Evidence map›Paper›PMID 37833779›Full record

ArticleDiabetology & metabolic syndrome2023

Development of a clinical automatic calculation of hypoglycemia during hemodialysis risk in patients with diabetic nephropathy.

Rui-Ting Zhang, Yu Liu, Ke-Ke Lin, Wan-Ning Jia, Quan-Ying Wu, Jing Wang, Xiao-Yan Bai

Open access · goldAbstract read
In one paragraph

Article in Diabetology & metabolic syndrome, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.6field-weighted citation impact, top 28% of its field
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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
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

7 authors at 3 institutions in 1 country.

Rui-Ting ZhangSchool of Nursing, Beijing University of Chinese Medicine, Beijing, China.
Yu LiuSchool of Nursing, Beijing University of Chinese Medicine, Beijing, China. liuyu222@hotmail.com.
Ke-Ke LinSchool of Nursing, Beijing University of Chinese Medicine, Beijing, China.
Wan-Ning JiaBlood Purification Center of China-Japan Friendship Hospital, Beijing, China.
Quan-Ying WuNursing Department, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Jing WangSchool of Nursing, Beijing University of Chinese Medicine, Beijing, China.
Xiao-Yan BaiSchool of Nursing, Beijing University of Chinese Medicine, Beijing, China.
Beijing University of Chinese Medicine · CNChina-Japan Friendship Hospital · CNChinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHypoglycemia is one of the most common complications in patients with DN during hemodialysis. The purpose of the study is to construct a clinical automatic calculation to predict risk of hypoglycemia during hemodialysis for patients with diabetic nephropathy.

methodsIn this cross-sectional study, patients provided information for the questionnaire and received blood glucose tests during hemodialysis. The data were analyzed with logistic regression and then an automated calculator for risk prediction was constructed based on the results. From May to November 2022, 207 hemodialysis patients with diabetes nephropathy were recruited. Patients were recruited at blood purifying facilities at two hospitals in Beijing and Inner Mongolia province, China. Hypoglycemia is defined according to the standards of medical care in diabetes issued by ADA (2021). The blood glucose meter was used uniformly for blood glucose tests 15 minutes before the end of hemodialysis or when the patient did not feel well during hemodialysis.

resultsThe incidence of hypoglycemia during hemodialysis was 50.2% (104/207). The risk prediction model included 6 predictors, and was constructed as follows: Logit (P) = 1.505×hemodialysis duration 8~15 years (OR = 4.506, 3 points) + 1.616×hemodialysis duration 16~21 years (OR = 5.032, 3 points) + 1.504×having hypotension during last hemodialysis (OR = 4.501, 3 points) + 0.788×having hyperglycemia during the latest hemodialysis night (OR = 2.199, 2 points) + 0.91×disturbance of potassium metabolism (OR = 2.484, 2 points) + 2.636×serum albumin<35 g/L (OR = 13.963, 5 points)-4.314. The AUC of the prediction model was 0.866, with Matthews correlation coefficient (MCC) of 0.633, and Hosmer-Lemeshow χ

conclusionsThe incidence of hypoglycemia during hemodialysis is high in patients with DN. The risk prediction model in this study had a good prediction outcome. The hypoglycemia prediction automatic calculation that was developed using this model can be used to predict the risk of hypoglycemia in DN patients during hemodialysis and also help identify those with a high risk of hypoglycemia during hemodialysis.

Indexed as

Diabetic nephropathyHemodialysisHypoglycemiaRisk prediction

Identifiers

PMID37833779
PMCPMC10571353
OpenAlexW4387603549

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.