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Record W4400193509 · doi:10.2215/cjn.0000000000000498

Frailty, Multimorbidity, and Polypharmacy

2024· article· en· W4400193509 on OpenAlexaff
Kaitlin J. Mayne, Rebecca J. Sardell, Natalie Staplin, Parminder K. Judge, Doreen Zhu, Emily Sammons, David Z.I. Cherney, Alfred K. Cheung, Aldo P. Maggioni, Masaomi Nangaku, Xavier Rosselló, Katherine R. Tuttle, Katsuhito Ihara, Tomoko Iwata, Christoph Wanner, Jonathan Emberson, David Preiss, Martin Landray, Colin Baigent, Richard Haynes, William G. Herrington

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2024
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of Toronto
FundersMedical Research CouncilBritish Heart FoundationKidney Research UKInternational Society of NephrologyUniversity of OxfordEli Lilly and Company
KeywordsMedicinePolypharmacyMultimorbidityComorbidityMEDLINEIntensive care medicineGerontologyInternal medicine

Abstract

fetched live from OpenAlex

Key Points Frailty, multimorbidity, and polypharmacy overlap and are associated with higher risk of adverse health outcomes in CKD. Empagliflozin was safe, well tolerated, and effectively reduced cardiorenal and hospitalization risk irrespective of these characteristics. Absolute benefits appeared greater in the most frail participants in this post hoc analysis of EMPA-KIDNEY. Background Sodium-glucose cotransporter-2 inhibitors are recommended treatment for adults with CKD, but uncertainty exists regarding their use in patients with frailty and/or multimorbidity, among whom polypharmacy is common. We derived a multivariable logistic regression model to predict hospitalization (reflecting frailty) and assessed empagliflozin's risk–benefit profile in a post hoc analysis of the double-blind, placebo-controlled EMPA-KIDNEY trial. Methods The EMPA-KIDNEY trial randomized 6609 patients with CKD (eGFR ≥20 to <45 ml/min per 1.73 m 2 , or ≥45 to <90 ml/min per 1.73 m 2 with urinary albumin-to-creatinine ratio ≥200 mg/g) to receive either empagliflozin 10 mg daily or matching placebo and followed them for 2 years (median). Additional characteristics analyzed in subgroups were multimorbidity, polypharmacy, and health-related quality of life at baseline. Cox regression analyses were performed with subgroups defined by approximate thirds of each variable. Results The strongest predictors of hospitalization were N -terminal prohormone of brain natriuretic peptide, poor mobility, and diabetes and then eGFR and other comorbidities. Empagliflozin was generally well tolerated independent of predicted risk of hospitalization. In relative terms, allocation to empagliflozin reduced the risk of the primary outcome of kidney disease progression or cardiovascular death by 28% (hazard ratio, 0.72; 95% confidence interval, 0.64 to 0.82) and all-cause hospitalization by 14% (hazard ratio, 0.86; 95% confidence interval, 0.78 to 0.95), with broadly consistent effects across subgroups of predicted risk of hospitalization, multimorbidity, polypharmacy, or health-related quality of life. In absolute terms, the estimated benefits of empagliflozin were greater in those at highest predicted risk of hospitalization (reflecting frailty) and outweighed potential serious harms. Conclusions These findings support the use of sodium-glucose cotransporter-2 inhibitors in CKD, irrespective of frailty, multimorbidity, or polypharmacy. Clinical Trial registration number: NCT03594110. Podcast This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2024_09_23_CJASNSeptember19992.mp3

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.079
GPT teacher head0.420
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations25
Published2024
Admission routes1
Has abstractyes

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