: 2015 ident: B1 article-title: Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study publication-title: Intensive Care Med. doi: 10.1007/s00134-015-3934-7 contributor: fullname: Hoste – volume: 379 start-page: 1431 year: 2018 ident: B6 article-title: Timing of renal-replacement therapy in patients with acute kidney injury and sepsis publication-title: New England J Med. doi: 10.1056/NEJMoa1803213 contributor: fullname: Barbar – volume: 161 start-page: 574 year: 1979 ident: B53 article-title: Renal denervation eliminates the renal response to continuous positive-pressure ventilation publication-title: Proc Soc Exp Biol Med. doi: 10.3181/00379727-161-40599 contributor: fullname: Fewell – volume: 25 start-page: 249 year: 2021 ident: B41 article-title: Impact of dexamethasone use to prevent from severe COVID-19-induced acute kidney injury publication-title: Critical Care. doi: 10.1186/s13054-021-03666-7 contributor: fullname: Orieux – volume: 107 start-page: 1095 year: 1995 ident: B52 article-title: Interactions between hemodynamic and hormonal modifications during peep-induced antidiuresis and antinatriuresis publication-title: Chest. doi: 10.1378/chest.107.4.1095 contributor: fullname: Farge – volume: 24 start-page: 150 year: 2020 ident: B15 article-title: Two subphenotypes of septic acute kidney injury are associated with different 90-day mortality and renal recovery publication-title: Crit Care. doi: 10.1186/s13054-020-02866-x contributor: fullname: Wiersema – volume: 375 start-page: 122 year: 2016 ident: B3 article-title: Initiation strategies for renal-replacement therapy in the intensive care unit publication-title: New England J Med. doi: 10.1056/NEJMoa1603017 contributor: fullname: Gaudry – volume: 10 start-page: 131 year: 2020 ident: B40 article-title: Characteristics and outcomes of acute respiratory distress syndrome related to COVID-19 in Belgian and French intensive care units according to antiviral strategies: the COVADIS multicentre observational study publication-title: Ann Intensive Care. doi: 10.1101/2020.06.28.20141911 contributor: fullname: Grimaldi – volume: 5 start-page: 1787 year: 2020 ident: B38 article-title: Acute kidney injury associated with lopinavir/ritonavir combined therapy in patients with COVID-19 publication-title: Kidney Int Rep. doi: 10.1016/j.ekir.2020.07.035 contributor: fullname: Binois – volume: 121 start-page: 1025 year: 2018 ident: B32 article-title: Development of a practical prediction score for chronic kidney disease after cardiac surgery publication-title: Br J Anaesth. doi: 10.1016/j.bja.2018.07.033 contributor: fullname: Legouis – volume: 13 start-page: 310 year: 2021 ident: B22 article-title: Finding optimal normalizing transformations via bestnormalize publication-title: RJ. doi: 10.32614/RJ-2021-041 contributor: fullname: Peterson – volume: 9 start-page: 1 year: 2019 ident: B11 article-title: Data-driven subtyping of Parkinson's disease using longitudinal clinical records: a cohort study publication-title: Sci Rep Nat Publ Group. doi: 10.1038/s41598-018-37545-z contributor: fullname: Zhang – volume: 69 start-page: 862 year: 1988 ident: B51 article-title: Atrial natriuretic factor may mediate the renal effects of PEEP ventilation publication-title: Anesthesiology. doi: 10.1097/00000542-198812000-00010 contributor: fullname: Kharasch – volume: 39 start-page: 420 year: 2013 ident: B2 article-title: Incidence, risk factors and 90-day mortality of patients with acute kidney injury in Finnish intensive care units: the FINNAKI study publication-title: Intensive Care Med. doi: 10.1007/s00134-012-2796-5 contributor: fullname: Nisula – volume: 98 start-page: 209 year: 2020 ident: B45 article-title: Acute kidney injury in patients hospitalized with COVID-19 publication-title: Kidney Int. doi: 10.1016/j.kint.2020.05.006 contributor: fullname: Hirsch – volume: 199 start-page: 863 year: 2019 ident: B16 article-title: Identification of acute kidney injury subphenotypes with differing molecular signatures and responses to vasopressin therapy publication-title: Am J Respir Crit Care Med. doi: 10.1164/rccm.201807-1346OC contributor: fullname: Bhatraju – volume: 8 start-page: 719472 year: 2021 ident: B44 article-title: Risk factors for acute kidney injury in adult patients with COVID-19: a systematic review and meta-analysis publication-title: Front Med. doi: 10.3389/fmed.2021.719472 contributor: fullname: Cai – volume: 18 start-page: 55 year: 2017 ident: B43 article-title: Latent variable modeling improves AKI risk factor identification and AKI prediction compared to traditional methods publication-title: BMC Nephrol. doi: 10.1186/s12882-017-0465-1 contributor: fullname: Smith – volume: 14 start-page: e0214904 year: 2019 ident: B26 article-title: Improved predictive models for acute kidney injury with IDEA: intraoperative data embedded analytics publication-title: PLoS ONE. doi: 10.1371/journal.pone.0214904 contributor: fullname: Adhikari |
SSID | ssj0001325413 |
Score | 2.24789 |
Snippet | Acute Kidney Injury (AKI) is a very frequent condition, occurring in about one in three patients admitted to an intensive care unit (ICU). AKI is a syndrome... Background Acute Kidney Injury (AKI) is a very frequent condition, occurring in about one in three patients admitted to an intensive care unit (ICU). AKI is a... BackgroundAcute Kidney Injury (AKI) is a very frequent condition, occurring in about one in three patients admitted to an intensive care unit (ICU). AKI is a... |
SourceID | doaj pubmedcentral proquest crossref pubmed |
SourceType | Open Website Open Access Repository Aggregation Database Index Database |
StartPage | 980160 |
SubjectTerms | AKI clustering COVID-19 critical care machine learning Medicine |
SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LS8NAEF6kB_Eivo0vVvDiIZrN5rE5alXainqx0tuyTyxIWkz7_51JUmlF8OI1CWT5vk3mm53Zbwm58AKiLoSpMFYsCxPBXKiwi1AoY0XmLaS5uKD_9Jz1hslglI6WjvrCnrDGHrgB7hoirsljkxWG2URorrTx9fbOPLOJcY00YulSMlWvrnBIfBhv6pKQhRVAk0Nj0Di-KgS6qq3Eodqu_zeN-bNVcin2PGyRzVY00ptmsNtkzZU7ZP2pLYvvksGwrOZT_OorZ6n5mKP7AcQkiv5MML8oNnJNcLW1ohNPbx77dFzSfndIuy9v_buQFbT1V632yPDh_rXbC9tDEkIDsXcWpsZo0Hh5FCmhPUOLQ-2Nxy2knDOP1xOe5C4y2ljFfBwZ79I0czy12oIa2yedclK6Q0KjwqVKW2sgLU6szbSNjcusU_APUioXAblcQCanjReGhBwC4ZUIr0R4ZQNvQG4R0-_n0MW6vgDcypZb-Re3ATlfMCJh1mMpQ5VuMq9knMeCoRjlATloGPp-FUfjZcHygOQr3K2MZfVOOX6vnbWxZgmK6ug_Bn9MNhCPuvEvPSGd2efcnYKAmemzeq5-AYmN7yA priority: 102 providerName: Directory of Open Access Journals – databaseName: Scholars Portal Open Access Journals dbid: M48 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB5BkRAXVN6BgozEhUOq2Ekc54CqslB1ixYuBO3N8hMqVUnZ7Er03zOTpIVFe-EYJ8rjGzvzjWf8GeBNVOh10U2lwnCZFoqH1FAVoTLOKxk9hrk0ob_4LE-b4mxZLv8sj54A7HeGdrSfVLO6OPz18-oIB_w7ijjR36IFAml-CnFYKxJMuw13RJEX1N8XE9kfZlxyDIaG_ZKFqGWqSrUc85Y7b7LlpwY5_10c9N9Syr9808k-3J9IJTsee8EDuBXah3B3MaXNH8FZ0_abS_or9MEzd7EhdQT0WYz0mxAHRoVeHc3G9qyL7PjTnJ23bD5r2OzLt_mHlNds0l_tH0Nz8vHr7DSdNlFIHfrmdVo6Z5EDVllmlI2cJBBtdJGWmOY5j9SOWFUhc9Z5w6PIXAxlKUNeeuuRrT2BvbZrwzNgWR1KY713GDYX3kvrhQvSB4P_KGMqlcDba8j05aiVoTHGIHg1wasJXj3Cm8B7wvTmOlK5Hhq61Xc9DRqNbMtVwsnacV8omxvr4rC0t5K-cMEk8PraIhpHBaU6TBu6Ta9FJRQnspon8HS00M2jchJmVrxKoNqy3da7bJ9pz38MytuU00TG9fw_PvQF3KOjof6vPIC99WoTXiKPWdtXQ_f8DZxg72Y priority: 102 providerName: Scholars Portal |
Title | Unsupervised clustering reveals phenotypes of AKI in ICU COVID-19 patients |
URI | https://www.ncbi.nlm.nih.gov/pubmed/36275817 https://search.proquest.com/docview/2728145653 https://pubmed.ncbi.nlm.nih.gov/PMC9579431 https://doaj.org/article/615c72c69c1d48b3abcf2358276d4cea |
Volume | 9 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Nb9QwELXaHhAXxDeBUhmJC4fsxnE-nGNZqLpFCxxY1Jtlj-2yUpusmt3_3xknW3URJy45OIlizUwybzzPL4x9DAqzLqapNDeiSgslfGqIRagMOFUFh2UuLegvvlfny-Lisrw8YOVuL0wk7YNdTdrrm0m7-hO5lesbmO54YtOfixm1ljDxTQ_ZYS3lgxI9LqxIrHmEHFqSWIA16CFPmqB5PmkUCartpaCo1P8vePk3S_JB2jl7yp6MeJGfDvN6xg58-5w9Wowd8RfsYtn22zW98L13HK63JHyA6YiTNBOGFicOV0cLrT3vAj_9Nuerls9nSz778Xv-JRUNH6VV-5dsefb11-w8Hf-PkAKm3U1aAliEd3WWGWWDIHVDGyDQ7lEpRaDxQha1z8CCMyLkGQRflpWXpbMOgdgrdtR2rX_DeNb40ljnACviwrnKuhx85bzBz48xtUrYp53J9HqQwdBYPpB5NZlXk3n1YN6EfSab3l9HAtZxoLu90qMbNQIpqHOoGhCuUFYaCyHu2q0rV4A3Cfuw84jGgKcuhml9t-11XudKEA6VCXs9eOj-UZI0l5WoE1bv-W5vLvtnMMaiqPYYU2__-8537DEZIRL9ymN2tLnd-vcIWDb2JBb6eFwU6iQG6x3RfO9K |
link.rule.ids | 230,315,733,786,790,870,891,2115,24346,27957,27958,53827,53829 |
linkProvider | National Library of Medicine |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB6VIgEX3oXwNBIXDsnGeTrHslDttt3CoVv1ZvkVWNEmq2Zz4dczk0fVrbjA1Y4Vx5_tmfF8_gLwsRRoddFM-ZHimZ8I7nxFLEKhjBVZaTHMpQP9xUk2WyaH5-n5DqTjXZiOtG_0KqguLoNq9bPjVq4vzWTkiU2-L6aUWkLDN7kDd3G9RvmNIL07Wokx6uFxn5TEEKxAjBypgkZRUAiSVNsyQp1W_98czNs8yRuG5-ARnI1d7vkmv4J2owPz-5aa4z9_02N4OLiibL-vfgI7rnoK9xZDsv0ZHC6rpl3TXtI4y8xFS5oKaOkYqT7hrGVED6vpDLdhdcn2j-ZsVbH5dMmm387mX3xesEG1tXkOy4Ovp9OZP_x6wTdo0Td-aoxGzzEPQyV0yUk4UZempIupccxLKk_iJHeh0cYqXkahKV2aZi5Orbbo4-3BblVX7iWwsHCp0tYaDLYTazNtI-My6xTubErlwoNPIxZy3StsSIxMCDdJuEnCTfa4efCZwLp-jrSxu4L66occRlOij2byyGSF4TYROlbalN2F4DyziXHKgw8j1BLXEiVIVOXqtpFRHglOLm7swYse-utXxSTnLHjuQb41Kbb6sl2DUHd63QO0r_675Xu4PztdHMvj-cnRa3hAA9LxCdM3sLu5at1b9Is2-l23Cv4AQ2IPiA |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3JbtswEB20KRD00n1xVxbopQfJolbqmDo14qROc6iLoBeCa2s0kYzIuvTrOyPJgR30lCtFQRQfyZnhPD4CfPQCrS6aqSBWPA9SwV2giEUolLEi9xbDXNrQn5_mR4v0-Dw737rqqyPtG70Mq4vLsFr-7riVq0sz3vDExmfzCaWW0PCNV9aP78I9nLNxuRWod9srCUY-POkTkxiGlYiTI2XQOA5LQbJqO4ao0-v_n5N5kyu5ZXymD-Hnptk95-RP2K51aP7eUHS81X89ggeDS8oO-iqP4Y6rnsD-fEi6P4XjRdW0K1pTGmeZuWhJWwEtHiP1Jxy9jGhiNe3lNqz27OBkxpYVm00WbPLtx-ww4CUb1FubZ7CYfvk-OQqGKxgCg5Z9HWTGaPQgiyhSQntOAoraG08HVJOEeypPk7RwkdHGKu7jyHiXZblLMqst-nrPYa-qK_cSWFS6TGlrDQbdqbW5trFxuXUKVzilCjGCTxs85KpX2pAYoRB2krCThJ3ssRvBZwLsuh5pZHcF9dUvOfSoRF_NFLHJS8NtKnSitPHdweAit6lxagQfNnBLnFOUKFGVq9tGxkUsOLm6yQhe9PBffyohWWfBixEUOwNjpy27TxDuTrd7gPfVrd98D_tnh1P5dXZ68hruU390tMLsDeytr1r3Ft2jtX7XTYR_uXESCA |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Unsupervised+clustering+reveals+phenotypes+of+AKI+in+ICU+COVID-19+patients&rft.jtitle=Frontiers+in+medicine&rft.au=Legouis%2C+David&rft.au=Criton%2C+Gilles&rft.au=Assouline%2C+Benjamin&rft.au=Le+Terrier%2C+Christophe&rft.date=2022-10-05&rft.issn=2296-858X&rft.eissn=2296-858X&rft.volume=9&rft_id=info:doi/10.3389%2Ffmed.2022.980160&rft.externalDBID=n%2Fa&rft.externalDocID=10_3389_fmed_2022_980160 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2296-858X&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2296-858X&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2296-858X&client=summon |