Evidence map›Paper›PMID 41767685›Full record

ArticleKidney medicine2026

Prognostic Significance of Baseline Lean Tissue Mass Percentage in Incident Peritoneal Dialysis Patients.

Lixing Xu, Jack Kit-Chung Ng, Gordon Chun-Kau Chan, Winston Wing-Shing Fung, Kai-Ming Chow, Cheuk-Chun Szeto

Abstract read
In one paragraph

Article in Kidney medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Lixing XuDepartment of Medicine & Therapeutics, Carol & Richard Yu Peritoneal Dialysis Research Centre, Prince of Wales Hospital.
Jack Kit-Chung NgDepartment of Medicine & Therapeutics, Carol & Richard Yu Peritoneal Dialysis Research Centre, Prince of Wales Hospital.
Gordon Chun-Kau ChanDepartment of Medicine & Therapeutics, Carol & Richard Yu Peritoneal Dialysis Research Centre, Prince of Wales Hospital.
Winston Wing-Shing FungDepartment of Medicine & Therapeutics, Carol & Richard Yu Peritoneal Dialysis Research Centre, Prince of Wales Hospital.
Kai-Ming ChowDepartment of Medicine & Therapeutics, Carol & Richard Yu Peritoneal Dialysis Research Centre, Prince of Wales Hospital.
Cheuk-Chun SzetoDepartment of Medicine & Therapeutics, Carol & Richard Yu Peritoneal Dialysis Research Centre, Prince of Wales Hospital.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rationale & Objective: Bioimpedance spectroscopy is a convenient method to measure lean tissue mass (LTM), which is commonly taken as the skeletal muscle mass, in peritoneal dialysis (PD) patients. We investigated the prognostic significance of LTM as the percentage of body weight (%LTM) and as the marker of sarcopenia in incident PD patients. Study Design: A retrospective review of a prospective cohort. Setting & Participants: 349 incident PD patients in a single center. Predictor: Baseline %LTM. Outcomes: Patient survival, technique survival, peritonitis-free survival, peritonitis rate, hospitalization rate, and the duration of hospitalization. Analytical Approach: Time-to-event survival analyses; linear regression for hospitalization. Results: The 5-year patient survival rates were 50.2%, 55.3%, 61.0%, and 72.6% for patients with %LTM quartiles I-IV, respectively (log-rank test; Limitations: Observational study with baseline measures only. Conclusions: Baseline %LTM as measured using bioimpedance spectroscopy is associated with patient survival, peritonitis-free survival, the number of hospital admissions, and the duration of hospital stay in incident PD patients.

Indexed as

frailtyKidney failuremalnutritionsarcopenia

Identifiers

PMID41767685
PMCPMC12936936

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.