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Record W2552147998 · doi:10.1053/j.ajkd.2016.08.030

Interdialytic Weight Gain: Trends, Predictors, and Associated Outcomes in the International Dialysis Outcomes and Practice Patterns Study (DOPPS)

2016· article· en· W2552147998 on OpenAlexaff
Michelle Wong, Keith McCullough, Brian Bieber, Juergen Bommer, Manfred Hecking, Nathan W. Levin, William M. McClellan, Ronald L. Pisoni, Rajiv Saran, Francesca Tentori, Tadashi Tomo, Friedrich K. Port, Bruce Robinson

Bibliographic record

VenueAmerican Journal of Kidney Diseases · 2016
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesFresenius Medical Care North AmericaAmgen
KeywordsMedicineHemodialysisProportional hazards modelRelative riskConfoundingWeight gainDialysisConfidence intervalDemographyInternal medicineBody weight

Abstract

fetched live from OpenAlex

BACKGROUND: High interdialytic weight gain (IDWG) is associated with adverse outcomes in hemodialysis (HD) patients. We identified temporal and regional trends in IDWG, predictors of IDWG, and associations of IDWG with clinical outcomes. STUDY DESIGN: Analysis 1: sequential cross-sections to identify facility- and patient-level predictors of IDWG and their temporal trends. Analysis 2: prospective cohort study to assess associations between IDWG and mortality and hospitalization risk. SETTING & PARTICIPANTS: 21,919 participants on HD therapy for 1 year or longer in the Dialysis Outcomes and Practice Patterns Study (DOPPS) phases 2 to 5 (2002-2014). PREDICTORS: Analysis 1: study phase, patient demographics and comorbid conditions, HD facility practices. Analysis 2: relative IDWG, expressed as percentage of post-HD weight (<0%, 0%-0.99%, 1%-2.49%, 2.5%-3.99% [reference], 4%-5.69%, and ≥5.7%). OUTCOMES: Analysis 1: relative IDWG as a continuous variable using linear mixed models; analysis 2: mortality; all-cause and cause-specific hospitalization using Cox regression, adjusting for potential confounders. RESULTS: From phase 2 to 5, IDWG declined in the United States (-0.29kg; -0.5% of post-HD weight), Canada (-0.25kg; -0.8%), and Europe (-0.22kg; -0.5%), with more modest declines in Japan and Australia/New Zealand. Among modifiable factors associated with IDWG, the most notable was facility mean dialysate sodium concentration: every 1-mEq/L greater dialysate sodium concentration was associated with 0.13 (95% CI, 0.11-0.16) greater relative IDWG. Compared to relative IDWG of 2.5% to 3.99%, there was elevated risk for mortality with relative IDWG≥5.7% (adjusted HR, 1.23; 95% CI, 1.08-1.40) and elevated risk for fluid-overload hospitalization with relative IDWG≥4% (HRs of 1.28 [95% CI, 1.09-1.49] and 1.64 [95% CI, 1.27-2.13] for relative IDWGs of 4%-5.69% and ≥5.7%, respectively). LIMITATIONS: Possible residual confounding. No dietary salt intake data. CONCLUSIONS: Reductions in IDWG during the past decade were partially explained by reductions in dialysate sodium concentration. Focusing quality improvement strategies on reducing occurrences of high IDWG may improve outcomes in HD patients.

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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.293
Teacher spread0.283 · 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

Citations140
Published2016
Admission routes1
Has abstractno

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