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Record W4403382611 · doi:10.2215/cjn.0000000000000548

Impairment of Cardiovascular Functional Capacity in Mild-to-Moderate Kidney Dysfunction

2024· article· en· W4403382611 on OpenAlexaff
Kenneth Lim, Matthew Nayor, Eliott Arroyo, Heather Burney, Xiaochun Li, Yang Li, Ravi V. Shah, Joseph Campain, Douglas Wan, Stephen Ting, Thomas Hiemstra, Ravi Thadhani, Sharon M. Moe, Daniel Zehnder, Martin G. Larson, Ramachandran S. Vasan, Gregory D. Lewis

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2024
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNational Heart, Lung, and Blood InstituteFramingham Heart StudyAmerican Heart Association
KeywordsMedicineRenal functionKidney diseaseInternal medicineCohortHeart failureCardiologyFramingham Heart StudyAnaerobic exercisePhysical therapyDiseaseFramingham Risk Score

Abstract

fetched live from OpenAlex

Key Points Mild-to-moderate CKD is associated with impairment in cardiovascular functional capacity as assessed by oxygen uptake at peak exercise (VO 2 Peak). Cardiac output is significantly reduced in patients with mild-to-moderate CKD and is associated with impaired VO 2 Peak. Assessment of VO 2 Peak by cardiopulmonary exercise testing can detect decrements in cardiovascular function during early stages of kidney function decline that may not be captured using resting left ventricular geometric indices alone. Background Traditional diagnostic tools that assess resting cardiac function and structure fail to accurately reflect cardiovascular alterations in patients with CKD. This study sought to determine whether multidimensional exercise response patterns related to cardiovascular functional capacity can detect abnormalities in mild-to-moderate CKD. Methods In a cross-sectional study, we examined 3075 participants from the Framingham Heart Study (FHS) and 451 participants from the Massachusetts General Hospital Exercise Study (MGH-ExS) who underwent cardiopulmonary exercise testing. Participants were stratified by eGFR: eGFR ≥90, eGFR 60–89, and eGFR 30–59. Our primary outcomes of interest were peak oxygen uptake (VO 2 Peak), VO 2 at anaerobic threshold (VO 2 AT), and ratio of minute ventilation to carbon dioxide production (VE/VCO 2 ). Multiple linear regression models were fitted to evaluate the associations between eGFR group and each outcome variable adjusted for covariates. Results In the FHS cohort, 1712 participants (56%) had an eGFR ≥90 ml/min per 1.73 m 2 , 1271 (41%) had an eGFR of 60–89 ml/min per 1.73 m 2 , and 92 (3%) had an eGFR of 30–59 ml/min per 1.73 m 2 . In the MGH-ExS cohort, 247 participants (55%) had an eGFR ≥90 ml/min per 1.73 m 2 , 154 (34%) had an eGFR of 60–89 ml/min per 1.73 m 2 , and 50 (11%) had an eGFR of 30–59 ml/min per 1.73 m 2 . In FHS, VO 2 Peak and VO 2 AT were incrementally impaired with declining kidney function ( P < 0.001); however, this pattern was attenuated after adjustment for age. Percent-predicted VO 2 Peak at AT was higher in the lower eGFR groups ( P < 0.001). In MGH-ExS, VO 2 Peak and VO 2 AT were incrementally impaired with declining kidney function in unadjusted and adjusted models ( P < 0.05). VO 2 Peak was associated with eGFR ( P < 0.05) in all models even after adjusting for age. On further mechanistic analysis, we directly measured cardiac output (CO) at peak exercise by right heart catheterization and found impaired CO in the lower eGFR groups ( P ≤ 0.007). Conclusions Cardiopulmonary exercise testing–derived indices may detect impairment in cardiovascular functional capacity and track CO declines in mild-to-moderate CKD.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.058
GPT teacher head0.322
Teacher spread0.264 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2024
Admission routes1
Has abstractyes

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