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Record W2804027411 · doi:10.1093/ndt/gfy104.fp633

FP633PULSE WAVE VELOCITY AND PROGNOSIS IN CHRONIC KIDNEY FAILURE

2018· article· en· W2804027411 on OpenAlexaffabout
Giovanni Tripepi, Mohsen Agharazii, Bruno Pannier, Graziella D’Arrigo, Francesca Mallamaci, Carmine Zoccali, Gérard M. London

Bibliographic record

VenueNephrology Dialysis Transplantation · 2018
Typearticle
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsHôtel-Dieu de Québec
Fundersnot available
KeywordsMedicineKidney diseaseChronic renal failureInternal medicineIntensive care medicineCardiology

Abstract

fetched live from OpenAlex

INTRODUCTION AND AIMS: Pulse wave velocity (PWV) is considered as an indicator of arterial damage and it is taken for granted that PWV is a powerful prognostic factor in the ESKD population. However, until now there is no study in ESKD patients that investigated the prognostic value of PWV by state-of-art prognostic analyses including discrimination analysis (Harrell‘s C statistics), risk re-classification and calibration. METHODS: We assessed whether PWV adds prognostic information to the prediction power of the two simple, well validated risk calculators for predicting 2-years all-cause and cardiovascular (CV) mortality in ESKD (the ARO risk scores). Our analysis is based on the two largest cohorts investigating PWV in ESKD patients, the Manes-Hospital (MH) cohort in Paris (n=287 HD patients) and Québec Research Center (QRC) cohort (n=246 HD patients) in Canada. RESULTS: In both cohorts, PWV had a positively skewed distribution with a median value of 11 m/sec (IQR: 9-13) in the MH cohort and of 13 m/sec (IQR: 10-16) in the QRC cohort. During a 2-year follow-up period, 16 deaths/100 person-years were observed in the MH cohort and a similar figure was observed in the QRC cohort (16 deaths/100 person-years). Likewise, the incidence rate of CV death in the MH cohort (12 cases/100 person-years) did not differ from that observed in the QRC cohort (9.4 cases/100 person-years). The discriminatory power (Harrell’C index, HC) of the ARO risk score was consistently higher than that provided by the PWV in the two cohorts for both all-cause (MH cohort, 77.5% vs 73.7%; QRC cohort, 61.5% vs 58.9%) and CV mortality (MH cohort, 77.9% vs 77.2%; QRC cohort, 63.8% vs 60.3%). The introduction of PWV into Cox analyses including the ARO risk score provided a very modest increase in the discriminatory power of the same models in both the MH (all-cause death, from 77.5% to 79.3%, P=0.02; CV mortality, from 77.9% to 81.2%, P<0.001) and QRC cohort (all-cause death, from 61.5% to 62.3%, P=0.32; CV mortality, from 63.8% to 64.3%, P=0.16). The additional prognostic value of PWV as assessed by the integrated discrimination improvement (IDI) index confirmed a very modest increase in prognostic accuracy by this biomarker beyond and above the ARO risk score (MH cohort, all-cause death: IDI=+2.7%, P=0.02; CV death: IDI=+5.1%, P=0.02; QRC cohort, all-cause death: IDI=+0.9%, P=0.16; CV death: IDI=+1.2%, P=0.18). Furthermore, the introduction of PWV into Cox analyses containing the ARO risk score actually determined a worsening in models calibration. CONCLUSIONS: Pulse wave velocity has prognostic power for all-cause and CV mortality inferior to that by simple risk calculators based on standard, easily available clinical data in this population. PWV only modestly improves the prediction of all-cause and CV death of the ARO clinical risk scores and fails to improve risk calibration or risk reclassification when considered in combination with the ARO risk scores. Thus, clinicians may better rely on the ARO clinical scores rather than on PWV for risk stratification in the ESKD population.

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)0.003
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 score0.022
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.012
GPT teacher head0.248
Teacher spread0.236 · 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".

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Citations0
Published2018
Admission routes2
Has abstractyes

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