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Record W2951177075 · doi:10.1093/ndt/gfz106.fp732

FP732PROTEIN-ENERGY WASTING IN ELDERLY MAINTENANCE HEMODIALYSIS PATIENTS

2019· article· en· W2951177075 on OpenAlexaboutno aff
Zauresh Amreyeva, Gulnar Chingayeva, Assya Kanatbayeva, Abai Shepetov, Elmira Alimzhanova, Makpal Kulkayeva

Bibliographic record

VenueNephrology Dialysis Transplantation · 2019
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineWastingHemodialysisIntensive care medicineCachexiaInternal medicineCancer

Abstract

fetched live from OpenAlex

INTRODUCTION: In Kazakhstan the number of geriatric patients on maintenance hemodialysis (MHD) has been increasing. Protein-energy wasting (PEW) is prevalent complication in these patients. The primary objectives of this study were to evaluate the prevalence of PEW in elderly patients on MHD by using different nutritional assessment tools and assess their association with clinical and laboratory measurements. A secondary objective was to investigate the relationship between nutritional status and frailty among these patients. METHODS: A multicenter cross-sectional study included 65 patients aged ≥ 65 years undergoing HD for at least 3 months in 7 outpatient HD facilities in Almaty, Kazakhstan. The study was performed from July to September 2018. Nutritional status of patients was evaluated by using Mini Nutritional assessment (MNA), Malnutrition-Inflammation Score (MIS), and anthropometric measurements (body mass index (BMI), triceps skinfold (TSF), mid-arm muscle circumference (MAMC)), biochemical data were collected from medical records. Frailty was defined in accordance with the Edmonton Frail scale (EFS). RESULTS: In the study, participants’ median age was 69 (range: 65–88) years old, and median dialysis vintage was 36 (IQR 15–60) months, 20% were aged 75 year old and older, 53.8% were female. The major causes of the ESRD were hypertension (38.5%) and diabetes (24.6%). Based on MIS the prevalence of PEW was 73.8%. According to MNA, the nutritional status was normal in 43.1% of patients, risk of malnutrition was detected in 47.7%, and malnutrition in 9.2% of patients on MHD. Mean body weight was 69,1±11.3kg, the mean BMI was slightly overweight 25.6±4.29kg/m2, while hand-grip strength was 21.33±3.36 in men and 15.5±5.51 in women, p=0.008, and it is lower than the normal population standard values. CRP was negative in 55.4% of cases. While MAMC was found to be significantly higher in male patients (24.87±1.93cm in men and 23.39±2.76cm in women, p=0.018), TSF was found to be significantly higher in female patients (9.56±3.74mm in men and 16.68±7.38cm in women, p<0.001). No significant difference was observed between genders in the frequency of malnutrition according to SGA, MNA, MIS, BMI, serum albumin, creatinine. The prevalence of frailty assessed by the EFS 43.1% were classified as non-frail patients, 33.8% as vulnerable, and 23.1% as frail. Among the frail patients, 86.7% were female (p=0.005) and 93.3% of frail patients had PEW (p=0.001) evaluated by MIS. CONCLUSIONS: Protein-energy wasting is common among elderly hemodialysis patients in Kazakhstan. Its prevalence varies between 56.9% and 73.8% depending on the measurement tool used to evaluate the nutritional status. In our country with limited resources, MIS and MNA nutritional scores could help to follow the nutritional status of our elderly hemodialysis patients. Also the study showed that the prevalence of frailty is high among female patients, and we detected that PEW increased in female frail 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)0.000
metaresearch head score (Gemma)0.001
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.006
GPT teacher head0.214
Teacher spread0.208 · 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
Published2019
Admission routes1
Has abstractyes

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