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Enregistrement W2182065036 · doi:10.1681/asn.2015050531

Not All Deaths in CKD Are from a Broken Heart

2015· letter· en· W2182065036 sur OpenAlexaffabout
Germaine Wong, Amit X. Garg

Notice bibliographique

RevueJournal of the American Society of Nephrology · 2015
Typeletter
Langueen
DomaineMedicine
ThématiqueChronic Kidney Disease and Diabetes
Établissements canadiensInstitute for Clinical Evaluative SciencesWestern University
Organismes subventionnairesnon disponible
Mots-clésCardiologyMedicineInternal medicineIntensive care medicine

Résumé

récupéré en direct d'OpenAlex

CKD is common, but outcomes remain poor, with a high incidence of death as eGFR declines.1 Two studies published in this issue of JASN examine the association between reduced kidney function and specific causes of death.2,3 Navaneethan et al.2 used electronic health records and a linked mortality registry from the state of Ohio in the United States to study adults with two eGFR values <60 ml/min per 1.73 m2. Thompson et al.3 used administrative health care data and linked laboratory information from the province of Alberta in Canada to study all adult deaths stratified by recent eGFR and urine protein results. Reliably ascertaining an individual’s cause of death is notoriously difficult, even in prospective cohort studies with central adjudication. In both of these retrospective analyses, administrative personnel recorded the cause of death in routine care using the International Classification of Diseases 10th Revision system. With this method of ascertainment, we expect that many deaths were classified incorrectly. Reasons for misclassification include scenarios with multiple causes of death, ill-defined causes of death, and a lack of autopsy data. Nonetheless, these studies by Navaneethan et al.2 and Thompson et al.3 provide several important messages for our consideration. Both studies confirm that a greater proportion of deaths is attributable to cardiovascular diseases as eGFR declines. Some would say this association between low eGFR and a higher risk of death “from a broken heart” is already well appreciated by the nephrology community.4 As might be expected, in the Alberta study, after adjustment for age and sex, 33% of deaths were attributed to cardiovascular disease when the eGFR was 45–59 ml/min per 1.73 m2, and 40% of deaths were attributed to cardiovascular disease when the eGFR was 15–29 ml/min per 1.73 m2.3 Similarly, in the Ohio study, the 3-year probability of mortality from cardiovascular disease increased in a graded manner as eGFR declined (from approximately 3% when the eGFR was 60 ml/min per 1.73 m2 to 7.5% when the eGFR was 10 ml/min per 1.73 m2).2 The Alberta study provides new insights into the nature of these cardiovascular deaths. As eGFR declined, a greater proportion of people died from cardiac failure and valvular disease rather than ischemic heart disease. This finding is consistent with prior data of chronic fluid overload coupled with anemia and chronic hypertension producing a high cardiac output state, leading to mechanical trauma.5 Valvular calcification is also prominent in CKD, where it is attributed to chronic inflammation and impairments of calcium phosphate metabolism.6 There are other prominent causes of death in CKD beyond a broken heart. In the Alberta study, infection accounted for 3% of deaths in those with an eGFR of 45–59 ml/min per 1.73 m2 and 5% of deaths in those with an eGFR of 15–29 ml/min per 1.73 m2. A similar graded relationship between CKD stage and severe infection requiring hospitalization was also observed in a recent meta-analysis.7 Better infection control may prevent some of these deaths. For example, in the study by Navaneethan et al.,2 approximately 10% of all CKD deaths were attributed to diabetic-related infections. Perhaps better diabetic foot care will prevent some deaths. In the Ohio study, the 3-year probability of mortality from cancer was approximately 2.5%, and this percentage remained unchanged through all stages of eGFR. Similar findings were observed in the Alberta cohort. Large population–based cohort studies suggest a graded association between reduced eGFR and cancer risk.8,9 However, the direction and the causality of this association require clarification. Although in the Ohio study, approximately 20% of those with CKD had cancer, it is unclear whether these cancers developed before or after the onset of CKD. Cancers, such as multiple myeloma and lymphoma, are established risk factors for CKD, whereas 1 in 10 patients has CKD in the setting of solid-organ malignancies.10 In other analyses restricted to those with cancer, CKD was associated with at least a 2-fold higher risk of death from cancer.11 Where do we go from here? These two studies shed new light on the association between reduced eGFR and cause of death.2,3 While we continue to search for therapies to prevent cardiovascular deaths in CKD, we also need to focus on strategies to prevent nonvascular deaths from infection and cancer. For example, the benefit of screening to reduce cancer-related deaths in CKD remains largely unknown.12 These studies serve as a call to bridge the gap between pathogenic understanding, experimental data, clinical testing, and high-quality care. We urgently need proven strategies to prevent deaths in CKD. Sadly, the results of intervention trials in CKD (for example, of antiplatelet and lipid–lowering therapies to prevent major cardiovascular events) have been disappointing or modest at best.13,14 Let us stop the heart break by working together on large–scale, practice–changing research. Finally, not all deaths should be prevented at the expense of quality of life, and we need to listen carefully to our patients and their families to ensure that we provide care according to their values and preferences. Disclosures None. We thank Drs. Jonathan Craig and Jessica Sontrop for their advice. A.X.G. was supported by the Dr. Adam Linton Chair in Kidney Health Analytics.

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)0,002
score de la tête « metaresearch » (Gemma)0,009
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: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,123
Score d'incertitude au seuil0,244

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,002
Communication savante0,0040,002
Science ouverte0,0020,001
Intégrité de la recherche0,0020,004
Charge utile insuffisante (le modèle a refusé de juger)0,0060,002

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 Opus0,029
Tête enseignante GPT0,293
Écart entre enseignants0,264 · 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
GenreCommentaire

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

En bref

Citations0
Publié2015
Routes d'admission2
Résumé présentoui

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Même revueJournal of the American Society of Nephrology→Même sujetChronic Kidney Disease and Diabetes→Travaux en français237 207→