Haemoglobin trajectories in chronic kidney disease and risk of major adverse cardiovascular events.

anaemia haemoglobin joint latent linear mixed model major adverse cardiovascular events trajectories

Journal

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
ISSN: 1460-2385
Titre abrégé: Nephrol Dial Transplant
Pays: England
ID NLM: 8706402

Informations de publication

Date de publication:
03 Nov 2023
Historique:
medline: 8 11 2023
pubmed: 8 11 2023
entrez: 7 11 2023
Statut: aheadofprint

Résumé

Trajectories of haemoglobin in patients with chronic kidney disease (CKD) have been poorly described. In such patients, we aimed to identify typical haemoglobin trajectory profiles and estimate their risks of major adverse cardiovascular events (MACE). We used 5-year longitudinal data from the CKD-REIN cohort patients with moderate to severe CKD enrolled from 40 nationally representative nephrology clinics in France. A joint latent class model was used to estimate, in different classes of haemoglobin trajectory, the competing risks of (i) MACE + defined as the first event among cardiovascular death, non-fatal myocardial infarction, stroke or hospitalization for acute heart failure, (ii) initiation of kidney replacement therapy (KRT), and (iii) non-cardiovascular death. During the follow-up, we gathered 33 874 haemoglobin measurements from 3 011 subjects (median, 10 per patient). We identified five distinct haemoglobin trajectory profiles. The predominant profile (n = 1885, 62.6%) showed an overall stable trajectory and low risks of events. The four other profiles had nonlinear declining trajectories: early strong decline (n = 257, 8.5%), late strong decline (n = 75, 2.5%), early moderate decline (n = 356, 11.8%) and late moderate decline (n = 438, 14.6%). The four profiles had different risks of MACE, while the risks of KRT and non-cardiovascular death consistently increased from the haemoglobin decline. In this study, we observed that two third of patients had stable haemoglobin trajectory and low risks of adverse events. The other third had a nonlinear trajectory declining at different rates, with increased risks of events. A better attention to dynamic changes of haemoglobin in CKD should be paid.

Sections du résumé

BACKGROUND BACKGROUND
Trajectories of haemoglobin in patients with chronic kidney disease (CKD) have been poorly described. In such patients, we aimed to identify typical haemoglobin trajectory profiles and estimate their risks of major adverse cardiovascular events (MACE).
METHODS METHODS
We used 5-year longitudinal data from the CKD-REIN cohort patients with moderate to severe CKD enrolled from 40 nationally representative nephrology clinics in France. A joint latent class model was used to estimate, in different classes of haemoglobin trajectory, the competing risks of (i) MACE + defined as the first event among cardiovascular death, non-fatal myocardial infarction, stroke or hospitalization for acute heart failure, (ii) initiation of kidney replacement therapy (KRT), and (iii) non-cardiovascular death.
RESULTS RESULTS
During the follow-up, we gathered 33 874 haemoglobin measurements from 3 011 subjects (median, 10 per patient). We identified five distinct haemoglobin trajectory profiles. The predominant profile (n = 1885, 62.6%) showed an overall stable trajectory and low risks of events. The four other profiles had nonlinear declining trajectories: early strong decline (n = 257, 8.5%), late strong decline (n = 75, 2.5%), early moderate decline (n = 356, 11.8%) and late moderate decline (n = 438, 14.6%). The four profiles had different risks of MACE, while the risks of KRT and non-cardiovascular death consistently increased from the haemoglobin decline.
CONCLUSION CONCLUSIONS
In this study, we observed that two third of patients had stable haemoglobin trajectory and low risks of adverse events. The other third had a nonlinear trajectory declining at different rates, with increased risks of events. A better attention to dynamic changes of haemoglobin in CKD should be paid.

Identifiants

pubmed: 37935529
pii: 7342465
doi: 10.1093/ndt/gfad235
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Investigateurs

Natalia Alencar De Pinho (NA)
Christian Combe (C)
Denis Fouque (D)
Luc Frimat (L)
Aghilès Hamroun (A)
Christian Jacquelinet (C)
Maurice Laville (M)
Sophie Liabeuf (S)
Ziad A Massy (ZA)
Christophe Pascal (C)
Roberto Pecoits-Filho (R)
Bénédicte Stengel (B)
Céline Lange (C)
Oriane Lambert (O)
Marie Metzger (M)

Informations de copyright

© The Author(s) 2023. Published by Oxford University Press on behalf of the ERA.

Auteurs

Lisa Le Gall (L)

Univ Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR1219, Bordeaux, France.
Univ Bordeaux, INSERM, CIC-1401-EC, Bordeaux, France.

Jérôme Harambat (J)

Univ Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR1219, Bordeaux, France.
Univ Bordeaux, INSERM, CIC-1401-EC, Bordeaux, France.
Bordeaux University Hospital, Pediatric Nephrology Unit, Centre de Référence des Maladies Rénales Rares Sorare, Pellegrin-Enfants Hospital, Bordeaux, France.

Christian Combe (C)

Bordeaux University Hospital, Department of Nephrology, transplantation, dialysis, Bordeaux, France.
Univ Bordeaux, INSERM U1026, Bordeaux, France.

Viviane Philipps (V)

Univ Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR1219, Bordeaux, France.

Cécile Proust-Lima (C)

Univ Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR1219, Bordeaux, France.

Maris Dussartre (M)

Univ Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR1219, Bordeaux, France.

Tilman Druëke (T)

Centre for research in Epidemiology and Population Health (CESP), Paris-Saclay university, Versailles Saint-Quentin university, Inserm U1018 Clinical Epidemiology Team, Villejuif, France.

Gabriel Choukroun (G)

University Hospital Centre Amiens-Picardie, Nephrology and Transplantation, Amiens, France.

Denis Fouque (D)

Hopital Lyon Sud, Département de néphrologie, Lyon, France.
Université Claude Bernard Lyon 1, Carmen INSERM U1060, Pierre-Bénite, France.

Luc Frimat (L)

CHRU de Nancy, Department of Nephrology, Vandoeuvre-lès-Nancy, France.

Christian Jacquelinet (C)

Agence de la biomedecine, Agence de la biomedicine, La Plaine-Saint-Denis, France.

Maurice Laville (M)

Université Claude Bernard Lyon 1, Carmen INSERM U1060, Pierre-Bénite, France.

Sophie Liabeuf (S)

Amiens-Picardie University Medical Center, Pharmacoepidemiology Unit, Department of Clinical Pharmacology, Amiens, France.
Jules Verne University of Picardie, MP3CV Laboratory, Amiens, France.

Roberto Pecoits-Filho (R)

Arbor Research Collaborative for Health, Arbor, MI, USA.

Ziad A Massy (ZA)

Centre for research in Epidemiology and Population Health (CESP), Paris-Saclay university, Versailles Saint-Quentin university, Inserm U1018 Clinical Epidemiology Team, Villejuif, France.
Ambroise Paré University Hospital, APHP, Department of Nephrology, Boulogne-Billancourt/Paris, France.

Bénédicte Stengel (B)

Centre for research in Epidemiology and Population Health (CESP), Paris-Saclay university, Versailles Saint-Quentin university, Inserm U1018 Clinical Epidemiology Team, Villejuif, France.

Natalia Alencar de Pinho (N)

Centre for research in Epidemiology and Population Health (CESP), Paris-Saclay university, Versailles Saint-Quentin university, Inserm U1018 Clinical Epidemiology Team, Villejuif, France.

Karen Leffondré (K)

Univ Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR1219, Bordeaux, France.
Univ Bordeaux, INSERM, CIC-1401-EC, Bordeaux, France.

Mathilde Prezelin-Reydit (M)

Univ Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR1219, Bordeaux, France.
Univ Bordeaux, INSERM, CIC-1401-EC, Bordeaux, France.
Maison du REIN AURAD Aquitaine, Néphrologie, Gradignan, Nouvelle-Aquitaine, FR.

Classifications MeSH