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Record W4206558653 · doi:10.1177/20543581211069008

Trajectories of Nutritional Parameters Before and After Prescribed Oral Nutritional Supplements: A Longitudinal Cohort Study of Patients With Chronic Kidney Disease Not Requiring Dialysis

2022· article· en· W4206558653 on OpenAlexaff
Michelle Wong, Yuyan Zheng, Dani Renouf, Zainab Sheriff, Adeera Levin

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2022
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsSt. Paul's HospitalProvidence Health CareUniversity of British Columbia
Fundersnot available
KeywordsMedicineKidney diseaseInternal medicineMalnutritionWastingMedical prescriptionDialysisCohortCohort studyConfidence intervalBody mass indexRenal functionPharmacology

Abstract

fetched live from OpenAlex

Background: The association between oral nutritional supplement use and nutritional parameters among patients with nondialysis chronic kidney disease (CKD-ND) with or at high risk of undernutrition/protein-energy wasting has not been previously studied. The definition of patient subgroups most likely to benefit from oral nutritional supplementation (ONS) is also an area where more research is needed. Objective: To assess nutritional parameter trajectories among patients with CKD-ND prescribed oral nutritional supplements in British Columbia, and to compare trajectories by nutritional phenotype. Design: Longitudinal cohort study, pre-post design. Setting: Multidisciplinary CKD clinics across British Columbia. Patients: A total of 3957 adult patients with CKD-ND, who entered multidisciplinary CKD clinics during 2010 to 2019, met criteria for oral nutritional supplement prescription based on dietitian assessment, and received ≥1 oral nutritional supplement prescription. Measurements: Longitudinal nutritional parameters, including body mass index (BMI), serum albumin, serum bicarbonate, serum phosphate, and neutrophil-to-lymphocyte ratio (NLR). Methods: Using linear mixed models, slopes for nutritional and inflammation parameters were assessed in the 2-year periods before and after the first oral nutritional supplement prescription. Hierarchical cluster analysis was applied to identify nutritional phenotypes using baseline data, and slope analysis was repeated by cluster. Results: In the pre-oral-nutritional-supplement period, declines in BMI (−0.87 kg/m 2 /year, 95% confidence interval [CI]: −0.99 to −0.75), albumin (−1.11 g/L/year, 95% CI: −1.27 to −0.95), and bicarbonate (−0.49 mmol/L/year; 95% CI: −0.59 to −0.39), and increases in NLR (+0.79/year; 95% CI: 0.60 to 0.98) and phosphate (+0.05 mmol/L/year; 95% CI: 0.04 to 0.06) were observed. Following oral nutritional supplement prescription, there were statistically significant increases in BMI slope (+0.91 kg/m 2 /year, P < .0001), albumin slope (+0.82 g/L/year, P < .0001), and phosphate slope (+0.02 mmol/L/year, P = .005), as well as a decline in NLR slope of −0.55/year ( P < .0001). There was no significant change in bicarbonate slope. Cluster analysis identified 5 distinct phenotypes. The cluster with the highest mean baseline NLR and lowest mean BMI demonstrated the greatest number of improvements in nutritional parameter slopes in the post-oral-nutritional-supplement period. Limitations: Possibility of residual confounding. Data on dietary intake, muscle mass, and nutritional scoring systems were not available in the registry. Conclusions: Among patients with CKD-ND prescribed oral nutritional supplements, there were improvements in nutrition/inflammation parameters over time following the first ONS prescription. The heterogeneity in response to ONS by cluster subgroup suggests an individualized approach to nutritional management may be beneficial.

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 score0.296
Threshold uncertainty score0.588

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.013
GPT teacher head0.253
Teacher spread0.240 · 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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Citations13
Published2022
Admission routes1
Has abstractyes

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