Evidence mapPaperPMID 39110305Full record

ArticleClinical and experimental medicine2024

Risk and mediation analyses of hemoglobin glycation index and survival prognosis in patients with sepsis.

Aifeng He, Juanli Liu, Jinxin Qiu, Xiaojie Zhu, Lulu Zhang, Leiming Xu, Jianyong Xu

Abstract read
In one paragraph

Article in Clinical and experimental medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

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12citing 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

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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

12 citing papers in PubMed.

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  9. Dynamic HGI trajectories and their impact on survival in patients with sepsis: a machine learning prognostic model.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2025
    Article
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

Aifeng He *Binhai County People's Hospital, Kangda College of Nanjing Medical University, Yancheng, Jiangsu Province, People's Republic of China.
Juanli Liu *Binhai County People's Hospital, Kangda College of Nanjing Medical University, Yancheng, Jiangsu Province, People's Republic of China.
Jinxin QiuBinhai County People's Hospital, Kangda College of Nanjing Medical University, Yancheng, Jiangsu Province, People's Republic of China.
Xiaojie ZhuBinhai County People's Hospital, Kangda College of Nanjing Medical University, Yancheng, Jiangsu Province, People's Republic of China.
Lulu ZhangBinhai County People's Hospital, Kangda College of Nanjing Medical University, Yancheng, Jiangsu Province, People's Republic of China.
Leiming XuBinhai County People's Hospital, Kangda College of Nanjing Medical University, Yancheng, Jiangsu Province, People's Republic of China. 1250965828@qq.com.
Jianyong XuBinhai County People's Hospital, Kangda College of Nanjing Medical University, Yancheng, Jiangsu Province, People's Republic of China. 2019630653@qq.com.

Funding

Yancheng Science and Technology Bureau YCBE202365
6 · The paper itself

Abstract

An increasing number of studies have reported the close relation of the hemoglobin glycation index (HGI) with metabolism, inflammation, and disease prognosis. However, the prognostic relationship between the HGI and patients with sepsis remains unclear. Thus, this study aimed to analyze the association between the HGI and all-cause mortality in patients with sepsis using data from the MIMIC-IV database. In this study, 2605 patients with sepsis were retrospectively analyzed. The linear regression equation was established by incorporating glycated hemoglobin (HbA1c) and fasting plasma glucose levels. Subsequently, the HGI was calculated based on the difference between the predicted and observed HbA1c levels. Furthermore, the HGI was divided into the following three groups using X-tile software: Q1 (HGI ≤  - 0.50%), Q2 (- 0.49% ≤ HGI ≤ 1.18%), and Q3 (HGI ≥ 1.19%). Kaplan-Meier survival curves were further plotted to analyze the differences in 28-day and 365-day mortality among patients with sepsis patients in these HGI groups. Multivariate corrected Cox proportional risk model and restricted cubic spline (RCS) were used. Lastly, mediation analysis was performed to assess the factors through which HGI affects sepsis prognosis. This study included 2605 patients with sepsis, and the 28-day and 365-day mortality rates were 19.7% and 38.9%, respectively. The Q3 group had the highest mortality risk at 28 days (HR = 2.55, 95% CI: 1.89-3.44, p < 0.001) and 365 days (HR = 1.59, 95% CI: 1.29-1.97, p < 0.001). In the fully adjusted multivariate Cox proportional hazards model, patients in the Q3 group still displayed the highest mortality rates at 28 days (HR = 2.02, 95% CI: 1.45-2.80, p < 0.001) and 365 days (HR = 1.28, 95% CI: 1.08-1.56, p < 0.001). The RCS analysis revealed that HGI was positively associated with adverse clinical outcomes. Finally, the mediation effect analysis demonstrated that the HGI might influence patient survival prognosis via multiple indicators related to the SOFA and SAPS II scores. There was a significant association between HGI and all-cause mortality in patients with sepsis, and patients with higher HGI values had a higher risk of death. Therefore, HGI can be used as a potential indicator to assess the prognostic risk of death in patients with sepsis.

Indexed as

Glycated HemoglobinSepsisAgedAged, 80 and overBlood GlucoseFemaleHumansKaplan-Meier EstimateMaleMiddle AgedPrognosisProportional Hazards ModelsRetrospective StudiesSurvival AnalysisBlood GlucoseGlycated HemoglobinFasting plasma glucoseGlycated hemoglobinHGIMortality ratesSepsis

Identifiers

PMID39110305
PMCPMC11306295

What Socratic holds

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LicenceCC BY-NC-ND
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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.