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Enregistrement W4387296715 · doi:10.1001/jama.2023.17002

Estimated Glomerular Filtration Rate, Albuminuria, and Adverse Outcomes

2023· review· en· W4387296715 sur OpenAlexafffund
Lawrence J. Appel, Morgan E. Grams, Mark Woodward, Katie Harris, Hisatomi Arima, John Chalmers, Hiroshi Yatsuya, Koji Tamakoshi, Yuanying Li, Josef Coresh, Yingying Sang, Kunihiro Matsushita, Kevan Polkinghorne, Steven J. Chadban, Adeera Levin, Ognjenka Djurdjev, Mila Tang, Luxia Zhang, Fang Wang, Jinwei Wang, Ming‐Hui Zhao, Elke Schäeffner, Natalie Ebert, Nina Mielke, Marcello Tonelli, Anita Lloyd, Frank M. Sacks, Michael G. Shlipak, Nisha Bansal, Mark J. Sarnak, Kazumasa Yamagishi, Isao Muraki, Yūji Shimizu, Hiroyasu Iso, Masafumi Fukagawa, Shoichi Maruyama, Takayuki Hamano, Naohiko Fujii, Takahiro Imaizumi, Natália Alencar de Pinho, Marie Metzger, Bénédicte Stengel, Aghilès Hamroun, Ziad Massy, Tazeen H. Jafar, Imtiaz Jehan, Juanita Hatcher, Nish Chaturvedi, Neil R Poulter, David C. Wheeler, Martin Landray, Amanda Anderson, Jing Chen, James Lash, Jon Taliercio, Peter Yang, Katherine R. Tuttle, Radica Alicic, Susanne B. Nicholas, Jenny I. Shen, Ben Schöttker, Hannah Stocker, Dietrich Rothenbacher, Hermann Brenner, Daniel Levy, Shih‐Jen Hwang, Markus P. Schneider, Anna Köttgen, Heike Meiselbach, Kai‐Uwe Eckardt, Alex R. Chang, Jamie A. Green, H. Lester Kirchner, Gurmukteshwar Singh, Simon Sawhney, Corri Black, Katie Wilde, Angharad Marks, Samira Bell, Moneeza K. Siddiqui, Ewan R. Pearson, Mariko Miyazaki, Masaaki Nakayama, Tae Yamamoto, Gen Yamada, Sadayoshi Ito, Massimo Círillo, Angela Yee‐Moon Wang, Henry H. L. Wu, Hoi Ching Cheung, Victoria Ngai, Tang Ka Tak, Amit X. Garg, Eric McArthur, Ann Young, Vivekanand Jha, Ashok Kumar Yadav, Vivek Kumar, April P. Carson, Bessie A Young, Clarissa J. Diamantidis, Yuan‐I Min, Tanjala S. Purnell, Shizukiyo Ishikawa, Makiko Mieno, Kunihiro Yamagata, Kunitoshi Iseki, Koichi Asahi, Tsuneo Konta, Brian J. Lee, Nigel J. Brunskill, Laura J. Gray, Rupert Major, James Medcalf, Gabriel Chodick, Cheli Melzer Cohen, Jack F.M. Wetzels, Peter J. Blankestijn, Arjan D. van Zuilen, Lesley A. Inker, Andrew S. Levey, Joachim H. Ix, Ian H. de Boer, Ronit Katz, Florian Kronenberg, Barbara Kollerits, Eberhard Ritz, Dorothea Nitsch, Girish N. Nadkarni, Lili Chan, Erwin P. Böttinger, Won-Suk Oh, Zhihong Liu, Haitao Zhang, Lihua Zhang, José Manuel Valdivielso, Marcelino Bermúdez-López, Milica Božić, Maite Caus, Juan Miguel Díaz‐Tocados, Katsuyuki Miura, Hirotsugu Ueshima, Akira Okayama, Aya Kadota, Tomonori Okamura, Laura Solá, Alejandro Ferreiro, José Luis Santiago, Pablo Ríos, Liliana Gadola, Ricardo Silvariño, Takayoshi Ohkubo, Michihiro Satoh, Hirohito Metoki, Masahiro Kikuya, Elizabeth L. Ciemins, Jeff T. Mohl, Robert G. Nelson, Robert L. Hanson, Helen C. Looker, Ron T. Gansevoort, Lyanne M. Kieneker, Stephan J. L. Bakker, Olivier Moranne, Cécile Couchoud, David Shepherd, Simerjot K Jassal, Jaclyn Bergstrom, Csaba P. Kövesdy, Keiichi Sumida, Prabin Shrestha, Orlando M. Gutiérrez, Katharine L. Cheung, Paul Muntner, Titilayo O. Ilori, Michelle J. Pena, Hiddo J.L. Heerspink, Edouard L. Fu, Carl‐Gustaf Elinder, Peter Bárány, Juan Jesús Carrero, Marie Evans, Charumathi Sabanayagam, Ching‐Yu Cheng, Tien Yin Wong, C Yuen, William G. Herrington, Natalie Staplin, Martin J Landray, Colin Baigent, Philip A. Kalra, Rajkumar Chinnadurai, Darren Green, Smeeta Sinha, James Ritchie, Frank L.J. Visseren, Pascal Burger, M H Emmelot, Berend J. van Welzen, Oommen John, Balaji Gummidi, Arpita Ghosh, David Naimark, Navdeep Tangri, Min‐Kuang Tsai, Yoshiyuki Ueno, Masafumi Watanabe, Kazunobu Ichikawa, Mohammadhassan Mirbolouk, Fereidoun Azizi, Farzad Hadaegh, Farhad Hosseinpanah, Wen Shi, Dan E. Arking, Johan Ärnlöv, Anders Larsson, Vilmantas Giedraitis, Patrick B. Mark, Jamie P. Traynor, Michael L. Sullivan, Jennifer S. Lees, Jae Won Yang, Jae il Shin, Jun Young Lee, Jae seok Kim, Henk J.G. Bilo, Peter R. van Dijk, Mireille A. Edens, Joep Dille, Shoshana H. Ballew, Jingsha Chen, Aditya Surapaneni, Amanda Anderson, Fereidoun Azizi, Jing Chen, Tazeen H. Jafar, Brian J. Lee, Olivier Moranne, Chi-Pang Wen, Kunihiro Yamagata, Jae Won Yang

Notice bibliographique

RevueJAMA · 2023
Typereview
Langueen
DomaineMedicine
ThématiqueChronic Kidney Disease and Diabetes
Établissements canadiensUniversity of TorontoUniversity of CalgaryUniversity of British Columbia
Organismes subventionnairesNHLBI Division of Intramural ResearchNational Heart, Lung, and Blood InstituteSchool of Medicine, Tokai UniversityNational Health and Medical Research CouncilMedical Research CouncilClinical and Translational Science Collaborative of Cleveland, School of Medicine, Case Western Reserve UniversityJohns Hopkins Bloomberg School of Public HealthPerelman School of Medicine, University of PennsylvaniaSchool of Medicine, New York UniversityUniversity of Illinois at Urbana-ChampaignNational Center for Research ResourcesNational Institute of General Medical SciencesDuke Global Health Institute, Duke UniversityCenters for Disease Control and PreventionNational Institutes of HealthHögskolan DalarnaUniversitair Medisch Centrum GroningenOtsuka PharmaceuticalKarolinska InstitutetBritish Heart FoundationBundesministerium für Bildung und ForschungRijksuniversiteit GroningenBoehringer Ingelheim FranceUniversity of New South WalesInstitut National de la Santé et de la Recherche MédicaleDeutsche ForschungsgemeinschaftUniversity of LeicesterAcademy of Medical SciencesBritish Renal SocietyUniversity of California, San FranciscoGlaxoSmithKlineShahid Beheshti University of Medical SciencesDiabetes UKKidney Research UKNational Center for Advancing Translational SciencesKaiser PermanenteUniversidad de la República UruguayNational Institute for Health and Care ResearchAlbert-Ludwigs-Universität FreiburgUniversity Hospitals of Leicester NHS TrustDeutsches KrebsforschungszentrumGeisinger Commonwealth School of MedicineGeorgia Clinical and Translational Science AllianceTulane UniversityMichigan Institute for Clinical and Health ResearchSanofiNational Institute of Diabetes and Digestive and Kidney DiseasesMSD FranceUniversity of PennsylvaniaResearch Institute for Endocrine Sciences, Shahid Beheshti University of Medical SciencesEli Lilly and CompanyBrigham and Women's HospitalWellcome TrustUniversity of OxfordUniversity of DundeeTufts Medical CenterYork UniversityAstraZenecaDuke-NUS Medical SchoolJohns Hopkins UniversityUniversity of California, Los AngelesUniversity of WashingtonImperial College LondonUniversità degli Studi di SalernoAmgen
Mots-clésMedicineAlbuminuriaRenal functionCreatinineKidney diseaseCystatin CInternal medicinePopulationUrology

Résumé

récupéré en direct d'OpenAlex

Importance: Chronic kidney disease (low estimated glomerular filtration rate [eGFR] or albuminuria) affects approximately 14% of adults in the US. Objective: To evaluate associations of lower eGFR based on creatinine alone, lower eGFR based on creatinine combined with cystatin C, and more severe albuminuria with adverse kidney outcomes, cardiovascular outcomes, and other health outcomes. Design, Setting, and Participants: Individual-participant data meta-analysis of 27 503 140 individuals from 114 global cohorts (eGFR based on creatinine alone) and 720 736 individuals from 20 cohorts (eGFR based on creatinine and cystatin C) and 9 067 753 individuals from 114 cohorts (albuminuria) from 1980 to 2021. Exposures: The Chronic Kidney Disease Epidemiology Collaboration 2021 equations for eGFR based on creatinine alone and eGFR based on creatinine and cystatin C; and albuminuria estimated as urine albumin to creatinine ratio (UACR). Main Outcomes and Measures: The risk of kidney failure requiring replacement therapy, all-cause mortality, cardiovascular mortality, acute kidney injury, any hospitalization, coronary heart disease, stroke, heart failure, atrial fibrillation, and peripheral artery disease. The analyses were performed within each cohort and summarized with random-effects meta-analyses. Results: Within the population using eGFR based on creatinine alone (mean age, 54 years [SD, 17 years]; 51% were women; mean follow-up time, 4.8 years [SD, 3.3 years]), the mean eGFR was 90 mL/min/1.73 m2 (SD, 22 mL/min/1.73 m2) and the median UACR was 11 mg/g (IQR, 8-16 mg/g). Within the population using eGFR based on creatinine and cystatin C (mean age, 59 years [SD, 12 years]; 53% were women; mean follow-up time, 10.8 years [SD, 4.1 years]), the mean eGFR was 88 mL/min/1.73 m2 (SD, 22 mL/min/1.73 m2) and the median UACR was 9 mg/g (IQR, 6-18 mg/g). Lower eGFR (whether based on creatinine alone or based on creatinine and cystatin C) and higher UACR were each significantly associated with higher risk for each of the 10 adverse outcomes, including those in the mildest categories of chronic kidney disease. For example, among people with a UACR less than 10 mg/g, an eGFR of 45 to 59 mL/min/1.73 m2 based on creatinine alone was associated with significantly higher hospitalization rates compared with an eGFR of 90 to 104 mL/min/1.73 m2 (adjusted hazard ratio, 1.3 [95% CI, 1.2-1.3]; 161 vs 79 events per 1000 person-years; excess absolute risk, 22 events per 1000 person-years [95% CI, 19-25 events per 1000 person-years]). Conclusions and Relevance: In this retrospective analysis of 114 cohorts, lower eGFR based on creatinine alone, lower eGFR based on creatinine and cystatin C, and more severe UACR were each associated with increased rates of 10 adverse outcomes, including adverse kidney outcomes, cardiovascular diseases, and hospitalizations.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,011
score de la tête « metaresearch » (Gemma)0,026
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,011
Score d'incertitude au seuil0,060

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0110,026
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,005
Bibliométrie0,0020,003
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,064
Tête enseignante GPT0,367
Écart entre enseignants0,303 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations315
Publié2023
Routes d'admission2
Résumé présentoui

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