Evidence map›Paper›PMID 39678542›Full record

ArticleAmerican journal of translational research2024

Analysis of risk factor for peritonitis in peritoneal dialysis patients.

Yanqiong Ding, Hongdan Tian, Qing Luo, Yanmin Zhang, Hongbo Li, Sheng Wan, Lulu Li, Li Sun

Abstract read
In one paragraph

Article in American journal of translational research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

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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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

8 authors.

Yanqiong DingDepartment of Nephrology, Wuhan No. 1 Hospital Wuhan 430022, Hubei, China.
Hongdan TianDepartment of Nephrology, Wuhan No. 1 Hospital Wuhan 430022, Hubei, China.
Qing LuoDepartment of Nephrology, Wuhan No. 1 Hospital Wuhan 430022, Hubei, China.
Yanmin ZhangDepartment of Nephrology, Wuhan No. 1 Hospital Wuhan 430022, Hubei, China.
Hongbo LiDepartment of Nephrology, Wuhan No. 1 Hospital Wuhan 430022, Hubei, China.
Sheng WanDepartment of Nephrology, Wuhan No. 1 Hospital Wuhan 430022, Hubei, China.
Lulu LiDepartment of Nephrology, Wuhan No. 1 Hospital Wuhan 430022, Hubei, China.
Li SunDepartment of Internal Medicine, Wuhan University Hospital Wuhan 430071, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo investigate the risk factors for peritonitis in peritoneal dialysis patients and to develop and validate a predictive model.

methodsA total of 219 patients undergoing continuous ambulatory peritoneal dialysis (CAPD) who had their first peritoneal dialysis catheter placement and regular follow-up at Wuhan No. 1 Hospital between April 2020 and August 2023 were included in this study. Patients were categorized into two groups: a peritoneal dialysis-associated peritonitis (PDAP) group and a non-PDAP group, based on the occurrence of PDAP. Univariate and multivariate logistic regression analyses were conducted to identify risk factors for PDAP in peritoneal dialysis patients. A risk prediction model was constructed, and its predictive performance was assessed using the receiver operating characteristic (ROC) curve.

resultsAmong the study population, 59 patients developed PDAP, with an incidence rate of 26.94%. Univariate and multivariate Logistic regression analyses identified serum albumin, age, hemoglobin, diabetes mellitus, and dialysis duration as independent risk factors for PDAP (all

conclusionSerum albumin, age, hemoglobin, diabetes, and dialysis duration are independent risk factors for PDAP in peritoneal dialysis patients. The developed predictive model demonstrates strong performance in identifying patients at high risk for PDAP.

Indexed as

clinical validationPeritoneal dialysisperitonitispredictive modelingrisk factors

Identifiers

PMID39678542
PMCPMC11645601

What Socratic holds

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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 Socratic graph.